diff --git a/.environment.yml b/.environment.yml
index 4a55fc9..eed4fee 100644
--- a/.environment.yml
+++ b/.environment.yml
@@ -6,13 +6,27 @@ dependencies:
   - python=3.11
   - affine
   - black
+  - contextily
   - geopandas
+  - jupyter
+  - matplotlib
   - nbstripout
+  - networkx
   - numpy
+  - openpyxl
+  - pandera
   - pip
+  - pyarrow
+  - pyogrio
   - pytest
   - pytest-cov
   - rasterio
+  - seaborn
   - shapely
+  - scipy
   - pip:
+    - hilbertcurve
+    - irv_autopkg_client
     - python-igraph
+    - snkit
+    - tqdm
diff --git a/README.md b/README.md
index ca9ef06..f966ffd 100644
--- a/README.md
+++ b/README.md
@@ -192,10 +192,10 @@ The `snail.core.intersections` module is built using `pybind11` with
 >
 > Copyright (c) 2020-23 Tom Russell and all [snail contributors](https://github.com/nismod/snail/graphs/contributors)
 
-This library is developed by researchers in the [Oxford Programme for Sustainable 
+This library is developed by researchers in the [Oxford Programme for Sustainable
 Infrastructure Systems](https://opsis.eci.ox.ac.uk/) at the University of Oxford,
 funded by multiple research projects.
 
-This research received funding from the FCDO Climate Compatible Growth Programme. 
-The views expressed here do not necessarily reflect the UK government's official 
+This research received funding from the FCDO Climate Compatible Growth Programme.
+The views expressed here do not necessarily reflect the UK government's official
 policies.
diff --git a/pyproject.toml b/pyproject.toml
index 9def7df..d6b760a 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,6 +1,6 @@
 [project]
 name="nismod-snail"
-version="0.3.2"
+version="0.4.0"
 license={file = "LICENSE"}
 description="The spatial networks impact assessment library"
 readme="README.md"
@@ -24,22 +24,37 @@ keywords=[]
 requires-python=">=3.8"
 dependencies=[
     "geopandas",
+    "matplotlib",
+    "openpyxl",
+    "pandera",
     "pyarrow",
+    "pyogrio",
     "python-igraph",
     "rasterio",
     "shapely",
+    "scipy"
 ]
 
 [project.optional-dependencies]
 dev=[
     "affine",
     "black",
+    "hilbertcurve",
     "nbstripout",
     "numpy",
     "pytest-cov",
     "pytest",
 ]
 docs=["sphinx", "m2r2"]
+tutorials=[
+    "contextily",
+    "irv_autopkg_client",
+    "jupyter",
+    "networkx",
+    "seaborn",
+    "snkit",
+    "tqdm",
+]
 
 [project.urls]
 Homepage = "https://github.com/nismod/snail"
diff --git a/src/snail/__init__.py b/src/snail/__init__.py
index b06d6dd..f9fb3e3 100644
--- a/src/snail/__init__.py
+++ b/src/snail/__init__.py
@@ -1,6 +1,5 @@
 """snail - the spatial networks impact assessment library
 """
-import pkg_resources
 
 # Import things to define what is accessible directly on snail, when a client
 # writes::
@@ -10,8 +9,10 @@
 # from snail.network import Network
 
 try:
-    __version__ = pkg_resources.get_distribution(__name__).version
-except pkg_resources.DistributionNotFound:
+    from importlib.metadata import version
+
+    __version__ = version("nismod-snail")
+except:
     __version__ = "unknown"
 
 
diff --git a/src/snail/cli.py b/src/snail/cli.py
index a4f45e3..8ce26af 100644
--- a/src/snail/cli.py
+++ b/src/snail/cli.py
@@ -8,8 +8,9 @@
 import pandas
 
 from snail.intersection import (
-    Transform,
+    GridDefinition,
     apply_indices,
+    get_raster_values_for_splits,
     prepare_linestrings,
     prepare_polygons,
     prepare_points,
@@ -19,7 +20,7 @@
     split_points,
 )
 from snail.io import (
-    associate_raster_file,
+    read_raster_band_data,
     associate_raster_files,
     read_features,
     read_raster_metadata,
@@ -171,7 +172,7 @@ def snail(args=None):
 def split(args):
     """snail split command"""
     if args.raster:
-        transform, all_bands = read_raster_metadata(args.raster)
+        grid, all_bands = read_raster_metadata(args.raster)
     else:
         crs = None
         width = args.width
@@ -181,26 +182,35 @@ def split(args):
             sys.exit(
                 "Error: Expected either a raster file or transform, width and height of splitting grid"
             )
-        transform = Transform(crs, width, height, affine_transform)
-    logging.info(f"Splitting grid {transform=}")
+        grid = GridDefinition(crs, width, height, affine_transform)
+    logging.info(f"Splitting {grid=}")
 
-    features = geopandas.read_file(args.features)
+    features = read_features(Path(args.features))
     features_crs = features.crs
     geom_type = _sample_geom_type(features)
 
     if "Point" in geom_type:
+        logging.info(f"Preparing points")
         prepared = prepare_points(features)
-        splits = split_points(prepared)
+        logging.info(f"Splitting points")
+        splits = split_features_for_rasters(prepared, [grid], split_points)
     elif "LineString" in geom_type:
+        logging.info(f"Preparing linestrings")
         prepared = prepare_linestrings(features)
-        splits = split_linestrings(prepared, transform)
+        logging.info(f"Splitting linestrings")
+        splits = split_features_for_rasters(
+            prepared, [grid], split_linestrings
+        )
     elif "Polygon" in geom_type:
+        logging.info(f"Preparing polygons")
         prepared = prepare_polygons(features)
-        splits = split_polygons(prepared, transform)
+        logging.info(f"Splitting polygons")
+        splits = split_features_for_rasters(prepared, [grid], split_polygons)
     else:
         raise ValueError("Could not process vector data of type %s", geom_type)
 
-    splits = apply_indices(splits, transform)
+    logging.info(f"Applying indices")
+    splits = apply_indices(splits, grid)
 
     if args.attribute and args.raster:
         if args.band:
@@ -225,9 +235,10 @@ def split(args):
                 args.raster,
                 band_index,
             )
-            splits[key] = associate_raster_file(
-                splits, args.raster, band_number=int(band_index)
+            band_data = read_raster_band_data(
+                args.raster, band_number=int(band_index)
             )
+            splits[key] = get_raster_values_for_splits(splits, band_data)
 
     splits.set_crs(features_crs, inplace=True)
     splits.to_file(args.output)
diff --git a/src/snail/core/__init__.py b/src/snail/core/__init__.py
index cb2c9ac..34217ab 100644
--- a/src/snail/core/__init__.py
+++ b/src/snail/core/__init__.py
@@ -1,4 +1,4 @@
-from .intersections import (get_cell_indices, split_linestring, split_polygon)
+from .intersections import get_cell_indices, split_linestring, split_polygon
 
 __all__ = [
     "get_cell_indices",
diff --git a/src/snail/damages.py b/src/snail/damages.py
new file mode 100644
index 0000000..5abf167
--- /dev/null
+++ b/src/snail/damages.py
@@ -0,0 +1,255 @@
+"""Damage assessment"""
+from abc import ABC
+import numpy
+import pandas
+import pandera
+from scipy.interpolate import interp1d
+from pandera.typing import DataFrame, Series
+
+
+# TODO check `nismod/east-africa-transport` and `nismod/jamaica-infrastructure`
+# manipulations of damage curves
+
+
+class DamageCurve(ABC):
+    """A damage curve"""
+
+    def __init__(self):
+        pass
+
+    def damage_fraction(exposure: numpy.array) -> numpy.array:
+        """Evaluate damage fraction for exposure to a given hazard intensity"""
+        pass
+
+
+class PiecewiseLinearDamageCurveSchema(pandera.DataFrameModel):
+    intensity: Series[float]
+    damage: Series[float]
+
+
+class PiecewiseLinearDamageCurve(DamageCurve):
+    """A piecewise-linear damage curve"""
+
+    intensity: Series[float]
+    damage: Series[float]
+
+    def __init__(self, curve: DataFrame[PiecewiseLinearDamageCurveSchema]):
+        curve = curve.copy()
+        self.intensity, self.damage = self.clip_curve_data(
+            curve.intensity, curve.damage
+        )
+
+        bounds = (self.damage.min(), self.damage.max())
+        self._interpolate = interp1d(
+            self.intensity,
+            self.damage,
+            kind="linear",
+            fill_value=bounds,
+            bounds_error=False,
+            copy=False,
+        )
+
+    def __eq__(self, other):
+        damage_eq = (self.damage == other.damage).all()
+        intensity_eq = (self.intensity == other.intensity).all()
+        return damage_eq and intensity_eq
+
+    @classmethod
+    def from_csv(
+        cls,
+        fname,
+        intensity_col="intensity",
+        damage_col="damage_ratio",
+        comment="#",
+        **kwargs,
+    ):
+        """Read a damage curve from a CSV file.
+
+        By default, the CSV should have columns named "intensity" and "damage_ratio",
+        with any additional header lines commented out by "#".
+
+        Any additional keyword arguments are passed through to ``pandas.read_csv``
+
+        Parameters
+        ----------
+        fname: str, path object or file-like object
+        intensity_col: str, default "intensity"
+            Column name to read hazard intensity values
+        damage_col: str, default "damage_ratio"
+            Column name to read damage values
+        comment: str, default "#"
+            Indicates remainder of the line in the CSV should not be parsed.
+            If found at the beginning of a line, the line will be ignored
+            altogether.
+        kwargs:
+            see pandas.read_csv documentation
+
+        Returns
+        -------
+        PiecewiseLinearDamageCurve
+        """
+        curve_data = pandas.read_csv(fname, comment=comment, **kwargs).rename(
+            columns={
+                intensity_col: "intensity",
+                damage_col: "damage",
+            }
+        )
+        return PiecewiseLinearDamageCurve(curve_data)
+
+    @classmethod
+    def from_excel(
+        cls,
+        fname,
+        sheet_name=0,
+        intensity_col="intensity",
+        damage_col="damage_ratio",
+        comment="#",
+        **kwargs,
+    ):
+        """Read a damage curve from an Excel file.
+
+        By default, the file should have columns named "intensity" and "damage_ratio",
+        with any additional header lines commented out by "#".
+
+        Any additional keyword arguments are passed through to ``pandas.read_excel``
+
+        Parameters
+        ----------
+        fname: str, path object or file-like object
+        sheet_name: str, int
+            Strings are used for sheet names. Integers are used in zero-indexed sheet
+            positions (chart sheets do not count as a sheet position).
+        intensity_col: str, default "intensity"
+            Column name to read hazard intensity values
+        damage_col: str, default "damage_ratio"
+            Column name to read damage values
+        comment: str, default "#"
+            Indicates remainder of the line in the CSV should not be parsed.
+            If found at the beginning of a line, the line will be ignored
+            altogether.
+        kwargs:
+            see pandas.read_csv documentation
+
+        Returns
+        -------
+        PiecewiseLinearDamageCurve
+        """
+        curve_data = pandas.read_excel(
+            fname, sheet_name=sheet_name, comment=comment, **kwargs
+        ).rename(
+            columns={
+                intensity_col: "intensity",
+                damage_col: "damage",
+            }
+        )
+        return PiecewiseLinearDamageCurve(curve_data)
+
+    def damage_fraction(self, exposure: numpy.array) -> numpy.array:
+        """Evaluate damage fraction for exposure to a given hazard intensity"""
+        return self._interpolate(exposure)
+
+    def translate_y(self, y: float):
+        damage = self.damage + y
+
+        return PiecewiseLinearDamageCurve(
+            pandas.DataFrame(
+                {
+                    "intensity": self.intensity,
+                    "damage": damage,
+                }
+            )
+        )
+
+    def scale_y(self, y: float):
+        damage = self.damage * y
+
+        return PiecewiseLinearDamageCurve(
+            pandas.DataFrame(
+                {
+                    "intensity": self.intensity,
+                    "damage": damage,
+                }
+            )
+        )
+
+    def translate_x(self, x: float):
+        intensity = self.intensity + x
+
+        return PiecewiseLinearDamageCurve(
+            pandas.DataFrame(
+                {
+                    "intensity": intensity,
+                    "damage": self.damage,
+                }
+            )
+        )
+
+    def scale_x(self, x: float):
+        intensity = self.intensity * x
+
+        return PiecewiseLinearDamageCurve(
+            pandas.DataFrame(
+                {
+                    "intensity": intensity,
+                    "damage": self.damage,
+                }
+            )
+        )
+
+    @staticmethod
+    def clip_curve_data(intensity, damage):
+        if (damage.max() > 1) or (damage.min() < 0):
+            # WARNING clipping out-of-bounds damage fractions
+            bounds = (
+                intensity.min(),
+                intensity.max(),
+            )
+            inverse = interp1d(
+                damage,
+                intensity,
+                kind="linear",
+                fill_value=bounds,
+                bounds_error=False,
+            )
+
+        if damage.max() > 1:
+            one_intercept = inverse(1)
+            idx = numpy.searchsorted(intensity, one_intercept)
+            # if one_intercept is in our intensities
+            if intensity[idx] == one_intercept:
+                # no action - damage fraction will be set to 1
+                pass
+            else:
+                # else insert new interpolation point
+                damage = numpy.insert(damage, idx, 1)
+                intensity = numpy.insert(intensity, idx, one_intercept)
+
+        if damage.min() < 0:
+            zero_intercept = inverse(0)
+            idx = numpy.searchsorted(intensity, zero_intercept)
+            # if zero_intercept is in our intensities
+            if intensity[idx] == zero_intercept:
+                # no action - damage fraction will be set to 0
+                pass
+            else:
+                # else insert new interpolation point
+                damage = numpy.insert(damage, idx, 0)
+                intensity = numpy.insert(intensity, idx, zero_intercept)
+
+        damage = numpy.clip(damage, 0, 1)
+
+        return intensity, damage
+
+    def plot(self, ax=None):
+        import matplotlib.pyplot as plt
+
+        if ax is None:
+            fig = plt.figure()
+            ax = fig.add_subplot(1, 1, 1)
+
+        ax.plot(self.intensity, self.damage, color="tab:blue")
+        ax.set_ylim([0, 1])
+        ax.set_ylabel("Damage Fraction")
+        ax.set_xlabel("Hazard Intensity")
+
+        return ax
diff --git a/src/snail/intersection.py b/src/snail/intersection.py
index 5b7017d..26b7686 100644
--- a/src/snail/intersection.py
+++ b/src/snail/intersection.py
@@ -1,13 +1,15 @@
 import logging
+import math
 import os
 from dataclasses import dataclass
-from itertools import product
 from typing import Callable, List, Tuple
 
 import geopandas
 import numpy
 import pandas
-from shapely.geometry import mapping, shape, box
+import rasterio
+from shapely import box
+from shapely.geometry import mapping, shape
 from shapely.ops import linemerge, polygonize
 
 from snail.core.intersections import (
@@ -33,29 +35,84 @@
 POLYGON_COORDINATE_PRECISION = 9
 
 
-@dataclass
-class Transform:
-    """Store a raster transform and CRS"""
+@dataclass(frozen=True)
+class GridDefinition:
+    """Store a raster transform and CRS
+
+    A note on `transform` - these six numbers define the transform from `i,j`
+    cell index (column/row) coordinates in the rectangular grid to `x,y`
+    geographic coordinates, in the coordinate reference system of the input and
+    output files. They effectively form the first two rows of a 3x3 matrix:
+
+
+    ```
+    | x |   | a  b  c | | i |
+    | y | = | d  e  f | | j |
+    | 1 |   | 0  0  1 | | 1 |
+    ```
+
+    In cases without shear or rotation, `a` and `e` define scaling or grid cell
+    size, while `c` and `f` define the offset or grid upper-left corner:
+
+    ```
+    | x_scale 0       x_offset |
+    | 0       y_scale y_offset |
+    | 0       0       1        |
+    ```
+    """
 
     crs: str
     width: int
     height: int
     transform: Tuple[float]
 
+    @classmethod
+    def from_rasterio_dataset(cls, dataset):
+        crs = dataset.crs
+        width = dataset.width
+        height = dataset.height
+        # trim transform to 6 - we expect the first two rows of 3x3 matrix
+        transform = tuple(dataset.transform)[:6]
+        return GridDefinition(crs, width, height, transform)
+
+    @classmethod
+    def from_raster(cls, fname):
+        with rasterio.open(fname) as dataset:
+            grid = GridDefinition.from_rasterio_dataset(dataset)
+        return grid
+
+    @classmethod
+    def from_extent(
+        cls,
+        xmin: float,
+        ymin: float,
+        xmax: float,
+        ymax: float,
+        cell_width: float,
+        cell_height: float,
+        crs,
+    ):
+        return GridDefinition(
+            crs=crs,
+            width=math.ceil((xmax - xmin) / cell_width),
+            height=math.ceil((ymax - ymin) / cell_height),
+            transform=(cell_width, 0, xmin, 0, cell_height, ymin),
+        )
+
 
 def split_features_for_rasters(
     features: geopandas.GeoDataFrame,
-    transforms: List[Transform],
+    grids: List[GridDefinition],
     split_func: Callable,
 ):
     # lookup per transform
-    for i, t in enumerate(transforms):
-        logging.info("Splitting on transform %s %s", i, t)
+    for i, grid in enumerate(grids):
+        logging.info("Splitting on grid %s %s", i, grid)
         # transform to grid CRS
-        crs_features = features.to_crs(t.crs)
-        crs_features = split_func(crs_features, t)
+        crs_features = features.to_crs(grid.crs)
+        crs_features = split_func(crs_features, grid)
         # save cell index for fast lookup of raster values
-        crs_features = apply_indices(crs_features, t, f"i_{i}", f"j_{i}")
+        crs_features = apply_indices(crs_features, grid, f"i_{i}", f"j_{i}")
         # transform back
         features = crs_features.to_crs(features.crs)
     return features
@@ -80,9 +137,9 @@ def prepare_polygons(
 
 
 def split_points(
-    points: geopandas.GeoDataFrame, t: Transform
+    points: geopandas.GeoDataFrame, grid: GridDefinition
 ) -> geopandas.GeoDataFrame:
-    """Split points along the grid defined by a transform
+    """Split points along a grid
 
     This is a no-op, written for equivalence when processing multiple
     geometry types.
@@ -91,14 +148,19 @@ def split_points(
 
 
 def split_linestrings(
-    linestring_features: geopandas.GeoDataFrame, t: Transform
+    linestring_features: geopandas.GeoDataFrame, grid: GridDefinition
 ) -> geopandas.GeoDataFrame:
-    """Split linestrings along the grid defined by a transform"""
+    """Split linestrings along a grid"""
+    # TODO check for MultiLineString
+    # throw error or coerce (df.explode)
     pieces = []
     for i in tqdm(range(len(linestring_features))):
         # split edge
         geom_splits = split_linestring(
-            linestring_features.geometry[i], t.width, t.height, t.transform
+            linestring_features.geometry[i],
+            grid.width,
+            grid.height,
+            grid.transform,
         )
         for j, s in enumerate(geom_splits):
             # splitting sometimes returns zero-length linestrings on edge of raster
@@ -117,7 +179,9 @@ def split_linestrings(
     logging.info(
         f"Split {len(linestring_features)} edges into {len(pieces)} pieces"
     )
-    splits_df = geopandas.GeoDataFrame(pieces, crs=t.crs, geometry="geometry")
+    splits_df = geopandas.GeoDataFrame(
+        pieces, crs=grid.crs, geometry="geometry"
+    )
     return splits_df
 
 
@@ -126,39 +190,38 @@ def _transform(i, j, a, b, c, d, e, f) -> Tuple[float]:
 
 
 def split_polygons(
-    polygon_features: geopandas.GeoDataFrame, t: Transform
+    polygon_features: geopandas.GeoDataFrame, grid: GridDefinition
 ) -> geopandas.GeoDataFrame:
-    """Split polygons along the grid defined by a transform"""
-    pieces = []
+    """Split polygons along a grid"""
     ##
-    # Fairly slow but solid approach, loop over cells and
+    # Fairly slow but solid approach, generate cells as boxes and
     # use geopandas (shapely/GEOS) intersection
     ##
-    a, b, c, d, e, f = t.transform
-    for i, j in tqdm(
-        product(range(t.width), range(t.height)), total=t.width * t.height
-    ):
-        ulx, uly = _transform(i, j, a, b, c, d, e, f)
-        lrx, lry = _transform(i + 1, j + 1, a, b, c, d, e, f)
-        cell_geom = box(ulx, uly, lrx, lry)
-        idx = polygon_features.geometry.sindex.query(cell_geom)
-        subset = polygon_features.iloc[idx].copy()
-        if len(subset):
-            subset.geometry = subset.intersection(cell_geom)
-            subset = subset[
-                ~(subset.geometry.is_empty | subset.geometry.isna())
-            ]
-            subset = subset.explode(ignore_index=True)
-            subset = subset[subset.geometry.type == "Polygon"]
-            pieces.append(subset)
-    splits_df = pandas.concat(pieces)
-    return splits_df
+    box_geoms = generate_grid_boxes(grid)
+    splits = polygon_features.overlay(box_geoms, how="intersection")
+    splits = splits[~(splits.geometry.is_empty | splits.geometry.isna())]
+    splits = splits.explode(ignore_index=True)
+    splits = splits[splits.geometry.type == "Polygon"]
+    return splits
+
+
+def generate_grid_boxes(grid):
+    a, b, c, d, e, f = grid.transform
+    idx = numpy.arange(grid.width * grid.height)
+    i, j = numpy.unravel_index(idx, (grid.height, grid.width))
+    ulx = i * a + j * b + c
+    uly = i * d + j * e + f
+    lrx = (i + 1) * a + (j + 1) * b + c
+    lry = (i + 1) * d + (j + 1) * e + f
+    return geopandas.GeoDataFrame(
+        data={}, geometry=box(ulx, lry, lrx, uly), crs=grid.crs
+    )
 
 
 def split_polygons_experimental(
-    polygon_features: geopandas.GeoDataFrame, t: Transform
+    polygon_features: geopandas.GeoDataFrame, grid: GridDefinition
 ) -> geopandas.GeoDataFrame:
-    """Split polygons along the grid defined by a transform
+    """Split polygons along a grid
 
     Experimental implementation of `split_polygons`, possibly fast/incorrect
     with some inputs.
@@ -177,7 +240,10 @@ def split_polygons_experimental(
     for i in tqdm(range(len(polygon_features))):
         # split area
         geom_splits = split_polygon(
-            polygon_features.geometry[i], t.width, t.height, t.transform
+            polygon_features.geometry[i],
+            grid.width,
+            grid.height,
+            grid.transform,
         )
         # round to high precision (avoid floating point errors)
         geom_splits = [
@@ -196,7 +262,7 @@ def split_polygons_experimental(
         f"  Split {len(polygon_features)} areas into {len(pieces)} pieces"
     )
     splits_df = geopandas.GeoDataFrame(pieces)
-    splits_df.crs = t.crs
+    splits_df.crs = grid.crs
     return splits_df
 
 
@@ -215,8 +281,8 @@ def _set_precision(geom, precision):
     return shape(geom_mapping)
 
 
-def associate_raster(
-    features: pandas.DataFrame,
+def get_raster_values_for_splits(
+    splits: pandas.DataFrame,
     data: numpy.ndarray,
     index_i: str = "index_i",
     index_j: str = "index_j",
@@ -228,54 +294,70 @@ def associate_raster(
 
     N.B. This will pass through no data values from the raster (no filtering).
 
-    Args:
-        df: Table of features, each with cell indices
-            to look up raster pixel. Indices must be stored under columns with
-            names referenced by index_i and index_j.
-        fname: Filename of raster file to read data from
-    Returns:
-        pd.Series: Series of raster values, with same row indexing as df.
+    Parameters
+    ----------
+    splits: pandas.DataFrame
+        Table of features, each with cell indices
+        to look up raster pixel. Indices must be stored under columns with
+        names referenced by index_i and index_j.
+    data:  numpy.ndarray
+        Raster data (2D array)
+    index_i: str
+        Column name for i-indices
+    index_j: str
+        Column name for j-indices
+
+    Returns
+    -------
+    pd.Series
+        Series of raster values, with same row indexing as df.
     """
     # 2D numpy indexing is j, i (i.e. row, column)
     with_data = pandas.Series(
-        index=features.index, data=data[features[index_j], features[index_i]]
+        index=splits.index, data=data[splits[index_j], splits[index_i]]
     )
     # set NaN for out-of-bounds
-    with_data[
-        (features[index_i] == -1) | (features[index_j] == -1)
-    ] = numpy.nan
+    with_data[(splits[index_i] == -1) | (splits[index_j] == -1)] = numpy.nan
     return with_data
 
 
 def apply_indices(
     features: geopandas.GeoDataFrame,
-    transform: Transform,
+    grid: GridDefinition,
     index_i="index_i",
     index_j="index_j",
 ) -> geopandas.GeoDataFrame:
     def f(geom, *args, **kwargs):
-        return get_indices(geom, transform, index_i, index_j)
+        return get_indices(geom, grid, index_i, index_j)
 
     indices = features.geometry.apply(f, result_type="expand")
     return pandas.concat([features, indices], axis="columns")
 
 
 def get_indices(
-    geom, t: Transform, index_i="index_i", index_j="index_j"
+    geom, grid: GridDefinition, index_i="index_i", index_j="index_j"
 ) -> pandas.Series:
     """Given a geometry, find the cell index (i, j) of its midpoint
-    for the enclosing raster transform.
+    for the enclosing grid.
 
     N.B. There is no checking whether a geometry spans more than one cell.
     """
-    i, j = get_cell_indices(geom, t.height, t.width, t.transform)
+    i, j = get_cell_indices(geom, grid.height, grid.width, grid.transform)
 
     # Raise error if cell index would be out of bounds
     # assert 0 <= i < t.width
     # assert 0 <= j < t.height
 
     # Or - special value (-1,-1) if cell would be out of bounds
-    if i >= t.width or i < 0 or j >= t.height or j < 0:
+    if i >= grid.width or i < 0 or j >= grid.height or j < 0:
         i = -1
         j = -1
     return pandas.Series(index=(index_i, index_j), data=[i, j])
+
+
+def idx_to_ij(idx: int, width: int, height: int) -> Tuple[int]:
+    return numpy.unravel_index(idx, (height, width))
+
+
+def ij_to_idx(ij: Tuple[int], width: int, height: int):
+    return numpy.ravel_multi_index(ij, (height, width))
diff --git a/src/snail/io.py b/src/snail/io.py
index 39a249a..d56c253 100644
--- a/src/snail/io.py
+++ b/src/snail/io.py
@@ -6,49 +6,60 @@
 import pandas
 import rasterio
 
-from snail.intersection import Transform, associate_raster
+from snail.intersection import GridDefinition, get_raster_values_for_splits
 
 
-def associate_raster_files(features, rasters):
+def associate_raster_files(splits, rasters):
+    """Read values from a list of raster files for a set of indexed split geometries
+
+    Parameters
+    ----------
+    splits: pandas.DataFrame
+        split geometries with raster indices in columns named "i_{grid_id}", "j_{grid_id}"
+        for each grid_id in `rasters`
+
+    rasters: pandas.DataFrame
+        table of raster metadata with columns: key, grid_id, path, bands
+
+    Returns
+    -------
+    pandas.DataFrame
+        split geometries with raster data values at indexed locations
+    """
     # to prevent a fragmented dataframe (and a memory explosion), add series to a dict
     # and then concat afterwards -- do not append to an existing dataframe
     raster_data: dict[str, pandas.Series] = {}
 
     # associate values
-    for raster in rasters.itertuples():
+    for raster, band_number, band_data in read_rasters(rasters):
         logging.info(
-            "Associating values from raster %s transform %s",
+            "Associating values from raster %s grid %s band %s",
             raster.key,
-            raster.transform_id,
+            raster.grid_id,
+            band_number,
+        )
+        raster_data[raster.key] = get_raster_values_for_splits(
+            splits,
+            band_data,
+            f"i_{raster.grid_id}",
+            f"j_{raster.grid_id}",
         )
-        for band_number in raster.bands:
-            raster_data[raster.key] = associate_raster_file(
-                features,
-                raster.path,
-                f"i_{raster.transform_id}",
-                f"j_{raster.transform_id}",
-                band_number,
-            )
 
     raster_data = pandas.DataFrame(raster_data)
-    features = pandas.concat([features, raster_data], axis="columns")
+    splits = pandas.concat([splits, raster_data], axis="columns")
 
-    return features
+    return splits
 
 
-def associate_raster_file(
-    df: pandas.DataFrame,
-    fname: str,
-    index_i: str = "index_i",
-    index_j: str = "index_j",
-    band_number: int = 1,
-) -> pandas.Series:
-    band_data = read_band_data(fname, band_number)
-    raster_values = associate_raster(df, band_data, index_i, index_j)
-    return raster_values
+def read_rasters(rasters):
+    for raster in rasters.itertuples():
+        for band_number in raster.bands:
+            yield raster, band_number, read_raster_band_data(
+                raster.path, band_number
+            )
 
 
-def read_band_data(
+def read_raster_band_data(
     fname: str,
     band_number: int = 1,
 ) -> numpy.ndarray:
@@ -59,50 +70,50 @@ def read_band_data(
 
 def extend_rasters_metadata(
     rasters: pandas.DataFrame,
-) -> Tuple[pandas.DataFrame, List[Transform]]:
-    transforms = []
-    transform_ids = []
+) -> Tuple[pandas.DataFrame, List[GridDefinition]]:
+    grids = []
+    grid_ids = []
     raster_bands = []
 
     for raster in rasters.itertuples():
         logging.info("Reading metadata from raster %s", raster.path)
-        transform, bands = read_raster_metadata(raster.path)
+        grid, bands = read_raster_metadata(raster.path)
 
         # add transform to list if not present
-        if transform not in transforms:
-            transforms.append(transform)
+        if grid not in grids:
+            grids.append(grid)
 
         # record raster/transform details
-        transform_id = transforms.index(transform)
-        transform_ids.append(transform_id)
+        grid_id = grids.index(grid)
+        grid_ids.append(grid_id)
         raster_bands.append(bands)
 
-    rasters["transform_id"] = transform_ids
+    rasters["grid_id"] = grid_ids
     if "bands" not in rasters.columns:
         rasters["bands"] = raster_bands
 
-    return rasters, transforms
+    return rasters, grids
 
 
-def read_raster_metadata(path) -> Tuple[Transform, Tuple[int]]:
+def read_raster_metadata(path) -> Tuple[GridDefinition, Tuple[int]]:
     with rasterio.open(path) as dataset:
-        crs = dataset.crs
-        width = dataset.width
-        height = dataset.height
-        affine_transform = tuple(dataset.transform)[
-            :6
-        ]  # trim to 6 - we expect the first two rows of 3x3 matrix
         bands = dataset.indexes
-    transform = Transform(crs, width, height, affine_transform)
-    return transform, bands
+        grid = GridDefinition.from_rasterio_dataset(dataset)
+    return grid, bands
 
