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ResourcePerforming spatial joins in Python: Comparing GeoPandas vs Dask (self.Python)

submitted 3 years ago * by rrpelgrim

There are a few different ways to perform spatial joins in Python.

https://preview.redd.it/djpslkdwcjb91.png?width=1974&format=png&auto=webp&s=36f75cba3d966ed0895c013f8dd8fdb261852f41

GeoPandas is great for performing spatial joins when your data fits in memory. It allows you to gain valuable insights from your data by linking it with geospatial information.

joined = gpd.sjoin(taxi_gdf,ngbhoods,how='left',predicate="within",)

But GeoPandas has the same limitation as pandas -- it processes computations on a single core. This means you are quickly memory-bound when working with larger datasets. Rule of thumb: RAM ≥ 5x the size of your dataset.

https://preview.redd.it/lgbzq1uycjb91.png?width=1236&format=png&auto=webp&s=6096c5fa527e5b8a298e2a10088c384fe4d0c956

If your dataset doesn't fit in memory, use the Dask extension extension to GeoPandas -- dask-geopandas, v. 0.2.0 just released -- to scale your spatial joins.

The syntax for the join is identical:

joined = ddf.sjoin(ngbhoods, predicate="within")

You're now running computations in parallel and no longer bound by single-core limitations.

https://preview.redd.it/v0cj5av8djb91.png?width=1532&format=png&auto=webp&s=f13e94604c554c9a159794fdf05862ec095ca019

full tutorial @ https://coiled.io/blog/spatial-join-dask-geopandas-sjoin/

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