diff --git a/08-mapping.ipynb b/08-mapping.ipynb
index d5d3f35b..6c66bdcf 100644
--- a/08-mapping.ipynb
+++ b/08-mapping.ipynb
@@ -6,9 +6,9 @@
"source": [
"# Making maps with Python {#sec-map-making}\n",
"\n",
- "## Prerequisites {.unnumbered}"
+ "## Prerequisites {.unnumbered}\n"
],
- "id": "65f0b74f"
+ "id": "b06a04d0"
},
{
"cell_type": "code",
@@ -36,9 +36,9 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "Let's import the required packages:"
+ "Let's import the required packages:\n"
],
- "id": "a819ef48"
+ "id": "6d660dcb"
},
{
"cell_type": "code",
@@ -51,7 +51,7 @@
"import contextily as cx\n",
"import folium"
],
- "id": "50d83c23",
+ "id": "ef9fdd3c",
"execution_count": null,
"outputs": []
},
@@ -67,7 +67,7 @@
"pd.options.display.max_colwidth = 35\n",
"plt.rcParams['figure.figsize'] = (5, 5)"
],
- "id": "4cc72590",
+ "id": "4a0adf8b",
"execution_count": null,
"outputs": []
},
@@ -75,9 +75,9 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "and load the sample data for this chapter:"
+ "and load the sample data for this chapter:\n"
],
- "id": "8935f8c5"
+ "id": "8f5ce72e"
},
{
"cell_type": "code",
@@ -90,7 +90,7 @@
"tanzania_buf = tanzania.to_crs(32736).buffer(50000).to_crs(4326)\n",
"tanzania_neigh = gpd.read_file('data/world.gpkg', mask=tanzania_buf)"
],
- "id": "a658a269",
+ "id": "26d2d326",
"execution_count": null,
"outputs": []
},
@@ -144,9 +144,9 @@
"### Minimal example\n",
"\n",
"A vector layer (`GeoDataFrame`) or a geometry column (`GeoSeries`) can be displayed using their `.plot` method (@sec-vector-layers).\n",
- "A minimal example of a vector layer map is obtained using `.plot` with nothing but the defaults (@fig-vector-minimal):"
+ "A minimal example of a vector layer map is obtained using `.plot` with nothing but the defaults (@fig-vector-minimal):\n"
],
- "id": "39cc5bc6"
+ "id": "1d180b71"
},
{
"cell_type": "code",
@@ -166,9 +166,9 @@
"metadata": {},
"source": [
"A `rasterio` raster file connection, or a numpy `ndarray`, can be displayed using `rasterio.plot.show` (@sec-using-rasterio).\n",
- "Here is a minimal example of a static raster map (@fig-raster-minimal):"
+ "Here is a minimal example of a static raster map (@fig-raster-minimal):\n"
],
- "id": "7d057122"
+ "id": "5c8a51fa"
},
{
"cell_type": "code",
@@ -189,9 +189,9 @@
"source": [
"### Styling {#sec-static-styling}\n",
"\n",
- "The most useful visual properties of the geometries, that can be specified in `.plot`, include `color`, `edgecolor`, and `markersize` (for points) (@fig-basic-plot):"
+ "The most useful visual properties of the geometries, that can be specified in `.plot`, include `color`, `edgecolor`, and `markersize` (for points) (@fig-basic-plot):\n"
],
- "id": "c6b20d14"
+ "id": "3b4814e2"
},
{
"cell_type": "code",
@@ -218,9 +218,9 @@
"metadata": {},
"source": [
"And here is an example of using `markersize` to get larger points (@fig-basic-plot-markersize).\n",
- "This example also demonstrates how to control the overall [figure size](https://matplotlib.org/stable/gallery/subplots_axes_and_figures/figure_size_units.html), such as $4 \\times 4$ $in$ in this case, using [`plt.subplots`](https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.subplots.html) to initialize the plot and its `figsize` parameter to specify dimensions:"
+ "This example also demonstrates how to control the overall [figure size](https://matplotlib.org/stable/gallery/subplots_axes_and_figures/figure_size_units.html), such as $4 \\times 4$ $in$ in this case, using [`plt.subplots`](https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.subplots.html) to initialize the plot and its `figsize` parameter to specify dimensions:\n"
],
- "id": "ce62e849"
+ "id": "79206a9f"
},
{
"cell_type": "code",
@@ -248,9 +248,9 @@
"- `legend`---Whether to show a legend\n",
"- `cmap`---Color map\n",
"\n",
- "For example, here we plot the `nz` polygons colored according to the `'Median_income'` attribute (@fig-plot-symbology):"
+ "For example, here we plot the `nz` polygons colored according to the `'Median_income'` attribute (@fig-plot-symbology):\n"
],
- "id": "bb3e9d45"
+ "id": "f7ede20c"
},
{
"cell_type": "code",
@@ -274,9 +274,9 @@
"A first safe choice is often the [ColorBrewer](https://colorbrewer2.org/#type=sequential&scheme=BuGn&n=3) collection of color scales, specifically designed for mapping.\n",
"Any color scale can be reversed, using the `_r` suffic.\n",
"Finally, other color scales are available, see the **matplotlib** [colormaps article](https://matplotlib.org/stable/tutorials/colors/colormaps.html) for details.\n",
- "The following code sections demonstrates three color scale specifications other than the default (@fig-plot-symbology-colors):"
