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...
Main Authors: | , , |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2018-09-01
|
Series: | Sustainability |
Subjects: | |
Online Access: | http://www.mdpi.com/2071-1050/10/9/3146 |
id |
doaj-b68f3855e5fa4c4fbae58d205d1c63b0 |
---|---|
record_format |
Article |
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 |
_version_ |
1724886403964207104 |