Geographically Weighted Regression (GWR)

Builds CARTO Workflows that model spatially varying relationships between a dependent variable and one or more independent variables using GWR. Unlike global regression (one set of coefficients for the entire study area), GWR produces local coefficients per spatial unit, revealing how relationships change across space. Example: "bedrooms add $50k to price in downtown but only $20k in suburbs."

Prerequisites: Load carto-create-workflow for the development process, JSON structure, and validation commands.

Instructions

A GWR workflow follows this pipeline:

Source Data -> (Filter) -> Spatial Indexing (H3/Quadbin) -> Aggregation (dependent + independent vars per cell) -> GWR -> Save

Step 1: Load Source Data

Use native.gettablebyname. The input table must contain at least one numeric dependent variable and one or more numeric independent (predictor) variables.