Abstract
ABSTRACTDwelling unit (population) data at finer zones are important for different applications. The census data are not available every year and published at coarse zones. While the remotely sensed data have addressed this limitation, analysis of spatial non-stationarity in the relationship between dwelling and detailed urban remote-sensing covariates for dwelling estimates is almost novel. Riyadh, Saudi Arabia, is chosen as a case study. The remote-sensing variables have been derived from QuickBird data using an object-based image analysis. To analyse the spatial non-stationarity, ordinary least squares (OLS) and geographically weighted regression (GWR) approaches are applied. The OLS models suggest that utilizing three-residential classes provides more accurate results than built area and residential built area. The GWR models are more accurate than the OLS model. This research proves that the GWR approach cannot completely handle the spatial non-stationarity problem without efficient explanatory variables. For example, the GWR model that uses three-residential classes is the best model to estimate dwelling compared with the GWR model that uses residential built area. This article confirms that the application of the OLS approach with efficient explanatory variables can successfully account the spatial non-stationarity and the results are relatively comparable with the GWR model.
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