 
 def read_features(path, layer=None):
     if path.suffix in (".parquet", ".geoparquet"):
         features = geopandas.read_parquet(path)
     else:
+        try:
+            import pyogrio
+
+            engine = "pyogrio"
+        except ImportError:
+            engine = "fiona"
         if layer:
-            features = geopandas.read_file(path, layer=layer)
+            features = geopandas.read_file(path, layer=layer, engine=engine)
         else:
-            features = geopandas.read_file(path)
+            features = geopandas.read_file(path, engine=engine)
     return features
diff --git a/tests/core/test_core.py b/tests/core/test_core.py
new file mode 100644
index 0000000..c50b916
--- /dev/null
+++ b/tests/core/test_core.py
@@ -0,0 +1,79 @@
+import pytest
+import snail.core.intersections
+
+from shapely.geometry import LineString, Polygon
+
+
+nrows = 2
+ncols = 2
+transform = [1, 0, 0, 0, 1, 0]
+
+
+@pytest.mark.parametrize(
+    "test_linestring,expected",
+    [
+        (
+            LineString([(0.5, 0.5), (0.75, 0.5), (1.5, 0.5), (1.5, 1.5)]),
+            [
+                LineString([(0.5, 0.5), (0.75, 0.5), (1.0, 0.5)]),
+                LineString([(1.0, 0.5), (1.5, 0.5), (1.5, 1.0)]),
+                LineString([(1.5, 1.0), (1.5, 1.5)]),
+            ],
+        ),
+        (
+            LineString([(0.5, 0.5), (0.75, 0.5), (1.5, 1.5)]),
+            [
+                LineString([(0.5, 0.5), (0.75, 0.5), (1.0, 0.8333333)]),
+                LineString([(1.0, 0.8333333), (1.125, 1.0)]),
+                LineString([(1.125, 1.0), (1.5, 1.5)]),
+            ],
+        ),
+    ],
+)
+def test_linestring_splitting(test_linestring, expected):
+    splits = snail.core.intersections.split_linestring(
+        test_linestring, nrows, ncols, transform
+    )
+    for split, expected_split in zip(splits, expected):
+        assert split.equals_exact(expected_split, 1e-7)
+
+
+@pytest.mark.parametrize(
+    "test_linestring,expected",
+    [
+        (
+            LineString([(0.25, 1.25), (0.5, 1.5), (0.5, 1.75)]),
+            (0, 1),
+        ),
+        (
+            LineString([(1.25, 1.25), (1.5, 1.5), (1.5, 1.75)]),
+            (1, 1),
+        ),
+    ],
+)
+def test_get_cell_indices(test_linestring, expected):
+    cell_indices = snail.core.intersections.get_cell_indices(
+        test_linestring, nrows, ncols, transform
+    )
+    assert cell_indices == expected
+
+
+@pytest.mark.xfail
+def test_split_polygons():
+    bad_poly = Polygon(
+        (
+            [-0.0062485600499826, 51.61041647955],
+            [-0.0062485600499826, 51.602083146149994],
+            [0.0020847733500204, 51.602083146149994],
+            # [0.0020847733500204, 51.61041647955],
+            # [-0.0062485600499826, 51.61041647955],
+        )
+    )
+
+    # expect a RuntimeError: Expected even number of crossings on gridline.
+    snail.core.intersections.split_polygon(
+        bad_poly,
+        36082,
+        18000,
+        (1000.0, 0.0, -18041000.0, 0.0, -1000.0, 9000000.0),
+    )
diff --git a/tests/core/test_intersections.py b/tests/core/test_intersections.py
deleted file mode 100644
index e3fc871..0000000
--- a/tests/core/test_intersections.py
+++ /dev/null
@@ -1,59 +0,0 @@
-import unittest
-
-from shapely.geometry import LineString
-
-import snail.core.intersections
-
-
-class TestIntersections(unittest.TestCase):
-    def setUp(self):
-        self.nrows = 2
-        self.ncols = 2
-        self.transform = [1, 0, 0, 0, 1, 0]
-
-    def test_linestring_splitting(self):
-        test_linestrings = [
-            LineString([(0.5, 0.5), (0.75, 0.5), (1.5, 0.5), (1.5, 1.5)]),
-            LineString([(0.5, 0.5), (0.75, 0.5), (1.5, 1.5)]),
-        ]
-        expected = [
-            [
-                LineString([(0.5, 0.5), (0.75, 0.5), (1.0, 0.5)]),
-                LineString([(1.0, 0.5), (1.5, 0.5), (1.5, 1.0)]),
-                LineString([(1.5, 1.0), (1.5, 1.5)]),
-            ],
-            [
-                LineString([(0.5, 0.5), (0.75, 0.5), (1.0, 0.8333)]),
-                LineString([(1.0, 0.8333), (1.125, 1.0)]),
-                LineString([(1.125, 1.0), (1.5, 1.5)]),
-            ],
-        ]
-
-        for i, test_data in enumerate(zip(test_linestrings, expected)):
-            test_linestring, expected_splits = test_data
-            with self.subTest(i=i):
-                splits = snail.core.intersections.split_linestring(
-                    test_linestring, self.nrows, self.ncols, self.transform
-                )
-                self.assertTrue(
-                    [
-                        split.equals_exact(expected_split, 0.5e-6)
-                        for split, expected_split in zip(
-                            splits, expected_splits
-                        )
-                    ]
-                )
-
-    def test_get_cell_indices(self):
-        test_linestrings = [
-            LineString([(0.25, 1.25), (0.5, 1.5), (0.5, 1.75)]),
-            LineString([(1.25, 1.25), (1.5, 1.5), (1.5, 1.75)]),
-        ]
-        expected_cell_indices = [(0, 1), (1, 1)]
-
-        for i, test_linestring in enumerate(test_linestrings):
-            with self.subTest(i=i):
-                cell_indices = snail.core.intersections.get_cell_indices(
-                    test_linestring, self.nrows, self.ncols, self.transform
-                )
-                self.assertEqual(cell_indices, expected_cell_indices[i])
diff --git a/tests/integration/paved-road-flood-depth-damage.csv b/tests/integration/paved-road-flood-depth-damage.csv
new file mode 100644
index 0000000..1a49f68
--- /dev/null
+++ b/tests/integration/paved-road-flood-depth-damage.csv
@@ -0,0 +1,8 @@
+# Unpaved road flood depth/damage curve
+inundation_depth_(m)	road_unpaved	unrelated
+0	0	a
+1	0.28	b
+2	0.46	c
+3	0.64	d
+4	0.82	e
+5	1	f
diff --git a/tests/integration/paved-road-flood-depth-damage.xlsx b/tests/integration/paved-road-flood-depth-damage.xlsx
new file mode 100644
index 0000000..4c709ed
Binary files /dev/null and b/tests/integration/paved-road-flood-depth-damage.xlsx differ
diff --git a/tests/integration/piecewise-linear-damage-curve.csv b/tests/integration/piecewise-linear-damage-curve.csv
new file mode 100644
index 0000000..cbc0b9d
--- /dev/null
+++ b/tests/integration/piecewise-linear-damage-curve.csv
@@ -0,0 +1,13 @@
+# Example damage curve,
+# - use # to add comment lines to a damage curve CSV
+# - default column names are "intensity" and ""damage_ratio"
+# - "damage_ratio" must be in the range [0, 1]
+# - ""intensity"" must be in some hazard/peril units and should be
+# documented in the header. E.g. "intensity is 10 minute mean surface
+# wind speeds in m/s" or "intensity is flood depth in metres"
+# - the header can also record source/citation details
+intensity,damage_ratio
+0,0
+10,0.1
+20,0.2
+30,1
diff --git a/tests/integration/piecewise-linear-damage-curve.xlsx b/tests/integration/piecewise-linear-damage-curve.xlsx
new file mode 100644
index 0000000..0071af6
Binary files /dev/null and b/tests/integration/piecewise-linear-damage-curve.xlsx differ
diff --git a/tests/split_polygons_rings.py b/tests/split_polygons_rings.py
deleted file mode 100644
index 751d772..0000000
--- a/tests/split_polygons_rings.py
+++ /dev/null
@@ -1,55 +0,0 @@
-expected_polygons_rings = [
-    [
-        (2.0, 0.875),
-        (1.5, 0.25),
-        (1.0, 0.875),
-        (1.0, 1.0),
-        (2.0, 1.0),
-        (2.0, 0.875),
-    ],
-    [(2.1, 1.0), (2.0, 0.875), (2.0, 1.0), (2.1, 1.0)],
-    [
-        (2.5, 2.0),
-        (2.5, 1.5),
-        (2.1, 1.0),
-        (2.0, 1.0),
-        (2.0, 2.0),
-        (2.5, 2.0),
-    ],
-    [
-        (2.5, 3.0),
-        (2.5, 2.0),
-        (2.0, 2.0),
-        (2.0, 2.875),
-        (2.1, 3.0),
-        (2.5, 3.0),
-    ],
-    [(2.1, 3.0), (2.5, 3.5), (2.5, 3.0), (2.1, 3.0)],
-    [
-        (1.0, 2.875),
-        (1.5, 2.25),
-        (2.0, 2.875),
-        (2.0, 2.0),
-        (1.0, 2.0),
-        (1.0, 2.875),
-    ],
-    [
-        (0.9, 3.0),
-        (1.0, 2.875),
-        (1.0, 2.0),
-        (0.5, 2.0),
-        (0.5, 3.0),
-        (0.9, 3.0),
-    ],
-    [(0.5, 3.0), (0.5, 3.5), (0.9, 3.0), (0.5, 3.0)],
-    [
-        (0.9, 1.0),
-        (0.5, 1.5),
-        (0.5, 2.0),
-        (1.0, 2.0),
-        (1.0, 1.0),
-        (0.9, 1.0),
-    ],
-    [(1.0, 0.875), (0.9, 1.0), (1.0, 1.0), (1.0, 0.875)],
-    [(2.0, 1.0), (1.0, 1.0), (1.0, 2.0), (2.0, 2.0), (2.0, 1.0)],
-]
diff --git a/tests/test_damages.py b/tests/test_damages.py
new file mode 100644
index 0000000..915ae61
--- /dev/null
+++ b/tests/test_damages.py
@@ -0,0 +1,194 @@
+"""Test damage assessment"""
+import os
+
+import numpy
+import pandas
+import pytest
+from numpy.testing import assert_allclose
+from snail.damages import DamageCurve, PiecewiseLinearDamageCurve
+
+
+@pytest.fixture
+def curve():
+    curve_data = pandas.DataFrame(
+        {"intensity": [0.0, 10, 20, 30], "damage": [0, 0.1, 0.2, 1.0]}
+    )
+    return PiecewiseLinearDamageCurve(curve_data)
+
+
+def test_linear_curve(curve):
+    # check inheritance
+    assert isinstance(curve, DamageCurve)
+
+
+def test_equality(curve):
+    curve_copy = PiecewiseLinearDamageCurve(
+        pandas.DataFrame(
+            {
+                "intensity": [0.0, 10, 20, 30],
+                "damage": [0, 0.1, 0.2, 1.0],
+            }
+        )
+    )
+    assert curve == curve_copy
+
+
+def test_read_csv(curve):
+    fname = os.path.join(
+        os.path.dirname(__file__),
+        "integration",
+        "piecewise-linear-damage-curve.csv",
+    )
+    curve_from_file = PiecewiseLinearDamageCurve.from_csv(fname)
+    assert curve == curve_from_file
+
+
+def test_read_csv_arguments():
+    fname = os.path.join(
+        os.path.dirname(__file__),
+        "integration",
+        "paved-road-flood-depth-damage.csv",
+    )
+    actual = PiecewiseLinearDamageCurve.from_csv(
+        fname,
+        intensity_col="inundation_depth_(m)",
+        damage_col="road_unpaved",
+        sep="\t",
+    )
+    expected = PiecewiseLinearDamageCurve(
+        pandas.DataFrame(
+            {
+                "intensity": [0, 1, 2, 3, 4, 5],
+                "damage": [0, 0.28, 0.46, 0.64, 0.82, 1],
+            }
+        )
+    )
+    assert actual == expected
+
+
+def test_read_excel(curve):
+    fname = os.path.join(
+        os.path.dirname(__file__),
+        "integration",
+        "piecewise-linear-damage-curve.xlsx",
+    )
+    curve_from_file = PiecewiseLinearDamageCurve.from_excel(fname)
+    assert curve == curve_from_file
+
+
+def test_read_excel_arguments():
+    fname = os.path.join(
+        os.path.dirname(__file__),
+        "integration",
+        "paved-road-flood-depth-damage.xlsx",
+    )
+    actual = PiecewiseLinearDamageCurve.from_excel(
+        fname,
+        sheet_name="flood",
+        intensity_col="inundation_depth_(m)",
+        damage_col="road_unpaved",
+    )
+    expected = PiecewiseLinearDamageCurve(
+        pandas.DataFrame(
+            {
+                "intensity": [0, 1, 2, 3, 4, 5],
+                "damage": [0, 0.28, 0.46, 0.64, 0.82, 1],
+            }
+        )
+    )
+    assert actual == expected
+
+
+def test_linear_curve_pass_through(curve):
+    # check specified intensities give specified damages
+    assert_allclose(curve.damage_fraction(curve.intensity), curve.damage)
+
+
+def test_linear_curve_interpolation(curve):
+    # sense-check interpolation
+    intensities = numpy.array([5, 15, 25])
+    expected = numpy.array([0.05, 0.15, 0.6])
+    actual = curve.damage_fraction(intensities)
+    assert_allclose(actual, expected)
+
+
+def test_linear_curve_out_of_bounds(curve):
+    # sense-check out-of-bounds
+    intensities = numpy.array(
+        [numpy.NINF, -999, numpy.NZERO, 0, 30, 999, numpy.inf, numpy.nan]
+    )
+    expected = numpy.array([0, 0, 0, 0, 1, 1, 1, numpy.nan])
+    actual = curve.damage_fraction(intensities)
+    assert_allclose(actual, expected)
+
+
+def test_linear_curve_translate_y(curve):
+    increased = curve.translate_y(0.1)
+    expected = numpy.array([0.1, 0.2, 0.3, 1.0, 1.0])
+    assert_allclose(increased.damage, expected)
+
+    expected = numpy.array([0, 10, 20, 28.75, 30])
+    assert_allclose(increased.intensity, expected)
+
+
+def test_linear_curve_translate_y_down(curve):
+    decreased = curve.translate_y(-0.1)
+    expected = numpy.array([0, 0, 0.1, 0.9])
+    assert_allclose(decreased.damage, expected)
+
+    expected = numpy.array([0, 10, 20, 30])
+    assert_allclose(decreased.intensity, expected)
+
+
+def test_linear_curve_scale_y(curve):
+    increased = curve.scale_y(2)
+    expected = numpy.array([0, 0.2, 0.4, 1.0, 1.0])
+    assert_allclose(increased.damage, expected)
+
+    expected = numpy.array([0, 10, 20, 23.75, 30])
+    assert_allclose(increased.intensity, expected)
+
+
+def test_linear_curve_scale_y_down(curve):
+    decreased = curve.scale_y(0.1)
+    expected = numpy.array([0, 0.01, 0.02, 0.1])
+    assert_allclose(decreased.damage, expected)
+
+    expected = numpy.array([0, 10, 20, 30])
+    assert_allclose(decreased.intensity, expected)
+
+
+def test_linear_curve_translate_x(curve):
+    increased = curve.translate_x(5)
+    expected = numpy.array([0, 0.1, 0.2, 1.0])
+    assert_allclose(increased.damage, expected)
+
+    expected = numpy.array([5, 15, 25, 35])
+    assert_allclose(increased.intensity, expected)
+
+
+def test_linear_curve_translate_x_down(curve):
+    decreased = curve.translate_x(-5)
+    expected = numpy.array([0, 0.1, 0.2, 1.0])
+    assert_allclose(decreased.damage, expected)
+
+    expected = numpy.array([-5, 5, 15, 25])
+    assert_allclose(decreased.intensity, expected)
+
+
+def test_linear_curve_scale_x(curve):
+    increased = curve.scale_x(2)
+    expected = numpy.array([0, 0.1, 0.2, 1.0])
+    assert_allclose(increased.damage, expected)
+
+    expected = numpy.array([0, 20, 40, 60])
+    assert_allclose(increased.intensity, expected)
+
+
+def test_linear_curve_scale_x_down(curve):
+    decreased = curve.scale_x(0.1)
+    expected = numpy.array([0, 0.1, 0.2, 1.0])
+    assert_allclose(decreased.damage, expected)
+
+    expected = numpy.array([0, 1, 2, 3])
+    assert_allclose(decreased.intensity, expected)
diff --git a/tests/test_intersection.py b/tests/test_intersection.py
new file mode 100644
index 0000000..6f1dfd9
--- /dev/null
+++ b/tests/test_intersection.py
@@ -0,0 +1,212 @@
+import os
+
+import geopandas as gpd
+import numpy as np
+import pytest
+from hilbertcurve.hilbertcurve import HilbertCurve
+from numpy.testing import assert_array_equal
+from rasterio.crs import CRS
+from shapely.geometry import LineString, Polygon
+from shapely.geometry.polygon import LinearRing, orient
+
+from snail.intersection import (
+    GridDefinition,
+    split_linestrings,
+    split_polygons,
+)
+
+
+@pytest.fixture
+def linestrings():
+    test_linestrings = [
+        LineString([(0.5, 0.5), (0.75, 0.5), (1.5, 0.5), (1.5, 1.5)]),
+        LineString([(0.5, 0.5), (0.75, 0.5), (1.5, 1.5)]),
+    ]
+    gdf = gpd.GeoDataFrame(
+        {"col1": ["name1", "name2"], "geometry": test_linestrings}
+    )
+    return gdf
+
+
+@pytest.fixture
+def linestrings_split():
+    expected_splits = [
+        LineString([(0.5, 0.5), (0.75, 0.5), (1.0, 0.5)]),
+        LineString([(1.0, 0.5), (1.5, 0.5), (1.5, 1.0)]),
+        LineString([(1.5, 1.0), (1.5, 1.5)]),
+    ] + [
+        LineString([(0.5, 0.5), (0.75, 0.5), (1.0, 0.8333)]),
+        LineString([(1.0, 0.8333), (1.125, 1.0)]),
+        LineString([(1.125, 1.0), (1.5, 1.5)]),
+    ]
+    expected_gdf = gpd.GeoDataFrame(
+        {"col1": ["name1"] * 3 + ["name2"] * 3, "geometry": expected_splits},
+        index=[0] * 3 + [1] * 3,
+    )
+    return expected_gdf
+
+
+@pytest.fixture
+def polygon():
+    test_linearing = LinearRing(
+        [
+            (1.5, 0.25),
+            (2.5, 1.5),
+            (2.5, 3.5),
+            (1.5, 2.25),
+            (0.5, 3.5),
+            (0.5, 1.5),
+        ]
+    )
+    counter_clockwise = 1
+    test_polygon = orient(Polygon(test_linearing), counter_clockwise)
+    return gpd.GeoDataFrame({"col1": ["name1"], "geometry": [test_polygon]})
+
+
+@pytest.fixture
+def polygon_split():
+    rings = [
+        [
+            (0.9, 1.0),
+            (0.5, 1.5),
+            (0.5, 2.0),
+            (1.0, 2.0),
+            (1.0, 1.0),
+            (0.9, 1.0),
+        ],
+        [
+            (0.9, 3.0),
+            (1.0, 2.875),
+            (1.0, 2.0),
+            (0.5, 2.0),
+            (0.5, 3.0),
+            (0.9, 3.0),
+        ],
+        [(1.0, 0.875), (0.9, 1.0), (1.0, 1.0), (1.0, 0.875)],
+        [(0.5, 3.0), (0.5, 3.5), (0.9, 3.0), (0.5, 3.0)],
+        [
+            (2.0, 0.875),
+            (1.5, 0.25),
+            (1.0, 0.875),
+            (1.0, 1.0),
+            (2.0, 1.0),
+            (2.0, 0.875),
+        ],
+        [
+            (1.0, 2.875),
+            (1.5, 2.25),
+            (2.0, 2.875),
+            (2.0, 2.0),
+            (1.0, 2.0),
+            (1.0, 2.875),
+        ],
+        [(2.0, 1.0), (1.0, 1.0), (1.0, 2.0), (2.0, 2.0), (2.0, 1.0)],
+        [(2.1, 1.0), (2.0, 0.875), (2.0, 1.0), (2.1, 1.0)],
+        [
+            (2.5, 2.0),
+            (2.5, 1.5),
+            (2.1, 1.0),
+            (2.0, 1.0),
+            (2.0, 2.0),
+            (2.5, 2.0),
+        ],
+        [
+            (2.5, 3.0),
+            (2.5, 2.0),
+            (2.0, 2.0),
+            (2.0, 2.875),
+            (2.1, 3.0),
+            (2.5, 3.0),
+        ],
+        [(2.1, 3.0), (2.5, 3.5), (2.5, 3.0), (2.1, 3.0)],
+    ]
+    expected_polygons = [Polygon(ring) for ring in rings]
+    expected_idx = ["name1"] * len(rings)
+    expected_gdf = gpd.GeoDataFrame(
+        {"col1": expected_idx, "geometry": expected_polygons}
+    )
+    expected_gdf["index"] = 0
+    return expected_gdf.set_index("index")
+
+
+@pytest.fixture
+def grid():
+    return GridDefinition(
+        crs=None, width=4, height=4, transform=(1, 0, 0, 0, 1, 0)
+    )
+
+
+def test_grid_from_extent(grid):
+    actual = GridDefinition.from_extent(
+        xmin=0, ymin=0, xmax=4, ymax=4, cell_width=1, cell_height=1, crs=None
+    )
+    assert actual == grid
+
+
+def test_grid_from_raster():
+    fname = os.path.join(
+        os.path.dirname(__file__),
+        "integration",
+        "range.tif",
+    )
+    actual = GridDefinition.from_raster(fname)
+    expected = GridDefinition(
+        crs=CRS.from_epsg(4326),
+        width=23,
+        height=14,
+        transform=(
+            0.008333333347826087,
+            0.0,
+            -1.341666667,
+            0.0,
+            -0.008333333285714315,
+            51.808333333,
+        ),
+    )
+    assert actual == expected
+
+
+class TestSnailIntersections:
+    def test_split_linestrings(self, grid, linestrings, linestrings_split):
+        actual = split_linestrings(linestrings, grid)
+        expected_gdf = linestrings_split
+
+        # Assertions
+
+        # Ideally we'd like to use geopandas.assert_geodataframe_equal to
+        # to compare both expected and actual geodfs, but this function offers
+        # little control over tolerance. When using option "check_less_precise",
+        # it used GeoSeries.geom_almost_equals under the hood, which has an kwarg
+        # "decimal". But assert_geodataframe_equal does not recognise kwarg "decimal".
+        assert (
+            actual["geometry"]
+            .geom_almost_equals(expected_gdf["geometry"], decimal=3)
+            .values.all()
+        )
+        assert_array_equal(actual["col1"].values, expected_gdf["col1"].values)
+
+    def test_split_polygons(self, grid, polygon, polygon_split):
+        actual = sort_polygons(split_polygons(polygon, grid))
+        expected = sort_polygons(polygon_split)
+
+        for i in range(len(actual)):
+            actual_geom = actual.iloc[i, 1]
+            expected_geom = expected.iloc[i, 1]
+            assert actual_geom.equals(expected_geom)
+        assert_array_equal(actual["col1"].values, expected["col1"].values)
+
+
+def sort_polygons(df):
+    iterations = 6  # all coords must be <= (2**p - 1) ; 2**6 - 1 == 63
+    ndimensions = 2
+    hilbert_curve = HilbertCurve(iterations, ndimensions)
+    points = df.geometry.centroid
+    coords = np.array(
+        list(zip(points.x.values.tolist(), points.y.values.tolist()))
+    )
+    int_coords = (coords * 10).astype(int)
+    distances = hilbert_curve.distances_from_points(int_coords)
+    df["hilbert_distance"] = distances
+    return df.sort_values(by="hilbert_distance").drop(
+        columns="hilbert_distance"
+    )
diff --git a/tests/test_multi_intersections.py b/tests/test_multi_intersections.py
deleted file mode 100644
index 8b6682b..0000000
--- a/tests/test_multi_intersections.py
+++ /dev/null
@@ -1,109 +0,0 @@
-import unittest
-
-from numpy.testing import assert_array_equal
-import geopandas as gpd
-from shapely.geometry import LineString, Polygon
-from shapely.geometry.polygon import LinearRing, orient
-
-from snail.intersection import (
-    Transform,
-    split_linestrings,
-    split_polygons,
-)
-
-from split_polygons_rings import expected_polygons_rings
-
-
-def get_couple_of_linestrings():
-    test_linestrings = [
-        LineString([(0.5, 0.5), (0.75, 0.5), (1.5, 0.5), (1.5, 1.5)]),
-        LineString([(0.5, 0.5), (0.75, 0.5), (1.5, 1.5)]),
-    ]
-    gdf = gpd.GeoDataFrame(
-        {"col1": ["name1", "name2"], "geometry": test_linestrings}
-    )
-    return gdf
-
-
-def get_polygon_vector_data():
-    test_linearing = LinearRing(
-        [
-            (1.5, 0.25),
-            (2.5, 1.5),
-            (2.5, 3.5),
-            (1.5, 2.25),
-            (0.5, 3.5),
-            (0.5, 1.5),
-        ]
-    )
-    counter_clockwise = 1
-    test_polygon = orient(Polygon(test_linearing), counter_clockwise)
-    return gpd.GeoDataFrame({"col1": ["name1"], "geometry": [test_polygon]})
-
-
-def get_split_polygons():
-    expected_polygons = [Polygon(ring) for ring in expected_polygons_rings]
-    expected_idx = ["name1"] * len(expected_polygons_rings)
-    expected_gdf = gpd.GeoDataFrame(
-        {"col1": expected_idx, "geometry": expected_polygons}
-    )
-    expected_gdf["index"] = 0
-    return expected_gdf.set_index("index")
-
-
-def get_split_linestrings():
-    expected_splits = [
-        LineString([(0.5, 0.5), (0.75, 0.5), (1.0, 0.5)]),
-        LineString([(1.0, 0.5), (1.5, 0.5), (1.5, 1.0)]),
-        LineString([(1.5, 1.0), (1.5, 1.5)]),
-    ] + [
-        LineString([(0.5, 0.5), (0.75, 0.5), (1.0, 0.8333)]),
-        LineString([(1.0, 0.8333), (1.125, 1.0)]),
-        LineString([(1.125, 1.0), (1.5, 1.5)]),
-    ]
-    expected_gdf = gpd.GeoDataFrame(
-        {"col1": ["name1"] * 3 + ["name2"] * 3, "geometry": expected_splits},
-        index=[0] * 3 + [1] * 3,
-    )
-    return expected_gdf
-
-
-class TestSnailIntersections(unittest.TestCase):
-    def setUp(self):
-        self.raster_dataset = Transform(
-            crs=None, width=4, height=4, transform=(1, 0, 0, 0, 1, 0)
-        )
-
-    def test_split_linestrings(self):
-        vector_data = get_couple_of_linestrings()
-        gdf = split_linestrings(vector_data, self.raster_dataset)
-        expected_gdf = get_split_linestrings()
-
-        # Assertions
-
-        # Ideally we'd like to use geopandas.assert_geodataframe_equal to
-        # to compare both expected and actual geodfs, but this function offers
-        # little control over tolerance. When using option "check_less_precise",
-        # it used GeoSeries.geom_almost_equals under the hood, which has an kwarg
-        # "decimal". But assert_geodataframe_equal does not recognise kwarg "decimal".
-        self.assertTrue(
-            list(
-                gdf["geometry"]
-                .geom_almost_equals(expected_gdf["geometry"], decimal=3)
-                .values
-            )
-        )
-        assert_array_equal(gdf["col1"].values, expected_gdf["col1"].values)
-
-    def test_split_polygons(self):
-        vector_data = get_polygon_vector_data()
-        gdf = split_polygons(vector_data, self.raster_dataset)
-        expected_gdf = get_split_polygons()
-        self.assertTrue(
-            list(
-                gdf["geometry"]
-                .geom_almost_equals(expected_gdf["geometry"], decimal=3)
-                .values
-            )
-        )
-        assert_array_equal(gdf["col1"].values, expected_gdf["col1"].values)
diff --git a/tests/test_routing.py b/tests/test_routing.py
index 635d79a..ec3c050 100644
--- a/tests/test_routing.py
+++ b/tests/test_routing.py
@@ -1,30 +1,27 @@
-import unittest
 from igraph import Graph
-
 from snail.routing import shortest_paths
 
