Geographically Weighted Regression on the Environmental-Ecological Factors of Human Longevity

Evaluating Geographically Weighted Regression Models for Environmental Chemical Risk Analysis

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 ...

Determining Effective Factors on Land Surface Temperature of Tehran Using LANDSAT Images And Integrating Geographically Weighted Regression With Genetic Algorithm

Due to urbanization and changes in the urban thermal environment and since the land surface temperature (LST) in urban areas are a few degrees higher than in surrounding non-urbanized areas, identifying spatial factors affecting on LST in urban areas is very important. Hence, by identifying these factors, preventing this phenomenon become possible using general education, inserting rules and al...

C.5 Geographically Weighted Regression

Geographically weighted regression (GWR) was introduced to the geography literature by Brunsdon et al. (1996) to study the potential for relationships in a regression model to vary in geographical space, or what is termed parametric nonstationarity. GWR is based on the non-parametric technique of locally weighted regression developed in statistics for curve-fitting and smoothing applications, w...

Mapping the Results of Geographically Weighted Regression

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...

Geographically Weighted Regression White Paper