Local Spatial Interaction Modelling Based on the Geographically Weighted Regression Approach

  • Tomoki Nakaya
Part of the The GeoJournal Library book series (GEJL, volume 70)


One of the recent major trends in spatial analysis is local modelling by which spatial analysts examine local properties in geographical phenomena (Fotheringham, 1997). Indeed, spatial processes tend to vary over space due to different geographical contexts so that spatial non-stationarity emerges (Jones III and Hanham, 1995). In such cases, global models that postulate universally acceptable properties fail to capture the real phenomena under study. We could say that inferences of local incidence rates in disease mapping are the simplest form of local modelling (Openshaw et al.,1987, Nakaya, 2000). As for more complicated association analyses, Casetti’s (1972) expansion method is popular to model explicitly the property of non-stationarity in regression analysis (e.g. Casetti, 1990). According to the method, we can specify geographical drifts of regression parameters by polynomial or harmonic expansion series of locational variables. Recently, the Newcastle school (Brunsdon et al., 1996, Fotheringham et al.,1998) has developed a more generalised local regression methodology, called geographically weighted regression (GWR). The approach estimates local regression coefficients with a moving weighting kernel.


Spatial Interaction Geographically Weight Regression Distance Decay Destination Choice Major Metropolitan Area 
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Copyright information

© Springer Science+Business Media Dordrecht 2003

Authors and Affiliations

  • Tomoki Nakaya
    • 1
  1. 1.Department of GeographyRitsumeikan UniversityKita-ku, KyotoJapan

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