title: Mapping the results of local statistics: Using geographically weighted regression.
abstract: The application of geographically weighted regression (GWR) - a local spatial statistical technique used to test for spatial nonstationarity - has grown rapidly in the social, health and demographic sciences. GWR is a useful exploratory analytical tool that generates a set of location-specific parameter estimates which can be mapped and analysed to provide information on spatial nonstationarity in relationships between predictors and the outcome variable. A major challenge to GWR users, however, is how best to map these parameter estimates. This paper introduces a simple mapping technique that combines local parameter estimates and local t-values on one map. The resultant map can facilitate the exploration and interpretation of nonstationarity.
authors.affiliation: Anthropology and Demography, Faculty Director of the Geographic Information Analysis Core, Population Research Institute, Social Science Research Institute, The Pennsylvania State University.
authors.forename: Stephen A
authors.initials: SA
authors.lastname: Matthews
authors.affiliation: Geographic Information Analysis Core Population Research Institute, Social Science Research Institute, The Pennsylvania State University.
authors.forename: Tse-Chuan
authors.initials: TC
authors.lastname: Yang
keywords: Geographically Weighted Regression (GWR); local statistics; mapping; nonstationarity