+ "The following code sections demonstrates three color scale specifications other than the default (@fig-plot-symbology-colors):\n"
],
- "id": "c0c63fe6"
+ "id": "74a67b61"
},
{
"cell_type": "code",
@@ -304,9 +304,9 @@
"source": [
"Categorical symbology is also supported, such as when `column` points to an `str` attribute.\n",
"For example, the following expression sets symbology according to the `'Island'` column.\n",
- "For categorical variables, it makes sense to use a qualitative color scale, such as `'Set1'` from ColorBrewer (@fig-plot-symbology-categorical):"
+ "For categorical variables, it makes sense to use a qualitative color scale, such as `'Set1'` from ColorBrewer (@fig-plot-symbology-categorical):\n"
],
- "id": "86fb3ead"
+ "id": "9916ff1f"
},
{
"cell_type": "code",
@@ -325,9 +325,9 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "In case the legend interferes with the contents (such as in @fig-plot-symbology-categorical), we can modify the legend position, as follows (@fig-plot-legend-pos):"
+ "In case the legend interferes with the contents (such as in @fig-plot-symbology-categorical), we can modify the legend position, as follows (@fig-plot-legend-pos):\n"
],
- "id": "e3153b0f"
+ "id": "578f0ce9"
},
{
"cell_type": "code",
@@ -347,9 +347,9 @@
"metadata": {},
"source": [
"The `rasterio.plot.show` function, based on **matplotlib** as well, supports the same kinds of `cmap` arguments.\n",
- "For example (@fig-plot-symbology-colors-r):"
+ "For example (@fig-plot-symbology-colors-r):\n"
],
- "id": "21f53d8e"
+ "id": "bc69f414"
},
{
"cell_type": "code",
@@ -376,9 +376,9 @@
"metadata": {},
"source": [
"Unfortunately, there is no built-in option to display a legend in `rasterio.plot.show`.\n",
- "The following [workaround](https://stackoverflow.com/questions/61327088/rio-plot-show-with-colorbar), reverting to **matplotlib** methods, can be used to acheive it instead (@fig-plot-symbology-colors-r-scale):"
+ "The following [workaround](https://stackoverflow.com/questions/61327088/rio-plot-show-with-colorbar), reverting to **matplotlib** methods, can be used to acheive it instead (@fig-plot-symbology-colors-r-scale):\n"
],
- "id": "dce55770"
+ "id": "d1ca2e68"
},
{
"cell_type": "code",
@@ -403,13 +403,13 @@
"### Labels {#sec-plot-static-labels}\n",
"\n",
"Labels are often useful to annotate maps, to identify the location of specific features. \n",
- "GIS software has specialized algorithms for label placement, e.g., to avoid overlaps. \n",
+ "GIS software has specialized algorithms for label placement, e.g., to avoid overlaps between adjacent labels. \n",
"Furthermore, editing in graphical editing software is sometimes used for fine tuning of label placement.\n",
- "Nevertheless, simple labels addend within the Python environment can be a good starting point.\n",
+ "Nevertheless, simple labels added within the Python environment can be a good starting point, both for interactive exploration and sharing analysis results.\n",
"\n",
- "To demonstrate, suppose that we have a layer of polygons comprising the New Zealand southern Island:"
+ "To demonstrate, suppose that we have a layer `nz1` of regions comprising the New Zealand southern Island:\n"
],
- "id": "f3b87572"
+ "id": "29227e2a"
},
{
"cell_type": "code",
@@ -417,7 +417,7 @@
"source": [
"nz1 = nz[nz['Island'] == 'South']"
],
- "id": "ff35e1d5",
+ "id": "e3f1ef86",
"execution_count": null,
"outputs": []
},
@@ -425,9 +425,15 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "... @fig-labels-polygon."
+ "To add a label in **matplotlib**, we use the [`.annotate`](https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.annotate.html) method where the important arguments are the label string and the placement (a `tuple` of the form `(x,y)`). \n",
+ "When labeling vector layers, we typically want to add numerous labels, based on (one or more) attribute of each feature. \n",
+ "To do that, we can run a `for` loop or the `.apply` method, passing the label text and the coordinates of each feature.\n",
+ "For example, in the following example we use the `.apply` method the pass the region name (`'Name'` attribute) and the geometry centroid coordinates, for each region, to `.annotate`.\n",
+ "We are also using `ha='center'` (short for `horizontalalignment`).\n",
+ "\n",
+ "The result is shown in @fig-labels-polygon.\n"
],
- "id": "8b336671"
+ "id": "283ef485"
},
{
"cell_type": "code",
@@ -455,9 +461,9 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "..."
+ "...\n"
],
- "id": "2e56cc89"
+ "id": "6f0afa78"
},
{
"cell_type": "code",
@@ -467,7 +473,7 @@
"ctr['geometry'] = ctr.centroid\n",
"ctr"
],
- "id": "9342120e",
+ "id": "39bc5dda",
"execution_count": null,
"outputs": []
},
@@ -475,9 +481,9 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "... @fig-labels-points1."
