Quantifying social vulnerability for flood disasters of insurance company

Social vulnerability assessments are largely ignored when compared with biophysical vulnerability assessments. This is mainly due to the fact that there are more difficulties in quantifying them. Aiming at several pitfalls still existing in the Hoovering approach which is widely accepted, a suitable modified model is provided. In this modified model, the integrated vulnerability is made an analogy to the elasticity coefficient of a spring, and an objective evaluation criterion is established. With the evaluation criterion, the assessment indicators of social vulnerability are filtered and their weight assignments are accomplished. There is an application in the city of Changsha where floods occur often. With the relative data from the PICC Hunan Province Branch, a generalized regression neural network model is established in Matlab 7.0 and used to evaluate a company's flood social vulnerability index (SoVI). The results show that the average flood social vulnerability in Yuhua district is the highest, while Yuelu district is the lowest. It is good for disaster risk management and decision-making of insurance companies.

An Index-Based Approach to Assess Social Vulnerability for Hamburg, Germany

In this study,we set out to develop a new social vulnerability index(SVI).In doing so,we suggest some conceptual improvements that can be made to existing methodical approaches to assessing social vulnerability.To make the entanglement of socio-spatial inequalities visible,we are conducting a small-scale study on heterogeneous urban development in the city of Hamburg,Germany.This kind of high-resolution analysis was not previously available,but is increasingly requested by political decision makers.We can thus show hot spots of social vulnerability(SV)in Hamburg,considering the effects of social welfare,education,and age.In doing so,we defined SV as a contextual concept that follows the recent shift in discourse in line with the Intergovernmental Panel on Climate Change’s(IPCC)concepts of risk and vulnerability.Our SVI consists of two subcomponents:sensitivity and coping capacity.Populated areas of Hamburg were identified using satellite information and merged with the social data units of the city.Areas with high SVI are distributed over the entire city,notably in the district of Harburg and the Reiherstieg quarter in Wilhelmsburg near the Elbe,as well as in the densely populated inner city areas of Eimsbüttel and St.Pauli.As a map at a detailed scale,our SVI can be a useful tool to identify areas where the population is most vulnerable to climate-related hazards.We conclude that an enhanced understanding of urban social vulnerability is a prerequisite for urban risk management and urban resilience planning.

Social Vulnerability to Natural Hazards in Brazil

Although social vulnerability has recently gained attention in academic studies, Brazil lacks frameworks and indicators to assess it for the entire country.Social vulnerability highlights differences in the human capacity to prepare for, respond to, and recover from disasters. It varies over space and time, and among and between social groups, largely due to differences in socioeconomic and demographic characteristics. This article provides a social vulnerability index(SoVI~) replication study for Brazil and shows how SoVI~concepts and indicators were adapted to the country. SoVI~Brazil follows the place-based framework adopted in the Social Vulnerability Index initially developed for the United States. Using a principal component analysis(PCA), 45city-level indicators were reduced to 10 factors that explain about 67 % of the variance in the data. Clearly identified spatial patterns showed a concentration of the most socially vulnerable cities in the North and Northeast regions of Brazil, as well as the social vulnerability of metropolitan areas and state capitals in the South and Southeast regions.The least vulnerable cities are mainly concentrated in the inland regions of the Southeast. Although different factors contribute to the social vulnerability in each city, the overall results confirm the social and economic disparities among Brazilian’s regions and reflect a differential vulnerability to natural hazards at local to regional scales.

Typhoon disaster risk zoning for China’s coastal area

Previous studies on typhoon disaster risk zoning in China have focused on individual provinces or small-scale areas and lack county-level results.In this study,typhoon disaster risk zoning is conducted for China’s coastal area,based on data at the county level.Using precipitation and wind data for China and typhoon disaster and social data at the county level for China’s coastal area from 2004 to 2013,first we analyze the characteristics of typhoon disasters in China’s coastal area and then develop an intensity index of factors causing typhoon disasters and a comprehensive social vulnerability index.Finally,by combining the two indices,we obtain a comprehensive risk index for typhoon disasters and conduct risk zoning.The results show that the maximum intensity areas are mainly the most coastal areas of both Zhejiang and Guangdong,and parts of Hainan Island,which is similar to the distribution of typhoon disasters.The maximum values of vulnerability in the northwest of Guangxi,parts of Fujian coastal areas and parts of the Shandong Peninsula.The comprehensive risk index generally decreases from coastal areas to inland areas.The high-risk areas are mainly distributed over Hainan Island,south-western Guangdong,most coastal Zhejiang,the coastal areas between Zhejiang and Fujian and parts of the Shandong Peninsula.