spatial-statistics

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SKILL.md

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
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spatial-statistics — muend/geoai-skills