Geographically weighted regression with parameter-specific distance metrics

Area-to-point parameter estimation with geographically weighted regression

The modifiable areal unit problems (MAUP) is a problem by which aggregated units of data influence the results of spatial data analysis. Standard GWR, which ignores aggregation mechanisms, cannot be considered to serve as an efficient countermeasure of MAUP. Accordingly, this study proposes a type of GWR with aggregation mechanisms, termed area-to-point (ATP) GWR herein. ATP GWR, which is close...

Parameter Estimation of Geographically Weighted Trivariate Weibull Regression Model

In this study, Geographically Weighted Trivariate Weibull Regression (GWTWR) model and parameter estimation procedure are proposed. GWTWR is trivariate Weibull regression model which all of the regression parameters depend on the geographical location, and parameter estimation is done locally at each location in the study area. The location is expressed as a point coordinate in two-dimensional ...

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

A Geographically Temporal Weighted Regression Approach with Travel Distance for House Price Estimation

Previous studies have demonstrated that non-Euclidean distance metrics can improve model fit in the geographically weighted regression (GWR) model. However, the GWR model often considers spatial nonstationarity and does not address variations in local temporal issues. Therefore, this paper explores a geographically temporal weighted regression (GTWR) approach that accounts for both spatial and ...