A CUDA-Based Parallel Geographically Weighted Regression for Large-Scale Geographic Data

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 modification to geographically weighted regression

BACKGROUND Geographically weighted regression (GWR) is a modelling technique designed to deal with spatial non-stationarity, e.g., the mean values vary by locations. It has been widely used as a visualization tool to explore the patterns of spatial data. However, the GWR tends to produce unsmooth surfaces when the mean parameters have considerable variations, partly due to that all parameter es...

A Family of Geographically Weighted Regression Models

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...

Study of the Geographically Weighted Regression Application on Climate Data

This study used Geographical Weighted Regression (GWR) technique to find spatial relationship between Elevation and climate (Rainfall, Temperature) in Northern Nigeria using climate (Rainfall, Temperature) data from weather stations from 1980 – 2010 obtained from Nigerian Meteorological Agency (Nimet). From the results of the analysis it was shown that there is significant relationship between ...