+ "... @fig-labels-points1.\n"
],
- "id": "18fd7b12"
+ "id": "6bce3c15"
},
{
"cell_type": "code",
@@ -506,9 +512,9 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "... @fig-labels-points2."
+ "... @fig-labels-points2.\n"
],
- "id": "f2685b0c"
+ "id": "68c72c74"
},
{
"cell_type": "code",
@@ -545,9 +551,9 @@
"- store the first plot in a variable (e.g., `base`), and\n",
"- pass it as the `ax` argument of any subsequent plot(s) (e.g., `ax=base`).\n",
"\n",
- "For example, here is how we can plot `nz` and `nz_height` together (@fig-two-layers):"
+ "For example, here is how we can plot `nz` and `nz_height` together (@fig-two-layers):\n"
],
- "id": "28dafaeb"
+ "id": "1d9f63f2"
},
{
"cell_type": "code",
@@ -574,9 +580,9 @@
"\n",
"- Panel (a) shows a raster (New Zealand elevation) and a vector layer (New Zealand administrative division)\n",
"- Panel (b) shows the raster with a buffer of 22.2 $km$ around the dissolved administrative borders, representing New Zealand's [territorial waters](https://en.wikipedia.org/wiki/Territorial_waters) (see @sec-global-operations-and-distances)\n",
- "- Panel (c) shows the raster with two vector layers: the territorial waters (in red) and elevation measurement points (in yellow)"
+ "- Panel (c) shows the raster with two vector layers: the territorial waters (in red) and elevation measurement points (in yellow)\n"
],
- "id": "3eb405d1"
+ "id": "70c88710"
},
{
"cell_type": "code",
@@ -630,9 +636,9 @@
"\n",
"Basemaps can be added to **geopandas** static plots using the [**contextily**](https://contextily.readthedocs.io/en/latest/index.html) package.\n",
"A preliminary step is to convert our layers to `EPSG:3857` ([\"Web Mercator\"](https://en.wikipedia.org/wiki/Web_Mercator_projection)), to be in agreement with the basemaps, which are typically provided in this CRS.\n",
- "For example, let's take the small `\"Nelson\"` polygon from `nz`, and reproject it to `3857`:"
+ "For example, let's take the small `\"Nelson\"` polygon from `nz`, and reproject it to `3857`:\n"
],
- "id": "8928c691"
+ "id": "3071d481"
},
{
"cell_type": "code",
@@ -640,7 +646,7 @@
"source": [
"nzw = nz[nz['Name'] == 'Nelson'].to_crs(epsg=3857)"
],
- "id": "3817c9c2",
+ "id": "5c7ac58c",
"execution_count": null,
"outputs": []
},
@@ -654,9 +660,9 @@
"The possible values are given in the `cx.providers`.\n",
"Also check out the [gallery](https://xyzservices.readthedocs.io/en/stable/gallery.html).\n",
"Finally, custom basemaps (such as from your own raster tile server) can be specified using a [URL](https://contextily.readthedocs.io/en/latest/providers_deepdive.html#Manually-specifying-a-provider).\n",
- "For example (@fig-basemap):"
+ "For example (@fig-basemap):\n"
],
- "id": "4949f901"
+ "id": "76689753"
},
{
"cell_type": "code",
@@ -699,9 +705,9 @@
"\n",
"Faceted maps are multiple maps displaying the same symbology for the same spatial layers, but with different data in each panel.\n",
"The data displayed in the different panels typically refer to different properties, or time steps.\n",
- "For example, the `nz` layer has several different properties for each polygon, stored as separate attributes:"
+ "For example, the `nz` layer has several different properties for each polygon, stored as separate attributes:\n"
],
- "id": "b69ca33e"
+ "id": "f879acc6"
},
{
"cell_type": "code",
@@ -710,7 +716,7 @@
"vars = ['Land_area', 'Population', 'Median_income', 'Sex_ratio']\n",
"nz[vars]"
],
- "id": "675f94c4",
+ "id": "b90fe48b",
"execution_count": null,
"outputs": []
},
@@ -719,9 +725,9 @@
"metadata": {},
"source": [
"We may want to plot them all in a faceted map, that is, four small maps of `nz` with the different variables.\n",
- "To do that, we initialize the plot with the right number of panels, such as `ncols=len(vars)` if we wish to have one row and four columns, and then go over the variables in a `for` loop, each time plotting `vars[i]` into the `ax[i]` panel (@fig-faceted-map):"
+ "To do that, we initialize the plot with the right number of panels, such as `ncols=len(vars)` if we wish to have one row and four columns, and then go over the variables in a `for` loop, each time plotting `vars[i]` into the `ax[i]` panel (@fig-faceted-map):\n"
],
- "id": "7af60a41"
+ "id": "6a091fdd"
},
{
"cell_type": "code",
@@ -750,9 +756,9 @@
"- plot into `ax[i]`\n",
"\n",
"For example, here is how we can reproduce the last plot, this time in a $2 \\times 2$ layout, instead of a $1 \\times 4$ layout (@fig-faceted-map2).\n",
- "One more modification we are doing here is hiding the axis ticks and labels, to make the map less \"crowded\", using `ax[i].xaxis.set_visible(False)` (and same for `.yaxis`):"
+ "One more modification we are doing here is hiding the axis ticks and labels, to make the map less \"crowded\", using `ax[i].xaxis.set_visible(False)` (and same for `.yaxis`):\n"
],
