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Geographically weighted regression (GWR) is a technique of spatial statistical modeling used to analyze spatially varying relationships between variables. Processes generating geographical patterns may vary under different geographical contexts. The identification of where and how such spatial heterogeneity in the processes appears on maps is a key in understanding complex geographical phenomena. To investigate this issue, several local spatial analysis techniques highlighting geographically “local” differences have been developed. Other techniques of local spatial analysis, such as Openshaw's GAM (geographical analysis machine) and Anselin's LISA (10cal indicators of spatial association), focus on the distribution of only one variable on a map. They are typically used for determining the geographical concentrations of high-risk diseases or crimes. GWR is also a common tool of local spatial analysis; however, ...

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