A hierarchical pattern of urban social vulnerability in Shanghai, China and its implications for risk management

Gu, Honghuan; Du, Shiqiang; Liao, Banggu; Wen, Jiahong; Wang, Congxiao; Chen, Ruishan; Chen, Bo

doi:10.1016/j.scs.2018.05.047

Cited by 61 publications

(41 citation statements)

References 46 publications

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“…These capitals are viewed as an interdependent capacity that capture the core capacities of a region system. The study is in continuation with the previous case studies (Shi et al, 2004;Keating et al, 2014;Gu et al, 2018) and considered socioeconomic aspects of Chongming Island. Accordingly, a three-tier system with 31 sub-indicators has been constructed (Table.…”

Section: Developing An Indicator Systemmentioning

confidence: 65%

Piloting a capital-based approach for characterizing and evaluating drivers of island sustainability- An application in Chongming Island

Fang

Liu

et al. 2020

Journal of Cleaner Production

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This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

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Section: Developing An Indicator Systemmentioning

confidence: 65%

Piloting a capital-based approach for characterizing and evaluating drivers of island sustainability- An application in Chongming Island

Fang

Liu

et al. 2020

Journal of Cleaner Production

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“…The study area consists of 15 districts, 209 subdistricts/towns and 5088 NCs. NCs are the smallest administrative unit in cities of China, which play a fundamental role in urban risk management [17,43]. Eight downtown districts are located on the west of the Huangpu River in the inner city area (within the outer ring) ( Figure 1b).…”

Section: Study Areamentioning

confidence: 99%

“…In recent years, flood risks in Shanghai have been highlighted in the field of flood risk analysis and management [7,11,[13][14][15]. Many studies conducted the hazard simulation of fluvial and pluvial floods [8,10,16], and assessed the exposure, vulnerability, loss and risk to floods for a neighborhood committee (NC) or district scale in Shanghai municipality [14,[17][18][19][20]. In particular, the extreme flood risk with low-probability and high consequence has been focused in recent decades because it can cause catastrophic damage and losses for the city's social, economic sectors, infrastructure and environment as well [1,4,11,[21][22][23][24][25].…”

Section: Introductionmentioning

confidence: 99%

“…Meanwhile, thanks to the rapid development of the Internet, open source data [36], and wide application of artificial intelligence in disaster risk assessment [37][38][39], we mapped the housing price for Shanghai at the neighborhood committee (NC) level [40], and developed reliable space-explicit datasets of residential buildings and household properties in Shanghai, using machine learning [40] to combine a variety of online datasets. These advances made it feasible for us to analyze exposure, losses and risks for residential buildings and household properties caused by extreme flooding at the NC level in the whole city of Shanghai, and to identify their spatial patterns with a spatial statistic method [17,41] in this study.…”

Section: Introductionmentioning

confidence: 99%

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Scenario-Based Extreme Flood Risk of Residential Buildings and Household Properties in Shanghai

et al. 2019

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Extreme flooding usually causes huge losses of residential buildings and household properties, which is critical to flood risk analysis and flood resilience building in Shanghai. We developed a scenario-based multidisciplinary approach to analyze the exposure, losses and risks of residential buildings and household properties, and their spatial patterns at the neighborhood committee level in Shanghai, based on extreme storm flood scenarios of 1/200, 1/500, 1/1000 and 1/5000-year. Our findings show that the inundation area of the residential buildings caused by a 1/200-year storm flood reaches 24.9 km2, and the total loss of residential buildings and household properties is 29.7 billion CNY (Chinese Yuan) (or 4.4 billion USD), while the inundation area of residential buildings and the total loss increases up to 162.4 km2 and 366.0 billion CNY (or 54.2 billion USD), respectively for a 1/5000-year storm flood. The estimated average annual loss (AAL) of residential buildings and household properties for Shanghai is 590 million CNY/year (or 87.4 million USD/year), with several hot spots distributed around the main urban area and on the bank of the Hangzhou Bay. Among sixteen districts, Pudong has the highest exposure and annual expected loss, while the inner city is also subject to extreme flooding with an AAL up to near half of the total. An analysis of flood risk in each of 209 subdistricts/towns finds that those most vulnerable to storm flooding are concentrated in Pudong, Jiading, Baoshan Districts and the inner city. Our work can provide meaningful information for risk-sensitive urban planning and resilience building in Shanghai. The methodology can also be used for risk analysis in other coastal cities facing the threat of storm flooding.

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“…Thus, the approach to the concept of social vulnerability has been opened up, placing people at the center. This approach concerns the people who have or do not have the capacity to overcome [8] or adapt to vicissitudes, which are not exclusively linked to environmental risks, and even incorporates a spatial aspect [9].…”

Section: Introductionmentioning

confidence: 99%

Decision Model for Predicting Social Vulnerability Using Artificial Intelligence

Abarca-Álvarez

Reinoso-Bellido

Campos-Sánchez

2019

IJGI

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Social vulnerability, from a socio-environmental point of view, focuses on the identification of disadvantaged or vulnerable groups and the conditions and dynamics of the environments in which they live. To understand this issue, it is important to identify the factors that explain the difficulty of facing situations with a social disadvantage. Due to its complexity and multidimensionality, it is not always easy to point out the social groups and urban areas affected. This research aimed to assess the connection between certain dimensions of social vulnerability and its urban and dwelling context as a fundamental framework in which it occurs using a decision model useful for the planning of social and urban actions. For this purpose, a holistic approximation was carried out on the census and demographic data commonly used in this type of study, proposing the construction of (i) a knowledge model based on Artificial Neural Networks (Self-Organizing Map), with which a demographic profile is identified and characterized whose indicators point to a presence of social vulnerability, and (ii) a predictive model of such a profile based on rules from dwelling variables constructed by conditional inference trees. These models, in combination with Geographic Information Systems, make a decision model feasible for the prediction of social vulnerability based on housing information.

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A hierarchical pattern of urban social vulnerability in Shanghai, China and its implications for risk management

Cited by 61 publications

References 46 publications

Piloting a capital-based approach for characterizing and evaluating drivers of island sustainability- An application in Chongming Island

Piloting a capital-based approach for characterizing and evaluating drivers of island sustainability- An application in Chongming Island

Scenario-Based Extreme Flood Risk of Residential Buildings and Household Properties in Shanghai

Decision Model for Predicting Social Vulnerability Using Artificial Intelligence

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