gwverse: A Template for a New Generic Geographically Weighted R Package

gwverse: A Template for a New Generic Geographically Weighted R Package
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DOI:
10.1111/gean.12337
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发表时间:
2021-09
影响因子:
3.6
通讯作者:
A. Comber;M. Callaghan;P. Harris;Binbin Lu;N. Malleson;C. Brunsdon
A. Comber;M. Callaghan;P. Harris;Binbin Lu;N. Malleson;C. Brunsdon
中科院分区:
地球科学3区
文献类型:
--
作者:
A. Comber;M. Callaghan;P. Harris;Binbin Lu;N. Malleson;C. Brunsdon

文献摘要

相似文献

GWR是一种流行的方法,用于调查响应变量和预测变量之间关系的空间变异,并对调查和理解过程的空间异质性至关重要。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,反映了探索模型参数或组成部分的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得到R和Python(空间分析的主要编码环境)中的包开发的支持。其结果是,在任何给定的包中,GWR和GW功能中的细化(如果有的话)并不一致。本文概述了一个新的`gwverse`包的结构,随着时间的推移,它将取代`GWmodel`,它利用了复杂的综合包的组成方面的最新发展。它将“gwverse”概念化为具有模块化结构,将核心GW功能和GWR等应用程序分开。它采用了函数工厂的方法,在这种方法中,根据用户定义的参数创建定制的函数并返回给用户。本文介绍了两个演示模块,可用于进行GWR,并确定了一些关键的考虑因素和后续步骤。
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.