How geographically weighted regression (GWR) works

Geographically Weighted Regression (GWR) is a statistical method that allows analysts to understand how relationships between variables vary spatially, offering a contrast to traditional regression models like ordinary least squares (OLS) that assume uniform relationships across a map. GWR gives more weight to data points that are geographically closer, revealing local variations that can be crucial for applications in urban planning, environmental management, and public education. By utilizing kernel functions such as Gaussian or bisquare, GWR adjusts the influence of data points based on their proximity, allowing for a nuanced analysis of spatial data. This method generates local regression models, each producing unique coefficients that illustrate how explanatory variables affect dependent variables in specific areas, helping to pinpoint where relationships strengthen or weaken. The resulting spatial patterns can be visualized using GIS tools like Felt, providing decision-makers with interactive maps to better understand complex spatial data and inform resource allocation or policy adjustments.