 
-class TestSnailRouting(unittest.TestCase):
-    def test_shortest_paths(self):
-        """
-             e4
-            0--4
-        e0  |  |
-            1  | e3
-        e1  |  |
-            2--3
-             e2
-        """
-        g = Graph.Ring(n=5, circular=True)
-        g.vs["name"] = ["node_" + str(i) for i in range(5)]
-        g.es["length_km"] = [1, 1, 1, 3, 1]
-        sps = shortest_paths(
-            ["node_0", "node_2"], ["node_4", "node_3"], g, "length_km"
-        )
-        expected_paths = [
-            [4],  # 0 to 4, along edge 4
-            [0, 1, 2],  # 0 to 3, avoids long edge 3
-            [1, 0, 4],  # 2 to 4, avoids long edge 3
-            [2],  # 2 to 3, along edge 2
-        ]
-        self.assertEqual(sps, expected_paths)
+def test_shortest_paths():
+    """
+            e4
+        0--4
+    e0  |  |
+        1  | e3
+    e1  |  |
+        2--3
+            e2
+    """
+    g = Graph.Ring(n=5, circular=True)
+    g.vs["name"] = ["node_" + str(i) for i in range(5)]
+    g.es["length_km"] = [1, 1, 1, 3, 1]
+    actual = shortest_paths(
+        ["node_0", "node_2"], ["node_4", "node_3"], g, "length_km"
+    )
+    expected = [
+        [4],  # 0 to 4, along edge 4
+        [0, 1, 2],  # 0 to 3, avoids long edge 3
+        [1, 0, 4],  # 2 to 4, avoids long edge 3
+        [2],  # 2 to 3, along edge 2
+    ]
+    assert actual == expected
diff --git a/tutorials/01-data-preparation-ghana.ipynb b/tutorials/01-data-preparation-ghana.ipynb
index 273c233..64d8fc2 100644
--- a/tutorials/01-data-preparation-ghana.ipynb
+++ b/tutorials/01-data-preparation-ghana.ipynb
@@ -1,6 +1,7 @@
 {
  "cells": [
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -15,6 +16,27 @@
    ]
   },
   {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# The os and subprocess modules are built into Python\n",
+    "# see https://docs.python.org/3/library/os.html\n",
+    "import os\n",
+    "\n",
+    "# see https://docs.python.org/3/library/subprocess.html\n",
+    "import subprocess\n",
+    "\n",
+    "# see https://docs.python.org/3/library/time.html\n",
+    "import time\n",
+    "\n",
+    "# see https://docs.python.org/3/library/pathlib.html\n",
+    "from pathlib import Path"
+   ]
+  },
+  {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -29,21 +51,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
     "# edit this if using a Mac (otherwise delete)\n",
-    "data_folder = \"/Users/NAME/Desktop/ghana_tutorial\"\n",
+    "data_folder = Path(\"/Users/NAME/Desktop/ghana_tutorial\")\n",
     "\n",
     "# edit this if using Windows (otherwise delete)\n",
-    "data_folder = \"C:\\\\Users\\\\NAME\\\\Desktop\\\\ghana_tutorial\"\n",
+    "data_folder = Path(\"C:/Users/NAME/Desktop/ghana_tutorial\")\n",
     "\n",
     "# delete this line\n",
-    "data_folder = \"../data\""
+    "data_folder = Path(\"../data\")"
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -52,17 +75,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# The os and subprocess modules are built into Python\n",
-    "# see https://docs.python.org/3/library/os.html\n",
-    "import os\n",
-    "\n",
-    "# see https://docs.python.org/3/library/subprocess.html\n",
-    "import subprocess\n",
-    "\n",
     "# Pandas and GeoPandas are libraries for working with datasets\n",
     "# see https://geopandas.org/\n",
     "import geopandas as gpd\n",
@@ -71,6 +87,14 @@
     "# see https://pandas.pydata.org/\n",
     "import pandas as pd\n",
     "\n",
+    "# This package interacts with a risk data extract service, also accessible at\n",
+    "# https://global.infrastructureresilience.org/downloads\n",
+    "import irv_autopkg_client\n",
+    "\n",
+    "# We'll use snail to intersect roads with flooding\n",
+    "import snail.intersection\n",
+    "import snail.io\n",
+    "\n",
     "# snkit helps generate connected networks from lines and nodes\n",
     "# see https://snkit.readthedocs.io/\n",
     "import snkit\n",
@@ -82,6 +106,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -89,6 +114,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -96,6 +122,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -104,18 +131,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
     "roads = gpd.read_file(\n",
-    "    os.path.join(\n",
-    "        data_folder, \"ghana-latest-free.shp\", \"gis_osm_roads_free_1.shp\"\n",
-    "    )\n",
+    "    data_folder / \"ghana-latest-free.shp\" / \"gis_osm_roads_free_1.shp\"\n",
     ")"
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -124,23 +150,282 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>osm_id</th>\n",
+       "      <th>code</th>\n",
+       "      <th>fclass</th>\n",
+       "      <th>name</th>\n",
+       "      <th>ref</th>\n",
+       "      <th>oneway</th>\n",
+       "      <th>maxspeed</th>\n",
+       "      <th>layer</th>\n",
+       "      <th>bridge</th>\n",
+       "      <th>tunnel</th>\n",
+       "      <th>geometry</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>4790591</td>\n",
+       "      <td>5121</td>\n",
+       "      <td>unclassified</td>\n",
+       "      <td>Airport Road</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>B</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>F</td>\n",
+       "      <td>F</td>\n",
+       "      <td>LINESTRING (-0.17184 5.60847, -0.17182 5.60849...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>4790592</td>\n",
+       "      <td>5122</td>\n",
+       "      <td>residential</td>\n",
+       "      <td>Nortei Ababio Road</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>B</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>F</td>\n",
+       "      <td>F</td>\n",
+       "      <td>LINESTRING (-0.18282 5.61197, -0.18336 5.61198...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>4790594</td>\n",
+       "      <td>5115</td>\n",
+       "      <td>tertiary</td>\n",
+       "      <td>Airport Road</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>F</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>F</td>\n",
+       "      <td>F</td>\n",
+       "      <td>LINESTRING (-0.17544 5.60550, -0.17418 5.60555...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4790596</td>\n",
+       "      <td>5121</td>\n",
+       "      <td>unclassified</td>\n",
+       "      <td>Airport Road</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>F</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>F</td>\n",
+       "      <td>F</td>\n",
+       "      <td>LINESTRING (-0.17207 5.60853, -0.17207 5.60844...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>4790597</td>\n",
+       "      <td>5122</td>\n",
+       "      <td>residential</td>\n",
+       "      <td>Volta Road</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>B</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>F</td>\n",
+       "      <td>F</td>\n",
+       "      <td>LINESTRING (-0.18282 5.61197, -0.18280 5.61262...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>338073</th>\n",
+       "      <td>1182192627</td>\n",
+       "      <td>5141</td>\n",
+       "      <td>service</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>B</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>F</td>\n",
+       "      <td>F</td>\n",
+       "      <td>LINESTRING (-0.17508 5.71756, -0.17511 5.71756...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>338074</th>\n",
+       "      <td>1182192628</td>\n",
+       "      <td>5141</td>\n",
+       "      <td>service</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>B</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>F</td>\n",
+       "      <td>F</td>\n",
+       "      <td>LINESTRING (-0.17501 5.71759, -0.17508 5.71756)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>338075</th>\n",
+       "      <td>1182192629</td>\n",
+       "      <td>5141</td>\n",
+       "      <td>service</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>B</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>F</td>\n",
+       "      <td>F</td>\n",
+       "      <td>LINESTRING (-0.17506 5.71778, -0.17500 5.71764...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>338076</th>\n",
+       "      <td>1182207852</td>\n",
+       "      <td>5114</td>\n",
+       "      <td>secondary</td>\n",
+       "      <td>Education Ridge Road</td>\n",
+       "      <td>R92</td>\n",
+       "      <td>B</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>F</td>\n",
+       "      <td>F</td>\n",
+       "      <td>LINESTRING (-0.97456 9.56428, -0.97542 9.56413...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>338077</th>\n",
+       "      <td>1182207853</td>\n",
+       "      <td>5114</td>\n",
+       "      <td>secondary</td>\n",
+       "      <td>Bontanga - Dalung Road</td>\n",
+       "      <td>R92</td>\n",
+       "      <td>B</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>F</td>\n",
+       "      <td>F</td>\n",
+       "      <td>LINESTRING (-0.99413 9.58079, -0.99425 9.58107...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>338078 rows × 11 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            osm_id  code        fclass                    name  ref oneway  \\\n",
+       "0          4790591  5121  unclassified            Airport Road  NaN      B   \n",
+       "1          4790592  5122   residential      Nortei Ababio Road  NaN      B   \n",
+       "2          4790594  5115      tertiary            Airport Road  NaN      F   \n",
+       "3          4790596  5121  unclassified            Airport Road  NaN      F   \n",
+       "4          4790597  5122   residential              Volta Road  NaN      B   \n",
+       "...            ...   ...           ...                     ...  ...    ...   \n",
+       "338073  1182192627  5141       service                     NaN  NaN      B   \n",
+       "338074  1182192628  5141       service                     NaN  NaN      B   \n",
+       "338075  1182192629  5141       service                     NaN  NaN      B   \n",
+       "338076  1182207852  5114     secondary    Education Ridge Road  R92      B   \n",
+       "338077  1182207853  5114     secondary  Bontanga - Dalung Road  R92      B   \n",
+       "\n",
+       "        maxspeed  layer bridge tunnel  \\\n",
+       "0              0      0      F      F   \n",
+       "1              0      0      F      F   \n",
+       "2              0      0      F      F   \n",
+       "3              0      0      F      F   \n",
+       "4              0      0      F      F   \n",
+       "...          ...    ...    ...    ...   \n",
+       "338073         0      0      F      F   \n",
+       "338074         0      0      F      F   \n",
+       "338075         0      0      F      F   \n",
+       "338076         0      0      F      F   \n",
+       "338077         0      0      F      F   \n",
+       "\n",
+       "                                                 geometry  \n",
+       "0       LINESTRING (-0.17184 5.60847, -0.17182 5.60849...  \n",
+       "1       LINESTRING (-0.18282 5.61197, -0.18336 5.61198...  \n",
+       "2       LINESTRING (-0.17544 5.60550, -0.17418 5.60555...  \n",
+       "3       LINESTRING (-0.17207 5.60853, -0.17207 5.60844...  \n",
+       "4       LINESTRING (-0.18282 5.61197, -0.18280 5.61262...  \n",
+       "...                                                   ...  \n",
+       "338073  LINESTRING (-0.17508 5.71756, -0.17511 5.71756...  \n",
+       "338074    LINESTRING (-0.17501 5.71759, -0.17508 5.71756)  \n",
+       "338075  LINESTRING (-0.17506 5.71778, -0.17500 5.71764...  \n",
+       "338076  LINESTRING (-0.97456 9.56428, -0.97542 9.56413...  \n",
+       "338077  LINESTRING (-0.99413 9.58079, -0.99425 9.58107...  \n",
+       "\n",
+       "[338078 rows x 11 columns]"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "roads"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['unclassified', 'residential', 'tertiary', 'tertiary_link',\n",
+       "       'secondary', 'trunk', 'service', 'primary', 'trunk_link',\n",
+       "       'primary_link', 'secondary_link', 'footway', 'path', 'track',\n",
+       "       'motorway', 'track_grade3', 'track_grade4', 'motorway_link',\n",
+       "       'steps', 'pedestrian', 'bridleway', 'cycleway', 'track_grade2',\n",
+       "       'track_grade5', 'track_grade1', 'living_street'], dtype=object)"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "roads.fclass.unique()"
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -148,6 +433,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -156,7 +442,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -188,6 +474,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -199,7 +486,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -208,7 +495,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -223,6 +510,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -231,7 +519,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -241,24 +529,166 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 11,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>osm_id</th>\n",
+       "      <th>road_type</th>\n",
+       "      <th>name</th>\n",
+       "      <th>geometry</th>\n",
+       "      <th>id</th>\n",
+       "      <th>from_id</th>\n",
+       "      <th>to_id</th>\n",
+       "      <th>length_m</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>15684</th>\n",
+       "      <td>1181982913</td>\n",
+       "      <td>secondary</td>\n",
+       "      <td>Kumbungu-Zangbalung road</td>\n",
+       "      <td>LINESTRING (-0.95804 9.56291, -0.95811 9.56294...</td>\n",
+       "      <td>roade_15684</td>\n",
+       "      <td>roadn_12219</td>\n",
+       "      <td>roadn_12220</td>\n",
+       "      <td>1870.991174</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15685</th>\n",
+       "      <td>1182141809</td>\n",
+       "      <td>secondary_link</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>LINESTRING (-1.59420 6.65761, -1.59426 6.65768...</td>\n",
+       "      <td>roade_15685</td>\n",
+       "      <td>roadn_12221</td>\n",
+       "      <td>roadn_12216</td>\n",
+       "      <td>47.244512</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15686</th>\n",
+       "      <td>1182207852</td>\n",
+       "      <td>secondary</td>\n",
+       "      <td>Education Ridge Road</td>\n",
+       "      <td>LINESTRING (-0.97456 9.56428, -0.97542 9.56413...</td>\n",
+       "      <td>roade_15686</td>\n",
+       "      <td>roadn_12220</td>\n",
+       "      <td>roadn_8005</td>\n",
+       "      <td>2242.279664</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15687</th>\n",
+       "      <td>1182207852</td>\n",
+       "      <td>secondary</td>\n",
+       "      <td>Education Ridge Road</td>\n",
+       "      <td>LINESTRING (-0.99028 9.57190, -0.99202 9.57587...</td>\n",
+       "      <td>roade_15687</td>\n",
+       "      <td>roadn_8005</td>\n",
+       "      <td>roadn_12222</td>\n",
+       "      <td>1069.950243</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15688</th>\n",
+       "      <td>1182207853</td>\n",
+       "      <td>secondary</td>\n",
+       "      <td>Bontanga - Dalung Road</td>\n",
+       "      <td>LINESTRING (-0.99413 9.58079, -0.99425 9.58107...</td>\n",
+       "      <td>roade_15688</td>\n",
+       "      <td>roadn_12222</td>\n",
+       "      <td>roadn_8006</td>\n",
+       "      <td>6604.650117</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           osm_id       road_type                      name  \\\n",
+       "15684  1181982913       secondary  Kumbungu-Zangbalung road   \n",
+       "15685  1182141809  secondary_link                       NaN   \n",
+       "15686  1182207852       secondary      Education Ridge Road   \n",
+       "15687  1182207852       secondary      Education Ridge Road   \n",
+       "15688  1182207853       secondary    Bontanga - Dalung Road   \n",
+       "\n",
+       "                                                geometry           id  \\\n",
+       "15684  LINESTRING (-0.95804 9.56291, -0.95811 9.56294...  roade_15684   \n",
+       "15685  LINESTRING (-1.59420 6.65761, -1.59426 6.65768...  roade_15685   \n",
+       "15686  LINESTRING (-0.97456 9.56428, -0.97542 9.56413...  roade_15686   \n",
+       "15687  LINESTRING (-0.99028 9.57190, -0.99202 9.57587...  roade_15687   \n",
+       "15688  LINESTRING (-0.99413 9.58079, -0.99425 9.58107...  roade_15688   \n",
+       "\n",
+       "           from_id        to_id     length_m  \n",
+       "15684  roadn_12219  roadn_12220  1870.991174  \n",
+       "15685  roadn_12221  roadn_12216    47.244512  \n",
+       "15686  roadn_12220   roadn_8005  2242.279664  \n",
+       "15687   roadn_8005  roadn_12222  1069.950243  \n",
+       "15688  roadn_12222   roadn_8006  6604.650117  "
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "roads.tail()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 12,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Geographic 2D CRS: EPSG:4326>\n",
+       "Name: WGS 84\n",
+       "Axis Info [ellipsoidal]:\n",
+       "- Lat[north]: Geodetic latitude (degree)\n",
+       "- Lon[east]: Geodetic longitude (degree)\n",
+       "Area of Use:\n",
+       "- name: World.\n",
+       "- bounds: (-180.0, -90.0, 180.0, 90.0)\n",
+       "Datum: World Geodetic System 1984 ensemble\n",
+       "- Ellipsoid: WGS 84\n",
+       "- Prime Meridian: Greenwich"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "roads.crs = {\"init\": \"epsg:4326\"}\n",
-    "road_nodes.crs = {\"init\": \"epsg:4326\"}"
+    "roads.set_crs(4326, inplace=True)\n",
+    "road_nodes.set_crs(4326, inplace=True)\n",
+    "road_nodes.crs"
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -267,127 +697,201 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [],
    "source": [
     "roads.to_file(\n",
-    "    os.path.join(data_folder, \"GHA_OSM_roads.gpkg\"),\n",
+    "    data_folder / \"GHA_OSM_roads.gpkg\",\n",
     "    layer=\"edges\",\n",
     "    driver=\"GPKG\",\n",
     ")\n",
     "road_nodes.to_file(\n",
-    "    os.path.join(data_folder, \"GHA_OSM_roads.gpkg\"),\n",
+    "    data_folder / \"GHA_OSM_roads.gpkg\",\n",
     "    layer=\"nodes\",\n",
     "    driver=\"GPKG\",\n",
     ")"
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Activity 2: Extract and polygonise hazard data"
+    "## Activity 2: Extract hazard data"
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Step 1) Download flood hazard data from Aqueduct"
+    "The full [Aqueduct dataset](https://www.wri.org/resources/data-sets/aqueduct-floods-hazard-maps) is available to download openly. \n",
+    "\n",
+    "Country-level extracts are available through the [Global Systemic Risk Assessment Tool (G-SRAT)](https://global.infrastructureresilience.org/downloads/). This section uses that service to download an extract for Ghana."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "country_iso = \"gha\""
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The full [Aqueduct dataset](https://www.wri.org/resources/data-sets/aqueduct-floods-hazard-maps) is available to download openly. There are some scripts and summary of the data you may find useful at [nismod/aqueduct](https://github.com/nismod/aqueduct).\n",
-    "\n",
-    "There are almost 700 files in the full Aqueduct dataset, of up to around 100MB each, so we don't recommend downloading all of them unless you intend to do further analysis.\n",
-    "\n",
-    "For later tutorials, we provide a preprocessed set of hazard polygons for the Ghana example. \n",
-    "\n",
-    "The next steps show how to clip a region out of the global dataset and polygonise it, in case you wish to reproduce this analysis in another part of the world.\n",
-    "\n",
-    "For now, we suggest downloading [inunriver_historical_000000000WATCH_1980_rp00100.tif](http://wri-projects.s3.amazonaws.com/AqueductFloodTool/download/v2/inunriver_historical_000000000WATCH_1980_rp00100.tif) to work through the next steps. Save the downloaded file in a new folder titled `flood_layer` under your data_folder."
+    "Create a client to connect to the data API:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "client = irv_autopkg_client.Client()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "job_id = client.job_submit(country_iso, [\"wri_aqueduct.version_2\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Processing...\n"
+     ]
+    }
+   ],
+   "source": [
+    "while not client.job_complete(job_id):\n",
+    "    print(\"Processing...\")\n",
+    "    time.sleep(1)"
    ]
   },
   {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "client.extract_download(\n",
+    "    country_iso,\n",
+    "    data_folder / \"flood_layer\",\n",
+    "    # there may be other datasets available, but only download the following\n",
+    "    dataset_filter=[\"wri_aqueduct.version_2\"],\n",
+    "    overwrite=True,\n",
+    ")"
+   ]
+  },
+  {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Step 2) Run the code below to polygonise the tif files\n",
+    "### Alternative: download flood hazard data from Aqueduct"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The full [Aqueduct dataset](https://www.wri.org/resources/data-sets/aqueduct-floods-hazard-maps) is available to download. There are some scripts and summary of the data you may find useful at [nismod/aqueduct](https://github.com/nismod/aqueduct).\n",
+    "\n",
+    "There are almost 700 files in the full Aqueduct dataset, of up to around 100MB each, so we don't recommend downloading all of them unless you intend to do further analysis.\n",
     "\n",
-    "This converts the flood maps from *tiff files (raster data)* into *shape files (vector data)*. It will take a little time to run."
+    "The next steps show how to clip a region out of the global dataset, in case you prefer to work from the original global Aqueduct files.\n",
+    "\n",
+    "To follow this step, we suggest downloading [inunriver_historical_000000000WATCH_1980_rp00100.tif](http://wri-projects.s3.amazonaws.com/AqueductFloodTool/download/v2/inunriver_historical_000000000WATCH_1980_rp00100.tif) to work through the next steps. Save the downloaded file in a new folder titled `flood_layer` under your data_folder."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 19,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Looking in ../data/flood_layer\n",
+      "Found tif file inunriver_historical_000000000WATCH_1980_rp00100.tif\n",
+      "['gdalwarp', '-te', '-3.262509', '4.737128', '1.187968', '11.162937', '../data/flood_layer/inunriver_historical_000000000WATCH_1980_rp00100.tif', '../data/flood_layer/gha/wri_aqueduct_version_2/inunriver_historical_000000000WATCH_1980_rp00100-gha.tif']\n",
+      "Creating output file that is 534P x 771L.\n",
+      "Processing ../data/flood_layer/inunriver_historical_000000000WATCH_1980_rp00100.tif [1/1] : 0Using internal nodata values (e.g. -9999) for image ../data/flood_layer/inunriver_historical_000000000WATCH_1980_rp00100.tif.\n",
+      "Copying nodata values from source ../data/flood_layer/inunriver_historical_000000000WATCH_1980_rp00100.tif to destination ../data/flood_layer/gha/wri_aqueduct_version_2/inunriver_historical_000000000WATCH_1980_rp00100-gha.tif.\n",
+      "...10...20...30...40...50...60...70...80...90...100 - done.\n",
+      "\n",
+      "\n",
+      "../data/flood_layer/gha/wri_aqueduct_version_2/inunriver_historical_000000000WATCH_1980_rp00100-gha.tif\n",
+      "Looking in ../data/flood_layer/gha\n",
+      "Looking in ../data/flood_layer/gha/wri_aqueduct_version_2\n"
+     ]
+    }
+   ],
    "source": [
     "xmin = \"-3.262509\"\n",
     "ymin = \"4.737128\"\n",
     "xmax = \"1.187968\"\n",
     "ymax = \"11.162937\"\n",
     "\n",
-    "for root, dirs, files in os.walk(data_folder, \"flood_layer\"):\n",
+    "for root, dirs, files in os.walk(os.path.join(data_folder, \"flood_layer\")):\n",
     "    print(\"Looking in\", root)\n",
-    "    for file in sorted(files):\n",
-    "        if file.endswith(\".tif\") and not file.endswith(\"p.tif\"):\n",
-    "            print(\"Found tif file\", file)\n",
-    "            stem = file[:-4]\n",
-    "            input_file = os.path.join(root, file)\n",
+    "    for file_ in sorted(files):\n",
+    "        if file_.endswith(\".tif\") and not file_.endswith(\n",
+    "            f\"-{country_iso}.tif\"\n",
+    "        ):\n",
+    "            print(\"Found tif file\", file_)\n",
+    "            stem = file_[:-4]\n",
+    "            input_file = os.path.join(root, file_)\n",
     "\n",
     "            # Clip file to bounds\n",
-    "            clip_file = os.path.join(root, f\"{stem}_clip.tif\")\n",
-    "            try:\n",
-    "                os.remove(clip_file)\n",
-    "            except FileNotFoundError:\n",
-    "                pass\n",
-    "            p = subprocess.run(\n",
-    "                [\n",
-    "                    \"gdalwarp\",\n",
-    "                    \"-te\",\n",
-    "                    xmin,\n",
-    "                    ymin,\n",
-    "                    xmax,\n",
-    "                    ymax,\n",
-    "                    input_file,\n",
-    "                    clip_file,\n",
-    "                ],\n",
-    "                capture_output=True,\n",
+    "            clip_file = os.path.join(\n",
+    "                root,\n",
+    "                \"gha\",\n",
+    "                \"wri_aqueduct_version_2\",\n",
+    "                f\"{stem}-{country_iso}.tif\",\n",
     "            )\n",
-    "            print(p.stdout.decode(\"utf8\"))\n",
-    "            print(p.stderr.decode(\"utf8\"))\n",
-    "            print(clip_file)\n",
-    "\n",
-    "            # Create vector outline of raster areas\n",
-    "            # note that this rounds the floating-point values of flood depth from\n",
-    "            # the raster to the nearest integer in the vector outlines\n",
-    "            polygons_file = os.path.join(root, f\"{stem}.gpkg\")\n",
     "            try:\n",
-    "                os.remove(polygons_file)\n",
+    "                os.remove(clip_file)\n",
     "            except FileNotFoundError:\n",
     "                pass\n",
-    "            p = subprocess.run(\n",
-    "                [\n",
-    "                    \"gdal_polygonize.py\",\n",
-    "                    clip_file,\n",
-    "                    \"-q\",\n",
-    "                    \"-f\",\n",
-    "                    \"GPKG\",\n",
-    "                    polygons_file,\n",
-    "                ],\n",
-    "                capture_output=True,\n",
-    "            )\n",
+    "            cmd = [\n",
+    "                \"gdalwarp\",\n",
+    "                \"-te\",\n",
+    "                xmin,\n",
+    "                ymin,\n",
+    "                xmax,\n",
+    "                ymax,\n",
+    "                input_file,\n",
+    "                clip_file,\n",
+    "            ]\n",
+    "            print(cmd)\n",
+    "            p = subprocess.run(cmd, capture_output=True)\n",
     "            print(p.stdout.decode(\"utf8\"))\n",
     "            print(p.stderr.decode(\"utf8\"))\n",
-    "            print(polygons_file)"
+    "            print(clip_file)"
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -395,6 +899,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -402,6 +907,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -410,27 +916,44 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [],
    "source": [
-    "flood_path = os.path.join(\n",
+    "flood_path = Path(\n",
     "    data_folder,\n",
     "    \"flood_layer\",\n",
-    "    \"inunriver_historical_000000000WATCH_1980_rp00100.gpkg\",\n",
+    "    \"gha\",\n",
+    "    \"wri_aqueduct_version_2\",\n",
+    "    \"inunriver_historical_000000000WATCH_1980_rp00100-gha.tif\",\n",
     ")\n",
     "\n",
-    "output_path = os.path.join(\n",
+    "output_path = Path(\n",
     "    data_folder,\n",
     "    \"results\",\n",
-    "    \"inunriver_historical_000000000WATCH_1980_rp00100_exposure.gpkg\",\n",
-    ")\n",
-    "\n",
-    "flood = gpd.read_file(flood_path).rename(columns={\"DN\": \"depth_m\"})\n",
-    "flood = flood[flood.depth_m > 0]"
+    "    \"inunriver_historical_000000000WATCH_1980_rp00100__roads_exposure.gpkg\",\n",
+    ")"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Read in pre-processed road edges, as created earlier."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "roads = gpd.read_file(data_folder / \"GHA_OSM_roads.gpkg\", layer=\"edges\")"
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -439,15 +962,27 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 27,
    "metadata": {},
    "outputs": [],
    "source": [
-    "flood_intersections = gpd.overlay(GHA_OSM_roads, flood, how=\"intersection\")\n",
-    "flood_intersections"
+    "grid, bands = snail.io.read_raster_metadata(flood_path)\n",
+    "\n",
+    "prepared = snail.intersection.prepare_linestrings(roads)\n",
+    "flood_intersections = snail.intersection.split_linestrings(prepared, grid)\n",
+    "flood_intersections = snail.intersection.apply_indices(\n",
+    "    flood_intersections, grid\n",
+    ")\n",
+    "flood_data = snail.io.read_raster_band_data(flood_path)\n",
+    "flood_intersections[\n",
+    "    \"inunriver__epoch_historical__rcp_baseline__rp_100\"\n",
+    "] = snail.intersection.get_raster_values_for_splits(\n",
+    "    flood_intersections, flood_data\n",
+    ")"
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -456,7 +991,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 28,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -468,14 +1003,115 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 29,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>osm_id</th>\n",
+       "      <th>road_type</th>\n",
+       "      <th>name</th>\n",
+       "      <th>id</th>\n",
+       "      <th>from_id</th>\n",
+       "      <th>to_id</th>\n",
+       "      <th>length_m</th>\n",
+       "      <th>geometry</th>\n",
+       "      <th>split</th>\n",
+       "      <th>index_i</th>\n",
+       "      <th>index_j</th>\n",
+       "      <th>inunriver__epoch_historical__rcp_baseline__rp_100</th>\n",
+       "      <th>flood_length_m</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>15688</th>\n",
+       "      <td>1182207853</td>\n",
+       "      <td>secondary</td>\n",
+       "      <td>Bontanga - Dalung Road</td>\n",
+       "      <td>roade_15688</td>\n",
+       "      <td>roadn_12222</td>\n",
+       "      <td>roadn_8006</td>\n",
+       "      <td>6604.650117</td>\n",
+       "      <td>LINESTRING (-1.00963 9.62941, -1.01021 9.63122...</td>\n",
+       "      <td>8</td>\n",
+       "      <td>270</td>\n",
+       "      <td>183</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>782.156843</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15688</th>\n",
+       "      <td>1182207853</td>\n",
+       "      <td>secondary</td>\n",
+       "      <td>Bontanga - Dalung Road</td>\n",
+       "      <td>roade_15688</td>\n",
+       "      <td>roadn_12222</td>\n",
+       "      <td>roadn_8006</td>\n",
+       "      <td>6604.650117</td>\n",
+       "      <td>LINESTRING (-1.01227 9.63597, -1.01230 9.63605...</td>\n",
+       "      <td>9</td>\n",
+       "      <td>269</td>\n",
+       "      <td>183</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>135.659825</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           osm_id  road_type                    name           id  \\\n",
+       "15688  1182207853  secondary  Bontanga - Dalung Road  roade_15688   \n",
+       "15688  1182207853  secondary  Bontanga - Dalung Road  roade_15688   \n",
+       "\n",
+       "           from_id       to_id     length_m  \\\n",
+       "15688  roadn_12222  roadn_8006  6604.650117   \n",
+       "15688  roadn_12222  roadn_8006  6604.650117   \n",
+       "\n",
+       "                                                geometry  split  index_i  \\\n",
+       "15688  LINESTRING (-1.00963 9.62941, -1.01021 9.63122...      8      270   \n",
+       "15688  LINESTRING (-1.01227 9.63597, -1.01230 9.63605...      9      269   \n",
+       "\n",
+       "       index_j  inunriver__epoch_historical__rcp_baseline__rp_100  \\\n",
+       "15688      183                                                0.0   \n",
+       "15688      183                                                0.0   \n",
+       "\n",
+       "       flood_length_m  \n",
+       "15688      782.156843  \n",
+       "15688      135.659825  "
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "flood_intersections.tail()"
+    "flood_intersections.tail(2)"
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -484,69 +1120,118 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 30,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "728.5879687723159"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "exposed_length = flood_intersections.flood_length_m.sum()\n",
-    "exposed_length"
+    "exposed_1m = flood_intersections[\n",
+    "    flood_intersections.inunriver__epoch_historical__rcp_baseline__rp_100 >= 1\n",
+    "]\n",
+    "exposed_length_km = exposed_1m.flood_length_m.sum() * 1e-3\n",
+    "exposed_length_km"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 31,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "29069.876011778793"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "all_roads_in_dataset_length = GHA_OSM_roads.length_m.sum()\n",
-    "all_roads_in_dataset_length"
+    "all_roads_in_dataset_length_km = roads.length_m.sum() * 1e-3\n",
+    "all_roads_in_dataset_length_km"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 32,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.025063332519103282"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "proportion = exposed_length / all_roads_in_dataset_length\n",
+    "proportion = exposed_length_km / all_roads_in_dataset_length_km\n",
     "proportion"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 33,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'2.5% of roads in this dataset are exposed to flood depths of >= 1m in a historical 1-in-100 year flood'"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "f\"{proportion:.0%} of roads in this dataset are exposed to flood depths of >= 1m in a historical 1-in-100 year flood\""
+    "f\"{proportion:.1%} of roads in this dataset are exposed to flood depths of >= 1m in a historical 1-in-100 year flood\""
    ]
   },
   {
-   "cell_type": "markdown",
+   "cell_type": "code",
+   "execution_count": 34,
    "metadata": {},
+   "outputs": [],
    "source": [
-    "Save to file (with spatial data)"
+    "output_path.parent.mkdir(parents=True, exist_ok=True)"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": null,
+   "attachments": {},
+   "cell_type": "markdown",
    "metadata": {},
-   "outputs": [],
    "source": [
-    "flood_intersections.to_file(output_path, driver=\"GPKG\")"
+    "Save to file (with spatial data)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 35,
    "metadata": {},
    "outputs": [],
    "source": [
-    "flood_intersections"
+    "flood_intersections.to_file(output_path, driver=\"GPKG\")"
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -555,12 +1240,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 36,
    "metadata": {},
    "outputs": [],
    "source": [
     "flood_intersections.drop(columns=\"geometry\").to_csv(\n",
-    "    output_path.replace(\".gpkg\", \".csv\")\n",
+    "    output_path.parent / output_path.name.replace(\".gpkg\", \".csv\")\n",
     ")"
    ]
   }
@@ -581,7 +1266,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.10"
+   "version": "3.11.4"
   }
  },
  "nbformat": 4,
diff --git a/tutorials/02-assess-damage-and-disruption.ipynb b/tutorials/02-assess-damage-and-disruption.ipynb
index 5c7555b..2bf11a0 100644
--- a/tutorials/02-assess-damage-and-disruption.ipynb
+++ b/tutorials/02-assess-damage-and-disruption.ipynb
@@ -1,6 +1,7 @@
 {
  "cells": [
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "spoken-texture",
    "metadata": {},
@@ -26,16 +27,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "id": "brief-stephen",
    "metadata": {},
    "outputs": [],
    "source": [
     "# Imports from Python standard library\n",
     "import os\n",
-    "\n",
-    "# see https://docs.python.org/3/library/warnings.html\n",
-    "import warnings\n",
+    "from pathlib import Path\n",
     "\n",
     "# see https://docs.python.org/3/library/glob.html\n",
     "from glob import glob\n",
@@ -52,6 +51,13 @@
     "# seaborn helps produce more complex plots\n",
     "# see https://seaborn.pydata.org/\n",
     "import seaborn as sns\n",
+    "\n",
+    "from scipy.integrate import simpson\n",
+    "\n",
+    "import snail.damages\n",
+    "import snail.intersection\n",
+    "import snail.io\n",
+    "\n",
     "from pyproj import Geod\n",
     "\n",
     "# tqdm lets us show progress bars (and تقدّم means \"progress\" in Arabic)\n",
@@ -60,6 +66,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "characteristic-reputation",
    "metadata": {},
@@ -69,15 +76,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "id": "twelve-threat",
    "metadata": {},
    "outputs": [],
    "source": [
-    "data_folder = \"../data\""
+    "data_folder = Path(\"../data\")"
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "hired-knife",
    "metadata": {},
@@ -86,6 +94,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "defensive-passion",
    "metadata": {},
@@ -95,30 +104,178 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 16,
    "id": "driven-restoration",
    "metadata": {},
-   "outputs": [],
-   "source": [
-    "hazard_files = sorted(glob(os.path.join(data_folder, \"flood_layer/*.gpkg\")))\n",
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>path</th>\n",
+       "      <th>key</th>\n",
+       "      <th>grid_id</th>\n",
+       "      <th>bands</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>../data/flood_layer/gha/wri_aqueduct_version_2...</td>\n",
+       "      <td>wri_aqueduct-version_2-inuncoast_historical_wt...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>(1,)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>../data/flood_layer/gha/wri_aqueduct_version_2...</td>\n",
+       "      <td>wri_aqueduct-version_2-inuncoast_historical_wt...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>(1,)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>../data/flood_layer/gha/wri_aqueduct_version_2...</td>\n",
+       "      <td>wri_aqueduct-version_2-inuncoast_historical_wt...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>(1,)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>../data/flood_layer/gha/wri_aqueduct_version_2...</td>\n",
+       "      <td>wri_aqueduct-version_2-inuncoast_historical_wt...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>(1,)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>../data/flood_layer/gha/wri_aqueduct_version_2...</td>\n",
+       "      <td>wri_aqueduct-version_2-inuncoast_historical_wt...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>(1,)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>374</th>\n",
+       "      <td>../data/flood_layer/gha/wri_aqueduct_version_2...</td>\n",
+       "      <td>wri_aqueduct-version_2-inunriver_rcp8p5_MIROC-...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>(1,)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>375</th>\n",
+       "      <td>../data/flood_layer/gha/wri_aqueduct_version_2...</td>\n",
+       "      <td>wri_aqueduct-version_2-inunriver_rcp8p5_MIROC-...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>(1,)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>376</th>\n",
