Evaluation Of Social Vulnerability Research Articles

Examining social vulnerability to multi-hazards in North-Western Himalayas, India.

The enhancing risk from human action and multi-hazard interaction has substantially complicated the hazard-society relationship. The underlying vulnerabilities are crucial in predicting the probable impact to be caused by multi-hazards. Thus, the evaluation of social vulnerability is decisive in inferring the driving factor and preparing for mitigation strategies. The Himalayan landscape is prone to multiple hazards as well as possesses a multitude of vulnerabilities owing to changing human landscape. Thus, an attempt has been made to inquire into the underlying socioeconomic factors enhancing the susceptibility of the region to multi-hazards. The social vulnerability index (SVIent) has been introduced, consisting of 13 indicators and 33 variables. The variables have been standardized using the maximum and minimum normalization method and the relative importance for each indicator has been determined using Shannon entropy methods to compute SVIent. The findings revealed that female population, population above 60 years old, net irrigated area, migrant population, dilapidated house, nonworkers, bank, and nonworkers seeking jobs were found to be relatively significant contributors to the vulnerability. The western part of the study area was classified as the highly vulnerable category (SVI>0.40628), attributed to high dependence, and higher share of unemployed workers and high poverty. The SVIent was shown to have positive correlation between unemployment, socioeconomic status, migration, dependency, and household structure significant at two-tailed test. The study's impact can be found in influencing the decision of policymakers and stakeholders in framing the mitigation strategies and policy documents.

The Spatiotemporal Characteristics of Extreme High Temperatures and Urban Vulnerability in Nanchong, China

It is necessary to alleviate the high temperatures and heat wave disasters in cities in southwest China that are beginning to occur because of global warming. During this study, the spatial and temporal characteristics of heat waves in Nanchong from 1961 to 2022 are analyzed by using the signal smooth method and mutation test. Based on the meteorological data and socioeconomic statistics, the entropy value method is used to obtain the indicator weights to construct a heat wave social vulnerability evaluation index system and conduct vulnerability assessments and classifications. The results show that: ① The heat wave indicators in Nanchong show an increasing trend, although there is a low period of heat waves from 1980 to 1995. Additionally, there are significant mutations in the number of days, frequency, and intensity of high-temperature heat waves from 2009 to 2011, which may be caused by the abnormal high-pressure belt in the mid-latitude. ② The distribution of exposure, sensitivity, and adaptability in Nanchong City, under high temperatures, is uneven in space. Generally, the indicators in the north are lower than those in the south. ③ The high-vulnerability counties are mainly distributed in the east and west of Nanchong, the proportion of the medium social vulnerability index areas are more than a half, while the dominant factor in the distribution pattern is natural factors. ④ The Western Pacific Subtropical High (WPSH) anomaly directly led to the extremely high temperature in Nanchong in the summer of 2022, and the urbanization process index shows a significant positive correlation with the trend of high temperatures and heat waves in Nanchong.

Machine learning-enabled regional multi-hazards risk assessment considering social vulnerability

The regional multi-hazards risk assessment poses difficulties due to data access challenges, and the potential interactions between multi-hazards and social vulnerability. For better natural hazards risk perception and preparedness, it is important to study the nature-hazards risk distribution in different areas, specifically a major priority in the areas of high hazards level and social vulnerability. We propose a multi-hazards risk assessment method which considers social vulnerability into the analyzing and utilize machine learning-enabled models to solve this issue. The proposed methodology integrates three aspects as follows: (1) characterization and mapping of multi-hazards (Flooding, Wildfires, and Seismic) using five machine learning methods including Naïve Bayes (NB), K-Nearest Neighbors (KNN), Logistic Regression (LR), Random Forest (RF), and K-Means (KM); (2) evaluation of social vulnerability with a composite index tailored for the case-study area and using machine learning models for classification; (3) risk-based quantification of spatial interaction mechanisms between multi-hazards and social vulnerability. The results indicate that RF model performs best in both hazard-related and social vulnerability datasets. The most cities at multi-hazards risk account for 34.12% of total studied cities (covering 20.80% land). Additionally, high multi-hazards level and socially vulnerable cities account for 15.88% (covering 4.92% land). This study generates a multi-hazards risk map which show a wide variety of spatial patterns and a corresponding understanding of where regional high hazards potential and vulnerable areas are. It emphasizes an urgent need to implement information-based prioritization when natural hazards coming, and effective policy measures for reducing natural-hazards risks in future.

