Geographically weighted regression (GWR) (Brunsdon et al. 1996; Fotheringham et al. 2002) is a useful technique for modelling local spatial relationships between variables. The essential idea of GWR is that observations near to a model calibration point have more influence in the estimation of regression coefficients than observations farther away do. The standard GWR model employs a single ban...

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...

The application of geographically weighted regression (GWR) - a local spatial statistical technique used to test for spatial nonstationarity - has grown rapidly in the social, health and demographic sciences. GWR is a useful exploratory analytical tool that generates a set of location-specific parameter estimates which can be mapped and analysed to provide information on spatial nonstationarity...

In the evaluation of cancer risk related to environmental chemical exposures, the effect of many correlated chemicals on disease is often of interest. The relationship between correlated environmental chemicals and health effects is not always constant across a study area, as exposure levels may change spatially due to various environmental factors. Geographically weighted regression (GWR) has ...

extended abstractintroductionin recent years, several studies around the world have shown that land use has a strong impact on water quality, and significant correlations exist between water quality parameters and land use types. generally land use types have adverse impacts on water quality, so positive relationships exist between percentages of these land use types and concentrations of water ...

Over the past decades, regional haze episodes have frequently occurred in eastern China, especially in the Yangtze River Delta (YRD). Satellite derived Aerosol Optical Depth (AOD) has been used to retrieve the spatial coverage of PM2.5 concentrations. To improve the retrieval accuracy of the daily AOD-PM2.5 model, various auxiliary variables like meteorological or geographical factors have been...

The effective use of spatial information, that is the geographic locations of population units, in a regression model-based approach to small area estimation is an important practical issue. One approach for incorporating such spatial information in a small area regression model is via Geographically Weighted Regression (GWR). In GWR the relationship between the outcome variable and the covaria...

BACKGROUND Limited data are available on imaging predictors of neurological outcomes after extracorporeal cardiopulmonary resuscitation (ECPR). We investigated the association of initial brain computed tomography (CT) findings with neurological outcomes following ECPR. METHODS Between February 2005 and December 2015, a total of 42 patients who underwent brain CT scans within 48 h after ECPR w...

Relationships between geographically referenced variables are usually spatially heterogeneous and, to account for such variations, local models are necessary. This paper compares the Geographically Weighted Regression (GWR) model, usually used to integrate and examine the spatial heterogeneity of a relationship, and the Fuzzy Clustering-Based Least Squares (FCBLS) model for the analysis of spat...

This study identifies drivers of deforestation in Mexico by applying Geographically Weighted Regression (GWR) models to cartographic and statistical data. A wall-to-wall multitemporal GIS database was constructed incorporating digital land use/land cover maps for 2002 and 2007; along with ancillary data (road network, settlements, topography and socioeconomical parameters). The database analysi...