The GWmodel R package: further topics for exploring spatial heterogeneity using geographically weighted models
The GWmodel R package: further topics for exploring spatial heterogeneity using geographically weighted models
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DOI：
10.1080/10095020.2014.917453
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发表时间：
2014-01-01
影响因子：
6
通讯作者：
Brunsdon, Chris
中科院分区：
文献类型：
--
作者：
Lu, Binbin;Harris, Paul;Brunsdon, Chris
在本研究中，我们提出了一组局部模型，称为地理加权（GW）模型，这些模型可以在GWmodel R包中找到。GW模型适用于空间数据难以用全局形式描述的情况，对于某些区域，局部拟合提供了更好的描述。该方法使用移动窗口加权技术，在目标位置估计局部模型的集合。通常，绘制模型参数或输出，以便探索和评估空间异质性的性质。特别是，我们使用以下方法进行案例研究：(i) GW汇总统计和GW主成分分析；（ii）高级GW回归拟合和诊断；（三）非平稳性的相关蒙特卡罗显著性检验；（iv） GW判别分析；(v)增强的内核带宽选择程序。来自爱尔兰共和国和美国的大选数据集用于演示。本研究旨在补充一项伴随的GW模型研究，该研究侧重于基本和稳健的GW模型。
In this study, we present a collection of local models, termed geographically weighted (GW) models, which can be found within the GWmodel R package. A GW model suits situations when spatial data are poorly described by the global form, and for some regions the localized fit provides a better description. The approach uses a moving window weighting technique, where a collection of local models are estimated at target locations. Commonly, model parameters or outputs are mapped so that the nature of spatial heterogeneity can be explored and assessed. In particular, we present case studies using: (i) GW summary statistics and a GW principal components analysis; (ii) advanced GW regression fits and diagnostics; (iii) associated Monte Carlo significance tests for non-stationarity; (iv) a GW discriminant analysis; and (v) enhanced kernel bandwidth selection procedures. General Election data-sets from the Republic of Ireland and US are used for demonstration. This study is designed to complement a companion GWmodel study, which focuses on basic and robust GW models.