Incorporating spatial non-stationarity of regression coefficients into predictive vegetation models

@article{Kupfer2006IncorporatingSN,
  title={Incorporating spatial non-stationarity of regression coefficients into predictive vegetation models},
  author={John A. Kupfer and Calvin A. Farris},
  journal={Landscape Ecology},
  year={2006},
  volume={22},
  pages={837-852}
}
The results of predictive vegetation models are often presented spatially as GIS-derived surfaces of vegetation attributes across a landscape or region, but spatial information is rarely included in the model itself. Geographically weighted regression (GWR), which extends the traditional regression framework by allowing regression coefficients to vary for individual locations (‘spatial non-stationarity’), is one method of utilizing spatial information to improve the predictive power of such… CONTINUE READING
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