Abstract
Spatial heterogeneity or non-stationarity is a prominent characteristic of data relationships. In line with Tobler’s first law of geography, a number of local statistics or local models have been proposed to explore spatial heterogeneities in spatial patterns or relationships. A particular branch of spatial statistics, termed geographically weighted (GW) models have evolved to encompass local techniques applicable in situations when data are not described well by such global models. Typical GW models and techniques include GW regression, GW descriptive statistics, GW principal components analysis, GW discriminant analysis, GW visualization techniques and GW artificial neural network. These GW models form a generic, open, and continually evolving technical framework to explore spatial heterogeneities from a wide range of disciplines in the natural and social sciences. In this study, we present a high-performance computing framework to incorporate the GW models with parallel computing techniques. We developed a software, namely GWmodelS to facilitate a flexible implementation of GW models. This study describes the procedures of geospatial data management, parameter optimization, model calibration and result visualization associated with GWmodelS. This software provides free services for scientific research and educational courses in the related domains.
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