+       "      <td>../data/flood_layer/gha/wri_aqueduct_version_2...</td>\n",
+       "      <td>wri_aqueduct-version_2-inunriver_rcp8p5_MIROC-...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>(1,)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>377</th>\n",
+       "      <td>../data/flood_layer/gha/wri_aqueduct_version_2...</td>\n",
+       "      <td>wri_aqueduct-version_2-inunriver_rcp8p5_MIROC-...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>(1,)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>378</th>\n",
+       "      <td>../data/flood_layer/gha/wri_aqueduct_version_2...</td>\n",
+       "      <td>wri_aqueduct-version_2-inunriver_rcp8p5_MIROC-...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>(1,)</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>379 rows × 4 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                                  path  \\\n",
+       "0    ../data/flood_layer/gha/wri_aqueduct_version_2...   \n",
+       "1    ../data/flood_layer/gha/wri_aqueduct_version_2...   \n",
+       "2    ../data/flood_layer/gha/wri_aqueduct_version_2...   \n",
+       "3    ../data/flood_layer/gha/wri_aqueduct_version_2...   \n",
+       "4    ../data/flood_layer/gha/wri_aqueduct_version_2...   \n",
+       "..                                                 ...   \n",
+       "374  ../data/flood_layer/gha/wri_aqueduct_version_2...   \n",
+       "375  ../data/flood_layer/gha/wri_aqueduct_version_2...   \n",
+       "376  ../data/flood_layer/gha/wri_aqueduct_version_2...   \n",
+       "377  ../data/flood_layer/gha/wri_aqueduct_version_2...   \n",
+       "378  ../data/flood_layer/gha/wri_aqueduct_version_2...   \n",
+       "\n",
+       "                                                   key  grid_id bands  \n",
+       "0    wri_aqueduct-version_2-inuncoast_historical_wt...        0  (1,)  \n",
+       "1    wri_aqueduct-version_2-inuncoast_historical_wt...        0  (1,)  \n",
+       "2    wri_aqueduct-version_2-inuncoast_historical_wt...        0  (1,)  \n",
+       "3    wri_aqueduct-version_2-inuncoast_historical_wt...        0  (1,)  \n",
+       "4    wri_aqueduct-version_2-inuncoast_historical_wt...        0  (1,)  \n",
+       "..                                                 ...      ...   ...  \n",
+       "374  wri_aqueduct-version_2-inunriver_rcp8p5_MIROC-...        0  (1,)  \n",
+       "375  wri_aqueduct-version_2-inunriver_rcp8p5_MIROC-...        0  (1,)  \n",
+       "376  wri_aqueduct-version_2-inunriver_rcp8p5_MIROC-...        0  (1,)  \n",
+       "377  wri_aqueduct-version_2-inunriver_rcp8p5_MIROC-...        0  (1,)  \n",
+       "378  wri_aqueduct-version_2-inunriver_rcp8p5_MIROC-...        0  (1,)  \n",
+       "\n",
+       "[379 rows x 4 columns]"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "hazard_paths = sorted(\n",
+    "    glob(str(data_folder / \"flood_layer/gha/wri_aqueduct_version_2/wri*.tif\"))\n",
+    ")\n",
+    "hazard_files = pd.DataFrame({\"path\": hazard_paths})\n",
+    "hazard_files[\"key\"] = [Path(path).stem for path in hazard_paths]\n",
+    "hazard_files, grids = snail.io.extend_rasters_metadata(hazard_files)\n",
     "hazard_files"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "headed-impression",
+   "execution_count": 19,
+   "id": "81fe43d5",
    "metadata": {},
    "outputs": [],
    "source": [
-    "def read_file_without_warnings(path, **kwd):\n",
-    "    with warnings.catch_warnings():\n",
-    "        warnings.simplefilter(\"ignore\")\n",
-    "        data = gpd.read_file(path, **kwd)\n",
-    "    return data"
+    "assert len(grids) == 1\n",
+    "grid = grids[0]"
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "described-consciousness",
    "metadata": {},
@@ -128,130 +285,1044 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "id": "recreational-renaissance",
    "metadata": {},
-   "outputs": [],
-   "source": [
-    "roads = read_file_without_warnings(\n",
-    "    os.path.join(data_folder, \"GHA_OSM_roads.gpkg\"), layer=\"edges\"\n",
-    ")\n",
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>osm_id</th>\n",
+       "      <th>road_type</th>\n",
+       "      <th>name</th>\n",
+       "      <th>id</th>\n",
+       "      <th>from_id</th>\n",
+       "      <th>to_id</th>\n",
+       "      <th>length_m</th>\n",
+       "      <th>geometry</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>4790594</td>\n",
+       "      <td>tertiary</td>\n",
+       "      <td>Airport Road</td>\n",
+       "      <td>roade_0</td>\n",
+       "      <td>roadn_0</td>\n",
+       "      <td>roadn_1</td>\n",
+       "      <td>236.526837</td>\n",
+       "      <td>LINESTRING (-0.17544 5.60550, -0.17418 5.60555...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>4790599</td>\n",
+       "      <td>tertiary</td>\n",
+       "      <td>South Liberation Link</td>\n",
+       "      <td>roade_1</td>\n",
+       "      <td>roadn_2</td>\n",
+       "      <td>roadn_10683</td>\n",
+       "      <td>18.539418</td>\n",
+       "      <td>LINESTRING (-0.17889 5.59979, -0.17872 5.59977)</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    osm_id road_type                   name       id  from_id        to_id  \\\n",
+       "0  4790594  tertiary           Airport Road  roade_0  roadn_0      roadn_1   \n",
+       "1  4790599  tertiary  South Liberation Link  roade_1  roadn_2  roadn_10683   \n",
+       "\n",
+       "     length_m                                           geometry  \n",
+       "0  236.526837  LINESTRING (-0.17544 5.60550, -0.17418 5.60555...  \n",
+       "1   18.539418    LINESTRING (-0.17889 5.59979, -0.17872 5.59977)  "
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "roads_file = data_folder / \"GHA_OSM_roads.gpkg\"\n",
+    "roads = gpd.read_file(roads_file, layer=\"edges\")\n",
     "roads.head(2)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 21,
    "id": "dressed-madrid",
    "metadata": {},
    "outputs": [],
    "source": [
-    "for hazard_file in tqdm(hazard_files):\n",
-    "    # read file\n",
-    "    flood = read_file_without_warnings(hazard_file).rename(\n",
-    "        columns={\"DN\": \"depth_m\"}\n",
-    "    )\n",
-    "    flood = flood[flood.depth_m > 0]\n",
-    "\n",
-    "    # run intersection\n",
-    "    intersections = gpd.overlay(roads, flood, how=\"intersection\")\n",
-    "    # calculate intersection lengths\n",
-    "    geod = Geod(ellps=\"WGS84\")\n",
-    "    intersections[\"flood_length_m\"] = intersections.geometry.apply(\n",
-    "        geod.geometry_length\n",
-    "    )\n",
-    "    # save file\n",
-    "    output_file = os.path.join(\n",
-    "        data_folder,\n",
-    "        \"results\",\n",
-    "        os.path.basename(hazard_file).replace(\".gpkg\", \"_exposure.gpkg\"),\n",
-    "    )\n",
-    "    if len(intersections):\n",
-    "        intersections.to_file(output_file, driver=\"GPKG\")"
+    "# split roads along hazard data grid\n",
+    "\n",
+    "# TODO top-level \"overlay_rasters\"\n",
+    "# TODO for vector in vectors / for raster in rasters \"overlay_raster\"\n",
+    "\n",
+    "\n",
+    "# push into split_linestrings, flag to disable\n",
+    "prepared = snail.intersection.prepare_linestrings(roads)\n",
+    "\n",
+    "flood_intersections = snail.intersection.split_linestrings(prepared, grid)\n",
+    "\n",
+    "# push into split_linestrings\n",
+    "flood_intersections = snail.intersection.apply_indices(\n",
+    "    flood_intersections, grid, index_i=\"i_0\", index_j=\"j_0\"\n",
+    ")\n",
+    "\n",
+    "flood_intersections = snail.io.associate_raster_files(\n",
+    "    flood_intersections, hazard_files\n",
+    ")\n",
+    "\n",
+    "# calculate the length of each stretch of road\n",
+    "# don't include in snail wrapper top-level function\n",
+    "geod = Geod(ellps=\"WGS84\")\n",
+    "flood_intersections[\"length_m\"] = flood_intersections.geometry.apply(\n",
+    "    geod.geometry_length\n",
+    ")"
    ]
   },
   {
-   "cell_type": "markdown",
-   "id": "interstate-chile",
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "ea2207fe",
    "metadata": {},
+   "outputs": [],
    "source": [
-    "List all the results just created:"
+    "# save to file\n",
+    "output_file = os.path.join(\n",
+    "    data_folder,\n",
+    "    \"results\",\n",
+    "    str(roads_file.name).replace(\".gpkg\", \"_edges___exposure.geoparquet\"),\n",
+    ")\n",
+    "\n",
+    "flood_intersections.to_parquet(output_file)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "short-enforcement",
+   "execution_count": 23,
+   "id": "cc2f238c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['osm_id', 'road_type', 'name', 'id', 'from_id', 'to_id', 'length_m',\n",
+       "       'geometry', 'split', 'i_0',\n",
+       "       ...\n",
+       "       'wri_aqueduct-version_2-inunriver_rcp8p5_MIROC-ESM-CHEM_2050_rp01000-gha',\n",
+       "       'wri_aqueduct-version_2-inunriver_rcp8p5_MIROC-ESM-CHEM_2080_rp00002-gha',\n",
+       "       'wri_aqueduct-version_2-inunriver_rcp8p5_MIROC-ESM-CHEM_2080_rp00005-gha',\n",
+       "       'wri_aqueduct-version_2-inunriver_rcp8p5_MIROC-ESM-CHEM_2080_rp00010-gha',\n",
+       "       'wri_aqueduct-version_2-inunriver_rcp8p5_MIROC-ESM-CHEM_2080_rp00025-gha',\n",
+       "       'wri_aqueduct-version_2-inunriver_rcp8p5_MIROC-ESM-CHEM_2080_rp00050-gha',\n",
+       "       'wri_aqueduct-version_2-inunriver_rcp8p5_MIROC-ESM-CHEM_2080_rp00100-gha',\n",
+       "       'wri_aqueduct-version_2-inunriver_rcp8p5_MIROC-ESM-CHEM_2080_rp00250-gha',\n",
+       "       'wri_aqueduct-version_2-inunriver_rcp8p5_MIROC-ESM-CHEM_2080_rp00500-gha',\n",
+       "       'wri_aqueduct-version_2-inunriver_rcp8p5_MIROC-ESM-CHEM_2080_rp01000-gha'],\n",
+       "      dtype='object', length=390)"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "flood_intersections.columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "8d7fbaa1",
    "metadata": {},
    "outputs": [],
    "source": [
-    "intersection_files = sorted(\n",
-    "    glob(os.path.join(data_folder, \"results/inunriver*.gpkg\"))\n",
-    ")\n",
-    "intersection_files"
+    "data_cols = [col for col in flood_intersections.columns if \"wri\" in col]"
    ]
   },
   {
-   "cell_type": "markdown",
-   "id": "formed-glory",
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "blond-intervention",
    "metadata": {},
-   "source": [
-    "Read and combine all the exposed lengths:"
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>id</th>\n",
+       "      <th>split</th>\n",
+       "      <th>road_type</th>\n",
+       "      <th>length_m</th>\n",
+       "      <th>key</th>\n",
+       "      <th>depth_m</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>445</th>\n",
+       "      <td>roade_1432</td>\n",
+       "      <td>9</td>\n",
+       "      <td>tertiary</td>\n",
+       "      <td>153.153316</td>\n",
+       "      <td>wri_aqueduct-version_2-inuncoast_historical_wt...</td>\n",
+       "      <td>0.466355</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>446</th>\n",
+       "      <td>roade_1432</td>\n",
+       "      <td>10</td>\n",
+       "      <td>tertiary</td>\n",
+       "      <td>28.151241</td>\n",
+       "      <td>wri_aqueduct-version_2-inuncoast_historical_wt...</td>\n",
+       "      <td>1.349324</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>450</th>\n",
+       "      <td>roade_1453</td>\n",
+       "      <td>9</td>\n",
+       "      <td>tertiary</td>\n",
+       "      <td>412.834012</td>\n",
+       "      <td>wri_aqueduct-version_2-inuncoast_historical_wt...</td>\n",
+       "      <td>0.006246</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>457</th>\n",
+       "      <td>roade_1459</td>\n",
+       "      <td>3</td>\n",
+       "      <td>primary</td>\n",
+       "      <td>929.965564</td>\n",
+       "      <td>wri_aqueduct-version_2-inuncoast_historical_wt...</td>\n",
+       "      <td>0.516396</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>459</th>\n",
+       "      <td>roade_1459</td>\n",
+       "      <td>5</td>\n",
+       "      <td>primary</td>\n",
+       "      <td>921.598630</td>\n",
+       "      <td>wri_aqueduct-version_2-inuncoast_historical_wt...</td>\n",
+       "      <td>0.407549</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2052280</th>\n",
+       "      <td>roade_15595</td>\n",
+       "      <td>35</td>\n",
+       "      <td>secondary</td>\n",
+       "      <td>1277.291170</td>\n",
+       "      <td>wri_aqueduct-version_2-inunriver_rcp8p5_MIROC-...</td>\n",
+       "      <td>1.505718</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2052281</th>\n",
+       "      <td>roade_15595</td>\n",
+       "      <td>37</td>\n",
+       "      <td>secondary</td>\n",
+       "      <td>411.835653</td>\n",
+       "      <td>wri_aqueduct-version_2-inunriver_rcp8p5_MIROC-...</td>\n",
+       "      <td>10.555718</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2052282</th>\n",
+       "      <td>roade_15663</td>\n",
+       "      <td>0</td>\n",
+       "      <td>tertiary</td>\n",
+       "      <td>835.677777</td>\n",
+       "      <td>wri_aqueduct-version_2-inunriver_rcp8p5_MIROC-...</td>\n",
+       "      <td>15.260432</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2052283</th>\n",
+       "      <td>roade_15663</td>\n",
+       "      <td>1</td>\n",
+       "      <td>tertiary</td>\n",
+       "      <td>341.015419</td>\n",
+       "      <td>wri_aqueduct-version_2-inunriver_rcp8p5_MIROC-...</td>\n",
+       "      <td>18.910431</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2052284</th>\n",
+       "      <td>roade_15663</td>\n",
+       "      <td>2</td>\n",
+       "      <td>tertiary</td>\n",
+       "      <td>1025.220133</td>\n",
+       "      <td>wri_aqueduct-version_2-inunriver_rcp8p5_MIROC-...</td>\n",
+       "      <td>21.370432</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>1070692 rows × 6 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                  id  split  road_type     length_m  \\\n",
+       "445       roade_1432      9   tertiary   153.153316   \n",
+       "446       roade_1432     10   tertiary    28.151241   \n",
+       "450       roade_1453      9   tertiary   412.834012   \n",
+       "457       roade_1459      3    primary   929.965564   \n",
+       "459       roade_1459      5    primary   921.598630   \n",
+       "...              ...    ...        ...          ...   \n",
+       "2052280  roade_15595     35  secondary  1277.291170   \n",
+       "2052281  roade_15595     37  secondary   411.835653   \n",
+       "2052282  roade_15663      0   tertiary   835.677777   \n",
+       "2052283  roade_15663      1   tertiary   341.015419   \n",
+       "2052284  roade_15663      2   tertiary  1025.220133   \n",
+       "\n",
+       "                                                       key    depth_m  \n",
+       "445      wri_aqueduct-version_2-inuncoast_historical_wt...   0.466355  \n",
+       "446      wri_aqueduct-version_2-inuncoast_historical_wt...   1.349324  \n",
+       "450      wri_aqueduct-version_2-inuncoast_historical_wt...   0.006246  \n",
+       "457      wri_aqueduct-version_2-inuncoast_historical_wt...   0.516396  \n",
+       "459      wri_aqueduct-version_2-inuncoast_historical_wt...   0.407549  \n",
+       "...                                                    ...        ...  \n",
+       "2052280  wri_aqueduct-version_2-inunriver_rcp8p5_MIROC-...   1.505718  \n",
+       "2052281  wri_aqueduct-version_2-inunriver_rcp8p5_MIROC-...  10.555718  \n",
+       "2052282  wri_aqueduct-version_2-inunriver_rcp8p5_MIROC-...  15.260432  \n",
+       "2052283  wri_aqueduct-version_2-inunriver_rcp8p5_MIROC-...  18.910431  \n",
+       "2052284  wri_aqueduct-version_2-inunriver_rcp8p5_MIROC-...  21.370432  \n",
+       "\n",
+       "[1070692 rows x 6 columns]"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# find any max depth and filter > 0\n",
+    "all_intersections = flood_intersections[\n",
+    "    flood_intersections[data_cols].max(axis=1) > 0\n",
+    "]\n",
+    "# subset columns\n",
+    "all_intersections = all_intersections.drop(\n",
+    "    columns=[\"osm_id\", \"name\", \"from_id\", \"to_id\", \"geometry\", \"i_0\", \"j_0\"]\n",
+    ")\n",
+    "# melt and check again for depth\n",
+    "all_intersections = all_intersections.melt(\n",
+    "    id_vars=[\"id\", \"split\", \"road_type\", \"length_m\"],\n",
+    "    value_vars=data_cols,\n",
+    "    var_name=\"key\",\n",
+    "    value_name=\"depth_m\",\n",
+    ").query(\"depth_m > 0\")\n",
+    "all_intersections"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "blond-intervention",
+   "execution_count": 26,
+   "id": "320791b0",
    "metadata": {},
    "outputs": [],
    "source": [
-    "all_intersections = []\n",
+    "river = all_intersections[all_intersections.key.str.contains(\"inunriver\")]\n",
+    "coast = all_intersections[all_intersections.key.str.contains(\"inuncoast\")]\n",
     "\n",
-    "for intersection_file in tqdm(intersection_files):\n",
-    "    # split up the filename to pull out metadata\n",
-    "    hazard, rcp, gcm, epoch, rp, _ = os.path.basename(intersection_file).split(\n",
-    "        \"_\"\n",
-    "    )\n",
-    "    gcm = gcm.replace(\"0\", \"\")\n",
-    "    rp = int(rp.replace(\"rp\", \"\"))\n",
-    "    epoch = int(epoch)\n",
-    "\n",
-    "    # read file\n",
-    "    intersections = read_file_without_warnings(intersection_file)\n",
-    "    # drop road length and geometry fields\n",
-    "    intersections.drop(columns=\"length_m\", inplace=True)\n",
-    "    # add metadata about the hazard and scenario\n",
-    "    intersections[\"hazard\"] = hazard\n",
-    "    intersections[\"rcp\"] = rcp\n",
-    "    intersections[\"gcm\"] = gcm\n",
-    "    intersections[\"epoch\"] = epoch\n",
-    "    intersections[\"rp\"] = rp\n",
-    "\n",
-    "    all_intersections.append(intersections)\n",
-    "\n",
-    "# group all together\n",
-    "all_intersections = pd.concat(all_intersections)\n",
-    "all_intersections"
+    "coast_keys = coast.key.str.extract(\n",
+    "    r\"wri_aqueduct-version_2-(?P<hazard>\\w+)_(?P<rcp>[^_]+)_(?P<sub>[^_]+)_(?P<epoch>[^_]+)_rp(?P<rp>[^-]+)-gha\"\n",
+    ")\n",
+    "coast = pd.concat([coast, coast_keys], axis=1)\n",
+    "river_keys = river.key.str.extract(\n",
+    "    r\"wri_aqueduct-version_2-(?P<hazard>\\w+)_(?P<rcp>[^_]+)_(?P<gcm>[^_]+)_(?P<epoch>[^_]+)_rp(?P<rp>[^-]+)-gha\"\n",
+    ")\n",
+    "river = pd.concat([river, river_keys], axis=1)"
    ]
   },
   {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "6430bb4a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>id</th>\n",
+       "      <th>split</th>\n",
+       "      <th>road_type</th>\n",
+       "      <th>length_m</th>\n",
+       "      <th>key</th>\n",
+       "      <th>depth_m</th>\n",
+       "      <th>hazard</th>\n",
+       "      <th>rcp</th>\n",
+       "      <th>sub</th>\n",
+       "      <th>epoch</th>\n",
+       "      <th>rp</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>445</th>\n",
+       "      <td>roade_1432</td>\n",
+       "      <td>9</td>\n",
+       "      <td>tertiary</td>\n",
+       "      <td>153.153316</td>\n",
+       "      <td>wri_aqueduct-version_2-inuncoast_historical_wt...</td>\n",
+       "      <td>0.466355</td>\n",
+       "      <td>inuncoast</td>\n",
+       "      <td>historical</td>\n",
+       "      <td>wtsub</td>\n",
+       "      <td>2030</td>\n",
+       "      <td>1.5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>446</th>\n",
+       "      <td>roade_1432</td>\n",
+       "      <td>10</td>\n",
+       "      <td>tertiary</td>\n",
+       "      <td>28.151241</td>\n",
+       "      <td>wri_aqueduct-version_2-inuncoast_historical_wt...</td>\n",
+       "      <td>1.349324</td>\n",
+       "      <td>inuncoast</td>\n",
+       "      <td>historical</td>\n",
+       "      <td>wtsub</td>\n",
+       "      <td>2030</td>\n",
+       "      <td>1.5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>450</th>\n",
+       "      <td>roade_1453</td>\n",
+       "      <td>9</td>\n",
+       "      <td>tertiary</td>\n",
+       "      <td>412.834012</td>\n",
+       "      <td>wri_aqueduct-version_2-inuncoast_historical_wt...</td>\n",
+       "      <td>0.006246</td>\n",
+       "      <td>inuncoast</td>\n",
+       "      <td>historical</td>\n",
+       "      <td>wtsub</td>\n",
+       "      <td>2030</td>\n",
+       "      <td>1.5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>457</th>\n",
+       "      <td>roade_1459</td>\n",
+       "      <td>3</td>\n",
+       "      <td>primary</td>\n",
+       "      <td>929.965564</td>\n",
+       "      <td>wri_aqueduct-version_2-inuncoast_historical_wt...</td>\n",
+       "      <td>0.516396</td>\n",
+       "      <td>inuncoast</td>\n",
+       "      <td>historical</td>\n",
+       "      <td>wtsub</td>\n",
+       "      <td>2030</td>\n",
+       "      <td>1.5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>459</th>\n",
+       "      <td>roade_1459</td>\n",
+       "      <td>5</td>\n",
+       "      <td>primary</td>\n",
+       "      <td>921.598630</td>\n",
+       "      <td>wri_aqueduct-version_2-inuncoast_historical_wt...</td>\n",
+       "      <td>0.407549</td>\n",
+       "      <td>inuncoast</td>\n",
+       "      <td>historical</td>\n",
+       "      <td>wtsub</td>\n",
+       "      <td>2030</td>\n",
+       "      <td>1.5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>541416</th>\n",
+       "      <td>roade_15317</td>\n",
+       "      <td>6</td>\n",
+       "      <td>secondary</td>\n",
+       "      <td>1072.888374</td>\n",
+       "      <td>wri_aqueduct-version_2-inuncoast_rcp8p5_wtsub_...</td>\n",
+       "      <td>1.180037</td>\n",
+       "      <td>inuncoast</td>\n",
+       "      <td>rcp8p5</td>\n",
+       "      <td>wtsub</td>\n",
+       "      <td>2080</td>\n",
+       "      <td>1000.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>541417</th>\n",
+       "      <td>roade_15317</td>\n",
+       "      <td>9</td>\n",
+       "      <td>secondary</td>\n",
+       "      <td>995.600041</td>\n",
+       "      <td>wri_aqueduct-version_2-inuncoast_rcp8p5_wtsub_...</td>\n",
+       "      <td>1.311388</td>\n",
+       "      <td>inuncoast</td>\n",
+       "      <td>rcp8p5</td>\n",
+       "      <td>wtsub</td>\n",
+       "      <td>2080</td>\n",
+       "      <td>1000.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>541446</th>\n",
+       "      <td>roade_15421</td>\n",
+       "      <td>0</td>\n",
+       "      <td>secondary</td>\n",
+       "      <td>126.501813</td>\n",
+       "      <td>wri_aqueduct-version_2-inuncoast_rcp8p5_wtsub_...</td>\n",
+       "      <td>0.307404</td>\n",
+       "      <td>inuncoast</td>\n",
+       "      <td>rcp8p5</td>\n",
+       "      <td>wtsub</td>\n",
+       "      <td>2080</td>\n",
+       "      <td>1000.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>541447</th>\n",
+       "      <td>roade_15421</td>\n",
+       "      <td>1</td>\n",
+       "      <td>secondary</td>\n",
+       "      <td>5.437718</td>\n",
+       "      <td>wri_aqueduct-version_2-inuncoast_rcp8p5_wtsub_...</td>\n",
+       "      <td>1.201466</td>\n",
+       "      <td>inuncoast</td>\n",
+       "      <td>rcp8p5</td>\n",
+       "      <td>wtsub</td>\n",
+       "      <td>2080</td>\n",
+       "      <td>1000.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>541448</th>\n",
+       "      <td>roade_15422</td>\n",
+       "      <td>0</td>\n",
+       "      <td>secondary</td>\n",
+       "      <td>480.887395</td>\n",
+       "      <td>wri_aqueduct-version_2-inuncoast_rcp8p5_wtsub_...</td>\n",
+       "      <td>0.528029</td>\n",
+       "      <td>inuncoast</td>\n",
+       "      <td>rcp8p5</td>\n",
+       "      <td>wtsub</td>\n",
+       "      <td>2080</td>\n",
+       "      <td>1000.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>11410 rows × 11 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                 id  split  road_type     length_m  \\\n",
+       "445      roade_1432      9   tertiary   153.153316   \n",
+       "446      roade_1432     10   tertiary    28.151241   \n",
+       "450      roade_1453      9   tertiary   412.834012   \n",
+       "457      roade_1459      3    primary   929.965564   \n",
+       "459      roade_1459      5    primary   921.598630   \n",
+       "...             ...    ...        ...          ...   \n",
+       "541416  roade_15317      6  secondary  1072.888374   \n",
+       "541417  roade_15317      9  secondary   995.600041   \n",
+       "541446  roade_15421      0  secondary   126.501813   \n",
+       "541447  roade_15421      1  secondary     5.437718   \n",
+       "541448  roade_15422      0  secondary   480.887395   \n",
+       "\n",
+       "                                                      key   depth_m  \\\n",
+       "445     wri_aqueduct-version_2-inuncoast_historical_wt...  0.466355   \n",
+       "446     wri_aqueduct-version_2-inuncoast_historical_wt...  1.349324   \n",
+       "450     wri_aqueduct-version_2-inuncoast_historical_wt...  0.006246   \n",
+       "457     wri_aqueduct-version_2-inuncoast_historical_wt...  0.516396   \n",
+       "459     wri_aqueduct-version_2-inuncoast_historical_wt...  0.407549   \n",
+       "...                                                   ...       ...   \n",
+       "541416  wri_aqueduct-version_2-inuncoast_rcp8p5_wtsub_...  1.180037   \n",
+       "541417  wri_aqueduct-version_2-inuncoast_rcp8p5_wtsub_...  1.311388   \n",
+       "541446  wri_aqueduct-version_2-inuncoast_rcp8p5_wtsub_...  0.307404   \n",
+       "541447  wri_aqueduct-version_2-inuncoast_rcp8p5_wtsub_...  1.201466   \n",
+       "541448  wri_aqueduct-version_2-inuncoast_rcp8p5_wtsub_...  0.528029   \n",
+       "\n",
+       "           hazard         rcp    sub epoch      rp  \n",
+       "445     inuncoast  historical  wtsub  2030     1.5  \n",
+       "446     inuncoast  historical  wtsub  2030     1.5  \n",
+       "450     inuncoast  historical  wtsub  2030     1.5  \n",
+       "457     inuncoast  historical  wtsub  2030     1.5  \n",
+       "459     inuncoast  historical  wtsub  2030     1.5  \n",
+       "...           ...         ...    ...   ...     ...  \n",
+       "541416  inuncoast      rcp8p5  wtsub  2080  1000.0  \n",
+       "541417  inuncoast      rcp8p5  wtsub  2080  1000.0  \n",
+       "541446  inuncoast      rcp8p5  wtsub  2080  1000.0  \n",
+       "541447  inuncoast      rcp8p5  wtsub  2080  1000.0  \n",
+       "541448  inuncoast      rcp8p5  wtsub  2080  1000.0  \n",
+       "\n",
+       "[11410 rows x 11 columns]"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "coast.rp = coast.rp.apply(lambda rp: float(rp.replace(\"_\", \".\").lstrip(\"0\")))\n",
+    "coast"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "849afbef",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>id</th>\n",
+       "      <th>split</th>\n",
+       "      <th>road_type</th>\n",
+       "      <th>length_m</th>\n",
+       "      <th>key</th>\n",
+       "      <th>depth_m</th>\n",
+       "      <th>hazard</th>\n",
+       "      <th>rcp</th>\n",
+       "      <th>gcm</th>\n",
+       "      <th>epoch</th>\n",
+       "      <th>rp</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>546915</th>\n",
+       "      <td>roade_56</td>\n",
+       "      <td>0</td>\n",
+       "      <td>trunk</td>\n",
+       "      <td>256.660267</td>\n",
+       "      <td>wri_aqueduct-version_2-inunriver_historical_00...</td>\n",
+       "      <td>2.243539</td>\n",
+       "      <td>inunriver</td>\n",
+       "      <td>historical</td>\n",
+       "      <td>WATCH</td>\n",
+       "      <td>1980</td>\n",
+       "      <td>00005</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>546917</th>\n",
+       "      <td>roade_126</td>\n",
+       "      <td>0</td>\n",
+       "      <td>trunk</td>\n",
+       "      <td>364.644366</td>\n",
+       "      <td>wri_aqueduct-version_2-inunriver_historical_00...</td>\n",
+       "      <td>0.073757</td>\n",
+       "      <td>inunriver</td>\n",
+       "      <td>historical</td>\n",
+       "      <td>WATCH</td>\n",
+       "      <td>1980</td>\n",
+       "      <td>00005</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>546918</th>\n",
+       "      <td>roade_126</td>\n",
+       "      <td>1</td>\n",
+       "      <td>trunk</td>\n",
+       "      <td>158.050565</td>\n",
+       "      <td>wri_aqueduct-version_2-inunriver_historical_00...</td>\n",
+       "      <td>0.073757</td>\n",
+       "      <td>inunriver</td>\n",
+       "      <td>historical</td>\n",
+       "      <td>WATCH</td>\n",
+       "      <td>1980</td>\n",
+       "      <td>00005</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>546919</th>\n",
+       "      <td>roade_127</td>\n",
+       "      <td>0</td>\n",
+       "      <td>trunk</td>\n",
+       "      <td>54.297481</td>\n",
+       "      <td>wri_aqueduct-version_2-inunriver_historical_00...</td>\n",
+       "      <td>0.073757</td>\n",
+       "      <td>inunriver</td>\n",
+       "      <td>historical</td>\n",
+       "      <td>WATCH</td>\n",
+       "      <td>1980</td>\n",
+       "      <td>00005</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>546920</th>\n",
+       "      <td>roade_128</td>\n",
+       "      <td>0</td>\n",
+       "      <td>trunk</td>\n",
+       "      <td>715.652789</td>\n",
+       "      <td>wri_aqueduct-version_2-inunriver_historical_00...</td>\n",
+       "      <td>0.073757</td>\n",
+       "      <td>inunriver</td>\n",
+       "      <td>historical</td>\n",
+       "      <td>WATCH</td>\n",
+       "      <td>1980</td>\n",
+       "      <td>00005</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2052280</th>\n",
+       "      <td>roade_15595</td>\n",
+       "      <td>35</td>\n",
+       "      <td>secondary</td>\n",
+       "      <td>1277.291170</td>\n",
+       "      <td>wri_aqueduct-version_2-inunriver_rcp8p5_MIROC-...</td>\n",
+       "      <td>1.505718</td>\n",
+       "      <td>inunriver</td>\n",
+       "      <td>rcp8p5</td>\n",
+       "      <td>MIROC-ESM-CHEM</td>\n",
+       "      <td>2080</td>\n",
+       "      <td>01000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2052281</th>\n",
+       "      <td>roade_15595</td>\n",
+       "      <td>37</td>\n",
+       "      <td>secondary</td>\n",
+       "      <td>411.835653</td>\n",
+       "      <td>wri_aqueduct-version_2-inunriver_rcp8p5_MIROC-...</td>\n",
+       "      <td>10.555718</td>\n",
+       "      <td>inunriver</td>\n",
+       "      <td>rcp8p5</td>\n",
+       "      <td>MIROC-ESM-CHEM</td>\n",
+       "      <td>2080</td>\n",
+       "      <td>01000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2052282</th>\n",
+       "      <td>roade_15663</td>\n",
+       "      <td>0</td>\n",
+       "      <td>tertiary</td>\n",
+       "      <td>835.677777</td>\n",
+       "      <td>wri_aqueduct-version_2-inunriver_rcp8p5_MIROC-...</td>\n",
+       "      <td>15.260432</td>\n",
+       "      <td>inunriver</td>\n",
+       "      <td>rcp8p5</td>\n",
+       "      <td>MIROC-ESM-CHEM</td>\n",
+       "      <td>2080</td>\n",
+       "      <td>01000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2052283</th>\n",
+       "      <td>roade_15663</td>\n",
+       "      <td>1</td>\n",
+       "      <td>tertiary</td>\n",
+       "      <td>341.015419</td>\n",
+       "      <td>wri_aqueduct-version_2-inunriver_rcp8p5_MIROC-...</td>\n",
+       "      <td>18.910431</td>\n",
+       "      <td>inunriver</td>\n",
+       "      <td>rcp8p5</td>\n",
+       "      <td>MIROC-ESM-CHEM</td>\n",
+       "      <td>2080</td>\n",
+       "      <td>01000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2052284</th>\n",
+       "      <td>roade_15663</td>\n",
+       "      <td>2</td>\n",
+       "      <td>tertiary</td>\n",
+       "      <td>1025.220133</td>\n",
+       "      <td>wri_aqueduct-version_2-inunriver_rcp8p5_MIROC-...</td>\n",
+       "      <td>21.370432</td>\n",
+       "      <td>inunriver</td>\n",
+       "      <td>rcp8p5</td>\n",
+       "      <td>MIROC-ESM-CHEM</td>\n",
+       "      <td>2080</td>\n",
+       "      <td>01000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>1059282 rows × 11 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                  id  split  road_type     length_m  \\\n",
+       "546915      roade_56      0      trunk   256.660267   \n",
+       "546917     roade_126      0      trunk   364.644366   \n",
+       "546918     roade_126      1      trunk   158.050565   \n",
+       "546919     roade_127      0      trunk    54.297481   \n",
+       "546920     roade_128      0      trunk   715.652789   \n",
+       "...              ...    ...        ...          ...   \n",
+       "2052280  roade_15595     35  secondary  1277.291170   \n",
+       "2052281  roade_15595     37  secondary   411.835653   \n",
+       "2052282  roade_15663      0   tertiary   835.677777   \n",
+       "2052283  roade_15663      1   tertiary   341.015419   \n",
+       "2052284  roade_15663      2   tertiary  1025.220133   \n",
+       "\n",
+       "                                                       key    depth_m  \\\n",
+       "546915   wri_aqueduct-version_2-inunriver_historical_00...   2.243539   \n",
+       "546917   wri_aqueduct-version_2-inunriver_historical_00...   0.073757   \n",
+       "546918   wri_aqueduct-version_2-inunriver_historical_00...   0.073757   \n",
+       "546919   wri_aqueduct-version_2-inunriver_historical_00...   0.073757   \n",
+       "546920   wri_aqueduct-version_2-inunriver_historical_00...   0.073757   \n",
+       "...                                                    ...        ...   \n",
+       "2052280  wri_aqueduct-version_2-inunriver_rcp8p5_MIROC-...   1.505718   \n",
+       "2052281  wri_aqueduct-version_2-inunriver_rcp8p5_MIROC-...  10.555718   \n",
+       "2052282  wri_aqueduct-version_2-inunriver_rcp8p5_MIROC-...  15.260432   \n",
+       "2052283  wri_aqueduct-version_2-inunriver_rcp8p5_MIROC-...  18.910431   \n",
+       "2052284  wri_aqueduct-version_2-inunriver_rcp8p5_MIROC-...  21.370432   \n",
+       "\n",
+       "            hazard         rcp             gcm epoch     rp  \n",
+       "546915   inunriver  historical           WATCH  1980  00005  \n",
+       "546917   inunriver  historical           WATCH  1980  00005  \n",
+       "546918   inunriver  historical           WATCH  1980  00005  \n",
+       "546919   inunriver  historical           WATCH  1980  00005  \n",
+       "546920   inunriver  historical           WATCH  1980  00005  \n",
+       "...            ...         ...             ...   ...    ...  \n",
+       "2052280  inunriver      rcp8p5  MIROC-ESM-CHEM  2080  01000  \n",
+       "2052281  inunriver      rcp8p5  MIROC-ESM-CHEM  2080  01000  \n",
+       "2052282  inunriver      rcp8p5  MIROC-ESM-CHEM  2080  01000  \n",
+       "2052283  inunriver      rcp8p5  MIROC-ESM-CHEM  2080  01000  \n",
+       "2052284  inunriver      rcp8p5  MIROC-ESM-CHEM  2080  01000  \n",
+       "\n",
+       "[1059282 rows x 11 columns]"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# river.rp = river.rp.apply(lambda rp: float(rp.replace(\"_\", \".\").lstrip(\"0\")))\n",
+    "river.gcm = river.gcm.str.replace(\"0\", \"\")\n",
+    "river"
+   ]
+  },
+  {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "removable-output",
    "metadata": {},
    "source": [
-    "Summarise total length of roads exposed to depth 2m or greater flooding, under different return periods and climate scenarios:\n"
+    "Summarise total length of roads exposed to depth 2m or greater river flooding, under different return periods and climate scenarios:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 29,
    "id": "measured-worst",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th>length_m</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>hazard</th>\n",
+       "      <th>rcp</th>\n",
+       "      <th>gcm</th>\n",
+       "      <th>epoch</th>\n",
+       "      <th>rp</th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"11\" valign=\"top\">inunriver</th>\n",
+       "      <th rowspan=\"5\" valign=\"top\">historical</th>\n",
+       "      <th rowspan=\"5\" valign=\"top\">WATCH</th>\n",
+       "      <th rowspan=\"5\" valign=\"top\">1980</th>\n",
+       "      <th>00005</th>\n",
+       "      <td>280529.521076</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>00010</th>\n",
+       "      <td>464062.951048</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>00025</th>\n",
+       "      <td>524330.918548</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>00050</th>\n",
+       "      <td>558540.210271</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>00100</th>\n",
+       "      <td>591176.629733</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <th>...</th>\n",
+       "      <th>...</th>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"5\" valign=\"top\">rcp8p5</th>\n",
+       "      <th rowspan=\"5\" valign=\"top\">NorESM1-M</th>\n",
+       "      <th rowspan=\"5\" valign=\"top\">2080</th>\n",
+       "      <th>00050</th>\n",
+       "      <td>305109.247009</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>00100</th>\n",
+       "      <td>344689.360705</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>00250</th>\n",
+       "      <td>406936.860184</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>00500</th>\n",
+       "      <td>505028.324108</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>01000</th>\n",
+       "      <td>573335.204890</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>208 rows × 1 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                                 length_m\n",
+       "hazard    rcp        gcm       epoch rp                  \n",
+       "inunriver historical WATCH     1980  00005  280529.521076\n",
+       "                                     00010  464062.951048\n",
+       "                                     00025  524330.918548\n",
+       "                                     00050  558540.210271\n",
+       "                                     00100  591176.629733\n",
+       "...                                                   ...\n",
+       "          rcp8p5     NorESM1-M 2080  00050  305109.247009\n",
+       "                                     00100  344689.360705\n",
+       "                                     00250  406936.860184\n",
+       "                                     00500  505028.324108\n",
+       "                                     01000  573335.204890\n",
+       "\n",
+       "[208 rows x 1 columns]"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "summary = (\n",
-    "    all_intersections[all_intersections.depth_m >= 2.0]\n",
+    "    river[river.depth_m >= 2.0]\n",
+    "    .drop(columns=[\"id\", \"split\", \"road_type\", \"key\"])\n",
     "    .groupby([\"hazard\", \"rcp\", \"gcm\", \"epoch\", \"rp\"])\n",
     "    .sum()\n",
     "    .drop(columns=[\"depth_m\"])\n",
@@ -260,6 +1331,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "southeast-berlin",
    "metadata": {},
@@ -269,22 +1341,227 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 30,
+   "id": "a0f3962b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>hazard</th>\n",
+       "      <th>rcp</th>\n",
+       "      <th>gcm</th>\n",
+       "      <th>epoch</th>\n",
+       "      <th>rp</th>\n",
+       "      <th>length_m</th>\n",