- "id": "7619048e"
+ "id": "e1e44ad2"
},
{
"cell_type": "code",
@@ -779,9 +785,9 @@
"source": [
"It is also possible to \"manually\" specify the properties of each panel, and which row/column it goes in (e.g., @fig-spatial-aggregation-different-functions).\n",
"This can be useful when the various panels have different components, or even completely different types of plots (e.g., @fig-zion-transect), making automation with a `for` loop less applicable.\n",
- "For example, here is a plot similar to @fig-faceted-map2, but specifying each panel using a separate expression instead of using a `for` loop (@fig-faceted-map3):"
+ "For example, here is a plot similar to @fig-faceted-map2, but specifying each panel using a separate expression instead of using a `for` loop (@fig-faceted-map3):\n"
],
- "id": "4f1108e6"
+ "id": "ab4ad75a"
},
{
"cell_type": "code",
@@ -813,9 +819,9 @@
"### Exporting static maps {#sec-exporting-static-maps}\n",
"\n",
"Static maps can be exported to a file using the [`matplotlib.pyplot.savefig`](https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.savefig.html) function.\n",
- "For example, the following code section recreates @fig-read-shp-query (see previous Chapter), but this time the last expression saves the image to a JPG image named `plot_geopandas.jpg`:"
+ "For example, the following code section recreates @fig-read-shp-query (see previous Chapter), but this time the last expression saves the image to a JPG image named `plot_geopandas.jpg`:\n"
],
- "id": "84b9fdab"
+ "id": "ee35f2ab"
},
{
"cell_type": "code",
@@ -833,7 +839,7 @@
"tanzania_neigh.apply(lambda x: axes[1].annotate(text=x['name_long'], xy=x.geometry.centroid.coords[0], ha='center'), axis=1);\n",
"plt.savefig('output/plot_geopandas.jpg')"
],
- "id": "d0a37fa8",
+ "id": "ca5167d6",
"execution_count": null,
"outputs": []
},
@@ -842,9 +848,9 @@
"metadata": {},
"source": [
"Figures with rasters can be exported exactly the same way.\n",
- "For example, the following code section (@sec-plot-static-layers) creates an image of a raster and a vector layer, which is then exported to a file named `plot_rasterio.jpg`:"
+ "For example, the following code section (@sec-plot-static-layers) creates an image of a raster and a vector layer, which is then exported to a file named `plot_rasterio.jpg`:\n"
],
- "id": "498c55e8"
+ "id": "793035b8"
},
{
"cell_type": "code",
@@ -857,7 +863,7 @@
"nz.to_crs(nz_elev.crs).plot(ax=ax, facecolor='none', edgecolor='r');\n",
"plt.savefig('output/plot_rasterio.jpg')"
],
- "id": "080b5a4b",
+ "id": "4147c422",
"execution_count": null,
"outputs": []
},
@@ -866,9 +872,9 @@
"metadata": {},
"source": [
"Image file properties can be controlled through the `plt.subplots` and `plt.savefig` parameters.\n",
- "For example, the following code section exports the same raster plot to a file named `plot_rasterio2.svg`, which has different dimensions (width = 5 $in$, height = 7 $in$), a different format (SVG), and different resolution (300 $DPI$:)"
+ "For example, the following code section exports the same raster plot to a file named `plot_rasterio2.svg`, which has different dimensions (width = 5 $in$, height = 7 $in$), a different format (SVG), and different resolution (300 $DPI$:)\n"
],
- "id": "364d27c4"
+ "id": "471f65a2"
},
{
"cell_type": "code",
@@ -881,7 +887,7 @@
"nz.to_crs(nz_elev.crs).plot(ax=ax, facecolor='none', edgecolor='r');\n",
"plt.savefig('output/plot_rasterio2.svg', dpi=300)"
],
- "id": "85a5ff74",
+ "id": "e4e504d8",
"execution_count": null,
"outputs": []
},
@@ -896,9 +902,9 @@
"### Minimal example\n",
"\n",
"An interactive map of a `GeoSeries` or `GeoDataFrame` can be created with `.explore` (@sec-vector-layers).\n",
- "Here is a minimal example:"
+ "Here is a minimal example:\n"
],
- "id": "52d055d1"
+ "id": "05f76ee5"
},
{
"cell_type": "code",
@@ -932,9 +938,9 @@
"- `fillColor`---Fill color\n",
"- `fillOpacity`---Fill opacity (from `0` to `1`)\n",
"\n",
- "For example, here is how we can set green fill color and 30% opaque black outline of `nz` polygons in `.explore` (@fig-explore-styling-polygons):"
+ "For example, here is how we can set green fill color and 30% opaque black outline of `nz` polygons in `.explore` (@fig-explore-styling-polygons):\n"
],
- "id": "87c50aed"
+ "id": "1c4f070a"
},
{
"cell_type": "code",
@@ -964,9 +970,9 @@
"- `'circle'`---A vector circle with radius specified in $m$\n",
"- `'circle_marker'`---A vector circle with radius specified in pixels (the default)\n",
"\n",
- "For example, the following expression draws `'circe_marker`' points with 20 pixel radius, green fill, and black outline (@fig-explore-styling-points):"
+ "For example, the following expression draws `'circe_marker`' points with 20 pixel radius, green fill, and black outline (@fig-explore-styling-points):\n"
],
- "id": "3c4ddaa2"
+ "id": "9f151f97"
},
{
