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Merge pull request #2035 from Esri/jy-rvw-sedf2
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Add missing images for map cells
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jyaistMap authored Sep 12, 2024
2 parents ff72690 + 49387b0 commit 8a1ebce
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"## Spatial Index\n",
"In addition to row and column indices to search a DataFrame, we can use a spatial indexes to quickly access information based on its location and relationship with other features. They are based on the concept of a **minimum bounding rectangle** - the smallest rectangle that contains an entire geometric shape. Each of these rectangles are then grouped into `leaf` nodes representing a single shape and `node` structures containing groups of shapes according to whatever algorithm the different types of spatial indexing use. Querying these rectangles requires magnitudes fewer compute resources for accessing and processing geometries relative to accessing the entire feature array of coordinate pairs that compose a shape. Access to points, complex lines and irregularly-shaped polygons becomes much quicker and easier through different flavors of spatial indexing.\n",
"In addition to row and column indexes to search a DataFrame, we can use spatial indexes to quickly access information based on its location and relationship with other features. They are based on the concept of a **minimum bounding rectangle** - the smallest rectangle that contains an entire geometric shape. Each of these rectangles are then grouped into `leaf` nodes representing a single shape and `node` structures containing groups of shapes according to whatever algorithm the different types of spatial indexing use. Querying these rectangles requires magnitudes fewer compute resources for accessing and processing geometries relative to accessing the entire feature array of coordinate pairs that compose a shape. Access to points, complex lines and irregularly-shaped polygons becomes much quicker and easier through different flavors of spatial indexing.\n",
"\n",
"The Spatially Enabled DataFrame uses an implementation of spatial indexing known as [QuadTree indexing](https://en.wikipedia.org/wiki/Quadtree), which searches nodes when determining locations, relationships and attributes of specific features. `QuadTree` indexes are the default spatial index, but the SEDF also supports `r-tree` implementations. In the [**DataFrame Index**](#DataFrame-index) section of this notebook, the USA Major Cities feature layer was queried and the `sdf` property was called on the results to create a DataFrame. The [`sindex`](https://developers.arcgis.com/python/api-reference/arcgis.features.toc.html#arcgis.features.GeoAccessor.sindex) method on the DataFrame creates a QuadTree index:"
"The Spatially Enabled DataFrame uses an implementation of spatial indexing known as [QuadTree indexing](https://en.wikipedia.org/wiki/Quadtree), which searches nodes when determining locations, relationships and attributes of specific features. `QuadTree` indexes are the default spatial index, but the SEDF also supports `r-tree` implementations. In the [**DataFrame Index**](#DataFrame-index) section of this notebook, the USA Major Cities feature layer was queried and the `sdf` property was called on the results to create a DataFrame. The [`sindex`](/python/api-reference/arcgis.features.toc.html#arcgis.features.GeoAccessor.sindex) method on the DataFrame creates a QuadTree index:"
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"A spatial join works similarly on matching attribute values. However, instead of joining on an attribue field (like you did earlier), you will join based on the spatial relationship between the records in the two tables.\n",
"\n",
"#### Example: Merging State Statistics Information with Cities\n",
"### Example: Merging State Statistics Information with Cities\n",
"\n",
"The goal is to get Wyoming's city locations and census data joined with Wyoming's state census data.\n",
"> If you do not have access to the `ArcPy` site-package from the Python interpreter used to execute the following cells, you must authenticate to an ArcGIS Online Organization or ArcGIS Enterprise portal."
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