Relationship Between Transport Accessibility and Land Value: Local Model Approach with Geographically Weighted 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...

Modelling accessibility via transportation networks based upon previous experience: a Geographically Weighted Regression approach

1.1 Motivation Much research is currently being undertaken in transportation to create more realistic models of transportation networks that account for the spatial heterogeneity in those transportation networks (Mark and Sadek, 2004), and also the different modes of transport that may be used. Simply assuming that users will travel at the maximum speed limit for a particular class of road fail...

Using Geographically Weighted Regression to Explore Local Crime Patterns

Mapping the results of local statistics: Using geographically weighted regression.

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...

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...