+       "      <th>probability</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>inunriver</td>\n",
+       "      <td>historical</td>\n",
+       "      <td>WATCH</td>\n",
+       "      <td>1980</td>\n",
+       "      <td>5</td>\n",
+       "      <td>280529.521076</td>\n",
+       "      <td>0.200</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>inunriver</td>\n",
+       "      <td>historical</td>\n",
+       "      <td>WATCH</td>\n",
+       "      <td>1980</td>\n",
+       "      <td>10</td>\n",
+       "      <td>464062.951048</td>\n",
+       "      <td>0.100</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>inunriver</td>\n",
+       "      <td>historical</td>\n",
+       "      <td>WATCH</td>\n",
+       "      <td>1980</td>\n",
+       "      <td>25</td>\n",
+       "      <td>524330.918548</td>\n",
+       "      <td>0.040</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>inunriver</td>\n",
+       "      <td>historical</td>\n",
+       "      <td>WATCH</td>\n",
+       "      <td>1980</td>\n",
+       "      <td>50</td>\n",
+       "      <td>558540.210271</td>\n",
+       "      <td>0.020</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>inunriver</td>\n",
+       "      <td>historical</td>\n",
+       "      <td>WATCH</td>\n",
+       "      <td>1980</td>\n",
+       "      <td>100</td>\n",
+       "      <td>591176.629733</td>\n",
+       "      <td>0.010</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>203</th>\n",
+       "      <td>inunriver</td>\n",
+       "      <td>rcp8p5</td>\n",
+       "      <td>NorESM1-M</td>\n",
+       "      <td>2080</td>\n",
+       "      <td>50</td>\n",
+       "      <td>305109.247009</td>\n",
+       "      <td>0.020</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>204</th>\n",
+       "      <td>inunriver</td>\n",
+       "      <td>rcp8p5</td>\n",
+       "      <td>NorESM1-M</td>\n",
+       "      <td>2080</td>\n",
+       "      <td>100</td>\n",
+       "      <td>344689.360705</td>\n",
+       "      <td>0.010</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>205</th>\n",
+       "      <td>inunriver</td>\n",
+       "      <td>rcp8p5</td>\n",
+       "      <td>NorESM1-M</td>\n",
+       "      <td>2080</td>\n",
+       "      <td>250</td>\n",
+       "      <td>406936.860184</td>\n",
+       "      <td>0.004</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>206</th>\n",
+       "      <td>inunriver</td>\n",
+       "      <td>rcp8p5</td>\n",
+       "      <td>NorESM1-M</td>\n",
+       "      <td>2080</td>\n",
+       "      <td>500</td>\n",
+       "      <td>505028.324108</td>\n",
+       "      <td>0.002</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>207</th>\n",
+       "      <td>inunriver</td>\n",
+       "      <td>rcp8p5</td>\n",
+       "      <td>NorESM1-M</td>\n",
+       "      <td>2080</td>\n",
+       "      <td>1000</td>\n",
+       "      <td>573335.204890</td>\n",
+       "      <td>0.001</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>73 rows × 7 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        hazard         rcp        gcm epoch    rp       length_m  probability\n",
+       "0    inunriver  historical      WATCH  1980     5  280529.521076        0.200\n",
+       "1    inunriver  historical      WATCH  1980    10  464062.951048        0.100\n",
+       "2    inunriver  historical      WATCH  1980    25  524330.918548        0.040\n",
+       "3    inunriver  historical      WATCH  1980    50  558540.210271        0.020\n",
+       "4    inunriver  historical      WATCH  1980   100  591176.629733        0.010\n",
+       "..         ...         ...        ...   ...   ...            ...          ...\n",
+       "203  inunriver      rcp8p5  NorESM1-M  2080    50  305109.247009        0.020\n",
+       "204  inunriver      rcp8p5  NorESM1-M  2080   100  344689.360705        0.010\n",
+       "205  inunriver      rcp8p5  NorESM1-M  2080   250  406936.860184        0.004\n",
+       "206  inunriver      rcp8p5  NorESM1-M  2080   500  505028.324108        0.002\n",
+       "207  inunriver      rcp8p5  NorESM1-M  2080  1000  573335.204890        0.001\n",
+       "\n",
+       "[73 rows x 7 columns]"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "plot_data = summary.reset_index()\n",
+    "plot_data = plot_data[plot_data.epoch.isin([\"1980\", \"2080\"])]\n",
+    "plot_data.rp = plot_data.rp.apply(lambda rp: int(rp.lstrip(\"0\")))\n",
+    "plot_data[\"probability\"] = 1 / plot_data.rp\n",
+    "plot_data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
    "id": "favorite-product",
    "metadata": {},
-   "outputs": [],
-   "source": [
-    "sns.lmplot(\n",
-    "    \"rp\",\n",
-    "    \"flood_length_m\",\n",
-    "    data=summary.reset_index(),\n",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<seaborn.axisgrid.FacetGrid at 0x7f05bee3a890>"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1676.5x500 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.relplot(\n",
+    "    data=plot_data,\n",
+    "    x=\"rp\",\n",
+    "    y=\"length_m\",\n",
     "    hue=\"gcm\",\n",
     "    col=\"rcp\",\n",
-    "    fit_reg=False,\n",
+    "    kind=\"line\",\n",
+    "    marker=\"o\",\n",
     ")"
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "global-technical",
    "metadata": {},
@@ -293,6 +1570,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "prostate-edinburgh",
    "metadata": {},
@@ -306,22 +1584,86 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 32,
    "id": "above-neighbor",
    "metadata": {},
-   "outputs": [],
-   "source": [
-    "fragility = pd.DataFrame(\n",
-    "    {\n",
-    "        \"paved\": [True, True, True, False, False, False],\n",
-    "        \"depth_m\": [\"1\", \"2\", \">=3\", \"1\", \"2\", \">=3\"],\n",
-    "        \"pfail\": [0.1, 0.3, 0.5, 0.9, 1.0, 1.0],\n",
-    "    }\n",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(<snail.damages.PiecewiseLinearDamageCurve at 0x7f05a9e46c50>,\n",
+       " <snail.damages.PiecewiseLinearDamageCurve at 0x7f05a9e2d850>)"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "paved = snail.damages.PiecewiseLinearDamageCurve(\n",
+    "    pd.DataFrame(\n",
+    "        {\n",
+    "            \"intensity\": [0.0, 0.999999999, 1, 2, 3],\n",
+    "            \"damage\": [0.0, 0.0, 0.1, 0.3, 0.5],\n",
+    "        }\n",
+    "    )\n",
+    ")\n",
+    "unpaved = snail.damages.PiecewiseLinearDamageCurve(\n",
+    "    pd.DataFrame(\n",
+    "        {\n",
+    "            \"intensity\": [0.0, 0.999999999, 1, 2, 3],\n",
+    "            \"damage\": [0.0, 0.0, 0.9, 1.0, 1.0],\n",
+    "        }\n",
+    "    )\n",
     ")\n",
-    "fragility"
+    "paved, unpaved"
    ]
   },
   {
+   "cell_type": "code",
+   "execution_count": 33,
+   "id": "7e54e59d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(<Axes: xlabel='Hazard Intensity', ylabel='Damage Fraction'>,\n",
+       " <Axes: xlabel='Hazard Intensity', ylabel='Damage Fraction'>)"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "paved.plot(), unpaved.plot()"
+   ]
+  },
+  {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "optical-vegetation",
    "metadata": {},
@@ -335,10 +1677,67 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 34,
    "id": "published-restriction",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>kind</th>\n",
+       "      <th>cost_usd_per_km</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>paved_four_lane</td>\n",
+       "      <td>3800000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>paved_two_lane</td>\n",
+       "      <td>932740</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>unpaved</td>\n",
+       "      <td>22780</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "              kind  cost_usd_per_km\n",
+       "0  paved_four_lane          3800000\n",
+       "1   paved_two_lane           932740\n",
+       "2          unpaved            22780"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "costs = pd.DataFrame(\n",
     "    {\n",
@@ -350,6 +1749,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "applied-communication",
    "metadata": {},
@@ -359,15 +1759,36 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 35,
    "id": "interracial-mason",
    "metadata": {},
-   "outputs": [],
-   "source": [
-    "sorted(all_intersections.road_type.unique())"
-   ]
-  },
-  {
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['motorway',\n",
+       " 'motorway_link',\n",
+       " 'primary',\n",
+       " 'primary_link',\n",
+       " 'secondary',\n",
+       " 'secondary_link',\n",
+       " 'tertiary',\n",
+       " 'tertiary_link',\n",
+       " 'trunk',\n",
+       " 'trunk_link']"
+      ]
+     },
+     "execution_count": 35,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sorted(river.road_type.unique())"
+   ]
+  },
+  {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "sonic-kernel",
    "metadata": {},
@@ -377,17 +1798,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 36,
    "id": "acting-publicity",
    "metadata": {},
    "outputs": [],
    "source": [
-    "all_intersections[\"paved\"] = ~(all_intersections.road_type == \"tertiary\")"
+    "river[\"paved\"] = ~(river.road_type == \"tertiary\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 37,
    "id": "lesser-portable",
    "metadata": {},
    "outputs": [],
@@ -401,55 +1822,151 @@
     "        return \"unpaved\"\n",
     "\n",
     "\n",
-    "all_intersections[\"kind\"] = all_intersections.road_type.apply(kind)"
+    "river[\"kind\"] = river.road_type.apply(kind)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 38,
    "id": "after-hungary",
    "metadata": {},
    "outputs": [],
    "source": [
-    "all_intersections = all_intersections.merge(costs, on=\"kind\")"
+    "river = river.merge(costs, on=\"kind\")"
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "turkish-friend",
    "metadata": {},
    "source": [
-    "Discard all information on flood depths greater than 3m in order to use the fragility curve to estimate `pfail` for each exposed section."
+    "Use the damage curve to estimate `proportion_damaged` for each exposed section."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 39,
    "id": "applied-operations",
    "metadata": {},
-   "outputs": [],
-   "source": [
-    "all_intersections_coarse_depth = all_intersections.copy()\n",
-    "all_intersections_coarse_depth.depth_m = (\n",
-    "    all_intersections_coarse_depth.depth_m.apply(\n",
-    "        lambda d: str(d) if d < 3 else \">=3\"\n",
-    "    )\n",
-    ")"
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>id</th>\n",
+       "      <th>split</th>\n",
+       "      <th>road_type</th>\n",
+       "      <th>length_m</th>\n",
+       "      <th>key</th>\n",
+       "      <th>depth_m</th>\n",
+       "      <th>hazard</th>\n",
+       "      <th>rcp</th>\n",
+       "      <th>gcm</th>\n",
+       "      <th>epoch</th>\n",
+       "      <th>rp</th>\n",
+       "      <th>paved</th>\n",
+       "      <th>kind</th>\n",
+       "      <th>cost_usd_per_km</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>roade_56</td>\n",
+       "      <td>0</td>\n",
+       "      <td>trunk</td>\n",
+       "      <td>256.660267</td>\n",
+       "      <td>wri_aqueduct-version_2-inunriver_historical_00...</td>\n",
+       "      <td>2.243539</td>\n",
+       "      <td>inunriver</td>\n",
+       "      <td>historical</td>\n",
+       "      <td>WATCH</td>\n",
+       "      <td>1980</td>\n",
+       "      <td>00005</td>\n",
+       "      <td>True</td>\n",
+       "      <td>paved_four_lane</td>\n",
+       "      <td>3800000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>roade_126</td>\n",
+       "      <td>0</td>\n",
+       "      <td>trunk</td>\n",
+       "      <td>364.644366</td>\n",
+       "      <td>wri_aqueduct-version_2-inunriver_historical_00...</td>\n",
+       "      <td>0.073757</td>\n",
+       "      <td>inunriver</td>\n",
+       "      <td>historical</td>\n",
+       "      <td>WATCH</td>\n",
+       "      <td>1980</td>\n",
+       "      <td>00005</td>\n",
+       "      <td>True</td>\n",
+       "      <td>paved_four_lane</td>\n",
+       "      <td>3800000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          id  split road_type    length_m  \\\n",
+       "0   roade_56      0     trunk  256.660267   \n",
+       "1  roade_126      0     trunk  364.644366   \n",
+       "\n",
+       "                                                 key   depth_m     hazard  \\\n",
+       "0  wri_aqueduct-version_2-inunriver_historical_00...  2.243539  inunriver   \n",
+       "1  wri_aqueduct-version_2-inunriver_historical_00...  0.073757  inunriver   \n",
+       "\n",
+       "          rcp    gcm epoch     rp  paved             kind  cost_usd_per_km  \n",
+       "0  historical  WATCH  1980  00005   True  paved_four_lane          3800000  \n",
+       "1  historical  WATCH  1980  00005   True  paved_four_lane          3800000  "
+      ]
+     },
+     "execution_count": 39,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "river.head(2)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 40,
    "id": "official-anchor",
    "metadata": {},
    "outputs": [],
    "source": [
-    "all_intersections_coarse_depth = all_intersections_coarse_depth.merge(\n",
-    "    fragility, on=[\"depth_m\", \"paved\"]\n",
-    ")"
+    "paved_depths = river.loc[river.paved, \"depth_m\"]\n",
+    "paved_damage = paved.damage_fraction(paved_depths)\n",
+    "river.loc[river.paved, \"proportion_damaged\"] = paved_damage\n",
+    "\n",
+    "unpaved_depths = river.loc[~river.paved, \"depth_m\"]\n",
+    "unpaved_damage = paved.damage_fraction(unpaved_depths)\n",
+    "river.loc[~river.paved, \"proportion_damaged\"] = unpaved_damage"
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "checked-offense",
    "metadata": {},
@@ -459,61 +1976,287 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 41,
    "id": "ranging-check",
    "metadata": {},
-   "outputs": [],
-   "source": [
-    "all_intersections_coarse_depth[\"damage_usd\"] = (\n",
-    "    all_intersections_coarse_depth.flood_length_m\n",
-    "    * all_intersections_coarse_depth.cost_usd_per_km\n",
-    "    / 1000\n",
-    ")\n",
-    "all_intersections_coarse_depth.head(2)"
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>id</th>\n",
+       "      <th>split</th>\n",
+       "      <th>road_type</th>\n",
+       "      <th>length_m</th>\n",
+       "      <th>key</th>\n",
+       "      <th>depth_m</th>\n",
+       "      <th>hazard</th>\n",
+       "      <th>rcp</th>\n",
+       "      <th>gcm</th>\n",
+       "      <th>epoch</th>\n",
+       "      <th>rp</th>\n",
+       "      <th>paved</th>\n",
+       "      <th>kind</th>\n",
+       "      <th>cost_usd_per_km</th>\n",
+       "      <th>proportion_damaged</th>\n",
+       "      <th>damage_usd</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>roade_56</td>\n",
+       "      <td>0</td>\n",
+       "      <td>trunk</td>\n",
+       "      <td>256.660267</td>\n",
+       "      <td>wri_aqueduct-version_2-inunriver_historical_00...</td>\n",
+       "      <td>2.243539</td>\n",
+       "      <td>inunriver</td>\n",
+       "      <td>historical</td>\n",
+       "      <td>WATCH</td>\n",
+       "      <td>1980</td>\n",
+       "      <td>00005</td>\n",
+       "      <td>True</td>\n",
+       "      <td>paved_four_lane</td>\n",
+       "      <td>3800000</td>\n",
+       "      <td>0.348708</td>\n",
+       "      <td>9.753090e+05</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>roade_126</td>\n",
+       "      <td>0</td>\n",
+       "      <td>trunk</td>\n",
+       "      <td>364.644366</td>\n",
+       "      <td>wri_aqueduct-version_2-inunriver_historical_00...</td>\n",
+       "      <td>0.073757</td>\n",
+       "      <td>inunriver</td>\n",
+       "      <td>historical</td>\n",
+       "      <td>WATCH</td>\n",
+       "      <td>1980</td>\n",
+       "      <td>00005</td>\n",
+       "      <td>True</td>\n",
+       "      <td>paved_four_lane</td>\n",
+       "      <td>3800000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1.385649e+06</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          id  split road_type    length_m  \\\n",
+       "0   roade_56      0     trunk  256.660267   \n",
+       "1  roade_126      0     trunk  364.644366   \n",
+       "\n",
+       "                                                 key   depth_m     hazard  \\\n",
+       "0  wri_aqueduct-version_2-inunriver_historical_00...  2.243539  inunriver   \n",
+       "1  wri_aqueduct-version_2-inunriver_historical_00...  0.073757  inunriver   \n",
+       "\n",
+       "          rcp    gcm epoch     rp  paved             kind  cost_usd_per_km  \\\n",
+       "0  historical  WATCH  1980  00005   True  paved_four_lane          3800000   \n",
+       "1  historical  WATCH  1980  00005   True  paved_four_lane          3800000   \n",
+       "\n",
+       "   proportion_damaged    damage_usd  \n",
+       "0            0.348708  9.753090e+05  \n",
+       "1            0.000000  1.385649e+06  "
+      ]
+     },
+     "execution_count": 41,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "river[\"damage_usd\"] = river.length_m * river.cost_usd_per_km * 1e-3\n",
+    "river.head(2)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "lightweight-recall",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "all_intersections_coarse_depth.to_file(\n",
-    "    os.path.join(data_folder, \"results/flood_exposure.gpkg\"), driver=\"GPKG\"\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 42,
    "id": "terminal-fundamentals",
    "metadata": {},
    "outputs": [],
    "source": [
-    "all_intersections_coarse_depth.drop(columns=\"geometry\").to_csv(\n",
-    "    os.path.join(data_folder, \"results/flood_exposure.csv\"), index=False\n",
+    "river.to_csv(\n",
+    "    os.path.join(data_folder, \"results/inunriver_damages_rp.csv\"), index=False\n",
     ")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 43,
    "id": "equivalent-billy",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th>damage_usd</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>road_type</th>\n",
+       "      <th>hazard</th>\n",
+       "      <th>rcp</th>\n",
+       "      <th>gcm</th>\n",
+       "      <th>epoch</th>\n",
+       "      <th>rp</th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"5\" valign=\"top\">motorway</th>\n",
+       "      <th rowspan=\"5\" valign=\"top\">inunriver</th>\n",
+       "      <th rowspan=\"5\" valign=\"top\">historical</th>\n",
+       "      <th rowspan=\"5\" valign=\"top\">WATCH</th>\n",
+       "      <th rowspan=\"5\" valign=\"top\">1980</th>\n",
+       "      <th>00005</th>\n",
+       "      <td>2.848105e+07</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>00010</th>\n",
+       "      <td>2.848105e+07</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>00025</th>\n",
+       "      <td>2.848105e+07</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>00050</th>\n",
+       "      <td>2.848105e+07</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>00100</th>\n",
+       "      <td>2.848105e+07</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <th>...</th>\n",
+       "      <th>...</th>\n",
+       "      <th>...</th>\n",
+       "      <th>...</th>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"5\" valign=\"top\">trunk_link</th>\n",
+       "      <th rowspan=\"5\" valign=\"top\">inunriver</th>\n",
+       "      <th rowspan=\"5\" valign=\"top\">rcp8p5</th>\n",
+       "      <th rowspan=\"5\" valign=\"top\">NorESM1-M</th>\n",
+       "      <th rowspan=\"5\" valign=\"top\">2080</th>\n",
+       "      <th>00050</th>\n",
+       "      <td>1.474321e+07</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>00100</th>\n",
+       "      <td>1.474321e+07</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>00250</th>\n",
+       "      <td>1.487881e+07</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>00500</th>\n",
+       "      <td>1.487881e+07</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>01000</th>\n",
+       "      <td>1.487881e+07</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>2780 rows × 1 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                                         damage_usd\n",
+       "road_type  hazard    rcp        gcm       epoch rp                 \n",
+       "motorway   inunriver historical WATCH     1980  00005  2.848105e+07\n",
+       "                                                00010  2.848105e+07\n",
+       "                                                00025  2.848105e+07\n",
+       "                                                00050  2.848105e+07\n",
+       "                                                00100  2.848105e+07\n",
+       "...                                                             ...\n",
+       "trunk_link inunriver rcp8p5     NorESM1-M 2080  00050  1.474321e+07\n",
+       "                                                00100  1.474321e+07\n",
+       "                                                00250  1.487881e+07\n",
+       "                                                00500  1.487881e+07\n",
+       "                                                01000  1.487881e+07\n",
+       "\n",
+       "[2780 rows x 1 columns]"
+      ]
+     },
+     "execution_count": 43,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "summary = (\n",
-    "    all_intersections_coarse_depth.groupby(\n",
-    "        [\"hazard\", \"rcp\", \"gcm\", \"epoch\", \"rp\"]\n",
+    "    river.drop(\n",
+    "        columns=[\n",
+    "            \"id\",\n",
+    "            \"split\",\n",
+    "            \"length_m\",\n",
+    "            \"key\",\n",
+    "            \"depth_m\",\n",
+    "            \"paved\",\n",
+    "            \"kind\",\n",
+    "            \"cost_usd_per_km\",\n",
+    "            \"proportion_damaged\",\n",
+    "        ]\n",
     "    )\n",
+    "    .groupby([\"road_type\", \"hazard\", \"rcp\", \"gcm\", \"epoch\", \"rp\"])\n",
     "    .sum()\n",
-    "    .drop(columns=[\"paved\", \"cost_usd_per_km\", \"pfail\"])\n",
     ")\n",
     "summary"
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "designed-interval",
    "metadata": {},
@@ -522,92 +2265,138 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "reverse-neutral",
    "metadata": {},
    "source": [
     "Calculate expected annual damages for each road under historical hazard.\n",
     "\n",
-    "Start by selecting only historical intersections, and keeping only the road ID, return period, probability of damage, and cost of rehabilitation if damaged."
+    "Start by selecting only historical intersections, and keeping only the road ID, return period, and cost of rehabilitation if damaged."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 44,
    "id": "resident-seating",
    "metadata": {},
    "outputs": [],
    "source": [
-    "historical = all_intersections_coarse_depth[\n",
-    "    all_intersections_coarse_depth.rcp == \"historical\"\n",
-    "][[\"id\", \"rp\", \"pfail\", \"damage_usd\"]]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "vocal-pierre",
-   "metadata": {},
-   "source": [
-    "Calculated the expected damage for each length exposed (under a given return period)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "exterior-soldier",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "historical[\"expected_damage_usd\"] = historical.pfail * historical.damage_usd"
+    "historical = river[river.rcp == \"historical\"][[\"id\", \"rp\", \"damage_usd\"]]"
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "juvenile-accident",
    "metadata": {},
    "source": [
-    "Sum up the expected damage for each road, per return period"
+    "Sum up the expected damage for each road, per return period, then pivot the table to create columns for each return period - now there is one row per road."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 45,
    "id": "comprehensive-separate",
    "metadata": {},
-   "outputs": [],
-   "source": [
-    "historical = (\n",
-    "    historical.groupby([\"id\", \"rp\"])\n",
-    "    .sum()\n",
-    "    .drop(columns=[\"pfail\", \"damage_usd\"])\n",
-    "    .reset_index()\n",
-    ")\n",
-    "historical.head(2)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "lonely-martial",
-   "metadata": {},
-   "source": [
-    "Pivot the table to create columns for each return period - now there is one row per road."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "absolute-duncan",
-   "metadata": {},
-   "outputs": [],
-   "source": [
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>rp5</th>\n",
+       "      <th>rp10</th>\n",
+       "      <th>rp25</th>\n",
+       "      <th>rp50</th>\n",
+       "      <th>rp100</th>\n",
+       "      <th>rp250</th>\n",
+       "      <th>rp500</th>\n",
+       "      <th>rp1000</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>id</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>roade_10012</th>\n",
+       "      <td>46079.916699</td>\n",
+       "      <td>46079.916699</td>\n",
+       "      <td>46079.916699</td>\n",
+       "      <td>46079.916699</td>\n",
+       "      <td>46079.916699</td>\n",
+       "      <td>46079.916699</td>\n",
+       "      <td>46079.916699</td>\n",
+       "      <td>46079.916699</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>roade_1002</th>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>5788.380563</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                      rp5          rp10          rp25          rp50  \\\n",
+       "id                                                                    \n",
+       "roade_10012  46079.916699  46079.916699  46079.916699  46079.916699   \n",
+       "roade_1002       0.000000      0.000000      0.000000      0.000000   \n",
+       "\n",
+       "                    rp100         rp250         rp500        rp1000  \n",
+       "id                                                                   \n",
+       "roade_10012  46079.916699  46079.916699  46079.916699  46079.916699  \n",
+       "roade_1002       0.000000      0.000000      0.000000   5788.380563  "
+      ]
+     },
+     "execution_count": 45,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "historical = historical.groupby([\"id\", \"rp\"]).sum().reset_index()\n",
     "historical = historical.pivot(index=\"id\", columns=\"rp\").replace(\n",
     "    float(\"NaN\"), 0\n",
     ")\n",
-    "historical.columns = [f\"rp{rp}\" for _, rp in historical.columns]\n",
+    "historical.columns = [f\"rp{int(rp)}\" for _, rp in historical.columns]\n",
     "historical.head(2)"
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "mexican-victor",
    "metadata": {},
@@ -617,32 +2406,134 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 46,
    "id": "legal-hello",
    "metadata": {},
-   "outputs": [],
-   "source": [
-    "def expected_annual_damages(row):\n",
-    "    return np.trapz([row.rp1000, row.rp100, row.rp10], x=[0.001, 0.01, 0.1])\n",
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>rp5</th>\n",
+       "      <th>rp10</th>\n",
+       "      <th>rp25</th>\n",
+       "      <th>rp50</th>\n",
+       "      <th>rp100</th>\n",
+       "      <th>rp250</th>\n",
+       "      <th>rp500</th>\n",
+       "      <th>rp1000</th>\n",
+       "      <th>ead_usd</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>id</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>roade_10012</th>\n",
+       "      <td>46079.916699</td>\n",
+       "      <td>46079.916699</td>\n",
+       "      <td>46079.916699</td>\n",
+       "      <td>46079.916699</td>\n",
+       "      <td>46079.916699</td>\n",
+       "      <td>46079.916699</td>\n",
+       "      <td>46079.916699</td>\n",
+       "      <td>46079.916699</td>\n",
+       "      <td>9169.903423</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>roade_1002</th>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>5788.380563</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                      rp5          rp10          rp25          rp50  \\\n",
+       "id                                                                    \n",
+       "roade_10012  46079.916699  46079.916699  46079.916699  46079.916699   \n",
+       "roade_1002       0.000000      0.000000      0.000000      0.000000   \n",
+       "\n",
+       "                    rp100         rp250         rp500        rp1000  \\\n",
+       "id                                                                    \n",
+       "roade_10012  46079.916699  46079.916699  46079.916699  46079.916699   \n",
+       "roade_1002       0.000000      0.000000      0.000000   5788.380563   \n",
+       "\n",
+       "                 ead_usd  \n",
+       "id                        \n",
+       "roade_10012  9169.903423  \n",
+       "roade_1002      0.000000  "
+      ]
+     },
+     "execution_count": 46,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "def calculate_ead(df):\n",
+    "    rp_cols = sorted(\n",
+    "        list(df.columns), key=lambda col: 1 / int(col.replace(\"rp\", \"\"))\n",
+    "    )\n",
+    "    rps = np.array([int(col.replace(\"rp\", \"\")) for col in rp_cols])\n",
+    "    probabilities = 1 / rps\n",
+    "    rp_damages = df[rp_cols]\n",
+    "    return simpson(rp_damages, x=probabilities, axis=1)\n",
     "\n",
     "\n",
-    "historical[\"ead_usd\"] = historical.apply(expected_annual_damages, axis=1)\n",
+    "historical[\"ead_usd\"] = calculate_ead(historical)\n",
     "historical.head(2)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 47,
    "id": "duplicate-wings",
    "metadata": {},
    "outputs": [],
    "source": [
     "historical.to_csv(\n",
-    "    os.path.join(data_folder, \"results/flood_risk_historical.csv\")\n",
+    "    os.path.join(data_folder, \"results/inunriver_damages_ead__historical.csv\")\n",
     ")"
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "thick-arlington",
    "metadata": {},
@@ -656,35 +2547,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 48,
    "id": "mathematical-istanbul",
    "metadata": {},
    "outputs": [],
    "source": [
-    "future = all_intersections_coarse_depth[\n",
-    "    [\"id\", \"rp\", \"rcp\", \"gcm\", \"pfail\", \"damage_usd\"]\n",
-    "].copy()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "disabled-warehouse",
-   "metadata": {},
-   "source": [
-    "Calculated the expected damage for each length exposed (under a given return period, gcm and rcp)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "smaller-librarian",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "future[\"expected_damage_usd\"] = future.pfail * future.damage_usd"
+    "future = river[[\"id\", \"rp\", \"rcp\", \"gcm\", \"epoch\", \"damage_usd\"]].copy()"
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "endless-origin",
    "metadata": {},
@@ -694,21 +2566,82 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 49,
    "id": "corporate-david",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>id</th>\n",
+       "      <th>rp</th>\n",
+       "      <th>rcp</th>\n",
+       "      <th>gcm</th>\n",
+       "      <th>epoch</th>\n",
+       "      <th>damage_usd</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>roade_10012</td>\n",
+       "      <td>00002</td>\n",
+       "      <td>rcp4p5</td>\n",
+       "      <td>GFDL-ESM2M</td>\n",
+       "      <td>2030</td>\n",
+       "      <td>46079.916699</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>roade_10012</td>\n",
+       "      <td>00002</td>\n",
+       "      <td>rcp4p5</td>\n",
+       "      <td>GFDL-ESM2M</td>\n",
+       "      <td>2050</td>\n",
+       "      <td>46079.916699</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            id     rp     rcp         gcm epoch    damage_usd\n",
+       "0  roade_10012  00002  rcp4p5  GFDL-ESM2M  2030  46079.916699\n",
+       "1  roade_10012  00002  rcp4p5  GFDL-ESM2M  2050  46079.916699"
+      ]
+     },
+     "execution_count": 49,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "future = (\n",
-    "    future.groupby([\"id\", \"rp\", \"rcp\", \"gcm\"])\n",
-    "    .sum()\n",
-    "    .drop(columns=[\"pfail\", \"damage_usd\"])\n",
-    "    .reset_index()\n",
+    "    future.groupby([\"id\", \"rp\", \"rcp\", \"gcm\", \"epoch\"]).sum().reset_index()\n",
     ")\n",
     "future.head(2)"
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "natural-frame",
    "metadata": {},
@@ -718,19 +2651,136 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 50,
    "id": "interpreted-compensation",
    "metadata": {},
-   "outputs": [],
-   "source": [
-    "future = future.pivot(index=[\"id\", \"rcp\", \"gcm\"], columns=\"rp\").replace(\n",
-    "    float(\"NaN\"), 0\n",
-    ")\n",
-    "future.columns = [f\"rp{rp}\" for _, rp in future.columns]\n",
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th>rp2</th>\n",
+       "      <th>rp5</th>\n",
+       "      <th>rp10</th>\n",
+       "      <th>rp25</th>\n",
+       "      <th>rp50</th>\n",
+       "      <th>rp100</th>\n",
+       "      <th>rp250</th>\n",
+       "      <th>rp500</th>\n",
+       "      <th>rp1000</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>id</th>\n",
+       "      <th>rcp</th>\n",
+       "      <th>gcm</th>\n",
+       "      <th>epoch</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"2\" valign=\"top\">roade_10012</th>\n",
+       "      <th>historical</th>\n",
+       "      <th>WATCH</th>\n",
+       "      <th>1980</th>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>46079.916699</td>\n",
+       "      <td>46079.916699</td>\n",
+       "      <td>46079.916699</td>\n",
+       "      <td>46079.916699</td>\n",
+       "      <td>46079.916699</td>\n",
+       "      <td>46079.916699</td>\n",
+       "      <td>46079.916699</td>\n",
+       "      <td>46079.916699</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>rcp4p5</th>\n",
+       "      <th>GFDL-ESM2M</th>\n",
+       "      <th>2030</th>\n",
+       "      <td>46079.916699</td>\n",
+       "      <td>46079.916699</td>\n",
+       "      <td>46079.916699</td>\n",
+       "      <td>46079.916699</td>\n",
+       "      <td>46079.916699</td>\n",
+       "      <td>46079.916699</td>\n",
+       "      <td>46079.916699</td>\n",
+       "      <td>46079.916699</td>\n",
+       "      <td>46079.916699</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                                  rp2           rp5  \\\n",
+       "id          rcp        gcm        epoch                               \n",
+       "roade_10012 historical WATCH      1980       0.000000  46079.916699   \n",
+       "            rcp4p5     GFDL-ESM2M 2030   46079.916699  46079.916699   \n",
+       "\n",
+       "                                                 rp10          rp25  \\\n",
+       "id          rcp        gcm        epoch                               \n",
+       "roade_10012 historical WATCH      1980   46079.916699  46079.916699   \n",
+       "            rcp4p5     GFDL-ESM2M 2030   46079.916699  46079.916699   \n",
+       "\n",
+       "                                                 rp50         rp100  \\\n",
+       "id          rcp        gcm        epoch                               \n",
+       "roade_10012 historical WATCH      1980   46079.916699  46079.916699   \n",
+       "            rcp4p5     GFDL-ESM2M 2030   46079.916699  46079.916699   \n",
+       "\n",
+       "                                                rp250         rp500  \\\n",
+       "id          rcp        gcm        epoch                               \n",
+       "roade_10012 historical WATCH      1980   46079.916699  46079.916699   \n",
+       "            rcp4p5     GFDL-ESM2M 2030   46079.916699  46079.916699   \n",
+       "\n",
+       "                                               rp1000  \n",
+       "id          rcp        gcm        epoch                \n",
+       "roade_10012 historical WATCH      1980   46079.916699  \n",
+       "            rcp4p5     GFDL-ESM2M 2030   46079.916699  "
+      ]
+     },
+     "execution_count": 50,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "future = future.pivot(\n",
+    "    index=[\"id\", \"rcp\", \"gcm\", \"epoch\"], columns=\"rp\"\n",
+    ").replace(float(\"NaN\"), 0)\n",
+    "future.columns = [f\"rp{int(rp)}\" for _, rp in future.columns]\n",
     "future.head(2)"
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "ceramic-china",
    "metadata": {},
@@ -740,25 +2790,26 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 51,
    "id": "heard-powell",
    "metadata": {},
    "outputs": [],
    "source": [
-    "future[\"ead_usd\"] = future.apply(expected_annual_damages, axis=1)"
+    "future[\"ead_usd\"] = calculate_ead(future)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 52,
    "id": "challenging-cutting",
    "metadata": {},
    "outputs": [],
    "source": [
-    "future.to_csv(os.path.join(data_folder, \"results/flood_risk.csv\"))"
+    "future.to_csv(os.path.join(data_folder, \"results/inunriver_damages_ead.csv\"))"
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "genuine-agenda",
    "metadata": {},
@@ -768,15 +2819,244 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "boxed-jacob",
+   "execution_count": 60,
+   "id": "42794000",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['roade_10012', 'roade_1002', 'roade_10027', ..., 'roade_9946',\n",
+       "       'roade_9947', 'roade_995'], dtype=object)"
+      ]
+     },
+     "execution_count": 60,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "future.loc[\"roade_10028\"]"
+    "future.reset_index().id.unique()"
    ]
   },
   {
+   "cell_type": "code",
+   "execution_count": 61,
+   "id": "boxed-jacob",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th>rp2</th>\n",
+       "      <th>rp5</th>\n",
+       "      <th>rp10</th>\n",
+       "      <th>rp25</th>\n",
+       "      <th>rp50</th>\n",
+       "      <th>rp100</th>\n",
+       "      <th>rp250</th>\n",
+       "      <th>rp500</th>\n",
+       "      <th>rp1000</th>\n",
+       "      <th>ead_usd</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>rcp</th>\n",
+       "      <th>gcm</th>\n",
+       "      <th>epoch</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>historical</th>\n",
+       "      <th>WATCH</th>\n",
+       "      <th>1980</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>5788.380563</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"4\" valign=\"top\">rcp4p5</th>\n",
+       "      <th rowspan=\"3\" valign=\"top\">MIROC-ESM-CHEM</th>\n",
+       "      <th>2030</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>5788.380563</td>\n",
+       "      <td>5788.380563</td>\n",
+       "      <td>5788.380563</td>\n",
+       "      <td>22.510369</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2050</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>5788.380563</td>\n",
+       "      <td>5788.380563</td>\n",
+       "      <td>5788.380563</td>\n",
+       "      <td>5788.380563</td>\n",
+       "      <td>5788.380563</td>\n",
+       "      <td>32.800823</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2080</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>5788.380563</td>\n",
+       "      <td>5788.380563</td>\n",
+       "      <td>5788.380563</td>\n",
+       "      <td>5788.380563</td>\n",
+       "      <td>5788.380563</td>\n",
+       "      <td>5788.380563</td>\n",
+       "      <td>444.418997</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>NorESM1-M</th>\n",
