the rapid change of land use and land cover in the metropolitan area of tehran has influenced the distribution pattern of land surface temperature (lst). in this study, a spatial autocorrelation analysis is adopted to process the spatial-temporal changes of lst and normalized difference vegetation index (ndvi) in tehran during the period of 1987 to 2010. global spatial autocorrelation analysis ...

Land resource management requires extensive land mapping. Conventional soil mapping takes a long time and is expensive; therefore, geographic information system data as predictor in texture modeling can be used an alternative solution to shorten reduce costs. Through digital elevation model data, topographic variability obtained independent variable predicting texture. Geographically weighted r...

Geographically weighted regression (GWR) is a local spatial statistical technique for exploring spatial nonstationarity. Previous approaches to mapping the results of GWR have primarily employed an equal step classification and sequential no-hue colour scheme for choropleth mapping of parameter estimates. This cartographic approach may hinder the exploration of spatial nonstationarity by inadeq...

A Bayesian treatment of locally linear regression methods introduced in McMillen (1996) and labeled geographically weighted regressions (GWR) in Brunsdon, Fotheringham and Charlton (1996) is set forth in this paper. GWR uses distance-decay-weighted sub-samples of the data to produce locally linear estimates for every point in space. While the use of locally linear regression represents a true c...

As an established spatial analytical tool, Geographically Weighted Regression (GWR) has been applied across a variety of disciplines. However, its usage can be challenging for large datasets, which are increasingly prevalent in today’s digital world. In this study, we propose two high-performance R solutions GWR via Multi-core Parallel (MP) and Compute Unified Device Architecture (CUDA) techniq...