"cell_type": "code",
@@ -990,9 +996,9 @@
"metadata": {},
"source": [
"The following expression demonstrates the `'marker'` option (@fig-explore-styling-points2).\n",
- "Note that the above-mentioned styling properties (other then `opacity`) are not applicable when using `marker_type='marker'`, because the markers are fixed PNG images:"
+ "Note that the above-mentioned styling properties (other then `opacity`) are not applicable when using `marker_type='marker'`, because the markers are fixed PNG images:\n"
],
- "id": "48b613fc"
+ "id": "ec3e91a0"
},
{
"cell_type": "code",
@@ -1013,9 +1019,9 @@
"source": [
"### Layers\n",
"\n",
- "To display multiple layers, one on top of another, with `.explore`, we use the `m` argument, which stands for the previous map (@fig-explore-layers):"
+ "To display multiple layers, one on top of another, with `.explore`, we use the `m` argument, which stands for the previous map (@fig-explore-layers):\n"
],
- "id": "422abec5"
+ "id": "44c0ceaf"
},
{
"cell_type": "code",
@@ -1038,9 +1044,9 @@
"One of the advantages of interactive maps is the ability to turn layers \"on\" and \"off\".\n",
"This capability is implemented in [`folium.LayerControl`](https://python-visualization.github.io/folium/latest/user_guide/ui_elements/layer_control.html#LayerControl) from package **folium**, which the **geopandas** `.explore` method is a wrapper of.\n",
"For example, this is how we can add a layer control for the `nz` and `nz_height` layers (@fig-explore-layers-controls).\n",
- "Note the `name` properties, used to specify layer names in the control, and the `collapsed` property, used to specify whether the control is fully visible at all times (`False`), or on mouse hover (`True`, the default):"
+ "Note the `name` properties, used to specify layer names in the control, and the `collapsed` property, used to specify whether the control is fully visible at all times (`False`), or on mouse hover (`True`, the default):\n"
],
- "id": "99096059"
+ "id": "e29fc973"
},
{
"cell_type": "code",
@@ -1065,9 +1071,9 @@
"### Symbology {#sec-explore-symbology}\n",
"\n",
"Symbology can be specified in `.explore` using similar arguments as in `.plot` (@sec-plot-symbology).\n",
- "For example, here is an interactive version of @fig-plot-symbology-colors (a)."
+ "For example, here is an interactive version of @fig-plot-symbology-colors (a).\n"
],
- "id": "85374ad8"
+ "id": "3c7de930"
},
{
"cell_type": "code",
@@ -1088,9 +1094,9 @@
"source": [
"Fixed styling (@sec-explore-symbology) can be combined with symbology settings.\n",
"For example, polygon outline colors in @fig-explore-symbology are styled according to `'Median_income'`, however, this layer has overlapping outlines and the color is arbitrarily set according to the order of features (top-most features), which may be misleading and confusing.\n",
- "To specify fixed outline colors (e.g., black), we can use the `color` and `weight` properties of `style_kwds` (@fig-explore-symbology2):"
+ "To specify fixed outline colors (e.g., black), we can use the `color` and `weight` properties of `style_kwds` (@fig-explore-symbology2):\n"
],
- "id": "94ec997c"
+ "id": "7795dd96"
},
{
"cell_type": "code",
@@ -1123,9 +1129,9 @@
"\n",
"Other basemaps are available through the **xyzservices** package, which needs to be installed (see `xyzservices.providers` for a list), or using a custom tile server URL.\n",
"\n",
- "For example, the following expression displays the `'CartoDB positron'` tiles in an `.explore` map (@fig-explore-basemaps):"
+ "For example, the following expression displays the `'CartoDB positron'` tiles in an `.explore` map (@fig-explore-basemaps):\n"
],
- "id": "a851369a"
+ "id": "bd9d17fa"
},
{
"cell_type": "code",
@@ -1150,9 +1156,9 @@
"The HTML file can then be shared with other people, or published on a server and shared through a URL.\n",
"A good free option for publishing a web map is [GitHub Pages](https://pages.github.com/).\n",
"\n",
- "For example, here is how we can export the map shown in @fig-explore-layers-controls, to a file named `map.html`:"
+ "For example, here is how we can export the map shown in @fig-explore-layers-controls, to a file named `map.html`:\n"
],
- "id": "b7049152"
+ "id": "8e1dad03"
},
{
"cell_type": "code",
@@ -1164,7 +1170,7 @@
"folium.LayerControl(collapsed=False).add_to(m)\n",
"m.save('output/map.html')"
],
- "id": "a08d43dd",
+ "id": "b3a3b59d",
"execution_count": null,
"outputs": []
},
@@ -1180,7 +1186,7 @@
"\n",
"## References"
],
- "id": "d6e56dd9"
+ "id": "0f1749dd"
}
],
"metadata": {
diff --git a/08-mapping.qmd b/08-mapping.qmd
index 30890c31..491c92ae 100644
--- a/08-mapping.qmd
+++ b/08-mapping.qmd
@@ -235,17 +235,23 @@ fig.colorbar(i, ax=ax);
### Labels {#sec-plot-static-labels}
Labels are often useful to annotate maps, to identify the location of specific features.