+       "      <th>2050</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>5788.380563</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"3\" valign=\"top\">rcp8p5</th>\n",
+       "      <th rowspan=\"3\" valign=\"top\">MIROC-ESM-CHEM</th>\n",
+       "      <th>2030</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>5788.380563</td>\n",
+       "      <td>5788.380563</td>\n",
+       "      <td>13.023856</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2050</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>5788.380563</td>\n",
+       "      <td>5788.380563</td>\n",
+       "      <td>5788.380563</td>\n",
+       "      <td>5788.380563</td>\n",
+       "      <td>5788.380563</td>\n",
+       "      <td>5788.380563</td>\n",
+       "      <td>5788.380563</td>\n",
+       "      <td>187.157638</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2080</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>5788.380563</td>\n",
+       "      <td>5788.380563</td>\n",
+       "      <td>5788.380563</td>\n",
+       "      <td>5788.380563</td>\n",
+       "      <td>5788.380563</td>\n",
+       "      <td>5788.380563</td>\n",
+       "      <td>5788.380563</td>\n",
+       "      <td>5788.380563</td>\n",
+       "      <td>2245.248505</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                 rp2          rp5         rp10         rp25  \\\n",
+       "rcp        gcm            epoch                                               \n",
+       "historical WATCH          1980   0.0     0.000000     0.000000     0.000000   \n",
+       "rcp4p5     MIROC-ESM-CHEM 2030   0.0     0.000000     0.000000     0.000000   \n",
+       "                          2050   0.0     0.000000     0.000000     0.000000   \n",
+       "                          2080   0.0     0.000000     0.000000  5788.380563   \n",
+       "           NorESM1-M      2050   0.0     0.000000     0.000000     0.000000   \n",
+       "rcp8p5     MIROC-ESM-CHEM 2030   0.0     0.000000     0.000000     0.000000   \n",
+       "                          2050   0.0     0.000000  5788.380563  5788.380563   \n",
+       "                          2080   0.0  5788.380563  5788.380563  5788.380563   \n",
+       "\n",
+       "                                        rp50        rp100        rp250  \\\n",
+       "rcp        gcm            epoch                                          \n",
+       "historical WATCH          1980      0.000000     0.000000     0.000000   \n",
+       "rcp4p5     MIROC-ESM-CHEM 2030      0.000000     0.000000  5788.380563   \n",
+       "                          2050   5788.380563  5788.380563  5788.380563   \n",
+       "                          2080   5788.380563  5788.380563  5788.380563   \n",
+       "           NorESM1-M      2050      0.000000     0.000000     0.000000   \n",
+       "rcp8p5     MIROC-ESM-CHEM 2030      0.000000     0.000000     0.000000   \n",
+       "                          2050   5788.380563  5788.380563  5788.380563   \n",
+       "                          2080   5788.380563  5788.380563  5788.380563   \n",
+       "\n",
+       "                                       rp500       rp1000      ead_usd  \n",
+       "rcp        gcm            epoch                                         \n",
+       "historical WATCH          1980      0.000000  5788.380563     0.000000  \n",
+       "rcp4p5     MIROC-ESM-CHEM 2030   5788.380563  5788.380563    22.510369  \n",
+       "                          2050   5788.380563  5788.380563    32.800823  \n",
+       "                          2080   5788.380563  5788.380563   444.418997  \n",
+       "           NorESM1-M      2050      0.000000  5788.380563     0.000000  \n",
+       "rcp8p5     MIROC-ESM-CHEM 2030   5788.380563  5788.380563    13.023856  \n",
+       "                          2050   5788.380563  5788.380563   187.157638  \n",
+       "                          2080   5788.380563  5788.380563  2245.248505  "
+      ]
+     },
+     "execution_count": 61,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "future.loc[\"roade_1002\"]"
+   ]
+  },
+  {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "bridal-hungarian",
    "metadata": {},
@@ -786,29 +3066,344 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 62,
    "id": "dominant-apparatus",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>rcp</th>\n",
+       "      <th>gcm</th>\n",
+       "      <th>epoch</th>\n",
+       "      <th>ead_usd</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>historical</td>\n",
+       "      <td>WATCH</td>\n",
+       "      <td>1980</td>\n",
+       "      <td>5.709742e+08</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>rcp4p5</td>\n",
+       "      <td>GFDL-ESM2M</td>\n",
+       "      <td>2030</td>\n",
+       "      <td>6.034515e+08</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>rcp4p5</td>\n",
+       "      <td>GFDL-ESM2M</td>\n",
+       "      <td>2050</td>\n",
+       "      <td>6.026048e+08</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>rcp4p5</td>\n",
+       "      <td>GFDL-ESM2M</td>\n",
+       "      <td>2080</td>\n",
+       "      <td>5.911836e+08</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>rcp4p5</td>\n",
+       "      <td>HadGEM2-ES</td>\n",
+       "      <td>2030</td>\n",
+       "      <td>6.192473e+08</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>rcp4p5</td>\n",
+       "      <td>HadGEM2-ES</td>\n",
+       "      <td>2050</td>\n",
+       "      <td>6.024512e+08</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>rcp4p5</td>\n",
+       "      <td>HadGEM2-ES</td>\n",
+       "      <td>2080</td>\n",
+       "      <td>5.444512e+08</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>rcp4p5</td>\n",
+       "      <td>IPSL-CM5A-LR</td>\n",
+       "      <td>2030</td>\n",
+       "      <td>5.128261e+08</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>rcp4p5</td>\n",
+       "      <td>IPSL-CM5A-LR</td>\n",
+       "      <td>2050</td>\n",
+       "      <td>5.010024e+08</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>rcp4p5</td>\n",
+       "      <td>IPSL-CM5A-LR</td>\n",
+       "      <td>2080</td>\n",
+       "      <td>5.141668e+08</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>rcp4p5</td>\n",
+       "      <td>MIROC-ESM-CHEM</td>\n",
+       "      <td>2030</td>\n",
+       "      <td>6.278343e+08</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>rcp4p5</td>\n",
+       "      <td>MIROC-ESM-CHEM</td>\n",
+       "      <td>2050</td>\n",
+       "      <td>6.189458e+08</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>rcp4p5</td>\n",
+       "      <td>MIROC-ESM-CHEM</td>\n",
+       "      <td>2080</td>\n",
+       "      <td>6.096544e+08</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>rcp4p5</td>\n",
+       "      <td>NorESM1-M</td>\n",
+       "      <td>2030</td>\n",
+       "      <td>5.523561e+08</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>rcp4p5</td>\n",
+       "      <td>NorESM1-M</td>\n",
+       "      <td>2050</td>\n",
+       "      <td>5.659124e+08</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>rcp4p5</td>\n",
+       "      <td>NorESM1-M</td>\n",
+       "      <td>2080</td>\n",
+       "      <td>5.592071e+08</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>rcp8p5</td>\n",
+       "      <td>GFDL-ESM2M</td>\n",
+       "      <td>2030</td>\n",
+       "      <td>5.975493e+08</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>rcp8p5</td>\n",
+       "      <td>GFDL-ESM2M</td>\n",
+       "      <td>2050</td>\n",
+       "      <td>5.928440e+08</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>rcp8p5</td>\n",
+       "      <td>GFDL-ESM2M</td>\n",
+       "      <td>2080</td>\n",
+       "      <td>5.636648e+08</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>rcp8p5</td>\n",
+       "      <td>HadGEM2-ES</td>\n",
+       "      <td>2030</td>\n",
+       "      <td>6.041759e+08</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
+       "      <td>rcp8p5</td>\n",
+       "      <td>HadGEM2-ES</td>\n",
+       "      <td>2050</td>\n",
+       "      <td>6.064462e+08</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21</th>\n",
+       "      <td>rcp8p5</td>\n",
+       "      <td>HadGEM2-ES</td>\n",
+       "      <td>2080</td>\n",
+       "      <td>6.008009e+08</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22</th>\n",
+       "      <td>rcp8p5</td>\n",
+       "      <td>IPSL-CM5A-LR</td>\n",
+       "      <td>2030</td>\n",
+       "      <td>5.118716e+08</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23</th>\n",
+       "      <td>rcp8p5</td>\n",
+       "      <td>IPSL-CM5A-LR</td>\n",
+       "      <td>2050</td>\n",
+       "      <td>5.061239e+08</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24</th>\n",
+       "      <td>rcp8p5</td>\n",
+       "      <td>IPSL-CM5A-LR</td>\n",
+       "      <td>2080</td>\n",
+       "      <td>4.933349e+08</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25</th>\n",
+       "      <td>rcp8p5</td>\n",
+       "      <td>MIROC-ESM-CHEM</td>\n",
+       "      <td>2030</td>\n",
+       "      <td>6.340810e+08</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26</th>\n",
+       "      <td>rcp8p5</td>\n",
+       "      <td>MIROC-ESM-CHEM</td>\n",
+       "      <td>2050</td>\n",
+       "      <td>6.829027e+08</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27</th>\n",
+       "      <td>rcp8p5</td>\n",
+       "      <td>MIROC-ESM-CHEM</td>\n",
+       "      <td>2080</td>\n",
+       "      <td>6.871308e+08</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>28</th>\n",
+       "      <td>rcp8p5</td>\n",
+       "      <td>NorESM1-M</td>\n",
+       "      <td>2030</td>\n",
+       "      <td>5.250524e+08</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>29</th>\n",
+       "      <td>rcp8p5</td>\n",
+       "      <td>NorESM1-M</td>\n",
+       "      <td>2050</td>\n",
+       "      <td>5.531403e+08</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>30</th>\n",
+       "      <td>rcp8p5</td>\n",
+       "      <td>NorESM1-M</td>\n",
+       "      <td>2080</td>\n",
+       "      <td>5.673106e+08</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           rcp             gcm  epoch       ead_usd\n",
+       "0   historical           WATCH   1980  5.709742e+08\n",
+       "1       rcp4p5      GFDL-ESM2M   2030  6.034515e+08\n",
+       "2       rcp4p5      GFDL-ESM2M   2050  6.026048e+08\n",
+       "3       rcp4p5      GFDL-ESM2M   2080  5.911836e+08\n",
+       "4       rcp4p5      HadGEM2-ES   2030  6.192473e+08\n",
+       "5       rcp4p5      HadGEM2-ES   2050  6.024512e+08\n",
+       "6       rcp4p5      HadGEM2-ES   2080  5.444512e+08\n",
+       "7       rcp4p5    IPSL-CM5A-LR   2030  5.128261e+08\n",
+       "8       rcp4p5    IPSL-CM5A-LR   2050  5.010024e+08\n",
+       "9       rcp4p5    IPSL-CM5A-LR   2080  5.141668e+08\n",
+       "10      rcp4p5  MIROC-ESM-CHEM   2030  6.278343e+08\n",
+       "11      rcp4p5  MIROC-ESM-CHEM   2050  6.189458e+08\n",
+       "12      rcp4p5  MIROC-ESM-CHEM   2080  6.096544e+08\n",
+       "13      rcp4p5       NorESM1-M   2030  5.523561e+08\n",
+       "14      rcp4p5       NorESM1-M   2050  5.659124e+08\n",
+       "15      rcp4p5       NorESM1-M   2080  5.592071e+08\n",
+       "16      rcp8p5      GFDL-ESM2M   2030  5.975493e+08\n",
+       "17      rcp8p5      GFDL-ESM2M   2050  5.928440e+08\n",
+       "18      rcp8p5      GFDL-ESM2M   2080  5.636648e+08\n",
+       "19      rcp8p5      HadGEM2-ES   2030  6.041759e+08\n",
+       "20      rcp8p5      HadGEM2-ES   2050  6.064462e+08\n",
+       "21      rcp8p5      HadGEM2-ES   2080  6.008009e+08\n",
+       "22      rcp8p5    IPSL-CM5A-LR   2030  5.118716e+08\n",
+       "23      rcp8p5    IPSL-CM5A-LR   2050  5.061239e+08\n",
+       "24      rcp8p5    IPSL-CM5A-LR   2080  4.933349e+08\n",
+       "25      rcp8p5  MIROC-ESM-CHEM   2030  6.340810e+08\n",
+       "26      rcp8p5  MIROC-ESM-CHEM   2050  6.829027e+08\n",
+       "27      rcp8p5  MIROC-ESM-CHEM   2080  6.871308e+08\n",
+       "28      rcp8p5       NorESM1-M   2030  5.250524e+08\n",
+       "29      rcp8p5       NorESM1-M   2050  5.531403e+08\n",
+       "30      rcp8p5       NorESM1-M   2080  5.673106e+08"
+      ]
+     },
+     "execution_count": 62,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "summary = (\n",
-    "    future.reset_index()\n",
-    "    .drop(columns=[\"id\", \"rp10\", \"rp100\", \"rp1000\"])\n",
-    "    .groupby([\"rcp\", \"gcm\"])\n",
+    "    future.reset_index()[[\"rcp\", \"gcm\", \"epoch\", \"ead_usd\"]]\n",
+    "    .groupby([\"rcp\", \"gcm\", \"epoch\"])\n",
     "    .sum()\n",
+    "    .reset_index()\n",
     ")\n",
+    "summary.epoch = summary.epoch.astype(int)\n",
     "summary"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 63,
    "id": "acoustic-exposure",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<seaborn.axisgrid.FacetGrid at 0x7f058850da90>"
+      ]
+     },
+     "execution_count": 63,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1676.5x500 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "sns.lmplot(\n",
-    "    \"rcp\", \"ead_usd\", data=summary.reset_index(), hue=\"gcm\", fit_reg=False\n",
+    "    data=summary,\n",
+    "    col=\"rcp\",\n",
+    "    x=\"epoch\",\n",
+    "    y=\"ead_usd\",\n",
+    "    hue=\"gcm\",  # fit_reg=False\n",
     ")"
    ]
   }
@@ -829,7 +3424,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.10"
+   "version": "3.11.4"
   }
  },
  "nbformat": 4,
diff --git a/tutorials/03-test-multiple-failures.ipynb b/tutorials/03-test-multiple-failures.ipynb
index 7f6b8c3..3db668b 100644
--- a/tutorials/03-test-multiple-failures.ipynb
+++ b/tutorials/03-test-multiple-failures.ipynb
@@ -22,7 +22,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 40,
    "id": "rolled-wireless",
    "metadata": {},
    "outputs": [],
@@ -32,8 +32,10 @@
     "import warnings\n",
     "from glob import glob\n",
     "from math import factorial\n",
+    "from pathlib import Path\n",
     "\n",
     "# Imports from other Python packages\n",
+    "import contextily as cx\n",
     "import geopandas as gpd\n",
     "import networkx as nx\n",
     "import numpy as np\n",
@@ -53,12 +55,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "id": "international-saint",
    "metadata": {},
    "outputs": [],
    "source": [
-    "data_folder = \"../data\""
+    "data_folder = Path(\"../data\")"
    ]
   },
   {
@@ -71,36 +73,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "pursuant-ribbon",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def read_file_without_warnings(path, **kwd):\n",
-    "    with warnings.catch_warnings():\n",
-    "        warnings.simplefilter(\"ignore\")\n",
-    "        data = gpd.read_file(path, **kwd)\n",
-    "    return data"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 7,
    "id": "middle-mercy",
    "metadata": {},
    "outputs": [],
    "source": [
-    "roads = read_file_without_warnings(\n",
-    "    os.path.join(data_folder, \"GHA_OSM_roads.gpkg\"), layer=\"edges\"\n",
-    ")\n",
-    "road_nodes = read_file_without_warnings(\n",
-    "    os.path.join(data_folder, \"GHA_OSM_roads.gpkg\"), layer=\"nodes\"\n",
-    ")"
+    "roads = gpd.read_file(data_folder / \"GHA_OSM_roads.gpkg\", layer=\"edges\")\n",
+    "road_nodes = gpd.read_file(data_folder / \"GHA_OSM_roads.gpkg\", layer=\"nodes\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 8,
    "id": "structured-hurricane",
    "metadata": {},
    "outputs": [],
@@ -117,7 +101,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 9,
    "id": "worse-million",
    "metadata": {},
    "outputs": [],
@@ -135,7 +119,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 10,
    "id": "alone-diameter",
    "metadata": {},
    "outputs": [],
@@ -145,19 +129,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 12,
    "id": "legitimate-metallic",
    "metadata": {},
    "outputs": [],
    "source": [
-    "exposure = read_file_without_warnings(\n",
-    "    os.path.join(data_folder, \"results/flood_exposure.gpkg\")\n",
+    "exposure = gpd.read_parquet(\n",
+    "    data_folder / \"results\" / \"GHA_OSM_roads_edges___exposure.geoparquet\"\n",
     ")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 13,
    "id": "raised-lafayette",
    "metadata": {},
    "outputs": [],
@@ -175,26 +159,369 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 16,
    "id": "presidential-writing",
    "metadata": {},
    "outputs": [],
    "source": [
-    "accra_exposure = exposure[\n",
-    "    (exposure.ADM1_EN == \"Greater Accra\")\n",
-    "    & (exposure.rcp == \"historical\")\n",
-    "    & (exposure.rp == 100)\n",
-    "]"
+    "accra_exposure = exposure[(exposure.ADM1_EN == \"Greater Accra\")]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "3ca9f390",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>osm_id</th>\n",
+       "      <th>road_type</th>\n",
+       "      <th>name</th>\n",
+       "      <th>id</th>\n",
+       "      <th>from_id</th>\n",
+       "      <th>to_id</th>\n",
+       "      <th>length_m</th>\n",
+       "      <th>geometry</th>\n",
+       "      <th>split</th>\n",
+       "      <th>i_0</th>\n",
+       "      <th>...</th>\n",
+       "      <th>wri_aqueduct-version_2-inunriver_rcp8p5_MIROC-ESM-CHEM_2080_rp00005-gha</th>\n",
+       "      <th>wri_aqueduct-version_2-inunriver_rcp8p5_MIROC-ESM-CHEM_2080_rp00010-gha</th>\n",
+       "      <th>wri_aqueduct-version_2-inunriver_rcp8p5_MIROC-ESM-CHEM_2080_rp00025-gha</th>\n",
+       "      <th>wri_aqueduct-version_2-inunriver_rcp8p5_MIROC-ESM-CHEM_2080_rp00050-gha</th>\n",
+       "      <th>wri_aqueduct-version_2-inunriver_rcp8p5_MIROC-ESM-CHEM_2080_rp00100-gha</th>\n",
+       "      <th>wri_aqueduct-version_2-inunriver_rcp8p5_MIROC-ESM-CHEM_2080_rp00250-gha</th>\n",
+       "      <th>wri_aqueduct-version_2-inunriver_rcp8p5_MIROC-ESM-CHEM_2080_rp00500-gha</th>\n",
+       "      <th>wri_aqueduct-version_2-inunriver_rcp8p5_MIROC-ESM-CHEM_2080_rp01000-gha</th>\n",
+       "      <th>ADM1_PCODE</th>\n",
+       "      <th>ADM1_EN</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>4790594</td>\n",
+       "      <td>tertiary</td>\n",
+       "      <td>Airport Road</td>\n",
+       "      <td>roade_0</td>\n",
+       "      <td>roadn_0</td>\n",
+       "      <td>roadn_1</td>\n",
+       "      <td>48.717294</td>\n",
+       "      <td>LINESTRING (-0.17544 5.60550, -0.17500 5.60552)</td>\n",
+       "      <td>0</td>\n",
+       "      <td>370</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>GH07</td>\n",
+       "      <td>Greater Accra</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>1 rows × 392 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    osm_id road_type          name       id  from_id    to_id   length_m  \\\n",
+       "0  4790594  tertiary  Airport Road  roade_0  roadn_0  roadn_1  48.717294   \n",
+       "\n",
+       "                                          geometry  split  i_0  ...  \\\n",
+       "0  LINESTRING (-0.17544 5.60550, -0.17500 5.60552)      0  370  ...   \n",
+       "\n",
+       "   wri_aqueduct-version_2-inunriver_rcp8p5_MIROC-ESM-CHEM_2080_rp00005-gha  \\\n",
+       "0                                                0.0                         \n",
+       "\n",
+       "   wri_aqueduct-version_2-inunriver_rcp8p5_MIROC-ESM-CHEM_2080_rp00010-gha  \\\n",
+       "0                                                0.0                         \n",
+       "\n",
+       "   wri_aqueduct-version_2-inunriver_rcp8p5_MIROC-ESM-CHEM_2080_rp00025-gha  \\\n",
+       "0                                                0.0                         \n",
+       "\n",
+       "   wri_aqueduct-version_2-inunriver_rcp8p5_MIROC-ESM-CHEM_2080_rp00050-gha  \\\n",
+       "0                                                0.0                         \n",
+       "\n",
+       "   wri_aqueduct-version_2-inunriver_rcp8p5_MIROC-ESM-CHEM_2080_rp00100-gha  \\\n",
+       "0                                                0.0                         \n",
+       "\n",
+       "   wri_aqueduct-version_2-inunriver_rcp8p5_MIROC-ESM-CHEM_2080_rp00250-gha  \\\n",
+       "0                                                0.0                         \n",
+       "\n",
+       "   wri_aqueduct-version_2-inunriver_rcp8p5_MIROC-ESM-CHEM_2080_rp00500-gha  \\\n",
+       "0                                                0.0                         \n",
+       "\n",
+       "   wri_aqueduct-version_2-inunriver_rcp8p5_MIROC-ESM-CHEM_2080_rp01000-gha  \\\n",
+       "0                                                0.0                         \n",
+       "\n",
+       "   ADM1_PCODE        ADM1_EN  \n",
+       "0        GH07  Greater Accra  \n",
+       "\n",
+       "[1 rows x 392 columns]"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "accra_exposure.head(1)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 22,
+   "id": "5e78168b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['wri_aqueduct-version_2-inunriver_historical_000000000WATCH_1980_rp00002-gha',\n",
+       " 'wri_aqueduct-version_2-inunriver_historical_000000000WATCH_1980_rp00005-gha',\n",
+       " 'wri_aqueduct-version_2-inunriver_historical_000000000WATCH_1980_rp00010-gha',\n",
+       " 'wri_aqueduct-version_2-inunriver_historical_000000000WATCH_1980_rp00025-gha',\n",
+       " 'wri_aqueduct-version_2-inunriver_historical_000000000WATCH_1980_rp00050-gha',\n",
+       " 'wri_aqueduct-version_2-inunriver_historical_000000000WATCH_1980_rp00100-gha',\n",
+       " 'wri_aqueduct-version_2-inunriver_historical_000000000WATCH_1980_rp00250-gha',\n",
+       " 'wri_aqueduct-version_2-inunriver_historical_000000000WATCH_1980_rp00500-gha',\n",
+       " 'wri_aqueduct-version_2-inunriver_historical_000000000WATCH_1980_rp01000-gha']"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "[col for col in accra_exposure.columns if \"1980\" in col]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
    "id": "proof-prague",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: >"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "accra_exposure.plot(\n",
+    "    column=\"wri_aqueduct-version_2-inunriver_historical_000000000WATCH_1980_rp00100-gha\"\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "2326d851",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>id</th>\n",
+       "      <th>road_type</th>\n",
+       "      <th>name</th>\n",
+       "      <th>length_m</th>\n",
+       "      <th>wri_aqueduct-version_2-inunriver_historical_000000000WATCH_1980_rp00100-gha</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>126</th>\n",
+       "      <td>roade_126</td>\n",
+       "      <td>trunk</td>\n",
+       "      <td>Winneba Road</td>\n",
+       "      <td>364.644366</td>\n",
+       "      <td>0.617383</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>126</th>\n",
+       "      <td>roade_126</td>\n",
+       "      <td>trunk</td>\n",
+       "      <td>Winneba Road</td>\n",
+       "      <td>158.050565</td>\n",
+       "      <td>0.617383</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>127</th>\n",
+       "      <td>roade_127</td>\n",
+       "      <td>trunk</td>\n",
+       "      <td>Winneba Road</td>\n",
+       "      <td>54.297481</td>\n",
+       "      <td>0.617383</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>128</th>\n",
+       "      <td>roade_128</td>\n",
+       "      <td>trunk</td>\n",
+       "      <td>Winneba Road</td>\n",
+       "      <td>715.652789</td>\n",
+       "      <td>0.617383</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>128</th>\n",
+       "      <td>roade_128</td>\n",
+       "      <td>trunk</td>\n",
+       "      <td>Winneba Road</td>\n",
+       "      <td>360.198545</td>\n",
+       "      <td>0.617383</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15368</th>\n",
+       "      <td>roade_15368</td>\n",
+       "      <td>primary</td>\n",
+       "      <td>Ring Road West</td>\n",
+       "      <td>45.136404</td>\n",
+       "      <td>0.617383</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15390</th>\n",
+       "      <td>roade_15390</td>\n",
+       "      <td>primary</td>\n",
+       "      <td>Ring Road West</td>\n",
+       "      <td>10.260176</td>\n",
+       "      <td>0.617383</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15663</th>\n",
+       "      <td>roade_15663</td>\n",
+       "      <td>tertiary</td>\n",
+       "      <td>None</td>\n",
+       "      <td>835.677777</td>\n",
+       "      <td>16.040001</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15663</th>\n",
+       "      <td>roade_15663</td>\n",
+       "      <td>tertiary</td>\n",
+       "      <td>None</td>\n",
+       "      <td>341.015419</td>\n",
+       "      <td>19.689999</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15663</th>\n",
+       "      <td>roade_15663</td>\n",
+       "      <td>tertiary</td>\n",
+       "      <td>None</td>\n",
+       "      <td>1025.220133</td>\n",
+       "      <td>22.150000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>900 rows × 5 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                id road_type            name     length_m  \\\n",
+       "126      roade_126     trunk    Winneba Road   364.644366   \n",
+       "126      roade_126     trunk    Winneba Road   158.050565   \n",
+       "127      roade_127     trunk    Winneba Road    54.297481   \n",
+       "128      roade_128     trunk    Winneba Road   715.652789   \n",
+       "128      roade_128     trunk    Winneba Road   360.198545   \n",
+       "...            ...       ...             ...          ...   \n",
+       "15368  roade_15368   primary  Ring Road West    45.136404   \n",
+       "15390  roade_15390   primary  Ring Road West    10.260176   \n",
+       "15663  roade_15663  tertiary            None   835.677777   \n",
+       "15663  roade_15663  tertiary            None   341.015419   \n",
+       "15663  roade_15663  tertiary            None  1025.220133   \n",
+       "\n",
+       "       wri_aqueduct-version_2-inunriver_historical_000000000WATCH_1980_rp00100-gha  \n",
+       "126                                             0.617383                            \n",
+       "126                                             0.617383                            \n",
+       "127                                             0.617383                            \n",
+       "128                                             0.617383                            \n",
+       "128                                             0.617383                            \n",
+       "...                                                  ...                            \n",
+       "15368                                           0.617383                            \n",
+       "15390                                           0.617383                            \n",
+       "15663                                          16.040001                            \n",
+       "15663                                          19.689999                            \n",
+       "15663                                          22.150000                            \n",
+       "\n",
+       "[900 rows x 5 columns]"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "accra_exposure.plot()"
+    "flood_col = \"wri_aqueduct-version_2-inunriver_historical_000000000WATCH_1980_rp00100-gha\"\n",
+    "accra_exposure_100yr = accra_exposure[accra_exposure[flood_col] > 0.5].copy()\n",
+    "accra_exposure_100yr[[\"id\", \"road_type\", \"name\", \"length_m\", flood_col]]"
    ]
   },
   {
@@ -215,15 +542,26 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 32,
    "id": "proprietary-drinking",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Total direct exposure, in Accra under a historical 100-year flood, is estimated to be 245km (of 2034km total roads).'"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "(\n",
-    "    \"Total direct damage, \"\n",
+    "    \"Total direct exposure, \"\n",
     "    \"in Accra under a historical 100-year flood, is estimated to be \"\n",
-    "    f\"USD${int(accra_exposure.damage_usd.sum() // 1e6)} million.\"\n",
+    "    f\"{int(accra_exposure_100yr.length_m.sum() // 1e3)}km (of {int(accra_exposure.length_m.sum() // 1e3)}km total roads).\"\n",
     ")"
    ]
   },
@@ -239,7 +577,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 33,
    "id": "superb-american",
    "metadata": {},
    "outputs": [],
@@ -261,7 +599,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 34,
    "id": "pleasant-saturn",
    "metadata": {},
    "outputs": [],
@@ -281,7 +619,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 35,
    "id": "sustained-irrigation",
    "metadata": {},
    "outputs": [],
@@ -297,7 +635,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 36,
    "id": "smoking-stockholm",
    "metadata": {},
    "outputs": [],
@@ -307,23 +645,56 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 37,
    "id": "guided-region",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Best route: 27.41km'"
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "f\"Best route: {round(route.length_m.sum() / 1e3, 2)}km\""
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 45,
    "id": "talented-contemporary",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: title={'center': 'Direct route'}>"
+      ]
+     },
+     "execution_count": 45,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "ax = route.plot()\n",
     "ax.set_title(\"Direct route\")\n",
+    "cx.add_basemap(ax, crs=route.crs, source=cx.providers.CartoDB.Positron)\n",
     "ax"
    ]
   },
@@ -337,7 +708,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 46,
    "id": "horizontal-prerequisite",
    "metadata": {},
    "outputs": [],
@@ -360,7 +731,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 47,
    "id": "nasty-camera",
    "metadata": {},
    "outputs": [],
@@ -386,6 +757,19 @@
     "    return route"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "id": "193fff97",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def plot_route(df):\n",
+    "    ax = df.plot()\n",
+    "    cx.add_basemap(ax, crs=route.crs, source=cx.providers.CartoDB.Positron)\n",
+    "    return ax"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "suspended-prague",
@@ -396,17 +780,45 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 52,
    "id": "chicken-session",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Best route: 27.41km\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: >"
+      ]
+     },
+     "execution_count": 52,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "single_failures = [(\"roadn_8900\", \"roadn_9227\")]\n",
     "single_fail_route = calc_route(\n",
     "    roads, single_failures, \"roadn_6700\", \"roadn_1011\"\n",
     ")\n",
     "print(f\"Best route: {round(single_fail_route.length_m.sum() / 1e3, 2)}km\")\n",
-    "single_fail_route.plot()"
+    "plot_route(single_fail_route)"
    ]
   },
   {
@@ -421,10 +833,38 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 53,
    "id": "decimal-cloud",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Best route: 27.4km\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: >"
+      ]
+     },
+     "execution_count": 53,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "both_lanes_failures = [\n",
     "    (\"roadn_8900\", \"roadn_9227\"),\n",
@@ -434,7 +874,7 @@
     "    roads, both_lanes_failures, \"roadn_6700\", \"roadn_1011\"\n",
     ")\n",
     "print(f\"Best route: {round(both_lanes_fail_route.length_m.sum() / 1e3, 1)}km\")\n",
-    "both_lanes_fail_route.plot()"
+    "plot_route(both_lanes_fail_route)"
    ]
   },
   {
@@ -449,10 +889,38 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 54,
    "id": "republican-payment",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Best route: 51.2km\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: >"
+      ]
+     },
+     "execution_count": 54,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "multi_failures = [\n",
     "    (road.from_id, road.to_id) for road in accra_exposure.itertuples()\n",
@@ -461,7 +929,7 @@
     "    roads, multi_failures, \"roadn_6700\", \"roadn_1011\"\n",
     ")\n",
     "print(f\"Best route: {round(multi_fail_route.length_m.sum() / 1e3, 1)}km\")\n",
-    "multi_fail_route.plot()"
+    "plot_route(multi_fail_route)"
    ]
   },
   {
@@ -504,7 +972,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 55,
    "id": "cognitive-absolute",
    "metadata": {},
    "outputs": [],
@@ -525,30 +993,63 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 56,
    "id": "breeding-pointer",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "3"
+      ]
+     },
+     "execution_count": 56,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "n_choose_k(3, 2)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 57,
    "id": "naughty-builder",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "19900"
+      ]
+     },
+     "execution_count": 57,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "n_choose_k(200, 2)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 58,
    "id": "figured-examination",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1313400"
+      ]
+     },
+     "execution_count": 58,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "n_choose_k(200, 3)"
    ]
@@ -563,10 +1064,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 59,
    "id": "compatible-surrey",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "With 15727 roads\n",
+      "there are 1 total possible combinations of 0 roads failing\n",
+      "there are 15,727 total possible combinations of 1 roads failing\n",
+      "there are 123,661,401 total possible combinations of 2 roads failing\n",
+      "there are 648,191,843,575 total possible combinations of 3 roads failing\n"
+     ]
+    }
+   ],
    "source": [
     "n = len(roads)\n",
     "print(f\"With {n} roads\")\n",
@@ -586,10 +1099,93 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 60,
    "id": "prerequisite-proof",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>osm_id</th>\n",
+       "      <th>road_type</th>\n",
+       "      <th>name</th>\n",
+       "      <th>id</th>\n",
+       "      <th>from_id</th>\n",
+       "      <th>to_id</th>\n",
+       "      <th>length_m</th>\n",
+       "      <th>geometry</th>\n",
+       "      <th>ADM1_PCODE</th>\n",
+       "      <th>ADM1_EN</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>4790608</td>\n",
+       "      <td>secondary</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>roade_12</td>\n",
+       "      <td>roadn_9</td>\n",
+       "      <td>roadn_10</td>\n",
+       "      <td>20.485494</td>\n",
+       "      <td>LINESTRING (-0.17548 5.60567, -0.17550 5.60569...</td>\n",
+       "      <td>GH07</td>\n",
+       "      <td>Greater Accra</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>4790611</td>\n",
+       "      <td>tertiary</td>\n",
+       "      <td>Patrice Lumumba Road</td>\n",
+       "      <td>roade_15</td>\n",
+       "      <td>roadn_5042</td>\n",
+       "      <td>roadn_14</td>\n",
+       "      <td>297.723047</td>\n",
+       "      <td>LINESTRING (-0.18609 5.60608, -0.18579 5.60652...</td>\n",
+       "      <td>GH07</td>\n",
+       "      <td>Greater Accra</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     osm_id  road_type                  name        id     from_id     to_id  \\\n",
+       "12  4790608  secondary                   NaN  roade_12     roadn_9  roadn_10   \n",
+       "15  4790611   tertiary  Patrice Lumumba Road  roade_15  roadn_5042  roadn_14   \n",
+       "\n",
+       "      length_m                                           geometry ADM1_PCODE  \\\n",
+       "12   20.485494  LINESTRING (-0.17548 5.60567, -0.17550 5.60569...       GH07   \n",
+       "15  297.723047  LINESTRING (-0.18609 5.60608, -0.18579 5.60652...       GH07   \n",
+       "\n",
+       "          ADM1_EN  \n",
+       "12  Greater Accra  \n",
+       "15  Greater Accra  "
+      ]
+     },
+     "execution_count": 60,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "k = 500\n",
     "ids = np.random.choice(roads.id, size=k, replace=False)\n",
@@ -599,10 +1195,38 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 62,
    "id": "underlying-popularity",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Best route: 27.4km\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: >"
+      ]
+     },
+     "execution_count": 62,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "random_failures = [\n",
     "    (road.from_id, road.to_id) for road in failed_roads.itertuples()\n",
@@ -612,7 +1236,7 @@
     ")\n",
     "\n",
     "print(f\"Best route: {round(random_fail_route.length_m.sum() / 1e3, 1)}km\")\n",
-    "random_fail_route.plot()"
+    "plot_route(random_fail_route)"
    ]
   },
   {
@@ -625,10 +1249,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 63,
    "id": "compound-lender",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "caa13b8b84814c2ebff6dbc72e801da7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/100 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "k = 500\n",
     "n_samples = 100\n",
@@ -648,7 +1287,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 64,
    "id": "finished-passenger",
    "metadata": {},
    "outputs": [],
@@ -666,10 +1305,88 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 65,
    "id": "engaging-conditions",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>length_km</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>100.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>28.355000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>1.538685</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>27.400000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>27.400000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>27.500000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>29.500000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>33.200000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        length_km\n",
+       "count  100.000000\n",
+       "mean    28.355000\n",
+       "std      1.538685\n",
+       "min     27.400000\n",
+       "25%     27.400000\n",
+       "50%     27.500000\n",
+       "75%     29.500000\n",
+       "max     33.200000"
+      ]
+     },
+     "execution_count": 65,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "sampled_failures.describe()"
    ]
@@ -684,10 +1401,31 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 66,
    "id": "sharing-button",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='index', ylabel='length_km'>"
+      ]
+     },
