文章基本信息

标题：Uncovering the Structural Effect Mechanisms of Natural and Social Factors on Land Subsidence: A Case Study in Beijing
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作者：Zhao, Bin ; Yang, Xuexi ; Wu, Qianhong 等
期刊名称：Sustainability
印刷版ISSN：2071-1050
出版年度：2022
卷号：14
期号：16
页码：1-18
DOI：10.3390/su141610139
语种：English
出版社：MDPI, Open Access Journal
摘要：Understanding the effect mechanisms of various factors on land subsidence may help in the development of scientific measures to control land subsidence. Previous studies mainly focused on exploring local effect mechanisms, such as extracting hotspots and analyzing their spatiotemporal distribution characteristics and identifying the interaction mechanisms of the associated factors. However, the scarcely discussed structural effect mechanisms on a small scale suggests a need to further explore the effects on land subsidence. Therefore, in this paper, an analytical framework was proposed to elaborate the structural effect mechanisms of influencing factors on land subsidence. First, the local effect mechanisms were identified using the geographically and temporally weighted regression (GTWR) model, followed by a spatial clustering analysis and the detection of their aggregation pattern using the spatially constrained multivariate clustering (SCMC) model to show the structural mechanisms. Study datasets included the monitoring results of land subsidence during 2003–2010 and the related socioeconomic factors by using synthetic aperture radar (SAR) images from Beijing. Factors such as population, annual average rainfall, underground water, and static load were identified to measure the changes in land subsidence, and all of these had both negative and positive impacts. Among these, the annual average rainfall had the largest coefficient variation range. These four geographically associated factors revealed various spatiotemporal effects on land subsidence in Beijing, showing land subsidence changes resulting from the urbanization process of Beijing during that period.
关键词：land subsidence; structural effect mechanisms; SAR; spatiotemporal variations; spatiotemporal regression models; spatiotemporal clustering analysis