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Precise_matches = possible_matchesįirst we have our GeoDataFrame (called gdf) create an R-tree spatial index for its geometry. Possible_matches_index = list(spatial_index.intersection(polygon.bounds)) We can use geopandas’s R-tree spatial index to find which street intersections lie within the boundaries of the city: Some of these points are within the city’s borders, but others are outside of them: Let’s say we have a polygon representing the city boundary of Walnut Creek, California:Īnd we also have a geopandas GeoDataFrame of lat-long points representing street intersections in the vicinity of this city. Python’s geopandas offers an implementation of R-tree to speed up spatial queries.
SCIPY SPATIAL HOW TO
Let’s look at how to use R-trees in Python and how to solve this limitation. Thus, using an R-tree spatial index makes the operation run no faster than it would without the spatial index! This proceeds recursively until all intersecting boxes are searched down to the lowest level, and returns the matching objects from the lowest level.īut what if the two sets of features (say, a polygon and a set of points) that you’re intersecting have approximately the same bounding boxes? Because the polygon’s minimum bounding box is approximately the same as the set of points’ minimum bounding box, the R-tree intersects every nested bounding box and considers every point to be a possible match. It then expands each intersecting bounding box and sees which of the child bounding boxes inside it intersect the query box. To search, the R-tree takes a query box and, starting at the top level, sees which (if any) bounding boxes intersect it. At yet higher levels, the R-tree aggregates bounding boxes and represents them by their bounding box, iteratively, until everything is nested into one top-level bounding box. It then aggregates nearby objects and represents them with their aggregate bounding box in the next higher level of the index.
SCIPY SPATIAL CODE
All of my code is in this notebook in this urban data science GitHub repo.Īn R-tree represents individual objects and their bounding boxes (the “R” is for “Rectangle”) as the lowest level of the spatial index. I’ll introduce how R-trees work and how to use them in Python and its geopandas library. Spatial indices are key features of spatial databases like PostGIS, but they’re also available for DIY coding in Python. Check out the journal article about OSMnx, which implements this technique.Ī spatial index such as R-tree can drastically speed up GIS operations like intersections and joins.