1998
DOI: 10.1111/1467-9884.00145
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Geographically Weighted Regression

Abstract: In regression models where the cases are geographical locations, sometimes regression coef®cients do not remain ®xed over space. A technique for exploring this phenomenon, geographically weighted regression is introduced. A related Monte Carlo signi®cance test for spatial non-stationarity is also considered. Finally, an example of the method is given, using limiting longterm illness data from the 1991 UK census.

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Cited by 976 publications
(410 citation statements)
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“…The GWR model is calibrated by weighting all observations around a grid using a distance decay function. The exponential function is the most common distance decay function (for more details on distance decay functions, see [36,64,65]). …”
Section: Estimating the Global (Ols) And Local (Gwr) Modelsmentioning
confidence: 99%
“…The GWR model is calibrated by weighting all observations around a grid using a distance decay function. The exponential function is the most common distance decay function (for more details on distance decay functions, see [36,64,65]). …”
Section: Estimating the Global (Ols) And Local (Gwr) Modelsmentioning
confidence: 99%
“…It can be in a fixed or adaptive distance form (see Gollini et al 2015) and can be optimally found by minimizing some GoF diagnostic, for example, via a cross-validation or an Akaike information criterion (AIC) approach. AIC (Akaike 1973) is derived from the KullbackLiebler information distance (Kullback and Leibler 1951) between two statistical distributions (Brunsdon et al 2000), and its minimization provides a trade-off between GoF and degrees of freedom (i.e. the goal is model parsimony).…”
Section: Gwr With Minkowski Distancesmentioning
confidence: 99%
“…The underlying idea of GWR is that parameters may be estimated anywhere in the study area given a dependent variable and a set of one or more independent variables which have been measured at places whose location is known (Brunsdon, Fotheringham and Charlton, 1998). GWR has been used to model local variations of residential segregation in a variety of studies (Levers, Bruckner and Lakes, 2010;Hwang, 2014;Lloyd, Catney and Shuttleworth, 2014) and it is for this reason that it was chosen to determine if there is a relationship between racial integration and levels of income in KDM.…”
Section: Segregation Perspectivementioning
confidence: 99%