+     "execution_count": 66,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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VlgQHAADQFFPJzMyZM/XGG2/otttuU6dOneRyuayOCwAAwCemkpkVK1Zo9erVGjNmjNXxAAAA+MXUmBm3260ePXpYHQsAAIDfTCUzzz77rH7961/rwoULVscDAADgF1O3me6//35lZ2erY8eO6t69u6Kjo73W796925LgAAAAmmIqmZk0aZJ27dqlRx55hAHAAADAVqaSmb/+9a/6+9//rqFDh1odDwAAgF9MjZlJTk5mNl0AABASTCUzS5Ys0RNPPKGCggKLwwEAAPCPqdtMjzzyiCorK3X99dcrNjb2qgHAxcXFlgQHAADQFFPJzEsvvcSgXwAAEBJMVzM1hLlnAABAMJkaMzN16tR6l1dUVGj06NHNCggAAMAfppKZ9evX66mnnvJaVlFRobvvvlvV1dWWBAYAAOALU7eZ1q9fr6FDh6pDhw6aPXu2ysrKdNddd6lly5b68MMPrY4RAACgQaaSmdTUVP3973/XiBEj1KJFC61cuVIxMTH661//qrZt21odIwAAQINMJTOS1LdvX61bt04jR47UrbfeqnXr1qlNmzZWxgYAANAkn5OZjIyMesuxY2JidOLECQ0ZMsSzjAdNAgCAYPE5mRk3blwAwwAAADDHZRiGEaidZ2dn67777rN1HE1paancbrdKSkp4nhQAAA7hz++3qdJsX/3iF7/Q6dOnA/kWAAAgwgU0mQngRR8AAABJAU5mAAAAAo1kBgAAOBrJDAAAcDSSGQAA4GgBTWZSUlIUHR0dyLcAAAARzvTjDCTp4sWLOnPmjGpqaryWd+vWTZK0d+/e5uweAACgSaaSmUOHDunRRx/Vtm3bvJYbhiGXy6Xq6mpLggMAAGiKqWRm0qRJatmypdatW6fOnTvX+8wmAACAYDCVzOTn52vXrl3q1auX1fEAAAD4xdQA4N69e6uoqMjqWAAAAPzmczJTWlrq+Xv++ef1xBNPaNOmTTp79qzXutLS0kDGCwAA4MXn20zXXHON19gYwzB0xx13eG3DAGAAABBsPiczGzduDGQcAAAApviczAwfPtzz319//bWSk5OvqmIyDEPHjx+3LjoAAIAmmBoAnJqaqsLCwquWFxcXKzU1tdlBAQAA+MpUMlM7Nqau8vJytW7dutlBAQAA+MqveWbmzJkjSXK5XHr66acVGxvrWVddXa1PP/1UAwYMsDRAAACAxviVzOTl5Un6/srMnj171KpVK8+6Vq1aqX///vrlL39pbYQAAPjpSGG5jhVXqnuHtkpNaGt3OAgwv5KZ2oqmn/70p3rllVcUHx8fkKAAADDjfOVFzcjO1+ZD/zeuc1h6opZmZsgdG21jZAgkU2Nmli9fTiIDAAg5M7LztfWw9wz1Ww8XaXp2nk0RIRhMPZvpJz/5Sb3LXS6XWrdurbS0ND388MPq2bNns4IDAMBXRwrLva7I1Ko2DG0+VKijRRXccgpTpq7MxMfH6+OPP9bu3bs9VU15eXn6+OOPdfnyZf3pT39S//79tXXrVkuDBQCgIceKKxtdX3C2IkiRINhMXZlJSkrSww8/rFdffVUtWnyfD9XU1GjmzJmKi4vTypUrNWXKFM2dO1dbtmyxNGAAsBuDS0NTSvvYRtd378C5Clemrsy89dZbmjVrlieRkaQWLVpo+vTpeuONN+RyuTRt2jTt3bu30f0sW7ZM/fr1U3x8vOLj4zV48GB9+OGHnvWGYejZZ59Vly5d1KZNG40YMUL79u0zEzIANNv5youa8NYO3b4kVz9d/plu+69NmvDWDpVUXrI7NEjqkdhOw9ITFVVnHrQol0vD0hNJPMOYqWTm8uXL+vLLL69a/uWXX3oeMtm6det6J9a7UteuXbV48WLt3LlTO3fu1O23366xY8d6Epbf/va3evHFF/Xqq6/qs88+U1JSku68806VlZWZCRsAmoXBpaFvaWaGhqQleC0bkpagpZkZNkWEYDB1m2n8+PGaPHmy5s+fr5tvvlkul0s7duzQokWLNGHCBElSbm6u+vTp0+h+7r33Xq9/L1y4UMuWLdP27dvVu3dvvfzyy1qwYIFnwPHbb7+tTp066b333tMvfvGLevdZVVWlqqoqz79LS0vNHCIAeGFwqTO4Y6P1zuRbdLSoQgVnK7gVGCFMJTMvvfSSOnXqpN/+9rc6ffq0JKlTp06aPXu25s6dK0kaNWqU7r77bp/3WV1drffff18VFRUaPHiwjh49qlOnTmnUqFGebWJiYjR8+HBt27atwWQmKytLv/71r80cFgA0yJfBpfxoho7UBJKYSGIqmYmKitKCBQu0YMECz5WPuvPOdOvWzad97dmzR4MHD9Z3332ndu3aac2aNerdu7e2bdsm6fsk6UqdOnXSsWPHGtzfvHnzPI9dkL6/MpOcnOxTLADQEAaXAqHLVDJzpeZOntezZ0/l5+fr/PnzWrVqlSZOnKjc3FzP+rrjbhp6yGWtmJgYxcTENCsmAKirdnDp1sNFqjYMz/Iol0sZ3a7xlP1yNQAIPlMDgE+fPq3x48erS5cuatmypaKiorz+/NGqVSulpaVp0KBBysrKUv/+/fXKK68oKSlJknTq1Cmv7c+cOXPV1RoACIb6BpfGt2mpncfOUd0E2MjUlZlJkybp66+/1tNPP63OnTs3WbXkD8MwVFVVpdTUVCUlJWnDhg3KyPh+FPrFixeVm5ur559/3rL3AwBf1R1c+trGw9p97LzXNrXVTe9MvsWeIIEIZCqZ2bJliz755BMNGDCgWW8+f/58jR49WsnJySorK9PKlSu1adMm5eTkyOVyadasWVq0aJHS09OVnp6uRYsWKTY2Vg8//HCz3hcAmiM1oa0Mw9BnBeeuWkd1ExB8ppKZ5ORkGVfcMzar9nbVyZMn5Xa71a9fP+Xk5OjOO++UJD3xxBO6cOGCHn/8cZ07d0633nqr1q9fr7i4uGa/NwA0B9VNQOhwGSaykvXr12vJkiV6/fXX1b179wCEZZ3S0lK53W6VlJTwpG8AljlSWK7bl+Q2uH7jL0eQzADN4M/vt6krMw8++KAqKyt1/fXXKzY2VtHR0V7ri4uLzewW8BnPxoHdGqtuGpKWQL8EgshUMvPyyy9bHAbgm/OVFzUjO99rJtZh6Ylampkhd2x0I68ErLc0M0PTs/O8+iNT5wPBZ+o2k5Nwmym8THhrR4P/J0z1COzC1PmA9fz5/TY1z4wkffXVV3rqqaeUmZmpM2fOSJJycnJ4qjUCpvbZONV18u8rq0cAO6QmtNVtPTuSyAA2MZXM5Obm6sYbb9Snn36q1atXq7y8XJL0xRdf6JlnnrE0QKCWL9UjAIDIYyqZefLJJ/Xcc89pw4YNatWqlWf5bbfdpn/84x+WBQdciWfjAADqYyqZ2bNnj3784x9ftTwxMVFnz55tdlBAfWqrR6LqzDgd5XJpWHoil/jhtyOF5dp44Ay3KBtBG8EJTFUzXXPNNTp58qRSU1O9lufl5em6666zJDCgPlSPwApUxTWNNoKTmKpmeuKJJ/SPf/xD77//vm644Qbt3r1bp0+f1oQJEzRhwoSQGjdDNVN4onoEzUFVXNNoI9gt4NVMCxcuVLdu3XTdddepvLxcvXv31r/+67/qhz/8oZ566ilTQQP+oHoEZlEV1zTaCE5j6jZTdHS03n33Xf3mN7/R7t27VVNTo4yMDKWnp1sdHwBYimcqNY02gtP4nMzMmTOn0fXbt2/3/PeLL75oPiIACCCq4ppGG8FpfE5m8vLyfNrOVafSBABCCc9UahptBKfhcQYAIk5J5aWrquKo1PFGG8Fu/vx+k8wAiFhUxTWNNoJd/Pn9NjUAGADCQWoCP9BNoY3gBKYfNAkAABAKuDLTDEcKy3WsuNKvy69mXhNsTogR1gj2ubbq/ZzQR50QY11OjBnmhNu5Jpkxwcw0306YGtwJMcIawT7XVr2fE/qoE2Ksy4kxw5xwPdfcZjJhRna+th4u8lq29XCRpmc3XL5u5jXB5oQYYY1gn2ur3s8JfdQJMdblxJhhTriea5IZP5mZ5tsJU4M7IUZYI9jn2qr3c0IfdUKMdTkxZpgTzueaZMZPvkzzbcVrgs0JMcIawT7XVr2fE/qoE2Ksy4kxw5xwPtckM34yM823E6YGd0KMsEawz7VV7+eEPuqEGOtyYswwJ5zPNcmMn2qn+Y6q89iGKJdLw9IT6x0VbuY1weaEGGGNYJ9rq97PCX3UCTHW5cSYYU44n2uSGROWZmZoSFqC17IhaQlamplh6WuCzQkxwhrBPtdWvZ8T+qgTYqzLiTHDnHA91zzOoBnMTPPthKnBnRAjrBHsc23V+zmhjzohxrqcGDPMccK55tlMV+DZTAAAOI8/v9/cZgIAAI5GMgMAAByNZAYAADgayQwAAHA0khkAAOBoJDMAAMDRSGYAAICjkcwAAABHI5kBAACORjIDAAAcjWQGAAA4GskMAABwNJIZAADgaCQzAADA0UhmAACAo5HMAAAARyOZAQAAjkYyAwAAHI1kBgAAOBrJDAAAcDRbk5msrCzdfPPNiouLU8eOHTVu3DgdOHDAa5vTp09r0qRJ6tKli2JjY3X33Xfr0KFDNkUMAABCja3JTG5urqZOnart27drw4YNunz5skaNGqWKigpJkmEYGjdunI4cOaIPPvhAeXl5SklJ0ciRIz3bAACAyOYyDMOwO4hahYWF6tixo3JzczVs2DAdPHhQPXv21N69e9WnTx9JUnV1tTp27Kjnn39eP/vZz67aR1VVlaqqqjz/Li0tVXJyskpKShQfHx+0YwEAAOaVlpbK7Xb79PsdUmNmSkpKJEnt27eXJE9S0rp1a882UVFRatWqlbZs2VLvPrKysuR2uz1/ycnJAY4aAADYKWSSGcMwNGfOHA0dOlR9+/aVJPXq1UspKSmaN2+ezp07p4sXL2rx4sU6deqUTp48We9+5s2bp5KSEs/f8ePHg3kYAAAgyFraHUCtadOm6YsvvvC64hIdHa1Vq1Zp8uTJat++vaKiojRy5EiNHj26wf3ExMQoJiYmGCEDAIAQEBLJzPTp07V27Vpt3rxZXbt29Vo3cOBA5efnq6SkRBcvXlRiYqJuvfVWDRo0yKZoAQBAKLH1NpNhGJo2bZpWr16tjz/+WKmpqQ1u63a7lZiYqEOHDmnnzp0aO3ZsECMFAAChytYrM1OnTtV7772nDz74QHFxcTp16pSk7xOXNm3aSJLef/99JSYmqlu3btqzZ49mzpypcePGadSoUXaGDgAAQoStycyyZcskSSNGjPBavnz5ck2aNEmSdPLkSc2ZM0enT59W586dNWHCBD399NNBjhQAAISqkJpnJhD8qVMHAAChwbHzzAAAAPiLZAYAADgayQwAAHA0khkAAOBoJDMAAMDRSGYAAICjkcwAAABHI5kBAACORjIDAAAcjWQGAAA4GskMAABwNFsfNAnU50hhuY4VV6p7h7ZKTWhrdzgAgBBHMoOQcb7yomZk52vzoULPsmHpiVqamSF3bLSNkQEAQhm3mRAyZmTna+vhIq9lWw8XaXp2nk0RAQCcgGQGIeFIYbk2HypUtWF4La82DG0+VKijRRU2RQYACHUkMwgJx4orG11fcJZkBgBQP5IZhISU9rGNru/egYHAAID6kcwgJPRIbKdh6YmKcrm8lke5XBqWnkhVEwCgQSQzCBlLMzM0JC3Ba9mQtAQtzcywKSIAgBNQmo2Q4Y6N1juTb9HRogoVnK1gnhkAgE9IZhByUhNIYgAAvuM2EwAAcDSSGQAA4GgkMwAAwNFIZgAAgKORzAAAAEcjmQEAAI5GMgMAAByNZAYAADgak+YhqI4UlutYcSWz+wIALEMyg6A4X3lRM7LztflQoWfZsPRELc3MkDs22sbIAABOx20mBMWM7HxtPVzktWzr4SJNz86zKSIAQLggmUHAHSks1+ZDhao2DK/l1YahzYcKdbSowqbIAADhgGQGAXesuLLR9QVnSWYAAOaRzCDgUtrHNrq+ewcGAgMAzCOZQcD1SGynYemJinK5vJZHuVwalp5IVROAgDtSWK6NB85wWztMUc2EoFiamaHp2Xle1UxD0hK0NDPDxqgAhDsqKSODyzDqjMoMM6WlpXK73SopKVF8fLzd4US8o0UVKjhbwTwzAIJiwls7tPVwkVcBQpTLpSFpCXpn8i02Roam+PP7zZUZBFVqAkkMgOCoraSs68pKSr6PwgNjZgAAYYlKyshBMgMACEtUUkYOkhkAQFiikjJykMwAAMLW0swMDUlL8FpGJWX4YQAwACBsuWOj9c7kW6ikDHMkMwCAsEclZXjjNhMAAHA0khkAAOBotiYzWVlZuvnmmxUXF6eOHTtq3LhxOnDggNc25eXlmjZtmrp27ao2bdroBz/4gZYtW2ZTxAAAINTYmszk5uZq6tSp2r59uzZs2KDLly9r1KhRqqj4v4mMZs+erZycHK1YsUL//Oc/NXv2bE2fPl0ffPCBjZEDAIBQEVLPZiosLFTHjh2Vm5urYcOGSZL69u2rBx98UE8//bRnu4EDB2rMmDH6zW9+c9U+qqqqVFVV5fl3aWmpkpOTeTYTAAAO4s+zmUJqzExJSYkkqX379p5lQ4cO1dq1a/Xtt9/KMAxt3LhRBw8e1F133VXvPrKysuR2uz1/ycnJQYkdAADYI2SuzBiGobFjx+rcuXP65JNPPMsvXryo//iP/9A777yjli1bqkWLFvrDH/6g8ePH17sfrswAAOB8jnxq9rRp0/TFF19oy5YtXst/97vfafv27Vq7dq1SUlK0efNmPf744+rcubNGjhx51X5iYmIUExMTrLABAIDNQuLKzPTp0/XnP/9ZmzdvVmpqqmf5hQsX5Ha7tWbNGt1zzz2e5T/72c/0zTffKCcnp8l9+5PZAQCA0OCYKzOGYWj69Olas2aNNm3a5JXISNKlS5d06dIltWjhPbQnKipKNTU1wQwVAACEKFuTmalTp+q9997TBx98oLi4OJ06dUqS5Ha71aZNG8XHx2v48OH61a9+pTZt2iglJUW5ubl655139OKLL9oZOgAACBG23mZy1Xkse63ly5dr0qRJkqRTp05p3rx5Wr9+vYqLi5WSkqKf//znmj17doOvvxK3mQAAcB5/fr9DYsxMIJHMAADgPI4ZMwMAcL4jheU6Vlyp7h14MjXsQTIDADDlfOVFzcjO1+ZDhZ5lw9ITtTQzQ+7YaBsjQ6QJqRmAAQDOMSM7X1sPF3kt23q4SNOz82yKCJGKZAYA4LcjheXafKhQ1XWGXVYbhjYfKtTRoooGXglYj2QGAOC3Y8WVja4vOEsyg+AhmQEA+C2lfWyj67t3YCAwgodkBj45UliujQfOcOk4Apk99/SZ8NYjsZ2GpScqqs58X1Eul4alJ0ZUVZMvfZ3PQ2BRzYRGUa0Qucyee/pM5FiamaHp2Xle53pIWoKWZmbYGFXw+NLX+TwEB5PmoVET3tqhrYeLvAb5RblcGpKWoHcm32JjZAg0s+eePhN5jhZVqOBsRcTNM+NLX+fzYJ4/v9/cZkKDqFaIXGbPPX0mMqUmtNVtPTtGVCLjS1/n8xA8JDNoENUKkcvsuafPIFL40tf5PAQPY2bCiNVTijutWoEp1a1j9tw7rc/UJ5L6USQdq9V86etNjeJwwufBKUhmwkCgBpjVVis0dL83VL78GGBnPbPn3il9pj6R1I8i6VgDxde+7tTPg9NwmykMBHJK8aWZGRqSluC1LNSqFZhSPTDMnnsn9Jn6RFI/iqRjDSRf+rpTPw9OQzWTwx0pLNftS3IbXL/xlyMsyf5DtVohWMcfycye+1DtM/WJpH4USccaLL70dSd9HkKFP7/f3GZyOF8GmFnxwUlNCM0PYLCOP5KZPfeh2mfqE0n9KJKONVh86etO+jw4EbeZHC4cBlw2R6QfP6wRSf0oko4VkYNkJgT5M+11U1OKG4bhta9QnFK7OTH5O6W62WnHzcRodj/BjNGsUOtHzW2PxvrRoJRrVXC2otnHGiptFqzHENQ9Xif062DzpY2sOv5APpYkFM4RY2ZCiNkKg5LKS1dNKT64Rwe5XNK2r856ll0bG61zlZf82ncgWVVRUd/xm5lSvL5t6mvHpmI0u59gxmhWqFXBWNke9fUjKz4zodZmkm+fGbPqO96m2tHufh1svrSRVccfyMeSBLpv+/P7TTITQpo77fWVA8ye+WDfVfuqy+4pta2e5ruxAXZmpx2vT1Mxmt1PMGM0K9SmZg9Ee9T2o9c2HtbuY+ebfayh1mZXCsSgVF/OSaj162Dz9XjrCmb/C4VHNfA4AweyYtrr2inFjf//mqY+KHZOqR2Iab4bmlK9OdOO16exGM3uJ5gxmhVqU7MHqj1SE9oqpX2sPis41+xjDbU2q8vqxxD4ek5CqV8Hmz/HW1ew+p8TH9VAMhMirJz2uql9NWffVgnmNN9WTDve0Ov8fS+z729ljGaF2tTsgWwPq4411Nos0Mx899jdr4PNzPHWFej+58RHNVCaHSKsrDBoal/N2bdVgllRYcW04w29zt/3Mvv+VsZoVqhVwZhtayv2Hez9OIWZ7x67+3Wwmem3dQW6/znxUQ1cmQkwX0eCHyuu1M3dr/WrwqChfTdUrVBXQxVPZo/Ln5H4gahCaogvFV8NtX99Gqtw8bXt6+7H9f9jaaw9zO7bqmoqfyvnzPK1Csbf9vAnRqsqfsy0mZkKl1CpAvL3uyeY/bo+Zj4PzW1Xf463Ll+Pv/bfvnyv+BOjv99ZwcQA4AAxOxLcl+oJX/btS2VGICt1fNm3VVVIvjBb8WWmKszK9wrmvs32q0BWXVjV1mZitKripzkxmumPdlcBmakKC2S/rk8gv599Yfb7OZj9IVC/If6gmukKdiUzzRkJflO3a/T47WkNVhj4M4K8brVCUxVPVlXq1MfXGM0eqy98Of762t9MhUt9x+XLfn49to+pqdEb23d9mlOZ0Nx+VB8zVTD1xVO3PZobo1UVP/5WG1rJjiqgxr57gtmv62NV5WBz29WXNvL3+BuK0ZfvlcZibO53lhk8zsBmtaO867pylHdtxVF923x27FyDncKXfV/5urpTaNf+29/9NPbevvI1xqber7EYm+LL8dfX/qkJ398j/qzgXLOOy5f9SNJtPTv6dBy+7rs+vvbHxo7NqnPka9/yt62tiNGqaeibiieQmvOZMauh7x5/XlO7zGy/Nvu90tDnwd/3aoovbeTv8TcUo9T090pDMVrxnRVojJkJgECOBLezysKKUfgN7bs+gRwtH4jjd8JxNfR+od4fze7Xl307oQrLKk6tArKy4ikQlYOBbtdgVkCZef9Q6FdcmQmAQI4Et7PKwopR+A3tuz6BrAQJxPE74bgaer9Q749m9+vLvp1QhWUVp1YBWVm5FojqxkC3azAroMy8fyj0K67MBIAvlRBmqyWCVWVR336aMwo/WDH6ysrjd8Jx1ccJ/bGxmH0VrGcRNTeeQLLrWK1itl/7sy8zFVbBatfmVkDZ8X0ZbCQzAbI0M0ND0hK8lg1JS9DSzAy/tjG7b6ti9OU1g3t00A+v79DksmDFGMh929n2zdm3L+cjFPvjtXUqIsy2USDb2wxfz1Hd4ze7jZ3HahWz/drXffnyebCqP5phts/Y+X0ZTFQzBZjZEfxW7TtQ+2mqeqSxZcGKMZD7dupxObE/WtlGgWxvq+Lxp8LFn23ChZXHaubzYHe7mu0PgXz/QKE0+wp2JzMAAMB/PGgSAABEDJIZAADgaCQzAADA0UhmAACAo5HMAAAARyOZAQAAjkYyAwAAHI1kBgAAOBrJDAAAcDSSGQAA4Ggt7Q4g0Gqf1lBaWmpzJAAAwFe1v9u+PHUp7JOZsrIySVJycrLNkQAAAH+VlZXJ7XY3uk3YP2iypqZGJ06cUFxcnFwul6X7Li0tVXJyso4fP85DLAOMtg4e2jp4aOvgoa2Dx6q2NgxDZWVl6tKli1q0aHxUTNhfmWnRooW6du0a0PeIj4/nwxEktHXw0NbBQ1sHD20dPFa0dVNXZGoxABgAADgayQwAAHA0kplmiImJ0TPPPKOYmBi7Qwl7tHXw0NbBQ1sHD20dPHa0ddgPAAYAAOGNKzMAAMDRSGYAAICjkcwAAABHI5kBAACORjJj0muvvabU1FS1bt1aAwcO1CeffGJ3SI6XlZWlm2++WXFxcerYsaPGjRunAwcOeG1jGIaeffZZdenSRW3atNGIESO0b98+myIOH1lZWXK5XJo1a5ZnGW1tnW+//VaPPPKIOnTooNjYWA0YMEC7du3yrKetrXH58mU99dRTSk1NVZs2bdSjRw/953/+p2pqajzb0NbmbN68Wffee6+6dOkil8ulP//5z17rfWnXqqoqTZ8+XQkJCWrbtq3uu+8+ffPNN9YEaMBvK1euNKKjo40333zT2L9/vzFz5kyjbdu2xrFjx+wOzdHuuusuY/ny5cbevXuN/Px845577jG6detmlJeXe7ZZvHixERcXZ6xatcrYs2eP8eCDDxqdO3c2SktLbYzc2Xbs2GF0797d6NevnzFz5kzPctraGsXFxUZKSooxadIk49NPPzWOHj1qfPTRR8bhw4c929DW1njuueeMDh06GOvWrTOOHj1qvP/++0a7du2Ml19+2bMNbW3O3/72N2PBggXGqlWrDEnGmjVrvNb70q5TpkwxrrvuOmPDhg3G7t27jdtuu83o37+/cfny5WbHRzJjwi233GJMmTLFa1mvXr2MJ5980qaIwtOZM2cMSUZubq5hGIZRU1NjJCUlGYsXL/Zs89133xlut9v4/e9/b1eYjlZWVmakp6cbGzZsMIYPH+5JZmhr68ydO9cYOnRog+tpa+vcc889xqOPPuq17Cc/+YnxyCOPGIZBW1ulbjLjS7ueP3/eiI6ONlauXOnZ5ttvvzVatGhh5OTkNDsmbjP56eLFi9q1a5dGjRrltXzUqFHatm2bTVGFp5KSEklS+/btJUlHjx7VqVOnvNo+JiZGw4cPp+1Nmjp1qu655x6NHDnSazltbZ21a9dq0KBBuv/++9WxY0dlZGTozTff9Kynra0zdOhQ/e///q8OHjwoSfr888+1ZcsWjRkzRhJtHSi+tOuuXbt06dIlr226dOmivn37WtL2Yf+gSasVFRWpurpanTp18lreqVMnnTp1yqaowo9hGJozZ46GDh2qvn37SpKnfetr+2PHjgU9RqdbuXKldu/erc8+++yqdbS1dY4cOaJly5Zpzpw5mj9/vnbs2KEZM2YoJiZGEyZMoK0tNHfuXJWUlKhXr16KiopSdXW1Fi5cqMzMTEn060DxpV1PnTqlVq1a6dprr71qGyt+O0lmTHK5XF7/NgzjqmUwb9q0afriiy+0ZcuWq9bR9s13/PhxzZw5U+vXr1fr1q0b3I62br6amhoNGjRIixYtkiRlZGRo3759WrZsmSZMmODZjrZuvj/96U9asWKF3nvvPfXp00f5+fmaNWuWunTpookTJ3q2o60Dw0y7WtX23GbyU0JCgqKioq7KJM+cOXNVVgpzpk+frrVr12rjxo3q2rWrZ3lSUpIk0fYW2LVrl86cOaOBAweqZcuWatmypXJzc/W73/1OLVu29LQnbd18nTt3Vu/evb2W/eAHP9DXX38tiX5tpV/96ld68skn9dBDD+nGG2/U+PHjNXv2bGVlZUmirQPFl3ZNSkrSxYsXde7cuQa3aQ6SGT+1atVKAwcO1IYNG7yWb9iwQT/84Q9tiio8GIahadOmafXq1fr444+VmprqtT41NVVJSUlebX/x4kXl5ubS9n664447tGfPHuXn53v+Bg0apH//939Xfn6+evToQVtbZMiQIVdNMXDw4EGlpKRIol9bqbKyUi1aeP+sRUVFeUqzaevA8KVdBw4cqOjoaK9tTp48qb1791rT9s0eQhyBakuz33rrLWP//v3GrFmzjLZt2xoFBQV2h+Zojz32mOF2u41NmzYZJ0+e9PxVVlZ6tlm8eLHhdruN1atXG3v27DEyMzMpq7TIldVMhkFbW2XHjh1Gy5YtjYULFxqHDh0y3n33XSM2NtZYsWKFZxva2hoTJ040rrvuOk9p9urVq42EhATjiSee8GxDW5tTVlZm5OXlGXl5eYYk48UXXzTy8vI8U5L40q5Tpkwxunbtanz00UfG7t27jdtvv53SbLv993//t5GSkmK0atXKuOmmmzzlwzBPUr1/y5cv92xTU1NjPPPMM0ZSUpIRExNjDBs2zNizZ499QYeRuskMbW2dv/zlL0bfvn2NmJgYo1evXsYbb7zhtZ62tkZpaakxc+ZMo1u3bkbr1q2NHj16GAsWLDCqqqo829DW5mzcuLHe7+eJEycahuFbu164cMGYNm2a0b59e6NNmzbGj370I+Prr7+2JD6XYRhG86/vAAAA2IMxMwAAwNFIZgAAgKORzAAAAEcjmQEAAI5GMgMAAByNZAYAADgayQwAAHA0khkAAOBoJDMAbDFixAjNmjXL9OsLCgrkcrmUn59vWUwAnKml3QEAiEyrV69WdHS03WEACAMkMwBs0b59e7tDABAmuM0EwBZX3mbq3r27Fi1apEcffVRxcXHq1q2b3njjDa/td+zYoYyMDLVu3VqDBg1SXl7eVfvcv3+/xowZo3bt2qlTp04aP368ioqKJEmbNm1Sq1at9Mknn3i2X7JkiRISEnTy5MnAHSiAgCOZARASlixZ4klSHn/8cT322GP68ssvJUkVFRX60Y9+pJ49e2rXrl169tln9ctf/tLr9SdPntTw4cM1YMAA7dy5Uzk5OTp9+rQeeOABSf+XPI0fP14lJSX6/PPPtWDBAr355pvq3Llz0I8XgHW4zQQgJIwZM0aPP/64JGnu3Ll66aWXtGnTJvXq1Uvvvvuuqqur9cc//lGxsbHq06ePvvnmGz322GOe1y9btkw33XSTFi1a5Fn2xz/+UcnJyTp48KBuuOEGPffcc/roo4/085//XPv27dP48eP14x//OOjHCsBaJDMAQkK/fv08/+1yuZSUlKQzZ85Ikv75z3+qf//+io2N9WwzePBgr9fv2rVLGzduVLt27a7a91dffaUbbrhBrVq10ooVK9SvXz+lpKTo5ZdfDszBAAgqkhkAIaFuZZPL5VJNTY0kyTCMJl9fU1Oje++9V88///xV6668jbRt2zZJUnFxsYqLi9W2bdvmhA0gBDBmBkDI6927tz7//HNduHDBs2z79u1e29x0003at2+funfvrrS0NK+/2oTlq6++0uzZs/Xmm2/qX/7lXzRhwgRPwgTAuUhmAIS8hx9+WC1atNDkyZO1f/9+/e1vf9N//dd/eW0zdepUFRcXKzMzUzt27NCRI0e0fv16Pfroo6qurlZ1dbXGjx+vUaNG6ac//amWL1+uvXv3asmSJTYdFQCrkMwACHnt2rXTX/7yF+3fv18ZGRlasGDBVbeTunTpoq1bt6q6ulp33XWX+vbtq5kzZ8rtdqtFixZauHChCgoKPCXfSUlJ+sMf/qCnnnqKWYQBh3MZvtyMBgAACFFcmQEAAI5GMgMAAByNZAYAADgayQwAAHA0khkAAOBoJDMAAMDRSGYAAICjkcwAAABHI5kBAACORjIDAAAcjWQGAAA42v8DRRqBrl03QQgAAAAASUVORK5CYII=",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "sampled_failures.reset_index().plot.scatter(x=\"index\", y=\"length_km\")"
    ]
@@ -704,10 +1442,31 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 67,
    "id": "swiss-singapore",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='length_km', ylabel='Density'>"
+      ]
+     },
+     "execution_count": 67,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "sns.kdeplot(sampled_failures.length_km, cumulative=True)"
    ]
@@ -729,7 +1488,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.10"
+   "version": "3.11.4"
   }
  },
  "nbformat": 4,
diff --git a/tutorials/04-evaluate-adaptation-options.ipynb b/tutorials/04-evaluate-adaptation-options.ipynb
index 65410dd..3b22415 100644
--- a/tutorials/04-evaluate-adaptation-options.ipynb
+++ b/tutorials/04-evaluate-adaptation-options.ipynb
@@ -1,6 +1,7 @@
 {
  "cells": [
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "global-length",
    "metadata": {},
@@ -20,7 +21,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 9,
    "id": "rocky-continent",
    "metadata": {},
    "outputs": [],
@@ -30,6 +31,7 @@
     "import os\n",
     "import warnings\n",
     "from glob import glob\n",
+    "from pathlib import Path\n",
     "\n",
     "# Imports from other Python packages\n",
     "import geopandas as gpd\n",
@@ -41,6 +43,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "found-equilibrium",
    "metadata": {},
@@ -50,29 +53,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 10,
    "id": "exciting-portal",
    "metadata": {},
    "outputs": [],
    "source": [
-    "data_folder = \"../data\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "dedicated-wyoming",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def read_file_without_warnings(path, **kwd):\n",
-    "    with warnings.catch_warnings():\n",
-    "        warnings.simplefilter(\"ignore\")\n",
-    "        data = gpd.read_file(path, **kwd)\n",
-    "    return data"
+    "data_folder = Path(\"../data\")"
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "hindu-orleans",
    "metadata": {},
@@ -81,6 +71,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "professional-plain",
    "metadata": {},
@@ -90,22 +81,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 11,
    "id": "essential-translation",
    "metadata": {},
    "outputs": [],
    "source": [
     "regions = gpd.read_file(\n",
-    "    os.path.join(\n",
-    "        data_folder,\n",
-    "        \"gha_admbnda_gss_20210308_shp\",\n",
-    "        \"gha_admbnda_gss_20210308_SHP\",\n",
-    "        \"gha_admbnda_adm1_gss_20210308.shp\",\n",
-    "    )\n",
+    "    data_folder\n",
+    "    / \"gha_admbnda_gss_20210308_shp\"\n",
+    "    / \"gha_admbnda_gss_20210308_SHP\"\n",
+    "    / \"gha_admbnda_adm1_gss_20210308.shp\"\n",
     ")[[\"ADM1_PCODE\", \"ADM1_EN\", \"geometry\"]]"
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "specific-failure",
    "metadata": {},
@@ -115,19 +105,151 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 12,
    "id": "wired-solomon",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>osm_id</th>\n",
+       "      <th>road_type</th>\n",
+       "      <th>name</th>\n",
+       "      <th>road_id</th>\n",
+       "      <th>from_id</th>\n",
+       "      <th>to_id</th>\n",
+       "      <th>length_m</th>\n",
+       "      <th>geometry</th>\n",
+       "      <th>ADM1_PCODE</th>\n",
+       "      <th>ADM1_EN</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>4790594</td>\n",
+       "      <td>tertiary</td>\n",
+       "      <td>Airport Road</td>\n",
+       "      <td>roade_0</td>\n",
+       "      <td>roadn_0</td>\n",
+       "      <td>roadn_1</td>\n",
+       "      <td>236.526837</td>\n",
+       "      <td>LINESTRING (-0.17544 5.60550, -0.17418 5.60555...</td>\n",
+       "      <td>GH07</td>\n",
+       "      <td>Greater Accra</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>4790599</td>\n",
+       "      <td>tertiary</td>\n",
+       "      <td>South Liberation Link</td>\n",
+       "      <td>roade_1</td>\n",
+       "      <td>roadn_2</td>\n",
+       "      <td>roadn_10683</td>\n",
+       "      <td>18.539418</td>\n",
+       "      <td>LINESTRING (-0.17889 5.59979, -0.17872 5.59977)</td>\n",
+       "      <td>GH07</td>\n",
+       "      <td>Greater Accra</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>4790599</td>\n",
+       "      <td>tertiary</td>\n",
+       "      <td>South Liberation Link</td>\n",
+       "      <td>roade_2</td>\n",
+       "      <td>roadn_10683</td>\n",
+       "      <td>roadn_3</td>\n",
+       "      <td>124.758045</td>\n",
+       "      <td>LINESTRING (-0.17872 5.59977, -0.17786 5.59960...</td>\n",
+       "      <td>GH07</td>\n",
+       "      <td>Greater Accra</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4790600</td>\n",
+       "      <td>tertiary</td>\n",
+       "      <td>Airport Road</td>\n",
+       "      <td>roade_3</td>\n",
+       "      <td>roadn_4</td>\n",
+       "      <td>roadn_6259</td>\n",
+       "      <td>38.030821</td>\n",
+       "      <td>LINESTRING (-0.17330 5.60560, -0.17327 5.60556...</td>\n",
+       "      <td>GH07</td>\n",
+       "      <td>Greater Accra</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>4790600</td>\n",
+       "      <td>tertiary</td>\n",
+       "      <td>Airport Road</td>\n",
+       "      <td>roade_4</td>\n",
+       "      <td>roadn_6259</td>\n",
+       "      <td>roadn_6258</td>\n",
+       "      <td>19.532483</td>\n",
+       "      <td>LINESTRING (-0.17300 5.60559, -0.17299 5.60561...</td>\n",
+       "      <td>GH07</td>\n",
+       "      <td>Greater Accra</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    osm_id road_type                   name  road_id      from_id  \\\n",
+       "0  4790594  tertiary           Airport Road  roade_0      roadn_0   \n",
+       "1  4790599  tertiary  South Liberation Link  roade_1      roadn_2   \n",
+       "2  4790599  tertiary  South Liberation Link  roade_2  roadn_10683   \n",
+       "3  4790600  tertiary           Airport Road  roade_3      roadn_4   \n",
+       "4  4790600  tertiary           Airport Road  roade_4   roadn_6259   \n",
+       "\n",
+       "         to_id    length_m                                           geometry  \\\n",
+       "0      roadn_1  236.526837  LINESTRING (-0.17544 5.60550, -0.17418 5.60555...   \n",
+       "1  roadn_10683   18.539418    LINESTRING (-0.17889 5.59979, -0.17872 5.59977)   \n",
+       "2      roadn_3  124.758045  LINESTRING (-0.17872 5.59977, -0.17786 5.59960...   \n",
+       "3   roadn_6259   38.030821  LINESTRING (-0.17330 5.60560, -0.17327 5.60556...   \n",
+       "4   roadn_6258   19.532483  LINESTRING (-0.17300 5.60559, -0.17299 5.60561...   \n",
+       "\n",
+       "  ADM1_PCODE        ADM1_EN  \n",
+       "0       GH07  Greater Accra  \n",
+       "1       GH07  Greater Accra  \n",
+       "2       GH07  Greater Accra  \n",
+       "3       GH07  Greater Accra  \n",
+       "4       GH07  Greater Accra  "
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "roads = read_file_without_warnings(\n",
-    "    os.path.join(data_folder, \"GHA_OSM_roads.gpkg\"), layer=\"edges\"\n",
+    "roads = gpd.read_file(\n",
+    "    data_folder / \"GHA_OSM_roads.gpkg\", layer=\"edges\"\n",
     ").rename(columns={\"id\": \"road_id\"})\n",
     "roads = gpd.sjoin(roads, regions).drop(columns=\"index_right\")\n",
     "roads.head()"
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "surprising-vision",
    "metadata": {},
@@ -137,23 +259,353 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 44,
    "id": "economic-technical",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>road_id</th>\n",
+       "      <th>rcp</th>\n",
+       "      <th>gcm</th>\n",
+       "      <th>epoch</th>\n",
+       "      <th>ead_usd</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>roade_10012</td>\n",
+       "      <td>historical</td>\n",
+       "      <td>WATCH</td>\n",
+       "      <td>1980</td>\n",
+       "      <td>17873.887689</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>roade_10012</td>\n",
+       "      <td>rcp4p5</td>\n",
+       "      <td>GFDL-ESM2M</td>\n",
+       "      <td>2030</td>\n",
+       "      <td>22993.878433</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>roade_10012</td>\n",
+       "      <td>rcp4p5</td>\n",
+       "      <td>GFDL-ESM2M</td>\n",
+       "      <td>2050</td>\n",
+       "      <td>22993.878433</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>roade_10012</td>\n",
+       "      <td>rcp4p5</td>\n",
+       "      <td>GFDL-ESM2M</td>\n",
+       "      <td>2080</td>\n",
+       "      <td>22993.878433</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>roade_10012</td>\n",
+       "      <td>rcp4p5</td>\n",
+       "      <td>HadGEM2-ES</td>\n",
+       "      <td>2030</td>\n",
+       "      <td>22993.878433</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       road_id         rcp         gcm  epoch       ead_usd\n",
+       "0  roade_10012  historical       WATCH   1980  17873.887689\n",
+       "1  roade_10012      rcp4p5  GFDL-ESM2M   2030  22993.878433\n",
+       "2  roade_10012      rcp4p5  GFDL-ESM2M   2050  22993.878433\n",
+       "3  roade_10012      rcp4p5  GFDL-ESM2M   2080  22993.878433\n",
+       "4  roade_10012      rcp4p5  HadGEM2-ES   2030  22993.878433"
+      ]
+     },
+     "execution_count": 44,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "risk = pd.read_csv(os.path.join(data_folder, \"results/flood_risk.csv\"))[\n",
-    "    [\"id\", \"rcp\", \"gcm\", \"ead_usd\"]\n",
+    "risk = pd.read_csv(data_folder / \"results\" / \"inunriver_damages_ead.csv\")[\n",
+    "    [\"id\", \"rcp\", \"gcm\", \"epoch\", \"ead_usd\"]\n",
     "].rename(columns={\"id\": \"road_id\"})\n",
     "risk.head()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 16,
    "id": "composed-objective",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>osm_id</th>\n",
+       "      <th>road_type</th>\n",
+       "      <th>name</th>\n",
+       "      <th>road_id</th>\n",
+       "      <th>from_id</th>\n",
+       "      <th>to_id</th>\n",
+       "      <th>length_m</th>\n",
+       "      <th>geometry</th>\n",
+       "      <th>ADM1_PCODE</th>\n",
+       "      <th>ADM1_EN</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>104</th>\n",
+       "      <td>11154880</td>\n",
+       "      <td>primary</td>\n",
+       "      <td>La Road</td>\n",
+       "      <td>roade_104</td>\n",
+       "      <td>roadn_111</td>\n",
+       "      <td>roadn_112</td>\n",
+       "      <td>443.190787</td>\n",
+       "      <td>LINESTRING (-0.17564 5.55326, -0.17568 5.55324...</td>\n",
+       "      <td>GH07</td>\n",
+       "      <td>Greater Accra</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>126</th>\n",
+       "      <td>11180537</td>\n",
+       "      <td>trunk</td>\n",
+       "      <td>Winneba Road</td>\n",
+       "      <td>roade_126</td>\n",
+       "      <td>roadn_135</td>\n",
+       "      <td>roadn_9182</td>\n",
+       "      <td>522.694931</td>\n",
+       "      <td>LINESTRING (-0.31338 5.55362, -0.31494 5.55356...</td>\n",
+       "      <td>GH07</td>\n",
+       "      <td>Greater Accra</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>127</th>\n",
+       "      <td>11180537</td>\n",
+       "      <td>trunk</td>\n",
+       "      <td>Winneba Road</td>\n",
+       "      <td>roade_127</td>\n",
+       "      <td>roadn_9182</td>\n",
+       "      <td>roadn_9181</td>\n",
+       "      <td>54.297481</td>\n",
+       "      <td>LINESTRING (-0.31809 5.55347, -0.31858 5.55345)</td>\n",
+       "      <td>GH07</td>\n",
+       "      <td>Greater Accra</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>128</th>\n",
+       "      <td>11180537</td>\n",
+       "      <td>trunk</td>\n",
+       "      <td>Winneba Road</td>\n",
+       "      <td>roade_128</td>\n",
+       "      <td>roadn_9181</td>\n",
+       "      <td>roadn_9527</td>\n",
+       "      <td>1075.851334</td>\n",
+       "      <td>LINESTRING (-0.31858 5.55345, -0.31866 5.55345...</td>\n",
+       "      <td>GH07</td>\n",
+       "      <td>Greater Accra</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>129</th>\n",
+       "      <td>11180537</td>\n",
+       "      <td>trunk</td>\n",
+       "      <td>Winneba Road</td>\n",
+       "      <td>roade_129</td>\n",
+       "      <td>roadn_9527</td>\n",
+       "      <td>roadn_402</td>\n",
+       "      <td>185.212407</td>\n",
+       "      <td>LINESTRING (-0.32808 5.55182, -0.32844 5.55168...</td>\n",
+       "      <td>GH07</td>\n",
+       "      <td>Greater Accra</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14304</th>\n",
+       "      <td>863659491</td>\n",
+       "      <td>trunk</td>\n",
+       "      <td>Annor Assemah High Street</td>\n",
+       "      <td>roade_14304</td>\n",
+       "      <td>roadn_11547</td>\n",
+       "      <td>roadn_11541</td>\n",
+       "      <td>106.305695</td>\n",
+       "      <td>LINESTRING (-2.82481 5.82056, -2.82494 5.82032...</td>\n",
+       "      <td>GH16</td>\n",
+       "      <td>Western North</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14305</th>\n",
+       "      <td>863659492</td>\n",
+       "      <td>trunk</td>\n",
+       "      <td>Annor Assemah High Street</td>\n",
+       "      <td>roade_14305</td>\n",
+       "      <td>roadn_11542</td>\n",
+       "      <td>roadn_11547</td>\n",
+       "      <td>17.716419</td>\n",
+       "      <td>LINESTRING (-2.82473 5.82070, -2.82481 5.82056)</td>\n",
+       "      <td>GH16</td>\n",
+       "      <td>Western North</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14368</th>\n",
+       "      <td>903998624</td>\n",
+       "      <td>tertiary</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>roade_14368</td>\n",
+       "      <td>roadn_8938</td>\n",
+       "      <td>roadn_11588</td>\n",
+       "      <td>40.821448</td>\n",
+       "      <td>LINESTRING (-2.75989 5.85919, -2.76025 5.85911)</td>\n",
+       "      <td>GH16</td>\n",
+       "      <td>Western North</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14369</th>\n",
+       "      <td>903998625</td>\n",
+       "      <td>tertiary</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>roade_14369</td>\n",
+       "      <td>roadn_11588</td>\n",
+       "      <td>roadn_9169</td>\n",
+       "      <td>1836.249395</td>\n",
+       "      <td>LINESTRING (-2.76025 5.85911, -2.76162 5.85884...</td>\n",
+       "      <td>GH16</td>\n",
+       "      <td>Western North</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14552</th>\n",
+       "      <td>970676815</td>\n",
+       "      <td>secondary</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>roade_14552</td>\n",
+       "      <td>roadn_4096</td>\n",
+       "      <td>roadn_4104</td>\n",
+       "      <td>835.847533</td>\n",
+       "      <td>LINESTRING (-2.59385 6.15169, -2.59397 6.15150...</td>\n",
+       "      <td>GH16</td>\n",
+       "      <td>Western North</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>2369 rows × 10 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          osm_id  road_type                       name      road_id  \\\n",
+       "104     11154880    primary                    La Road    roade_104   \n",
+       "126     11180537      trunk               Winneba Road    roade_126   \n",
+       "127     11180537      trunk               Winneba Road    roade_127   \n",
+       "128     11180537      trunk               Winneba Road    roade_128   \n",
+       "129     11180537      trunk               Winneba Road    roade_129   \n",
+       "...          ...        ...                        ...          ...   \n",
+       "14304  863659491      trunk  Annor Assemah High Street  roade_14304   \n",
+       "14305  863659492      trunk  Annor Assemah High Street  roade_14305   \n",
+       "14368  903998624   tertiary                        NaN  roade_14368   \n",
+       "14369  903998625   tertiary                        NaN  roade_14369   \n",
+       "14552  970676815  secondary                        NaN  roade_14552   \n",
+       "\n",
+       "           from_id        to_id     length_m  \\\n",
+       "104      roadn_111    roadn_112   443.190787   \n",
+       "126      roadn_135   roadn_9182   522.694931   \n",
+       "127     roadn_9182   roadn_9181    54.297481   \n",
+       "128     roadn_9181   roadn_9527  1075.851334   \n",
+       "129     roadn_9527    roadn_402   185.212407   \n",
+       "...            ...          ...          ...   \n",
+       "14304  roadn_11547  roadn_11541   106.305695   \n",
+       "14305  roadn_11542  roadn_11547    17.716419   \n",
+       "14368   roadn_8938  roadn_11588    40.821448   \n",
+       "14369  roadn_11588   roadn_9169  1836.249395   \n",
+       "14552   roadn_4096   roadn_4104   835.847533   \n",
+       "\n",
+       "                                                geometry ADM1_PCODE  \\\n",
+       "104    LINESTRING (-0.17564 5.55326, -0.17568 5.55324...       GH07   \n",
+       "126    LINESTRING (-0.31338 5.55362, -0.31494 5.55356...       GH07   \n",
+       "127      LINESTRING (-0.31809 5.55347, -0.31858 5.55345)       GH07   \n",
+       "128    LINESTRING (-0.31858 5.55345, -0.31866 5.55345...       GH07   \n",
+       "129    LINESTRING (-0.32808 5.55182, -0.32844 5.55168...       GH07   \n",
+       "...                                                  ...        ...   \n",
+       "14304  LINESTRING (-2.82481 5.82056, -2.82494 5.82032...       GH16   \n",
+       "14305    LINESTRING (-2.82473 5.82070, -2.82481 5.82056)       GH16   \n",
+       "14368    LINESTRING (-2.75989 5.85919, -2.76025 5.85911)       GH16   \n",
+       "14369  LINESTRING (-2.76025 5.85911, -2.76162 5.85884...       GH16   \n",
+       "14552  LINESTRING (-2.59385 6.15169, -2.59397 6.15150...       GH16   \n",
+       "\n",
+       "             ADM1_EN  \n",
+       "104    Greater Accra  \n",
+       "126    Greater Accra  \n",
+       "127    Greater Accra  \n",
+       "128    Greater Accra  \n",
+       "129    Greater Accra  \n",
+       "...              ...  \n",
+       "14304  Western North  \n",
+       "14305  Western North  \n",
+       "14368  Western North  \n",
+       "14369  Western North  \n",
+       "14552  Western North  \n",
+       "\n",
+       "[2369 rows x 10 columns]"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "exposed_roads = roads[roads.road_id.isin(risk.road_id.unique())]\n",
     "exposed_roads"
@@ -161,40 +613,305 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 41,
    "id": "sophisticated-pickup",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>road_id</th>\n",
+       "      <th>rcp</th>\n",
+       "      <th>gcm</th>\n",
+       "      <th>epoch</th>\n",
+       "      <th>rp</th>\n",
+       "      <th>flood_length_m</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>roade_10012</td>\n",
+       "      <td>historical</td>\n",
+       "      <td>WATCH</td>\n",
+       "      <td>1980</td>\n",
+       "      <td>1000</td>\n",
+       "      <td>49.402745</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>roade_10012</td>\n",
+       "      <td>rcp4p5</td>\n",
+       "      <td>GFDL-ESM2M</td>\n",
+       "      <td>2030</td>\n",
+       "      <td>1000</td>\n",
+       "      <td>49.402745</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>roade_10012</td>\n",
+       "      <td>rcp4p5</td>\n",
+       "      <td>GFDL-ESM2M</td>\n",
+       "      <td>2050</td>\n",
+       "      <td>1000</td>\n",
+       "      <td>49.402745</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>roade_10012</td>\n",
+       "      <td>rcp4p5</td>\n",
+       "      <td>GFDL-ESM2M</td>\n",
+       "      <td>2080</td>\n",
+       "      <td>1000</td>\n",
+       "      <td>49.402745</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>roade_10012</td>\n",
+       "      <td>rcp4p5</td>\n",
+       "      <td>HadGEM2-ES</td>\n",
+       "      <td>2030</td>\n",
+       "      <td>1000</td>\n",
+       "      <td>49.402745</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>64810</th>\n",
+       "      <td>roade_995</td>\n",
+       "      <td>rcp8p5</td>\n",
+       "      <td>MIROC-ESM-CHEM</td>\n",
+       "      <td>2050</td>\n",
+       "      <td>1000</td>\n",
+       "      <td>1776.210973</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>64811</th>\n",
+       "      <td>roade_995</td>\n",
+       "      <td>rcp8p5</td>\n",
+       "      <td>MIROC-ESM-CHEM</td>\n",
+       "      <td>2080</td>\n",
+       "      <td>1000</td>\n",
+       "      <td>2708.082749</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>64812</th>\n",
+       "      <td>roade_995</td>\n",
+       "      <td>rcp8p5</td>\n",
+       "      <td>NorESM1-M</td>\n",
+       "      <td>2030</td>\n",
+       "      <td>1000</td>\n",
+       "      <td>727.427009</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>64813</th>\n",
+       "      <td>roade_995</td>\n",
+       "      <td>rcp8p5</td>\n",
+       "      <td>NorESM1-M</td>\n",
+       "      <td>2050</td>\n",
+       "      <td>1000</td>\n",
+       "      <td>727.427009</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>64814</th>\n",
+       "      <td>roade_995</td>\n",
+       "      <td>rcp8p5</td>\n",
+       "      <td>NorESM1-M</td>\n",
+       "      <td>2080</td>\n",
+       "      <td>1000</td>\n",
+       "      <td>727.427009</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>64815 rows × 6 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           road_id         rcp             gcm  epoch    rp  flood_length_m\n",
+       "0      roade_10012  historical           WATCH   1980  1000       49.402745\n",
+       "1      roade_10012      rcp4p5      GFDL-ESM2M   2030  1000       49.402745\n",
+       "2      roade_10012      rcp4p5      GFDL-ESM2M   2050  1000       49.402745\n",
+       "3      roade_10012      rcp4p5      GFDL-ESM2M   2080  1000       49.402745\n",
+       "4      roade_10012      rcp4p5      HadGEM2-ES   2030  1000       49.402745\n",
+       "...            ...         ...             ...    ...   ...             ...\n",
+       "64810    roade_995      rcp8p5  MIROC-ESM-CHEM   2050  1000     1776.210973\n",
+       "64811    roade_995      rcp8p5  MIROC-ESM-CHEM   2080  1000     2708.082749\n",
+       "64812    roade_995      rcp8p5       NorESM1-M   2030  1000      727.427009\n",
+       "64813    roade_995      rcp8p5       NorESM1-M   2050  1000      727.427009\n",
+       "64814    roade_995      rcp8p5       NorESM1-M   2080  1000      727.427009\n",
+       "\n",
+       "[64815 rows x 6 columns]"
+      ]
+     },
+     "execution_count": 41,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "exposure = pd.read_csv(\n",
-    "    os.path.join(data_folder, \"results/flood_exposure.csv\")\n",
-    ")[[\"id\", \"flood_length_m\", \"hazard\", \"rcp\", \"gcm\", \"rp\"]].rename(\n",
-    "    columns={\"id\": \"road_id\"}\n",
-    ")\n",
+    "exposure = pd.read_csv(data_folder / \"results\" / \"inunriver_damages_rp.csv\")[\n",
+    "    [\"id\", \"length_m\", \"rcp\", \"gcm\", \"epoch\", \"rp\"]\n",
+    "].rename(columns={\"id\": \"road_id\", \"length_m\": \"flood_length_m\"})\n",
     "\n",
     "# sum over any segments exposed within the same return period\n",
-    "exposure = exposure.groupby([\"road_id\", \"rcp\", \"gcm\", \"rp\"]).sum()\n",
+    "exposure = exposure.groupby([\"road_id\", \"rcp\", \"gcm\", \"epoch\", \"rp\"]).sum()\n",
     "\n",
-    "# pick max length exposed over all return periods\n",
-    "exposure = exposure.groupby([\"road_id\", \"rcp\", \"gcm\"]).max().reset_index()\n",
+    "# # pick max length exposed over all return periods\n",
+    "exposure = (\n",
+    "    exposure.reset_index()\n",
+    "    .groupby([\"road_id\", \"rcp\", \"gcm\", \"epoch\"])\n",
+    "    .max()\n",
+    "    .reset_index()\n",
+    ")\n",
     "\n",
     "exposure"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 45,
    "id": "italian-color",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>osm_id</th>\n",
+       "      <th>road_type</th>\n",
+       "      <th>name</th>\n",
+       "      <th>road_id</th>\n",
+       "      <th>from_id</th>\n",
+       "      <th>to_id</th>\n",
+       "      <th>length_m</th>\n",
+       "      <th>geometry</th>\n",
+       "      <th>ADM1_PCODE</th>\n",
+       "      <th>ADM1_EN</th>\n",
+       "      <th>rcp</th>\n",
+       "      <th>gcm</th>\n",
+       "      <th>epoch</th>\n",
+       "      <th>ead_usd</th>\n",
+       "      <th>rp</th>\n",
+       "      <th>flood_length_m</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>11154880</td>\n",
+       "      <td>primary</td>\n",
+       "      <td>La Road</td>\n",
+       "      <td>roade_104</td>\n",
+       "      <td>roadn_111</td>\n",
+       "      <td>roadn_112</td>\n",
+       "      <td>443.190787</td>\n",
+       "      <td>LINESTRING (-0.17564 5.55326, -0.17568 5.55324...</td>\n",
+       "      <td>GH07</td>\n",
+       "      <td>Greater Accra</td>\n",
+       "      <td>historical</td>\n",
+       "      <td>WATCH</td>\n",
+       "      <td>1980</td>\n",
+       "      <td>2342.496724</td>\n",
+       "      <td>1000</td>\n",
+       "      <td>443.190787</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>11154880</td>\n",
+       "      <td>primary</td>\n",
+       "      <td>La Road</td>\n",
+       "      <td>roade_104</td>\n",
+       "      <td>roadn_111</td>\n",
+       "      <td>roadn_112</td>\n",
+       "      <td>443.190787</td>\n",
+       "      <td>LINESTRING (-0.17564 5.55326, -0.17568 5.55324...</td>\n",
+       "      <td>GH07</td>\n",
+       "      <td>Greater Accra</td>\n",
+       "      <td>rcp4p5</td>\n",
+       "      <td>GFDL-ESM2M</td>\n",
+       "      <td>2030</td>\n",
+       "      <td>2342.496724</td>\n",
+       "      <td>1000</td>\n",
+       "      <td>443.190787</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     osm_id road_type     name    road_id    from_id      to_id    length_m  \\\n",
+       "0  11154880   primary  La Road  roade_104  roadn_111  roadn_112  443.190787   \n",
+       "1  11154880   primary  La Road  roade_104  roadn_111  roadn_112  443.190787   \n",
+       "\n",
+       "                                            geometry ADM1_PCODE  \\\n",
+       "0  LINESTRING (-0.17564 5.55326, -0.17568 5.55324...       GH07   \n",
+       "1  LINESTRING (-0.17564 5.55326, -0.17568 5.55324...       GH07   \n",
+       "\n",
+       "         ADM1_EN         rcp         gcm  epoch      ead_usd    rp  \\\n",
+       "0  Greater Accra  historical       WATCH   1980  2342.496724  1000   \n",
+       "1  Greater Accra      rcp4p5  GFDL-ESM2M   2030  2342.496724  1000   \n",
