Statistics that respect location: hot spots with Getis-Ord Gi*, spatial autocorrelation, kriging and fast vector overlays.
Run a hot spot analysis in Python with PySAL: aggregate 2025 STATS19 road collisions in Birmingham to a hexagon grid, test for clustering with global Moran's I, map Getis-Ord Gi* hot spots and LISA clusters with permutation inference and FDR correction, and see how spatial weights, the areal unit and exposure change the answer.
A practical spatial interpolation tutorial in Python: download real 2025 NO₂ annual means from the UK AURN network, fit spherical, exponential and Gaussian variograms, run ordinary kriging with PyKrige, compare it with inverse distance weighting, score both with leave-one-out cross-validation, map the kriging variance and add Sentinel-5P satellite NO₂ as a covariate.
Benchmark point-in-polygon joins, buffers, overlays and dissolves on 200,000 points with GeoPandas and Shapely 2, see why spatial indexes give a 100x+ speed-up, and compare Shapefile, GeoPackage, FlatGeobuf and GeoParquet.