What are the assumptions of Geographically Weighted Regression?
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Geographically Weighted Regression (GWR) is an exploratory technique that aims to capture spatial nonstationarity by calibrating a multiple regression model that allows for different relationships to exist at different points in space . The assumptions of GWR include the presence of spatial heterogeneity, where a simple global model cannot explain the relationships between variables . GWR assumes that the relationships between variables vary over space and that the local coefficients estimated at each location are representative of the local variations in the data . Additionally, GWR assumes that the choice of a spatial weighting function, such as a Gaussian kernel, is appropriate for capturing the spatial relationships . These assumptions are fundamental to the application of GWR in various fields, including ecology, agriculture, social science, and public health .
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