gwverse: a template for a new generic Geographically Weighted Rpackage

Abstract

Geographically weighted regression (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. It has been refined to accommodate outliers, hetroskedasticity and local collinearity and extended to LASSO and elastic net forms. 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 and to move away from global, "whole map" approaches. 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. As an example, the GWmodel R package, which despite including the greatest number of GWR and GW related tools, has been developed and extended on a piecemeal basis with no overarching schema. 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 of complex, integrated packages. It conceptualises gwverse as having a modular structure, that separates core GW functionality and applications such as GWR. It adopts a function factory approach, in which bespoke functions are created and returned to the user based on user-defined parameters. Function factory approaches and the functions they generate have the advantage of enclosing environments that are execution environments of the function. The paper introduces and demonstrates two modules (written as linked packages) that can be used to undertake GWR as an initial transect through the proposed gwverse schema, and to support user defined GW modules, before discussing a number of key considerations and next steps.