Social Vulnerability Evaluation of Natural Disasters and Its Spatiotemporal Evolution in Zhejiang Province, China

Natural disasters present a significant challenge to the productivity of Zhejiang Province. This paper is the first to evaluate social vulnerability to natural disasters in Zhejiang Province and provides a scientific foundation for disaster prevention, mitigation, and risk management. In this paper, we construct an indicator system for evaluating social vulnerability of natural disasters in Zhejiang Province through demand analysis, frequency analysis, and applicability analysis. The methodology employed in this paper reduces errors arising from subjective indicator selection and provides a reference for future international research on evaluating social vulnerability to natural disasters. This study analyzes the spatiotemporal evolution of social vulnerability to natural disasters in 11 cities from 2011 to 2020. The results indicate an overall downward trend of social vulnerability to natural disasters in Zhejiang. Social vulnerability to natural disasters exhibits significant spatial variability. The evaluation can help to bridge the knowledge gap regarding the social vulnerability of Zhejiang Province to natural disasters. The analysis of the spatiotemporal evolution of social vulnerability provides insights into the contributing factors to vulnerability and the effectiveness of past disaster management strategies. The findings of this study can serve as a valuable reference for future research in Zhejiang Province and other regions facing similar challenges. The results can contribute to the advancement of comprehensive knowledge of social vulnerability to natural disasters, which can inform the development of policies and strategies aimed at mitigating disaster risk and promoting effective disaster management globally.

Evaluation of Social Vulnerability to Flood Hazard in Basilicata Region (Southern Italy)

Floods are the most common natural hazards causing damage to properties and loss of life worldwide. They are not preventable but vulnerability assessments, hazard mitigation, and effective emergency management plans can reduce their impacts and facilitate recovery actions. Floods can have different impacts depending on the local physical conditions and on the social context represented by the economic and cultural patterns of a specific community. Social vulnerability is the susceptibility of social groups to the adverse impacts of natural hazards, including disproportionate death, injury, loss, or disruption of livelihood. Therefore, the social vulnerability analysis becomes of primary importance in understanding the main factors influencing the capacity of a specific community to anticipate, cope with, and recover from a flood event. In this context, this paper investigates the correlation between flood hazard and socio-economic factors across the Basilicata Region (southern Italy). The aim of this research is to evaluate flood hazard and social vulnerability index through a Geographic Information System (GIS) approach. Multivariate factor analysis was applied in this work to construct an overall social vulnerability index which was combined with the flood hazard distribution. Our results underline the presence of 107,587 people with a high level of both flood hazard and social vulnerability.

A fine-grained evaluation of social vulnerability to COVID-19, healthcare access and built environmental disparities in Tokyo, Japan

ABSTRACT Measuring vulnerability to COVID-19 and healthcare accessibility at the fine-grained level serves as the foundation for spatially explicit health planning and policy making in response to future public health crises. However, the evaluation of vulnerability and healthcare accessibility is insufficient in Japan – a nation with high population density and super-aging challenges. Drawing on the 2022 census data, transport network, medical and digital cadastral data, land use maps, and points of interest data, our study extends the concept of vulnerability in the context of COVID-19 and constructs the first fine-grained measure of vulnerability and healthcare accessibility in Tokyo Metropolis, Japan – the most populated metropolitan region in the world. We delineate the vulnerable neighbourhoods with low healthcare access and further evaluate the disparity in healthcare access and built environment of areas at different levels of vulnerability. Our outcome datasets and findings provide nuanced and timely evidence to government and health authorities to have a holistic and latest understanding of social vulnerability to COVID-19 and healthcare access at a fine-grained level. Our analytical framework can be employed in different geographic contexts, guiding through place-based health planning and policy making in the post-COVID era and beyond.

Social vulnerability assessment of landslide disaster based on improved TOPSIS method: Case study of eleven small towns in China

