MGWR: A Python Implementation of Multiscale Geographically Weighted Regression for Investigating Process Spatial Heterogeneity and Scale
MGWR: A Python Implementation of Multiscale Geographically Weighted Regression for Investigating Process Spatial Heterogeneity and Scale
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DOI：
10.3390/ijgi8060269
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发表时间：
2019-06-01
影响因子：
3.4
通讯作者：
Fotheringham, A. Stewart
中科院分区：
文献类型：
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作者：
Oshan, Taylor M.;Li, Ziqi;Fotheringham, A. Stewart
地理加权回归(GWR)是一种空间统计技术，它认识到当空间过程随空间环境变化时，传统的“全局”回归模型可能是有限的。GWR通过允许影响在空间上的变化来捕捉过程的空间异质性。为此，GWR使用“借来的”附近数据校准任意数量位置的局部线性模型集合。这提供了模型中允许在空间上变化的每个关系的位置特定参数估计的表面，以及提供关于过程的地理规模的直观的单个带宽参数。该框架的最新扩展允许每个关系根据不同的空间尺度参数而变化，因此被称为多尺度(M)GWR。本文介绍了MGWR，这是一个基于Python的MGWR实现，它明确地专注于空间异质性的多尺度分析。它为局部空间过程的推理和探索性分析提供了新的功能，为多尺度局部模式提供了独特的新诊断，并大幅提高了估计例程的效率。除了回顾本地模型的核心概念外，我们还提供了两个使用MGWR的案例研究。我们以识字的编程风格介绍这一点，提供了主要软件功能的概述和建议用法的演示，同时讨论了主要概念并演示了MGWR中所做的改进。
Geographically weighted regression (GWR) is a spatial statistical technique that recognizes that traditional "global' regression models may be limited when spatial processes vary with spatial context. GWR captures process spatial heterogeneity by allowing effects to vary over space. To do this, GWR calibrates an ensemble of local linear models at any number of locations using "borrowed' nearby data. This provides a surface of location-specific parameter estimates for each relationship in the model that is allowed to vary spatially, as well as a single bandwidth parameter that provides intuition about the geographic scale of the processes. A recent extension to this framework allows each relationship to vary according to a distinct spatial scale parameter, and is therefore known as multiscale (M)GWR. This paper introduces mgwr, a Python-based implementation of MGWR that explicitly focuses on the multiscale analysis of spatial heterogeneity. It provides novel functionality for inference and exploratory analysis of local spatial processes, new diagnostics unique to multi-scale local models, and drastic improvements to efficiency in estimation routines. We provide two case studies using mgwr, in addition to reviewing core concepts of local models. We present this in a literate programming style, providing an overview of the primary software functionality and demonstrations of suggested usage alongside the discussion of primary concepts and demonstration of the improvements made in mgwr.