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I have a global coastline grid made up of polygons (hexagons) and I am trying to aggregate global data onto these hexagons. For example, I am trying to compute the distance of each hexagon to the nearest coral reef (also defined as a polygon). Since I use global datasets, which are quite large, I am using GeoPandas sjoin_nearest() function (both datasets are geodataframes), which links each hexagon to the nearest coral polygon (typically the distance will be in not be more than a few hundred km).

Do the distances that GeoPandas uses for this actually make sense? The CRS I am using for both datasets is EPSG:6933, which is an ellipsoidal, equal area projection with unit meters. However, the documentation of sjoin_nearest states that "Since this join relies on distances, results will be inaccurate if your geometries are in a geographic CRS. Every operation in GeoPandas is planar, i.e. the potential third dimension is not taken into account."

Does this mean I cannot use sjoin_nearest at all for geographic calculations, even with an area-preserving projection? And if not, does someone know a better way to do this?

I know that I will never get perfectly accurate distances, but I only need an accuracy in the order of 10km. Also, the analysis is limited to between 47.5 and -37.5 degrees latitude, so relatively far from the poles.

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  • Distance measurements on a global scale are always wrong, whatever CRS you use: No projection can preserve distances but only for a small part of the map - be it along a line or only for distances from one (or two, using Two-point equidistant projection) points to all other points on the map. See here for details: gis.stackexchange.com/a/434196/88814 Commented Aug 9, 2022 at 13:26
  • A rough approximation is to use ellipsoidal distances (Great-circle distance), that should be good enought for your use case. Commented Aug 9, 2022 at 13:27
  • I know that there is no distance preserving projection, but I'm mainly wondering if the error is acceptable for my purpose (O(10km)). But I guess it also depends on which distance and where on Earth. The problem is that using other distance functions take more computational effort, whereas the GeoPandas sjoin_nearest function is very quick, but only does planar. Commented Aug 9, 2022 at 16:53
  • This may or may not work, but I think it will work under your conditions: Use a conformal projection to find the nearest. Then, if needed, compute the ellipsoidal distance between both geometries using a geodetic library. Commented Aug 10, 2022 at 2:31

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In order to calculate a meaningful distance, we must treat geographic coordinates not as approximate Cartesian coordinates but rather as true spherical coordinates. We must measure the distances between points as true paths over a sphere.

So we should use geographic types instead of geometric, that are planar and don't take into account earth's curvature (inaccurate distances), so for your task is better PostgreSQL/PostGIS geography type and functions

http://postgis.net/workshops/postgis-intro/geography.html

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