Geographically Weighted Regression in Geospatial Analysis

  • Rajesh Bahadur Thapa
  • Ronald C. Estoque


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).


Geographically Weighted Regression Akaike Information Criterion Ordinary Little Square Model Landscape Fragmentation Geographically Weighted Regression Model 
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Copyright information

© Springer Japan 2012

Authors and Affiliations

  1. 1.Earth Observation Research Center, Space Applications Mission DirectorateJapan Aerospace Exploration Agency (JAXA)TsukubaJapan
  2. 2.Division of Spatial Information Science, Graduate School of Life and Environmental SciencesUniversity of TsukubaTsukubaJapan
  3. 3.Don Mariano Marcos Memorial State UniversityBacnotanPhilippines

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