spatial-statistics

From README

Spatial Statistics Purpose: answer "is it clustered, where, and why" with defensible inference. The core discipline: spatial data violates independence assumptions, so standard statistics silently overstate significance — every analysis here starts with weights design and ends with residual diagnostics. Spatial weights (W) — the analysis IS the weights Every result downstream depends on W; choose it for substantive reasons and run a sensitivity check with one alternative: | Weights | Use when | |---|---| | Queen/Rook contiguity | Irregular polygons (admin units, parcels) | | K-nearest neighbors | Points; islands present (contiguity leaves them unconnected) | | Distance band | Physical process with known range | | Kernel (distance-decayed) | Smooth influence, GWR-style local models | Always report: weights type, parameters, number of islands, and whether results survive an alternative W.