-GIS software has specialized algorithms for label placement, e.g., to avoid overlaps.
+GIS software has specialized algorithms for label placement, e.g., to avoid overlaps between adjacent labels.
Furthermore, editing in graphical editing software is sometimes used for fine tuning of label placement.
-Nevertheless, simple labels addend within the Python environment can be a good starting point.
+Nevertheless, simple labels added within the Python environment can be a good starting point, both for interactive exploration and sharing analysis results.
-To demonstrate, suppose that we have a layer of polygons comprising the New Zealand southern Island:
+To demonstrate, suppose that we have a layer `nz1` of regions comprising the New Zealand southern Island:
```{python}
nz1 = nz[nz['Island'] == 'South']
```
-... @fig-labels-polygon.
+To add a label in **matplotlib**, we use the [`.annotate`](https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.annotate.html) method where the important arguments are the label string and the placement (a `tuple` of the form `(x,y)`).
+When labeling vector layers, we typically want to add numerous labels, based on (one or more) attribute of each feature.
+To do that, we can run a `for` loop or the `.apply` method, passing the label text and the coordinates of each feature.
+For example, in the following example we use the `.apply` method the pass the region name (`'Name'` attribute) and the geometry centroid coordinates, for each region, to `.annotate`.
+We are also using `ha='center'` (short for `horizontalalignment`).
+
+The result is shown in @fig-labels-polygon.
```{python}
#| label: fig-labels-polygon
diff --git a/08-mapping_files/execute-results/html.json b/08-mapping_files/execute-results/html.json
new file mode 100644
index 00000000..171ad6e3
--- /dev/null
+++ b/08-mapping_files/execute-results/html.json
@@ -0,0 +1,15 @@
+{
+ "hash": "8f829f31bbf063562aafb637f04184b1",
+ "result": {
+ "markdown": "# Making maps with Python {#sec-map-making}\n\n## Prerequisites {.unnumbered}\n\n\n\nLet's import the required packages:\n\n::: {.cell execution_count=2}\n``` {.python .cell-code}\nimport matplotlib.pyplot as plt\nimport geopandas as gpd\nimport rasterio\nimport rasterio.plot\nimport contextily as cx\nimport folium\n```\n:::\n\n\n\n\nand load the sample data for this chapter:\n\n::: {.cell execution_count=4}\n``` {.python .cell-code}\nnz = gpd.read_file('data/nz.gpkg')\nnz_height = gpd.read_file('data/nz_height.gpkg')\nnz_elev = rasterio.open('data/nz_elev.tif')\ntanzania = gpd.read_file('data/world.gpkg', where='name_long=\"Tanzania\"')\ntanzania_buf = tanzania.to_crs(32736).buffer(50000).to_crs(4326)\ntanzania_neigh = gpd.read_file('data/world.gpkg', mask=tanzania_buf)\n```\n:::\n\n\n## Introduction\n\n\n\n\n\n\n\nA satisfying and important aspect of geographic research is communicating the results.\nMap making---the art of cartography---is an ancient skill that involves communication, intuition, and an element of creativity.\nIn addition to being fun and creative, cartography also has important practical applications.\nA carefully crafted map can be the best way of communicating the results of your work, but poorly designed maps can leave a bad impression.\nCommon design issues include poor placement, size and readability of text and careless selection of colors, as outlined in the style guide of the Journal of Maps.\nFurthermore, poor map making can hinder the communication of results [@brewer_designing_2015]:\n\n> Amateur-looking maps can undermine your audience's ability to understand important information and weaken the presentation of a professional data investigation.\n\nMaps have been used for several thousand years for a wide variety of purposes.\nHistoric examples include maps of buildings and land ownership in the Old Babylonian dynasty more than 3000 years ago and Ptolemy's world map in his masterpiece Geography nearly 2000 years ago [@talbert_ancient_2014].\n\nMap making has historically been an activity undertaken only by, or on behalf of, the elite.\nThis has changed with the emergence of open source mapping software such as mapping packages in Python, R, and other languages, and the \"print composer\" in QGIS, which enable anyone to make high-quality maps, enabling \"citizen science\".\nMaps are also often the best way to present the findings of geocomputational research in a way that is accessible.\nMap making is therefore a critical part of geocomputation and its emphasis not only on describing, but also changing the world.\n\nBasic static display of vector layers in Python is done with the `.plot` method or the `rasterio.plot.show` function, for vector layers and rasters, as we saw in Sections @sec-vector-layers and @sec-using-rasterio, respectively.