Generalized geographically and temporally weighted regression

IF 7.1 1区地球科学 Q1 ENVIRONMENTAL STUDIES

Computers Environment and Urban Systems Pub Date : 2024-12-24 DOI:10.1016/j.compenvurbsys.2024.102244

Hanchen Yu

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引用次数: 0

Abstract

This paper proposes Generalized Geographically and Temporally Weighted Regression (GGTWR) to address the limitations of Geographically and Temporally Weighted Regression (GTWR). The proposed GGTWR framework encompasses various generalized linear models, e.g. Poisson regression, negative binomial regression, and other models of the exponential distribution family. The paper also shows the classic GTWR bandwidth search algorithm is not suitable for GGTWR and proposes a new bandwidth search algorithm for GGTWR. Several simulation experiments are used to prove that GGTWR can effectively capture spatiotemporal non-stationary. The GGTWR framework enables the estimation of varying regression coefficients that capture spatial and temporal heterogeneity for generalized linear relationships, providing a comprehensive understanding of how predictor variables influence the response variable across different locations and time periods. An application to interprovincial population migration in China using 2005–2020 census data demonstrates the interpretability of the GGTWR framework. GGTWR provides a flexible modeling approach that more accurately explains real-world phenomena.

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广义地理和时间加权回归

为了解决地理和时间加权回归（GTWR）的局限性，本文提出了广义地理和时间加权回归（GGTWR）。提出的GGTWR框架包括各种广义线性模型，如泊松回归、负二项回归和指数分布族的其他模型。本文还指出了经典的GTWR带宽搜索算法不适用于GGTWR，并提出了一种新的GTWR带宽搜索算法。多个仿真实验证明了GGTWR能够有效捕获时空非平稳。GGTWR框架能够估计各种回归系数，这些回归系数捕捉广义线性关系的时空异质性，从而全面了解预测变量如何影响不同地点和时间段的响应变量。利用2005-2020年人口普查数据对中国省际人口迁移进行实证分析，证明了GGTWR框架的可解释性。GGTWR提供了一种灵活的建模方法，可以更准确地解释现实世界的现象。

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来源期刊

Computers Environment and Urban Systems Multiple-

CiteScore

13.30

自引率

7.40%

发文量

111

审稿时长

32 days

期刊介绍： Computers, Environment and Urban Systemsis an interdisciplinary journal publishing cutting-edge and innovative computer-based research on environmental and urban systems, that privileges the geospatial perspective. The journal welcomes original high quality scholarship of a theoretical, applied or technological nature, and provides a stimulating presentation of perspectives, research developments, overviews of important new technologies and uses of major computational, information-based, and visualization innovations. Applied and theoretical contributions demonstrate the scope of computer-based analysis fostering a better understanding of environmental and urban systems, their spatial scope and their dynamics.