Abstract
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.
Highlights
Sea-level rise has the most impact among the natural factors, and permanent residents have the most impact among the social factors
Land subsidence is the downward displacement of a land surface relative to a certain reference surface, such as mean sea level (MSL) or a reference ellipsoid [1]
Land subsidence leads to serious water hazards in urban areas [56], poor inland navigation, frequent damages to municipal infrastructure, and other urban problems
Summary
Land subsidence is the downward displacement of a land surface relative to a certain reference surface, such as mean sea level (MSL) or a reference ellipsoid [1]. The land subsidence in Shanghai causes frequent damages of municipal infrastructure and impedes the construction and resource development activities, and is responsible for huge economic and ecological losses. The reasons for these factors are selected as follows: (1) Over-exploitation of groundwater reduces the buoyancy of the underground aquifer [19,20] and changes the stress of the stratum Both of these reactions cause land subsidence [21,22,23,24] by affecting the consolidation of the soil. Sea-level rise is usually attributed to global climate change by thermal expansion of ocean water and melting of ice sheets and glaciers [25] Compared with the previous studies, innovations of the study are as follows: (1) It supplements social factors and compares them with natural factors [35]. (2) This paper expands the new perspective by analysing annual growth (i.e., the incremental effect) of impacting factors while most of the existing researches [7,8,9] analyse the influence of statistics (i.e., the scale effect in the paper) on the land subsidence. (3) GCDA is used in the study instead of mathematical statistics, because of in the analysis of long-term variable data, the GCDA results in an improved integrity and dynamic than those of the traditional mathematical statistics [36]. (4) ESDA was used in the spatial analysis of land subsidence in Shanghai to describe and analyse its spatial characteristics quantitatively in detail
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