\nOther, more advaned uses of these methods, were also encountered in subsequent chapters, when demonstrating the various outputs we got.\nIn this chapter, we provide a comprehensive summary of the most useful workflows of these two methods for creating static maps (@sec-static-maps).\nThen, we move on to elaborate on the `.explore` method for creating interactive maps, which was also briefly introduced earlier (@sec-vector-layers).\n\n## Static maps {#sec-static-maps}\n\nStatic maps are the most common type of visual output from geocomputation.\nWhen stored in a file, standard formats include `.png` and `.pdf` for raster and vector outputs, respectively.\nStatic maps can be easily shared and viewed (whether digitally or in print), however they can only convey as much information as a static image can.\nInteractive maps (@sec-interactive-maps) provide much more flexibilty in terms of user experience and amount of information, however they often require more work to design and effectively share.\n\n\n\n\n\nLet's move on to the basics of static mapping with Python.\n\n### Minimal example\n\nA vector layer (`GeoDataFrame`) or a geometry column (`GeoSeries`) can be displayed using their `.plot` method (@sec-vector-layers).\nA minimal example of a vector layer map is obtained using `.plot` with nothing but the defaults (@fig-vector-minimal):\n\n::: {.cell execution_count=5}\n``` {.python .cell-code}\nnz.plot();\n```\n\n::: {.cell-output .cell-output-display}\n{#fig-vector-minimal width=306 height=442}\n:::\n:::\n\n\nA `rasterio` raster file connection, or a numpy `ndarray`, can be displayed using `rasterio.plot.show` (@sec-using-rasterio).\nHere is a minimal example of a static raster map (@fig-raster-minimal):\n\n::: {.cell execution_count=6}\n``` {.python .cell-code}\nrasterio.plot.show(nz_elev);\n```\n\n::: {.cell-output .cell-output-display}\n{#fig-raster-minimal width=334 height=442}\n:::\n:::\n\n\n### Styling {#sec-static-styling}\n\nThe most useful visual properties of the geometries, that can be specified in `.plot`, include `color`, `edgecolor`, and `markersize` (for points) (@fig-basic-plot):\n\n::: {#fig-basic-plot .cell layout-ncol='3' execution_count=7}\n``` {.python .cell-code}\nnz.plot(color='grey');\nnz.plot(color='none', edgecolor='blue');\nnz.plot(color='grey', edgecolor='blue');\n```\n\n::: {.cell-output .cell-output-display}\n{#fig-basic-plot-1 width=306 height=442}\n:::\n\n::: {.cell-output .cell-output-display}\n{#fig-basic-plot-2 width=306 height=442}\n:::\n\n::: {.cell-output .cell-output-display}\n{#fig-basic-plot-3 width=306 height=442}\n:::\n\nSetting `color` and `edgecolor` in static maps of a vector layer\n:::\n\n\nAnd here is an example of using `markersize` to get larger points (@fig-basic-plot-markersize).\nThis example also demonstrates how to control the overall [figure size](https://matplotlib.org/stable/gallery/subplots_axes_and_figures/figure_size_units.html), such as $4 \\times 4$ $in$ in this case, using [`plt.subplots`](https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.subplots.html) to initialize the plot and its `figsize` parameter to specify dimensions:\n\n::: {.cell execution_count=8}\n``` {.python .cell-code}\nfig, ax = plt.subplots(figsize=(4,4))\nnz_height.plot(markersize=100, ax=ax);\n```\n\n::: {.cell-output .cell-output-display}\n{#fig-basic-plot-markersize width=347 height=362}\n:::\n:::\n\n\n### Symbology {#sec-plot-symbology}\n\nWe can set symbology in a `.plot` using the following parameters:\n\n- `column`---A column name\n- `legend`---Whether to show a legend\n- `cmap`---Color map\n\nFor example, here we plot the `nz` polygons colored according to the `'Median_income'` attribute (@fig-plot-symbology):\n\n::: {.cell execution_count=9}\n``` {.python .cell-code}\nnz.plot(column='Median_income', legend=True);\n```\n\n::: {.cell-output .cell-output-display}\n{#fig-plot-symbology width=395 height=442}\n:::\n:::\n\n\nThe default color scale which you see in @fig-plot-symbology is `cmap='viridis'`.\nThe `cmap` (\"color map\") argument can be used to specify any of countless other color scales.\nA first safe choice is often the [ColorBrewer](https://colorbrewer2.org/#type=sequential&scheme=BuGn&n=3) collection of color scales, specifically designed for mapping.\nAny color scale can be reversed, using the `_r` suffic.\nFinally, other color scales are available, see the **matplotlib** [colormaps article](https://matplotlib.org/stable/tutorials/colors/colormaps.html) for details.