Thapa, Rajesh Bahadur,Estoque, Ronald C.2026-09-012026-09-012012Thapa, R. B., & Estoque, R. C. (2012). Geographically weighted regression in geospatial analysis. Progress in Geospatial Analysis, 9784431540007, 85 - 96. https://www.researchgate.net/profile/Rajesh-Thapa-4/publication/261296287_Progress_in_Geospatial_Analysis/links/5cc930e492851c8d22106171/Progress-in-Geospatial-Analysis.pdf10.1007/978-4-431-54000-7_6https://lakasa.dmmmsu.edu.ph/handle/123456789/2429Full text.Geographically weighted regression (GWR) is a local spatial statistical technique for exploring spatial non-stationarity. The assumption in GWR is that observations nearby have a greater influence on parameter estimates than observations at a greater distance. This is very close to Tobler's first law of geography-everything is related to everything else, but near things are more related than distant things (Tobler 1970). GWR was developed on the basis of the traditional regression framework which incorporates local spatial relationships into the framework in an intuitive and explicit manner (Brunsdon et al. 1996; Fotheringham and Brunsdon 1999; Fotheringham et al. 2002). © 2012 Springer Japan. All rights reserved.enGeospatial analysisGeographically weighted regressionSpatial statisticsSpatial relationships519.5Geographic information systemsSpatial analysis (Statistics)Regression analysisSpatial statisticsBook chapter