Analysis of Land Development Drivers Using Geographically Weighted Ridge Regression

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...

Geographically Weighted Regression White Paper

Anticipating Land Use Change Using Geographically Weighted Regression Models for Discrete Response

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...

Geographically Weighted Regression the analysis of spatially varying relationships