GWmodel: an R Package for Exploring Spatial Heterogeneity using Geographically Weighted Models

TL;DR

GWmodel is an R package that implements geographically weighted models to analyze spatial heterogeneity, providing local statistical techniques and visualization tools for spatial data exploration.

Contribution

It introduces a comprehensive suite of GW spatial analysis methods in R, including regression, PCA, and summary statistics, with robust and predictive options.

Findings

Enables detailed exploration of spatial heterogeneity.

Provides robust GW regression techniques.

Supports local PCA and summary statistics.

Abstract

Spatial statistics is a growing discipline providing important analytical techniques in a wide range of disciplines in the natural and social sciences. In the R package GWmodel, we introduce techniques from a particular branch of spatial statistics, termed geographically weighted (GW) models. GW models suit situations when data are not described well by some global model, but where there are spatial regions where a suitably localised calibration provides a better description. The approach uses a moving window weighting technique, where localised models are found at target locations. Outputs are mapped to provide a useful exploratory tool into the nature of the data spatial heterogeneity. GWmodel includes: GW summary statistics, GW principal components analysis, GW regression, GW regression with a local ridge compensation, and GW regression for prediction; some of which are provided in basic and robust forms.