Multi-Scale Analysis of Surface Building Density and Land Subsidence Using a Combination of Wavelet Transform and Spatial Autocorrelation in the Plains of Beijing

Shuai Jiao, Xiaojuan Li, Jie Yu (), Mingyuan Lyu, Ke Zhang, Yuehui Li and Pengyuan Shi
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Shuai Jiao: College of Resource Environment and Tourism, Capital Normal University, Beijing 100048, China
Xiaojuan Li: College of Resource Environment and Tourism, Capital Normal University, Beijing 100048, China
Jie Yu: College of Resource Environment and Tourism, Capital Normal University, Beijing 100048, China
Mingyuan Lyu: College of Resource Environment and Tourism, Capital Normal University, Beijing 100048, China
Ke Zhang: College of Resource Environment and Tourism, Capital Normal University, Beijing 100048, China
Yuehui Li: CAUPD Beijing Planning & Design Consultants Ltd., Beijing 100037, China
Pengyuan Shi: CAUPD Beijing Planning & Design Consultants Ltd., Beijing 100037, China

Sustainability, 2024, vol. 16, issue 7, 1-23

Abstract: Land subsidence is a major issue in the Beijing Plain in China, caused by the construction of new buildings and infrastructure combined with groundwater extraction. This study employs a multi-level two-dimensional wavelet decomposition to decompose land subsidence into high- and low-frequency components, and Moran’s I index to analyze the spatial distribution of land subsidence and its main influencing factors. By comparing the spatial distributions of the high- and low-frequency components, we estimate the correlation between land subsidence and influencing factors at different scales. Utilizing a combination of wavelet decomposition and Moran’s I analysis, our study establishes a clear spatial correlation between continuously varying factors such as groundwater and clay layer thickness, and the low-frequency components of land subsidence, allowing for a focused analysis of the relationship between surface building density and the high-frequency components of land subsidence. Quantitatively, the study identifies a significant correlation at specific granularities, particularly at 480 m and 960 m, underscoring the nuanced interaction between urban development and land subsidence patterns. These insights into the spatial distribution of land subsidence and its contributing factors can inform the development of effective strategies to address this issue.

Keywords: land subsidence; wavelet transform; spatial autocorrelation; surface building density; groundwater (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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