scholarly journals Assessing the effectiveness of a social vulnerability index in predicting heterogeneity in the impacts of natural hazards: Case study of the Tropical Storm Washi flood in the Philippines

2016 ◽  
Vol 1 ◽  
pp. 91-129
Author(s):  
J. Andres F. Ignacio ◽  
Grace T. Cruz ◽  
Fernando Nardi ◽  
Sabine Henry
Keyword(s):  
Natural Hazards ◽  
Social Vulnerability ◽  
Vulnerability Index ◽  
The Philippines ◽  
Tropical Storm ◽  
Social Vulnerability Index

scholarly journals ASSESSMENT OF SOCIAL VULNERABILITY TO THE IMPACT OF FLOOD HAZARD: A CASE STUDY OF KOPILI RIVER BASIN, ASSAM, INDIA

2018 ◽  
Vol XLII-5 ◽  
pp. 455-460
Author(s):  
S. V. Shiva Prasad Sharma ◽  
P. S. Roy ◽  
V. Chakravarthi
Keyword(s):  
Social Vulnerability ◽  
Flood Hazard ◽  
Vulnerability Index ◽  
Social Vulnerability Index ◽  
The Social ◽  
Vulnerability Indicators ◽  
Multi Temporal ◽  
The Impact ◽  
Vulnerable Zones

<p><strong>Abstract.</strong> In the present study, an attempt is made to understand the impact on Social Vulnerability of the Kopili basin due to various severities of flood hazard. The flood hazard is generated using multi-temporal historical satellite based analysis and integration of annual flood inundation layers. The census of India data of 2001 and 2011 is spatially joined with village database to study the impact at village level. Using 5 Census variables from both Census 2001 &amp;amp; 2011 as vulnerability indicators, the Social Vulnerability Index (SVI) is derived and classified into various vulnerable zones namely Low, Moderate and High Vulnerable zones. The findings of the study show that the number of villages falling in Low and High Vulnerable zones had decreased during Census 2011 when compared to 2001 and a rise of 6% in villages falling in moderate vulnerable zones during 2011 is observed. The spatial database generated is useful to understand the impact of floods on the Social Vulnerability status of the basin and can be a useful input to further study the Physical, Economic and Environmental Vulnerabilities of the basin.</p>

Social Vulnerability Index: Barbados – A Case Study

2020 ◽  
Vol 48 (5) ◽  
pp. 505-526
Author(s):  
Janice Cumberbatch ◽  
Crystal Drakes ◽  
Tara Mackey ◽  
Mohammad Nagdee ◽  
Jehroum Wood ◽  
...  
Keyword(s):  
Social Vulnerability ◽  
Vulnerability Index ◽  
Social Vulnerability Index

scholarly journals QUANTITATIVE ASSESSMENT OF SOCIAL VULNERABILITY FOR LANDSLIDE DISASTER RISK REDUCTION USING GIS APPROACH (CASE STUDY: CILACAP REGENCY, PROVINCE OF CENTRAL JAVA, INDONESIA)

2018 ◽  
Vol XLII-4 ◽  
pp. 703-709
Author(s):  
A. P. Wijaya ◽  
J.-H. Hong
Keyword(s):  
Social Vulnerability ◽  
Vulnerability Index ◽  
Spatial Diversity ◽  
Disaster Risk ◽  
Social Vulnerability Index ◽  
Internal Factors ◽  
Central Java ◽  
National Disaster ◽  
Quantitative Assessments

<p><strong>Abstract.</strong> Social vulnerability is an important aspect in determining the level of disaster risk in a region. Social vulnerability index (SoVI) is influenced by several supporting factors, such as age, gender, health, education, etc. When different sets of parameters are considered, the SoVI analyzed results are likely to be also different from one to another. In this paper, we will discuss the quantitative assessments of SoVI based on two different models. The first model, proposed by Frigerio et al. (2016), is used to analyze the spatial diversity of social vulnerability due to seismic hazards in Italy. The second model is based on the regulations of the head of the National Disaster Management Agency (BNPB) No. 2 of 2012. GIS is used to present and compare the results of the two selected models. In additive impact factor on the SoVI is also done. The result is that there are regions that belong to the same class on both models such as Pemalang, there are regions that enter in different classes on both models such as Cilacap. The result also shows the model of Frigerio et al. (2016) is more representative than the BNPB model (2012) by additionally considering the education and unemployment factors in determining the SoVI, while the BNPB model (2012) only includes internal factors such as age, gender. By considering education and unemployment factors, we get more detailed conditions about society from social vulnerability.</p>

scholarly journals Assessment of social vulnerability to natural hazards in Nepal

2017 ◽  
Vol 17 (12) ◽  
pp. 2313-2320 ◽  
Author(s):  
Dipendra Gautam
Keyword(s):  
Natural Hazards ◽  
Social Vulnerability ◽  
Public Awareness ◽  
Vulnerability Index ◽  
District Level ◽  
Vulnerability Mapping ◽  
Contingency Planning ◽  
Social Vulnerability Index ◽  
Preparedness Planning ◽  
The Social

Abstract. This paper investigates district-wide social vulnerability to natural hazards in Nepal. Disasters such as earthquakes, floods, landslides, epidemics, and droughts are common in Nepal. Every year thousands of people are killed and huge economic and environmental losses occur in Nepal due to various natural hazards. Although natural hazards are well recognized, quantitative and qualitative social vulnerability mapping has not existed until now in Nepal. This study aims to quantify the social vulnerability on a local scale, considering all 75 districts using the available census. To perform district-level vulnerability mapping, 13 variables were selected and aggregated indexes were plotted in an ArcGIS environment. The sum of results shows that only 4 districts in Nepal have a very low social vulnerability index whereas 46 districts (61 %) are at moderate to high social vulnerability levels. Vulnerability mapping highlights the immediate need for decentralized frameworks to tackle natural hazards in district level; additionally, the results of this study can contribute to preparedness, planning and resource management, inter-district coordination, contingency planning, and public awareness efforts.

scholarly journals Social Vulnerability across the Great Lakes Basin: A County-Level Comparative and Spatial Analysis

2021 ◽  
Vol 13 (13) ◽  
pp. 7274
Author(s):  
Joshua T. Fergen ◽  
Ryan D. Bergstrom
Keyword(s):  
Great Lakes ◽  
Spatial Patterns ◽  
Social Vulnerability ◽  
Vulnerability Index ◽  
Social Vulnerability Index ◽  
Great Lakes Basin ◽  
Empirical Examination ◽  
Regional Patterns ◽  
Urban Rural ◽  
Social Vulnerabilities

Social vulnerability refers to how social positions affect the ability to access resources during a disaster or disturbance, but there is limited empirical examination of its spatial patterns in the Great Lakes Basin (GLB) region of North America. In this study, we map four themes of social vulnerability for the GLB by using the Center for Disease Control’s Social Vulnerability Index (CDC SVI) for every county in the basin and compare mean scores for each sub-basin to assess inter-basin differences. Additionally, we map LISA results to identify clusters of high and low social vulnerability along with the outliers across the region. Results show the spatial patterns depend on the social vulnerability theme selected, with some overlapping clusters of high vulnerability existing in Northern and Central Michigan, and clusters of low vulnerability in Eastern Wisconsin along with outliers across the basins. Differences in these patterns also indicate the existence of an urban–rural dimension to the variance in social vulnerabilities measured in this study. Understanding regional patterns of social vulnerability help identify the most vulnerable people, and this paper presents a framework for policymakers and researchers to address the unique social vulnerabilities across heterogeneous regions.

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