\nThe following code sections demonstrates three color scale specifications other than the default (@fig-plot-symbology-colors):\n\n::: {#fig-plot-symbology-colors .cell layout-ncol='3' execution_count=10}\n``` {.python .cell-code}\nnz.plot(column='Median_income', legend=True, cmap='Reds');\nnz.plot(column='Median_income', legend=True, cmap='Reds_r');\nnz.plot(column='Median_income', legend=True, cmap='spring');\n```\n\n::: {.cell-output .cell-output-display}\n{#fig-plot-symbology-colors-1 width=395 height=442}\n:::\n\n::: {.cell-output .cell-output-display}\n{#fig-plot-symbology-colors-2 width=395 height=442}\n:::\n\n::: {.cell-output .cell-output-display}\n{#fig-plot-symbology-colors-3 width=395 height=442}\n:::\n\nSymbology in a static map of a vector layer, created with `.plot`\n:::\n\n\nCategorical symbology is also supported, such as when `column` points to an `str` attribute.\nFor example, the following expression sets symbology according to the `'Island'` column.\nFor categorical variables, it makes sense to use a qualitative color scale, such as `'Set1'` from ColorBrewer (@fig-plot-symbology-categorical):\n\n::: {.cell execution_count=11}\n``` {.python .cell-code}\nnz.plot(column='Island', legend=True, cmap='Set1');\n```\n\n::: {.cell-output .cell-output-display}\n{#fig-plot-symbology-categorical width=306 height=442}\n:::\n:::\n\n\nIn case the legend interferes with the contents (such as in @fig-plot-symbology-categorical), we can modify the legend position, as follows (@fig-plot-legend-pos):\n\n::: {.cell execution_count=12}\n``` {.python .cell-code}\nnz.plot(column='Island', legend=True, cmap='Set1', legend_kwds={'loc': 4});\n```\n\n::: {.cell-output .cell-output-display}\n{#fig-plot-legend-pos width=306 height=442}\n:::\n:::\n\n\nThe `rasterio.plot.show` function, based on **matplotlib** as well, supports the same kinds of `cmap` arguments.\nFor example (@fig-plot-symbology-colors-r):\n\n::: {#fig-plot-symbology-colors-r .cell layout-ncol='3' execution_count=13}\n``` {.python .cell-code}\nrasterio.plot.show(nz_elev, cmap='BrBG');\nrasterio.plot.show(nz_elev, cmap='BrBG_r');\nrasterio.plot.show(nz_elev, cmap='nipy_spectral');\n```\n\n::: {.cell-output .cell-output-display}\n{#fig-plot-symbology-colors-r-1 width=334 height=442}\n:::\n\n::: {.cell-output .cell-output-display}\n{#fig-plot-symbology-colors-r-2 width=334 height=442}\n:::\n\n::: {.cell-output .cell-output-display}\n{#fig-plot-symbology-colors-r-3 width=334 height=442}\n:::\n\nSymbology in a static map of a raster, with `rasterio.plot.show`\n:::\n\n\nUnfortunately, there is no built-in option to display a legend in `rasterio.plot.show`.\nThe following [workaround](https://stackoverflow.com/questions/61327088/rio-plot-show-with-colorbar), reverting to **matplotlib** methods, can be used to acheive it instead (@fig-plot-symbology-colors-r-scale):\n\n::: {.cell execution_count=14}\n``` {.python .cell-code}\nfig, ax = plt.subplots()\ni = ax.imshow(nz_elev.read(1), cmap='BrBG')\nrasterio.plot.show(nz_elev, cmap='BrBG', ax=ax);\nfig.colorbar(i, ax=ax);\n```\n\n::: {.cell-output .cell-output-display}\n{#fig-plot-symbology-colors-r-scale width=414 height=442}\n:::\n:::\n\n\n### Labels {#sec-plot-static-labels}\n\n...\n\n::: {.cell execution_count=15}\n``` {.python .cell-code}\nnz1 = nz[nz['Island'] == 'South']\n```\n:::\n\n\n... @fig-labels-polygon.\n\n::: {.cell execution_count=16}\n``` {.python .cell-code}\nfig, ax = plt.subplots()\nnz1.plot(ax=ax, color='lightgrey', edgecolor='grey')\nnz1.apply(\n lambda x: ax.annotate(\n text=x['Name'], \n xy=x.geometry.centroid.coords[0], \n ha='center'\n ), \n axis=1\n);\n```\n\n::: {.cell-output .cell-output-display}\n{#fig-labels-polygon width=348 height=442}\n:::\n:::\n\n\n...\n\n::: {.cell execution_count=17}\n``` {.python .cell-code}\nctr = nz[['Island', 'geometry']].dissolve(by='Island').reset_index()\nctr['geometry'] = ctr.centroid\nctr\n```\n\n::: {.cell-output .cell-output-display execution_count=430}\n```{=html}\n\n\n
\n \n \n | \n Island | \n geometry | \n
\n \n \n \n | 0 | \n North | \n POINT (1834096.904 5732233.908) | \n
\n \n | 1 | \n South | \n POINT (1401304.646 5125013.652) | \n
\n \n
\n
\n\n
\n \n \n | \n Land_area | \n Population | \n Median_income | \n Sex_ratio | \n
\n \n \n \n | 0 | \n 12500.561149 | \n 175500.0 | \n 23400 | \n 0.942453 | \n
\n \n | 1 | \n 4941.572557 | \n 1657200.0 | \n 29600 | \n 0.944286 | \n
\n \n | 2 | \n 23900.036383 | \n 460100.0 | \n 27900 | \n 0.952050 | \n
\n \n | ... | \n ... | \n ... | \n ... | \n ... | \n
\n \n | 13 | \n 9615.976035 | \n 51100.0 | \n 25700 | \n 0.971898 | \n
\n \n | 14 | \n 422.195242 | \n 51400.0 | \n 27200 | \n 0.925967 | \n
\n \n | 15 | \n 10457.745485 | \n 46200.0 | \n 27900 | \n 0.957792 | \n
\n \n
\n
16 rows × 4 columns
\n
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