How geographically weighted regression (GWR) works

Geographically Weighted Regression (GWR) is a statistical method designed to reveal how relationships between variables vary across different spatial locations, providing insights into local rather than global patterns. Unlike traditional regression models that apply a single equation across an entire dataset, GWR breaks down the dataset into smaller zones and gives more weight to data points that are closer together, allowing analysts to observe how variables like income or environmental factors impact specific areas differently. This approach uses a kernel function to determine the influence each data point has on a local regression model, which can vary depending on the chosen bandwidth and kernel type, such as Gaussian or bisquare. GWR is particularly useful in urban planning, environmental management, and public education, where it helps decision-makers understand spatial variations in factors like property values, pollution impacts, and educational outcomes. Tools like Felt AI facilitate the visualization of GWR outputs, transforming complex geospatial data into interactive maps that enhance decision-making and collaboration by illustrating how relationships between variables change across space.