A faithful Python tutorial on Li & Fotheringham (2026) — using a two-stage MGWFER algorithm to remove time-invariant spatial confounders from Multiscale GWR and recover both unbiased spatially varying slopes and intrinsic contextual effects from simulated panel data (225 units x 3 periods).
Applying Multiscale Geographically Weighted Regression (MGWR) to reveal how economic catching-up varies across Indonesia's 514 districts, with each variable operating at its own spatial scale
A geocomputational notebook to study spatial heterogeneity using the GWR and MGWR frameworks.