• Constructing evaluation index system of social vulnerability of small towns in China. • Exploring the spatial differences of social vulnerability landslide disaster areas. • Risk perception and social structure are the two key factors of social vulnerability in landslide disaster areas. • It provides a case and method reference for structural and non-structural disaster prevention & mitigation.a. There are few studies on the social vulnerability of small towns in landslide disaster areas. The occurrence of geological disasters will directly threaten the local socioeconomic growth, it is particularly important to identify vulnerable areas and populations by using the social vulnerability index system. Eleven small towns of landslide areas after Wenchuan earthquake were taken as examples, based on 863 questionnaires and statistical data, this study established a new comprehensive evaluation index system of social vulnerability evaluation in landslide areas, and conducted quantitative evaluation by using the improved TOPSIS (Technique for Order Preference by Similarity to an Ideal Solution) model, the social vulnerability index was adopted to compare, classify and rank the social vulnerability levels in townships. The empirical results indicate that the social vulnerability showed the characteristics of regional heterogeneity, most towns showed a high or medium level of social vulnerability. Only Yingxiu showed a low level of social vulnerability, Shuimo presented the highest evaluation score of social vulnerability in eleven towns, the inter-group difference between these two towns is 0.286. Risk perception is an important factor affecting social vulnerability in landslide areas, involving disaster awareness, disaster cognitive approach, and community publicity. Additionally, social structure is another key factor affecting these areas based on the scores of the top three areas with the highest social vulnerability, including disaster prevention facilities, residents' income, and infrastructure accessibility. The index system can evaluate the social vulnerability scientifically in landslide areas, it can be used for urban disaster management and socioeconomic development in geological disaster areas.

Trends in social vulnerability to storm surges in Shenzhen, China

Abstract. An evaluation of social vulnerability to storm surges is important for any coastal city to provide marine disaster preparedness and mitigation procedures and to formulate post-disaster emergency plans for coastal communities. This study establishes an integrated evaluation system of social vulnerability by blending a variety of single-evaluation methods, which are subsequently combined by weighting in order to calculate a common social vulnerability index. Shenzhen has a current reputation of having considerable economic development potential and is a representative city in China. It is chosen for an evaluation of its social vulnerability to storm surges via a historical social and economic statistical dataset spanning the period 1986–2016. Exposure and sensitivity increased slowly with some fluctuation, leading to some alterations of the social vulnerability trend. Social vulnerability stayed almost constant during 1986–1991 and 1993–2004, while it decreased sharply afterwards to form a “stair-type” declining curve over the past 31 years. Resilience is progressively increasing by virtue of a continuous increase in medical services supply, fixed asset investments, and salary levels of employees. These determinants contribute to the overall downward trend of social vulnerability for Shenzhen.

A fuzzy social vulnerability evaluation from the perception of disaster bearers against meteorological disasters

Because of climatic hazards and extreme weather events, meteorological disasters attract more and more attention of government, national, and international agencies. Every event tests people’s ability to cope with meteorological disasters and generates the need for disaster risk research and assessments. Social vulnerability is an important measure of disaster risk assessments. Social vulnerability assessment problem can be viewed as a multi-criteria decision-making problem. In order to satisfy the perception of special disaster bearers, we need a local-context approach to construct a social vulnerability evaluation index system. The key to this approach is to identify the evaluation criteria structure by analyzing the complicated information gathering from special disaster bearers. It’s natural to use fuzzy language to express disaster bearers’ preferences in a complicated context. This paper attempts to describe the interrelationship between the evaluation factors with linguistic preferences since linguistic variables can better reflect the vagueness of human being. The fuzzy interpretive structural modeling (FISM) approach has been employed to develop the structural relationship between social vulnerability evaluation factors. In FISM, we apply some computational models of computing with words to quantify the fuzzy interrelationship. Finally, we give an example to show the process of our method.

Evaluation of social vulnerability to natural hazards: a case of Barton on Sea, England

In recent decades, Barton on Sea, which is a small coastal town in the south of England, has been exposed to rapid coastal, climatic and socioeconomic changes. The area is vulnerable to natural hazards such as landslides, coastal and cliff erosion and wave-induced erosion. However, there is a dearth of consistent data on the state of the environment, the level of social vulnerability and the socioeconomic status of the town’s residents. This research bridges the gap in knowledge by identifying the intensity of Barton on Sea’s social vulnerability in current scenarios. Accordingly, a Social Vulnerability Survey experiment (n = 72) was carried out in 2017. The survey results showed that natural hazards, particularly coastal and cliff erosion, have a negligible impact on people’s income. Home insurance can be acquired for the majority of coastal properties without much difficulty. This research also revealed that social vulnerability is not necessarily associated with social inequality in all environmental and geographical circumstances, and no substantial evidence was found that economic inequality increases vulnerability to natural hazards. The communities of Barton on Sea are happy even though they frequently experience the effects of natural hazards. This study provides vital data about the socioeconomic life of the Barton on Sea population that is invaluable for decision-makers’ recognition of coastal areas that require intervention, and that can guide policy measures to reduce the risk and intensity of natural hazards in order to protect the coastal frontage in the future.

