Multiscale Geographically Weighted Regression Considering Boundary Effects

ABSTRACT Multiscale geographically weighted regression (MGWR) is a widely used spatial coefficient of variation model that assumes nearby sample points exhibit similar characteristics. However, geographical processes may exist cross‐border differences in space due to boundary effects, leading MGWR to suffer from precision loss and local coefficient distortion. To address this problem, we propose an MGWR that considers boundary effects (MGWRBE). MGWRBE first uses a priori knowledge to identify boundaries where changes non‐stationarily in a particular spatial process occur. These boundaries are used to construct a boundary‐aware matrix for each independent variable. MGWRBE then reformulates the spatial weight function of MGWR using these matrices, thereby incorporating boundary effects from different spatial processes into the modeling. MGWRBE was evaluated based on both simulated and real datasets. Results showed that compared to MGWR, MGWRBE achieved higher fitting accuracy and more reliable local parameter estimates.