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 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. The paper introduces two demonstrator modules that can be used to undertake GWR and identifies a number of key considerations and next steps.
翻译:地理加权(GW)框架越来越多地用于容纳不同类型的模型和分析,反映探索模型参数或组成部分空间变化的更广泛愿望;然而,GWR和不同GW模型的使用增长仅部分地得到R和Python软件包开发的支持,这两个软件包是空间分析的主要编码环境,其结果是,在任何特定软件包中,GWR和GW功能中的改进(如果有的话)前后不一。本文件概述了新的`Gwverse'软件包的结构,它将随着时间的推移取代`GW模型 ',利用复杂综合软件包构成方面的最新发展。该文件将`gwverse'和不同的GW模型的概念理解为一个模块结构,将GW的功能和诸如GWW等应用区分开来。它采用了一种功能工厂方法,根据用户定义的参数创建和返回用户功能。本文件提出了两个演示模块,可以用来确定用于进行GWR的关键性考虑和关键步骤。