Research on social vulnerability evaluation of urban fringe based on street scale—Taking Chang’an district of Xi’an as an example

Vulnerability theory is an important tool for analyzing the degree, mechanism and process of human-land interaction, and provides a new perspective for sustainability assessment research.The interference factor of this study is rapid urbanization, and the entry point is the street space scale.Taking the northwestern part of Chang’an District of Xi’an as an example, the social vulnerability assessment index suitable for the study area is selected, and the social vulnerability theoretical framework and the index evaluation method are combined. The RS/GIS spatial statistical analysis method analyzes the social vulnerability assessment and its influencing factors of the street units in the study area. The research results show that: (1) The spatial distribution of social vulnerability is unbalanced and the contradiction between human and land is prominent.The spatial unit is highly correlated with the system in which it is located.(2) Changes in land use patterns and landscape fragmentation are the direct causes of the vulnerability of social systems in urban fringe areas, while the social system transition and urbanization in urban fringe areas are dislocated to aggravate the negative impact of urbanization.

Socio-Environmental Vulnerability Mapping for Environmental and Flood Resilience Assessment: The Case of Ageing and Poverty in the City of Wrocław, Poland.

The phenomena of urbanization and climate change interact with the growing number of older people living in cities. One of the effects of climate change is an increased riverine flooding hazard, and when floods occur this has a severe impact on human lives and comes with vast economic losses. Flood resilience management procedures should be supported by a combination of complex social and environmental vulnerability assessments. Therefore, new methodologies and tools should be developed for this purpose. One way to achieve such inclusive procedures is by incorporating a social vulnerability evaluation methodology for environmental and flood resilience assessment. These are illustrated for application in the Polish city of Wrocław. Socio-environmental vulnerability mapping, based on spatial analyses using the poverty risk index, data on the ageing population, as well as the distribution of the areas vulnerable to floods, was conducted with use of a location intelligence system combining Geographic Information System (GIS) and Business Intelligence (BI) tools. The new methodology allows for the identification of areas populated by social groups that are particularly vulnerable to the negative effects of flooding. Integr Environ Assess Manag 2018;14:592-597. © 2018 SETAC.

Spatiotemporal Pattern of Social Vulnerability in Italy

Evaluation of social vulnerability (SV) against natural hazards remains a big challenge for disaster risk reduction. Spatiotemporal analysis of SV is important for successful implementation of prevision and prevention measures for risk mitigation. This study examined the spatiotemporal pattern of SV in Italy, and also analyzed socioeconomic factors that may influence how the Italian population reacts to catastrophic natural events. We identified 16 indicators that quantify SV and collected data for the census years 1991, 2001, and 2011. We created a social vulnerability index (SVI) for each year by using principal component analysis outputs and an additive method. Exploratory spatial data analysis, including global and local autocorrelations, was used to understand the spatial patterns of social vulnerability across the country. Specifically, univariate local Moran’s index was performed for the SVI of each of the three most recent census years in order to detect changes in spatial clustering during the whole study period. The original contribution of this Italy case study was to use a bivariate spatial correlation to describe the spatiotemporal correlation between the threes annual SV indices. The temporal analysis shows that the percentage of municipalities with medium social vulnerability in Italy increased from 1991 to 2011 and those with very high social vulnerability decreased. Spatial analysis provided evidence of clusters that maintained significant high values of social vulnerability throughout the study periods. The SVI of many areas in the center and the south of the peninsula remained stable, and the people living there have continued to be potentially vulnerable to natural hazards.

Models of Social Vulnerability to Disasters*

This paper discusses the bases of theory in the evaluation of social vulnerability to disasters. Vulnerability is shown to be the vital component of risk and the principal element of disaster impacts. Perception is a key process in decision making in disasters. It is affected by culture and symbolism, which are analysed in the context of disaster risk. A model of cultural metamorphosis is used to explain changes and discrepancies in attitudes to disaster and recovery processes. The response to the L'Aquila (central Italy) earthquake of 6 April 2009 is discussed as an illustration of processes of cultural metamorphosis and symbolic interpretation of disasters. The response was influenced by both modern and inherited cultural traits, which can be identified and analysed in order to explain public reactions to the event. A new model is proposed in which culture and history combine with physical hazards to influence vulnerability.