+       "\n",
+       "   flood_length_m  \n",
+       "0      443.190787  \n",
+       "1      443.190787  "
+      ]
+     },
+     "execution_count": 45,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "roads_with_risk = exposed_roads.merge(risk, on=\"road_id\").merge(\n",
-    "    exposure, on=[\"road_id\", \"rcp\", \"gcm\"]\n",
+    "    exposure, on=[\"road_id\", \"rcp\", \"gcm\", \"epoch\"]\n",
     ")\n",
     "roads_with_risk.head(2)"
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "prime-perception",
    "metadata": {},
@@ -203,6 +920,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "encouraging-poverty",
    "metadata": {},
@@ -216,10 +934,80 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 24,
    "id": "furnished-closer",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>kind</th>\n",
+       "      <th>initial_cost_usd_per_km</th>\n",
+       "      <th>routine_usd_per_km</th>\n",
+       "      <th>periodic_usd_per_km</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>four_lane</td>\n",
+       "      <td>1000000</td>\n",
+       "      <td>20000</td>\n",
+       "      <td>100000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>two_lane</td>\n",
+       "      <td>500000</td>\n",
+       "      <td>10000</td>\n",
+       "      <td>50000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>single_lane</td>\n",
+       "      <td>125000</td>\n",
+       "      <td>5000</td>\n",
+       "      <td>25000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          kind  initial_cost_usd_per_km  routine_usd_per_km  \\\n",
+       "0    four_lane                  1000000               20000   \n",
+       "1     two_lane                   500000               10000   \n",
+       "2  single_lane                   125000                5000   \n",
+       "\n",
+       "   periodic_usd_per_km  \n",
+       "0               100000  \n",
+       "1                50000  \n",
+       "2                25000  "
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "options = pd.DataFrame(\n",
     "    {\n",
@@ -233,6 +1021,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "elder-prince",
    "metadata": {},
@@ -244,7 +1033,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 25,
    "id": "rapid-award",
    "metadata": {},
    "outputs": [],
@@ -253,6 +1042,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "refined-success",
    "metadata": {},
@@ -267,10 +1057,191 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 26,
    "id": "indoor-digit",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>year</th>\n",
+       "      <th>year_from_start</th>\n",
+       "      <th>discount_rate_norm</th>\n",
+       "      <th>kind</th>\n",
+       "      <th>initial_cost_usd_per_km</th>\n",
+       "      <th>routine_usd_per_km</th>\n",
+       "      <th>periodic_usd_per_km</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2020</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>four_lane</td>\n",
+       "      <td>1000000</td>\n",
+       "      <td>20000.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2020</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>two_lane</td>\n",
+       "      <td>500000</td>\n",
+       "      <td>10000.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2020</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>single_lane</td>\n",
+       "      <td>125000</td>\n",
+       "      <td>5000.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2021</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.970874</td>\n",
+       "      <td>four_lane</td>\n",
+       "      <td>0</td>\n",
+       "      <td>19417.475728</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2021</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.970874</td>\n",
+       "      <td>two_lane</td>\n",
+       "      <td>0</td>\n",
+       "      <td>9708.737864</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>178</th>\n",
+       "      <td>2079</td>\n",
+       "      <td>59</td>\n",
+       "      <td>0.174825</td>\n",
+       "      <td>two_lane</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1748.250827</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>179</th>\n",
+       "      <td>2079</td>\n",
+       "      <td>59</td>\n",
+       "      <td>0.174825</td>\n",
+       "      <td>single_lane</td>\n",
+       "      <td>0</td>\n",
+       "      <td>874.125414</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>180</th>\n",
+       "      <td>2080</td>\n",
+       "      <td>60</td>\n",
+       "      <td>0.169733</td>\n",
+       "      <td>four_lane</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3394.661800</td>\n",
+       "      <td>16973.309002</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>181</th>\n",
+       "      <td>2080</td>\n",
+       "      <td>60</td>\n",
+       "      <td>0.169733</td>\n",
+       "      <td>two_lane</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1697.330900</td>\n",
+       "      <td>8486.654501</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>182</th>\n",
+       "      <td>2080</td>\n",
+       "      <td>60</td>\n",
+       "      <td>0.169733</td>\n",
+       "      <td>single_lane</td>\n",
+       "      <td>0</td>\n",
+       "      <td>848.665450</td>\n",
+       "      <td>4243.327250</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>183 rows × 7 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     year  year_from_start  discount_rate_norm         kind  \\\n",
+       "0    2020                0            1.000000    four_lane   \n",
+       "1    2020                0            1.000000     two_lane   \n",
+       "2    2020                0            1.000000  single_lane   \n",
+       "3    2021                1            0.970874    four_lane   \n",
+       "4    2021                1            0.970874     two_lane   \n",
+       "..    ...              ...                 ...          ...   \n",
+       "178  2079               59            0.174825     two_lane   \n",
+       "179  2079               59            0.174825  single_lane   \n",
+       "180  2080               60            0.169733    four_lane   \n",
+       "181  2080               60            0.169733     two_lane   \n",
+       "182  2080               60            0.169733  single_lane   \n",
+       "\n",
+       "     initial_cost_usd_per_km  routine_usd_per_km  periodic_usd_per_km  \n",
+       "0                    1000000        20000.000000             0.000000  \n",
+       "1                     500000        10000.000000             0.000000  \n",
+       "2                     125000         5000.000000             0.000000  \n",
+       "3                          0        19417.475728             0.000000  \n",
+       "4                          0         9708.737864             0.000000  \n",
+       "..                       ...                 ...                  ...  \n",
+       "178                        0         1748.250827             0.000000  \n",
+       "179                        0          874.125414             0.000000  \n",
+       "180                        0         3394.661800         16973.309002  \n",
+       "181                        0         1697.330900          8486.654501  \n",
+       "182                        0          848.665450          4243.327250  \n",
+       "\n",
+       "[183 rows x 7 columns]"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "# set up a costs dataframe\n",
     "costs = pd.DataFrame()\n",
@@ -308,6 +1279,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "missing-disclaimer",
    "metadata": {},
@@ -317,10 +1289,84 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 27,
    "id": "intermediate-mouth",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>kind</th>\n",
+       "      <th>initial_cost_usd_per_km</th>\n",
+       "      <th>routine_usd_per_km</th>\n",
+       "      <th>periodic_usd_per_km</th>\n",
+       "      <th>total_cost_usd_per_km</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>four_lane</td>\n",
+       "      <td>1000000</td>\n",
+       "      <td>573511.273322</td>\n",
+       "      <td>521281.893260</td>\n",
+       "      <td>2.094793e+06</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>single_lane</td>\n",
+       "      <td>125000</td>\n",
+       "      <td>143377.818331</td>\n",
+       "      <td>130320.473315</td>\n",
+       "      <td>3.986983e+05</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>two_lane</td>\n",
+       "      <td>500000</td>\n",
+       "      <td>286755.636661</td>\n",
+       "      <td>260640.946630</td>\n",
+       "      <td>1.047397e+06</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          kind  initial_cost_usd_per_km  routine_usd_per_km  \\\n",
+       "0    four_lane                  1000000       573511.273322   \n",
+       "1  single_lane                   125000       143377.818331   \n",
+       "2     two_lane                   500000       286755.636661   \n",
+       "\n",
+       "   periodic_usd_per_km  total_cost_usd_per_km  \n",
+       "0        521281.893260           2.094793e+06  \n",
+       "1        130320.473315           3.986983e+05  \n",
+       "2        260640.946630           1.047397e+06  "
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "npv_costs = (\n",
     "    costs[\n",
@@ -344,6 +1390,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "grand-depth",
    "metadata": {},
@@ -352,6 +1399,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "honest-relation",
    "metadata": {},
@@ -361,7 +1409,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 48,
    "id": "tracked-wagner",
    "metadata": {},
    "outputs": [],
@@ -379,6 +1427,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "compound-spine",
    "metadata": {},
@@ -388,7 +1437,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 49,
    "id": "initial-independence",
    "metadata": {},
    "outputs": [],
@@ -399,6 +1448,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "foreign-quilt",
    "metadata": {},
@@ -408,7 +1458,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 50,
    "id": "floppy-crowd",
    "metadata": {},
    "outputs": [],
@@ -421,6 +1471,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "simple-secretariat",
    "metadata": {},
@@ -430,7 +1481,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 51,
    "id": "judicial-rehabilitation",
    "metadata": {},
    "outputs": [],
@@ -442,15 +1493,27 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 52,
    "id": "preliminary-plenty",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "28.675563666119398"
+      ]
+     },
+     "execution_count": 52,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "discount_rate_norm"
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "criminal-burning",
    "metadata": {},
@@ -460,7 +1523,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 53,
    "id": "accepted-charger",
    "metadata": {},
    "outputs": [],
@@ -472,6 +1535,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "noble-newark",
    "metadata": {},
@@ -481,16 +1545,187 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 54,
    "id": "affecting-piano",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>length_m</th>\n",
+       "      <th>epoch</th>\n",
+       "      <th>ead_usd</th>\n",
+       "      <th>rp</th>\n",
+       "      <th>flood_length_m</th>\n",
+       "      <th>total_cost_usd_per_km</th>\n",
+       "      <th>total_adaptation_cost_usd</th>\n",
+       "      <th>total_adaptation_benefit_usd</th>\n",
+       "      <th>bcr</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>2264.000000</td>\n",
+       "      <td>2264.0</td>\n",
+       "      <td>2.264000e+03</td>\n",
+       "      <td>2264.0</td>\n",
+       "      <td>2264.000000</td>\n",
+       "      <td>2.264000e+03</td>\n",
+       "      <td>2.264000e+03</td>\n",
+       "      <td>2.264000e+03</td>\n",
+       "      <td>2264.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>3409.722027</td>\n",
+       "      <td>1980.0</td>\n",
+       "      <td>2.593277e+05</td>\n",
+       "      <td>1000.0</td>\n",
+       "      <td>1027.421512</td>\n",
+       "      <td>1.049971e+06</td>\n",
+       "      <td>8.582112e+05</td>\n",
+       "      <td>7.436369e+06</td>\n",
+       "      <td>7.745543</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>7251.226282</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>7.533146e+05</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1959.983688</td>\n",
+       "      <td>6.369807e+05</td>\n",
+       "      <td>1.709022e+06</td>\n",
+       "      <td>2.160172e+07</td>\n",
+       "      <td>7.461552</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>1.290015</td>\n",
+       "      <td>1980.0</td>\n",
+       "      <td>0.000000e+00</td>\n",
+       "      <td>1000.0</td>\n",
+       "      <td>0.303766</td>\n",
+       "      <td>3.986983e+05</td>\n",
+       "      <td>1.211108e+02</td>\n",
+       "      <td>0.000000e+00</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>47.555476</td>\n",
+       "      <td>1980.0</td>\n",
+       "      <td>3.338947e+03</td>\n",
+       "      <td>1000.0</td>\n",
+       "      <td>42.211808</td>\n",
+       "      <td>3.986983e+05</td>\n",
+       "      <td>4.214351e+04</td>\n",
+       "      <td>9.574619e+04</td>\n",
+       "      <td>0.635519</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>366.251343</td>\n",
+       "      <td>1980.0</td>\n",
+       "      <td>1.977872e+04</td>\n",
+       "      <td>1000.0</td>\n",
+       "      <td>224.304937</td>\n",
+       "      <td>1.047397e+06</td>\n",
+       "      <td>1.980604e+05</td>\n",
+       "      <td>5.671659e+05</td>\n",
+       "      <td>9.905326</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>3309.033698</td>\n",
+       "      <td>1980.0</td>\n",
+       "      <td>1.516975e+05</td>\n",
+       "      <td>1000.0</td>\n",
+       "      <td>970.686131</td>\n",
+       "      <td>1.047397e+06</td>\n",
+       "      <td>8.338113e+05</td>\n",
+       "      <td>4.350010e+06</td>\n",
+       "      <td>9.905326</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>73318.612176</td>\n",
+       "      <td>1980.0</td>\n",
+       "      <td>1.306064e+07</td>\n",
+       "      <td>1000.0</td>\n",
+       "      <td>17981.326559</td>\n",
+       "      <td>2.094793e+06</td>\n",
+       "      <td>1.856157e+07</td>\n",
+       "      <td>3.745212e+08</td>\n",
+       "      <td>20.177240</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           length_m   epoch       ead_usd      rp  flood_length_m  \\\n",
+       "count   2264.000000  2264.0  2.264000e+03  2264.0     2264.000000   \n",
+       "mean    3409.722027  1980.0  2.593277e+05  1000.0     1027.421512   \n",
+       "std     7251.226282     0.0  7.533146e+05     0.0     1959.983688   \n",
+       "min        1.290015  1980.0  0.000000e+00  1000.0        0.303766   \n",
+       "25%       47.555476  1980.0  3.338947e+03  1000.0       42.211808   \n",
+       "50%      366.251343  1980.0  1.977872e+04  1000.0      224.304937   \n",
+       "75%     3309.033698  1980.0  1.516975e+05  1000.0      970.686131   \n",
+       "max    73318.612176  1980.0  1.306064e+07  1000.0    17981.326559   \n",
+       "\n",
+       "       total_cost_usd_per_km  total_adaptation_cost_usd  \\\n",
+       "count           2.264000e+03               2.264000e+03   \n",
+       "mean            1.049971e+06               8.582112e+05   \n",
+       "std             6.369807e+05               1.709022e+06   \n",
+       "min             3.986983e+05               1.211108e+02   \n",
+       "25%             3.986983e+05               4.214351e+04   \n",
+       "50%             1.047397e+06               1.980604e+05   \n",
+       "75%             1.047397e+06               8.338113e+05   \n",
+       "max             2.094793e+06               1.856157e+07   \n",
+       "\n",
+       "       total_adaptation_benefit_usd          bcr  \n",
+       "count                  2.264000e+03  2264.000000  \n",
+       "mean                   7.436369e+06     7.745543  \n",
+       "std                    2.160172e+07     7.461552  \n",
+       "min                    0.000000e+00     0.000000  \n",
+       "25%                    9.574619e+04     0.635519  \n",
+       "50%                    5.671659e+05     9.905326  \n",
+       "75%                    4.350010e+06     9.905326  \n",
+       "max                    3.745212e+08    20.177240  "
+      ]
+     },
+     "execution_count": 54,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "historical = roads_with_costs[roads_with_costs.rcp == \"historical\"]\n",
     "historical.describe()"
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "rubber-destruction",
    "metadata": {},
@@ -500,16 +1735,418 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 55,
    "id": "treated-average",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>osm_id</th>\n",
+       "      <th>road_type</th>\n",
+       "      <th>name</th>\n",
+       "      <th>road_id</th>\n",
+       "      <th>from_id</th>\n",
+       "      <th>to_id</th>\n",
+       "      <th>length_m</th>\n",
+       "      <th>geometry</th>\n",
+       "      <th>ADM1_PCODE</th>\n",
+       "      <th>ADM1_EN</th>\n",
+       "      <th>...</th>\n",
+       "      <th>gcm</th>\n",
+       "      <th>epoch</th>\n",
+       "      <th>ead_usd</th>\n",
+       "      <th>rp</th>\n",
+       "      <th>flood_length_m</th>\n",
+       "      <th>kind</th>\n",
+       "      <th>total_cost_usd_per_km</th>\n",
+       "      <th>total_adaptation_cost_usd</th>\n",
+       "      <th>total_adaptation_benefit_usd</th>\n",
+       "      <th>bcr</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>31</th>\n",
+       "      <td>11287763</td>\n",
+       "      <td>primary</td>\n",
+       "      <td>Obetsebi Lamptey Circle</td>\n",
+       "      <td>roade_153</td>\n",
+       "      <td>roadn_162</td>\n",
+       "      <td>roadn_163</td>\n",
+       "      <td>53.770003</td>\n",
+       "      <td>LINESTRING (-0.22956 5.56170, -0.22962 5.56166...</td>\n",
+       "      <td>GH07</td>\n",
+       "      <td>Greater Accra</td>\n",
+       "      <td>...</td>\n",
+       "      <td>WATCH</td>\n",
+       "      <td>1980</td>\n",
+       "      <td>19453.959302</td>\n",
+       "      <td>1000</td>\n",
+       "      <td>53.770003</td>\n",
+       "      <td>two_lane</td>\n",
+       "      <td>1.047397e+06</td>\n",
+       "      <td>56318.517592</td>\n",
+       "      <td>5.578532e+05</td>\n",
+       "      <td>9.905326</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>62</th>\n",
+       "      <td>11664722</td>\n",
+       "      <td>primary</td>\n",
+       "      <td>Ring Road Central</td>\n",
+       "      <td>roade_181</td>\n",
+       "      <td>roadn_194</td>\n",
+       "      <td>roadn_195</td>\n",
+       "      <td>115.414521</td>\n",
+       "      <td>LINESTRING (-0.21575 5.56968, -0.21578 5.56965...</td>\n",
+       "      <td>GH07</td>\n",
+       "      <td>Greater Accra</td>\n",
+       "      <td>...</td>\n",
+       "      <td>WATCH</td>\n",
+       "      <td>1980</td>\n",
+       "      <td>2287.927108</td>\n",
+       "      <td>1000</td>\n",
+       "      <td>6.323743</td>\n",
+       "      <td>two_lane</td>\n",
+       "      <td>1.047397e+06</td>\n",
+       "      <td>6623.467290</td>\n",
+       "      <td>6.560760e+04</td>\n",
+       "      <td>9.905326</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>93</th>\n",
+       "      <td>11665216</td>\n",
+       "      <td>primary</td>\n",
+       "      <td>Ring Road West</td>\n",
+       "      <td>roade_183</td>\n",
+       "      <td>roadn_198</td>\n",
+       "      <td>roadn_199</td>\n",
+       "      <td>528.631407</td>\n",
+       "      <td>LINESTRING (-0.22543 5.54138, -0.22519 5.54122...</td>\n",
+       "      <td>GH07</td>\n",
+       "      <td>Greater Accra</td>\n",
+       "      <td>...</td>\n",
+       "      <td>WATCH</td>\n",
+       "      <td>1980</td>\n",
+       "      <td>170494.756904</td>\n",
+       "      <td>1000</td>\n",
+       "      <td>471.241020</td>\n",
+       "      <td>two_lane</td>\n",
+       "      <td>1.047397e+06</td>\n",
+       "      <td>493576.233867</td>\n",
+       "      <td>4.889033e+06</td>\n",
+       "      <td>9.905326</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>124</th>\n",
+       "      <td>11665277</td>\n",
+       "      <td>primary</td>\n",
+       "      <td>Ring Road Central</td>\n",
+       "      <td>roade_184</td>\n",
+       "      <td>roadn_200</td>\n",
+       "      <td>roadn_10659</td>\n",
+       "      <td>305.440550</td>\n",
+       "      <td>LINESTRING (-0.22908 5.56154, -0.22904 5.56167...</td>\n",
+       "      <td>GH07</td>\n",
+       "      <td>Greater Accra</td>\n",
+       "      <td>...</td>\n",
+       "      <td>WATCH</td>\n",
+       "      <td>1980</td>\n",
+       "      <td>110508.232976</td>\n",
+       "      <td>1000</td>\n",
+       "      <td>305.440550</td>\n",
+       "      <td>two_lane</td>\n",
+       "      <td>1.047397e+06</td>\n",
+       "      <td>319917.388862</td>\n",
+       "      <td>3.168886e+06</td>\n",
+       "      <td>9.905326</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>155</th>\n",
+       "      <td>11665277</td>\n",
+       "      <td>primary</td>\n",
+       "      <td>Ring Road Central</td>\n",
+       "      <td>roade_185</td>\n",
+       "      <td>roadn_10659</td>\n",
+       "      <td>roadn_201</td>\n",
+       "      <td>23.379035</td>\n",
+       "      <td>LINESTRING (-0.22726 5.56358, -0.22718 5.56366...</td>\n",
+       "      <td>GH07</td>\n",
+       "      <td>Greater Accra</td>\n",
+       "      <td>...</td>\n",
+       "      <td>WATCH</td>\n",
+       "      <td>1980</td>\n",
+       "      <td>8458.522645</td>\n",
+       "      <td>1000</td>\n",
+       "      <td>23.379035</td>\n",
+       "      <td>two_lane</td>\n",
+       "      <td>1.047397e+06</td>\n",
+       "      <td>24487.121053</td>\n",
+       "      <td>2.425529e+05</td>\n",
+       "      <td>9.905326</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>40958</th>\n",
+       "      <td>634994013</td>\n",
+       "      <td>trunk</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>roade_12169</td>\n",
+       "      <td>roadn_10178</td>\n",
+       "      <td>roadn_10180</td>\n",
+       "      <td>64.086457</td>\n",
+       "      <td>LINESTRING (-2.82071 5.82178, -2.82097 5.82180...</td>\n",
+       "      <td>GH16</td>\n",
+       "      <td>Western North</td>\n",
+       "      <td>...</td>\n",
+       "      <td>WATCH</td>\n",
+       "      <td>1980</td>\n",
+       "      <td>94462.014008</td>\n",
+       "      <td>1000</td>\n",
+       "      <td>64.086457</td>\n",
+       "      <td>four_lane</td>\n",
+       "      <td>2.094793e+06</td>\n",
+       "      <td>134247.872952</td>\n",
+       "      <td>2.708751e+06</td>\n",
+       "      <td>20.177240</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>40989</th>\n",
+       "      <td>862409400</td>\n",
+       "      <td>trunk</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>roade_14299</td>\n",
+       "      <td>roadn_11541</td>\n",
+       "      <td>roadn_5925</td>\n",
+       "      <td>42.726103</td>\n",
+       "      <td>LINESTRING (-2.82515 5.81966, -2.82522 5.81928)</td>\n",
+       "      <td>GH16</td>\n",
+       "      <td>Western North</td>\n",
+       "      <td>...</td>\n",
+       "      <td>WATCH</td>\n",
+       "      <td>1980</td>\n",
+       "      <td>62977.325869</td>\n",
+       "      <td>1000</td>\n",
+       "      <td>42.726103</td>\n",
+       "      <td>four_lane</td>\n",
+       "      <td>2.094793e+06</td>\n",
+       "      <td>89502.347911</td>\n",
+       "      <td>1.805910e+06</td>\n",
+       "      <td>20.177240</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>41020</th>\n",
+       "      <td>862409401</td>\n",
+       "      <td>trunk</td>\n",
+       "      <td>Annor Assemah High Street</td>\n",
+       "      <td>roade_14300</td>\n",
+       "      <td>roadn_10179</td>\n",
+       "      <td>roadn_11542</td>\n",
+       "      <td>121.522488</td>\n",
+       "      <td>LINESTRING (-2.82397 5.82148, -2.82399 5.82146...</td>\n",
+       "      <td>GH16</td>\n",
+       "      <td>Western North</td>\n",
+       "      <td>...</td>\n",
+       "      <td>WATCH</td>\n",
+       "      <td>1980</td>\n",
+       "      <td>179121.446702</td>\n",
+       "      <td>1000</td>\n",
+       "      <td>121.522488</td>\n",
+       "      <td>four_lane</td>\n",
+       "      <td>2.094793e+06</td>\n",
+       "      <td>254564.477292</td>\n",
+       "      <td>5.136408e+06</td>\n",
+       "      <td>20.177240</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>41063</th>\n",
+       "      <td>863659491</td>\n",
+       "      <td>trunk</td>\n",
+       "      <td>Annor Assemah High Street</td>\n",
+       "      <td>roade_14304</td>\n",
+       "      <td>roadn_11547</td>\n",
+       "      <td>roadn_11541</td>\n",
+       "      <td>106.305695</td>\n",
+       "      <td>LINESTRING (-2.82481 5.82056, -2.82494 5.82032...</td>\n",
+       "      <td>GH16</td>\n",
+       "      <td>Western North</td>\n",
+       "      <td>...</td>\n",
+       "      <td>WATCH</td>\n",
+       "      <td>1980</td>\n",
+       "      <td>156692.232328</td>\n",
+       "      <td>1000</td>\n",
+       "      <td>106.305695</td>\n",
+       "      <td>four_lane</td>\n",
+       "      <td>2.094793e+06</td>\n",
+       "      <td>222688.443806</td>\n",
+       "      <td>4.493238e+06</td>\n",
+       "      <td>20.177240</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>41094</th>\n",
+       "      <td>863659492</td>\n",
+       "      <td>trunk</td>\n",
+       "      <td>Annor Assemah High Street</td>\n",
+       "      <td>roade_14305</td>\n",
+       "      <td>roadn_11542</td>\n",
+       "      <td>roadn_11547</td>\n",
+       "      <td>17.716419</td>\n",
+       "      <td>LINESTRING (-2.82473 5.82070, -2.82481 5.82056)</td>\n",
+       "      <td>GH16</td>\n",
+       "      <td>Western North</td>\n",
+       "      <td>...</td>\n",
+       "      <td>WATCH</td>\n",
+       "      <td>1980</td>\n",
+       "      <td>26113.608546</td>\n",
+       "      <td>1000</td>\n",
+       "      <td>17.716419</td>\n",
+       "      <td>four_lane</td>\n",
+       "      <td>2.094793e+06</td>\n",
+       "      <td>37112.234365</td>\n",
+       "      <td>7.488224e+05</td>\n",
+       "      <td>20.177240</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>1303 rows × 21 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          osm_id road_type                       name      road_id  \\\n",
+       "31      11287763   primary    Obetsebi Lamptey Circle    roade_153   \n",
+       "62      11664722   primary          Ring Road Central    roade_181   \n",
+       "93      11665216   primary             Ring Road West    roade_183   \n",
+       "124     11665277   primary          Ring Road Central    roade_184   \n",
+       "155     11665277   primary          Ring Road Central    roade_185   \n",
+       "...          ...       ...                        ...          ...   \n",
+       "40958  634994013     trunk                        NaN  roade_12169   \n",
+       "40989  862409400     trunk                        NaN  roade_14299   \n",
+       "41020  862409401     trunk  Annor Assemah High Street  roade_14300   \n",
+       "41063  863659491     trunk  Annor Assemah High Street  roade_14304   \n",
+       "41094  863659492     trunk  Annor Assemah High Street  roade_14305   \n",
+       "\n",
+       "           from_id        to_id    length_m  \\\n",
+       "31       roadn_162    roadn_163   53.770003   \n",
+       "62       roadn_194    roadn_195  115.414521   \n",
+       "93       roadn_198    roadn_199  528.631407   \n",
+       "124      roadn_200  roadn_10659  305.440550   \n",
+       "155    roadn_10659    roadn_201   23.379035   \n",
+       "...            ...          ...         ...   \n",
+       "40958  roadn_10178  roadn_10180   64.086457   \n",
+       "40989  roadn_11541   roadn_5925   42.726103   \n",
+       "41020  roadn_10179  roadn_11542  121.522488   \n",
+       "41063  roadn_11547  roadn_11541  106.305695   \n",
+       "41094  roadn_11542  roadn_11547   17.716419   \n",
+       "\n",
+       "                                                geometry ADM1_PCODE  \\\n",
+       "31     LINESTRING (-0.22956 5.56170, -0.22962 5.56166...       GH07   \n",
+       "62     LINESTRING (-0.21575 5.56968, -0.21578 5.56965...       GH07   \n",
+       "93     LINESTRING (-0.22543 5.54138, -0.22519 5.54122...       GH07   \n",
+       "124    LINESTRING (-0.22908 5.56154, -0.22904 5.56167...       GH07   \n",
+       "155    LINESTRING (-0.22726 5.56358, -0.22718 5.56366...       GH07   \n",
+       "...                                                  ...        ...   \n",
+       "40958  LINESTRING (-2.82071 5.82178, -2.82097 5.82180...       GH16   \n",
+       "40989    LINESTRING (-2.82515 5.81966, -2.82522 5.81928)       GH16   \n",
+       "41020  LINESTRING (-2.82397 5.82148, -2.82399 5.82146...       GH16   \n",
+       "41063  LINESTRING (-2.82481 5.82056, -2.82494 5.82032...       GH16   \n",
+       "41094    LINESTRING (-2.82473 5.82070, -2.82481 5.82056)       GH16   \n",
+       "\n",
+       "             ADM1_EN  ...    gcm epoch        ead_usd    rp  flood_length_m  \\\n",
+       "31     Greater Accra  ...  WATCH  1980   19453.959302  1000       53.770003   \n",
+       "62     Greater Accra  ...  WATCH  1980    2287.927108  1000        6.323743   \n",
+       "93     Greater Accra  ...  WATCH  1980  170494.756904  1000      471.241020   \n",
+       "124    Greater Accra  ...  WATCH  1980  110508.232976  1000      305.440550   \n",
+       "155    Greater Accra  ...  WATCH  1980    8458.522645  1000       23.379035   \n",
+       "...              ...  ...    ...   ...            ...   ...             ...   \n",
+       "40958  Western North  ...  WATCH  1980   94462.014008  1000       64.086457   \n",
+       "40989  Western North  ...  WATCH  1980   62977.325869  1000       42.726103   \n",
+       "41020  Western North  ...  WATCH  1980  179121.446702  1000      121.522488   \n",
+       "41063  Western North  ...  WATCH  1980  156692.232328  1000      106.305695   \n",
+       "41094  Western North  ...  WATCH  1980   26113.608546  1000       17.716419   \n",
+       "\n",
+       "            kind total_cost_usd_per_km  total_adaptation_cost_usd  \\\n",
+       "31      two_lane          1.047397e+06               56318.517592   \n",
+       "62      two_lane          1.047397e+06                6623.467290   \n",
+       "93      two_lane          1.047397e+06              493576.233867   \n",
+       "124     two_lane          1.047397e+06              319917.388862   \n",
+       "155     two_lane          1.047397e+06               24487.121053   \n",
+       "...          ...                   ...                        ...   \n",
+       "40958  four_lane          2.094793e+06              134247.872952   \n",
+       "40989  four_lane          2.094793e+06               89502.347911   \n",
+       "41020  four_lane          2.094793e+06              254564.477292   \n",
+       "41063  four_lane          2.094793e+06              222688.443806   \n",
+       "41094  four_lane          2.094793e+06               37112.234365   \n",
+       "\n",
+       "       total_adaptation_benefit_usd        bcr  \n",
+       "31                     5.578532e+05   9.905326  \n",
+       "62                     6.560760e+04   9.905326  \n",
+       "93                     4.889033e+06   9.905326  \n",
+       "124                    3.168886e+06   9.905326  \n",
+       "155                    2.425529e+05   9.905326  \n",
+       "...                             ...        ...  \n",
+       "40958                  2.708751e+06  20.177240  \n",
+       "40989                  1.805910e+06  20.177240  \n",
+       "41020                  5.136408e+06  20.177240  \n",
+       "41063                  4.493238e+06  20.177240  \n",
+       "41094                  7.488224e+05  20.177240  \n",
+       "\n",
+       "[1303 rows x 21 columns]"
+      ]
+     },
+     "execution_count": 55,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "candidates = historical[historical.bcr > 1]\n",
     "candidates"
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "caring-trader",
    "metadata": {},
@@ -521,10 +2158,206 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 58,
    "id": "negative-liquid",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>flood_length_m</th>\n",
+       "      <th>total_adaptation_benefit_usd</th>\n",
+       "      <th>total_adaptation_cost_usd</th>\n",
+       "      <th>bcr</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ADM1_EN</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Ahafo</th>\n",
+       "      <td>6169.925987</td>\n",
+       "      <td>5.334659e+07</td>\n",
+       "      <td>6.462359e+06</td>\n",
+       "      <td>9.201183</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Ashanti</th>\n",
+       "      <td>22377.092972</td>\n",
+       "      <td>3.282192e+08</td>\n",
+       "      <td>2.826401e+07</td>\n",
+       "      <td>11.931011</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Bono</th>\n",
+       "      <td>7400.527071</td>\n",
+       "      <td>5.550888e+07</td>\n",
+       "      <td>7.751287e+06</td>\n",
+       "      <td>9.460365</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Bono East</th>\n",
+       "      <td>27762.639348</td>\n",
+       "      <td>4.182545e+08</td>\n",
+       "      <td>3.792551e+07</td>\n",
+       "      <td>8.978280</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Central</th>\n",
+       "      <td>315825.739173</td>\n",
+       "      <td>5.869595e+09</td>\n",
+       "      <td>4.171591e+08</td>\n",
+       "      <td>15.218221</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Eastern</th>\n",
+       "      <td>95586.571989</td>\n",
+       "      <td>1.185412e+09</td>\n",
+       "      <td>1.208687e+08</td>\n",
+       "      <td>11.219343</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Greater Accra</th>\n",
+       "      <td>144951.652415</td>\n",
+       "      <td>2.633143e+09</td>\n",
+       "      <td>1.993335e+08</td>\n",
+       "      <td>12.986069</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Northern</th>\n",
+       "      <td>36063.690743</td>\n",
+       "      <td>3.179162e+08</td>\n",
+       "      <td>4.066786e+07</td>\n",
+       "      <td>11.922071</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Northern East</th>\n",
+       "      <td>25863.284557</td>\n",
+       "      <td>2.136089e+08</td>\n",
+       "      <td>2.902658e+07</td>\n",
+       "      <td>10.220424</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Oti</th>\n",
+       "      <td>23611.523422</td>\n",
+       "      <td>4.552750e+08</td>\n",
+       "      <td>3.292477e+07</td>\n",
+       "      <td>15.858581</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Savannah</th>\n",
+       "      <td>55578.630958</td>\n",
+       "      <td>5.244536e+08</td>\n",
+       "      <td>7.486831e+07</td>\n",
+       "      <td>11.413993</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Upper East</th>\n",
+       "      <td>12836.310274</td>\n",
+       "      <td>2.867787e+08</td>\n",
+       "      <td>1.871576e+07</td>\n",
+       "      <td>15.420944</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Upper West</th>\n",
+       "      <td>10474.289978</td>\n",
+       "      <td>1.950791e+08</td>\n",
+       "      <td>1.380859e+07</td>\n",
+       "      <td>12.351019</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Volta</th>\n",
+       "      <td>209314.033479</td>\n",
+       "      <td>2.743280e+09</td>\n",
+       "      <td>2.465202e+08</td>\n",
+       "      <td>13.729495</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Western</th>\n",
+       "      <td>61050.730289</td>\n",
+       "      <td>9.696906e+08</td>\n",
+       "      <td>7.783973e+07</td>\n",
+       "      <td>11.085770</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Western North</th>\n",
+       "      <td>19296.238399</td>\n",
+       "      <td>3.133976e+08</td>\n",
+       "      <td>2.537202e+07</td>\n",
+       "      <td>12.967275</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "               flood_length_m  total_adaptation_benefit_usd  \\\n",
+       "ADM1_EN                                                       \n",
+       "Ahafo             6169.925987                  5.334659e+07   \n",
+       "Ashanti          22377.092972                  3.282192e+08   \n",
+       "Bono              7400.527071                  5.550888e+07   \n",
+       "Bono East        27762.639348                  4.182545e+08   \n",
+       "Central         315825.739173                  5.869595e+09   \n",
+       "Eastern          95586.571989                  1.185412e+09   \n",
+       "Greater Accra   144951.652415                  2.633143e+09   \n",
+       "Northern         36063.690743                  3.179162e+08   \n",
+       "Northern East    25863.284557                  2.136089e+08   \n",
+       "Oti              23611.523422                  4.552750e+08   \n",
+       "Savannah         55578.630958                  5.244536e+08   \n",
+       "Upper East       12836.310274                  2.867787e+08   \n",
+       "Upper West       10474.289978                  1.950791e+08   \n",
+       "Volta           209314.033479                  2.743280e+09   \n",
+       "Western          61050.730289                  9.696906e+08   \n",
+       "Western North    19296.238399                  3.133976e+08   \n",
+       "\n",
+       "               total_adaptation_cost_usd        bcr  \n",
+       "ADM1_EN                                              \n",
+       "Ahafo                       6.462359e+06   9.201183  \n",
+       "Ashanti                     2.826401e+07  11.931011  \n",
+       "Bono                        7.751287e+06   9.460365  \n",
+       "Bono East                   3.792551e+07   8.978280  \n",
+       "Central                     4.171591e+08  15.218221  \n",
+       "Eastern                     1.208687e+08  11.219343  \n",
+       "Greater Accra               1.993335e+08  12.986069  \n",
+       "Northern                    4.066786e+07  11.922071  \n",
+       "Northern East               2.902658e+07  10.220424  \n",
+       "Oti                         3.292477e+07  15.858581  \n",
+       "Savannah                    7.486831e+07  11.413993  \n",
+       "Upper East                  1.871576e+07  15.420944  \n",
+       "Upper West                  1.380859e+07  12.351019  \n",
+       "Volta                       2.465202e+08  13.729495  \n",
+       "Western                     7.783973e+07  11.085770  \n",
+       "Western North               2.537202e+07  12.967275  "
+      ]
+     },
+     "execution_count": 58,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "candidates.groupby(\"ADM1_EN\").agg(\n",
     "    {\n",
@@ -537,6 +2370,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "helpful-negative",
    "metadata": {},
@@ -561,7 +2395,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.10"
+   "version": "3.11.4"
   }
  },
  "nbformat": 4,