Based on annual average PM2.5 gridded dataset, this study first analyzed the spatiotemporal pattern of PM2.5 across Mainland China during 1998-2012. Then facilitated with meteorological site data, land cover data, population and Gross Domestic Product (GDP) data, etc., the contributions of latent geographic factors, including socioeconomic factors (e.g., road, agriculture, population, industry)...

Geographically weighted regression (GWR) enjoys wide application in regional science, thanks to its relatively straightforward formulation and explicit treatment of spatial effects. However, its application to discrete-response data sets and land use change at the level of urban parcels has remained a novelty. This work combines logit specifications with GWR techniques to anticipate five catego...

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...

Geographically Weighted Regression (GWR) is increasingly used in spatial analyses of social and environmental data. It allows heterogeneities processes relationships to be investigated through a series local regression models rather than single global one. Standard GWR assumes that between the response predictor variables operate at same scale, which frequently not case. To address this, severa...

BACKGROUND The spatial interplay between socioeconomic factors and tuberculosis (TB) cases contributes to the understanding of regional tuberculosis burdens. Historically, local Poisson Geographically Weighted Regression (GWR) has allowed for the identification of the geographic disparities of TB cases and their relevant socioeconomic determinants, thereby forecasting local regression coefficie...

This study applies a hedonic model to assess the economic benefits of air quality improvement following the 1990 Clean Air Act Amendment at the county level in the lower 48 United States. An instrumental variable approach that combines geographically weighted regression and spatial autoregression methods (GWR-SEM) is adopted to simultaneously account for spatial heterogeneity and spatial autoco...

There is an increasing interest in the relationship between area-based disadvantage and obesity but the extent to which the poverty-obesity relationship remains constant across geographical areas remains unclear. We examined geographical variations in the relationship between poverty and obesity in Taiwan using geographically weighted regression (GWR). A representative sample of 27,293 Taiwanes...

This paper proposes an extended semi-supervised regression approach to enhance the prediction accuracy of housing prices within the geographical information science field. The method, referred to as co-training geographical weighted regression (COGWR), aims to fully utilize the positive aspects of both the geographical weighted regression (GWR) method and the semi-supervised learning paradigm. ...

County-based spatial distribution characteristics and the related geological factors for iodine in drinking-water were studied in Shandong Province (China). Spatial autocorrelation analysis and spatial scan statistic were applied to analyze the spatial characteristics. Generalized linear models (GLMs) and geographically weighted regression (GWR) studies were conducted to explore the relationshi...

Geographic analyses in ecology may be separated into those that attempt generalizations to achieve ‘global’ insights, and those that attempt to explore and document local variation. Ecological studies at the broad scale usually set out to test specific hypotheses (such as the effect of energy on species richness) and focus on establishing global relationships before examining local residual var...