Faster way of polygon intersection with shapely

Question

I have a large number of polygons (~100000) and try to find a smart way of calculating their intersecting area with a regular grid cells.

Currently, I am creating th

Answer 1

Since 2013/2014 Shapely has STRtree. I have used it and it seems to work well.

Here is a snippet from the docstring:

STRtree is an R-tree that is created using the Sort-Tile-Recursive algorithm. STRtree takes a sequence of geometry objects as initialization parameter. After initialization the query method can be used to make a spatial query over those objects.

>>> from shapely.geometry import Polygon
>>> from shapely.strtree import STRtree
>>> polys = [Polygon(((0, 0), (1, 0), (1, 1))), Polygon(((0, 1), (0, 0), (1, 0))), Polygon(((100, 100), (101, 100), (101, 101)))]
>>> s = STRtree(polys)
>>> query_geom = Polygon([(-1, -1), (2, 0), (2, 2), (-1, 2)])
>>> result = s.query(query_geom)
>>> polys[0] in result
True

Answer 2

Consider using Rtree to help identify which grid cells that a polygon may intersect. This way, you can remove the for loop used with the array of lat/lons, which is probably the slow part.

Structure your code something like this:

from shapely.ops import cascaded_union
from rtree import index
idx = index.Index()

# Populate R-tree index with bounds of grid cells
for pos, cell in enumerate(grid_cells):
    # assuming cell is a shapely object
    idx.insert(pos, cell.bounds)

# Loop through each Shapely polygon
for poly in polygons:
    # Merge cells that have overlapping bounding boxes
    merged_cells = cascaded_union([grid_cells[pos] for pos in idx.intersection(poly.bounds)])
    # Now do actual intersection
    print(poly.intersection(merged_cells).area)