gwverse: a template for a new generic Geographically Weighted Rpackage
Alexis Comber, Chris Brunsdon, Martin Callaghan, Paul Harris, Binbin, Lu, Nick Malleson

TL;DR
The paper introduces `gwverse`, a modular and flexible R package designed to improve and unify the implementation of Geographically Weighted Regression and related spatial analysis models.
Contribution
It presents a new, modular package structure for GWR that overcomes limitations of existing tools and facilitates future development and integration.
Findings
Design of a modular package structure for GWR
Development of demonstrator modules for GWR analysis
Discussion of key considerations for future development
Abstract
GWR is a popular approach for investigating the spatial variation in relationships between response and predictor variables, and critically for investigating and understanding process spatial heterogeneity. The geographically weighted (GW) framework is increasingly used to accommodate different types of models and analyses reflecting a wider desire to explore spatial variation in model parameters or components. However the growth in the use of GWR and different GW models has only been partially supported by package development in both R and Python, the major coding environments for spatial analysis. The result is that refinements have been inconsistently included (if at all) within GWR and GW functions in any given package. This paper outlines the structure of a new `gwverse` package, that will over time replace `GWmodel`, that takes advantage of recent developments in the composition…
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Taxonomy
TopicsLand Use and Ecosystem Services · Spatial and Panel Data Analysis · Regional Economics and Spatial Analysis
