Impacting Factors and Temporal and Spatial Differentiation of Land Subsidence in Shanghai

This paper uses Grey Correlation Degree Analysis (GCDA) to obtain and compare the relationships between major impacting factors and land subsidence, and finds the spatial characteristics of subsidence in the urban centre by Exploratory Spatial Data Analysis (ESDA). The results show the following: (1...

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Main Authors: Yishao Shi, Donghui Shi, Xiangyang Cao
Format: Article
Language:English
Published: MDPI AG 2018-09-01
Series:Sustainability
Subjects:
Online Access:http://www.mdpi.com/2071-1050/10/9/3146
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spelling doaj-b68f3855e5fa4c4fbae58d205d1c63b02020-11-25T02:17:26ZengMDPI AGSustainability2071-10502018-09-01109314610.3390/su10093146su10093146Impacting Factors and Temporal and Spatial Differentiation of Land Subsidence in ShanghaiYishao Shi0Donghui Shi1Xiangyang Cao2College of Surveying and Geo-Informatics, Tongji University, Shanghai 200092, ChinaCollege of Surveying and Geo-Informatics, Tongji University, Shanghai 200092, ChinaCollege of Surveying and Geo-Informatics, Tongji University, Shanghai 200092, ChinaThis paper uses Grey Correlation Degree Analysis (GCDA) to obtain and compare the relationships between major impacting factors and land subsidence, and finds the spatial characteristics of subsidence in the urban centre by Exploratory Spatial Data Analysis (ESDA). The results show the following: (1) Annual ground subsidence in Shanghai has occurred in four stages: slow growth in the 1980s, rapid growth in the 1990s, gradual decline in the first decade of the 21st century, and steady development currently. (2) In general, natural impact factors on land subsidence are more significant than social factors. Sea-level rise has the most impact among the natural factors, and permanent residents have the most impact among the social factors. (3) The average annual subsidence of the urban centre has undergone the following stages: “weak spatial autocorrelation” → “strong spatial autocorrelation” → “weak spatial autocorrelation”. (4) The “high clustering” spatial pattern in 1978 gradually disintegrated. There has been no obvious spatial clustering since 2000, and the spatial distribution of subsidence tends to be discrete and random.http://www.mdpi.com/2071-1050/10/9/3146land subsidencenatural factorssocial factorsspatial distributionclustering pattern
collection DOAJ
language English
format Article
sources DOAJ
author Yishao Shi
Donghui Shi
Xiangyang Cao
spellingShingle Yishao Shi
Donghui Shi
Xiangyang Cao
Impacting Factors and Temporal and Spatial Differentiation of Land Subsidence in Shanghai
Sustainability
land subsidence
natural factors
social factors
spatial distribution
clustering pattern
author_facet Yishao Shi
Donghui Shi
Xiangyang Cao
author_sort Yishao Shi
title Impacting Factors and Temporal and Spatial Differentiation of Land Subsidence in Shanghai
title_short Impacting Factors and Temporal and Spatial Differentiation of Land Subsidence in Shanghai
title_full Impacting Factors and Temporal and Spatial Differentiation of Land Subsidence in Shanghai
title_fullStr Impacting Factors and Temporal and Spatial Differentiation of Land Subsidence in Shanghai
title_full_unstemmed Impacting Factors and Temporal and Spatial Differentiation of Land Subsidence in Shanghai
title_sort impacting factors and temporal and spatial differentiation of land subsidence in shanghai
publisher MDPI AG
series Sustainability
issn 2071-1050
publishDate 2018-09-01
description This paper uses Grey Correlation Degree Analysis (GCDA) to obtain and compare the relationships between major impacting factors and land subsidence, and finds the spatial characteristics of subsidence in the urban centre by Exploratory Spatial Data Analysis (ESDA). The results show the following: (1) Annual ground subsidence in Shanghai has occurred in four stages: slow growth in the 1980s, rapid growth in the 1990s, gradual decline in the first decade of the 21st century, and steady development currently. (2) In general, natural impact factors on land subsidence are more significant than social factors. Sea-level rise has the most impact among the natural factors, and permanent residents have the most impact among the social factors. (3) The average annual subsidence of the urban centre has undergone the following stages: “weak spatial autocorrelation” → “strong spatial autocorrelation” → “weak spatial autocorrelation”. (4) The “high clustering” spatial pattern in 1978 gradually disintegrated. There has been no obvious spatial clustering since 2000, and the spatial distribution of subsidence tends to be discrete and random.
topic land subsidence
natural factors
social factors
spatial distribution
clustering pattern
url http://www.mdpi.com/2071-1050/10/9/3146
work_keys_str_mv AT yishaoshi impactingfactorsandtemporalandspatialdifferentiationoflandsubsidenceinshanghai
AT donghuishi impactingfactorsandtemporalandspatialdifferentiationoflandsubsidenceinshanghai
AT xiangyangcao impactingfactorsandtemporalandspatialdifferentiationoflandsubsidenceinshanghai
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