Comparative Analysis of Social Vulnerability Indices: CDC’s SVI and SoVI®
(2017) VBRM15 20171Division of Risk Management and Societal Safety
- Abstract
- As interest in social vulnerability to hazards grows, more indices are formulated for identifying and mapping population groups that may experience differential consequences from natural hazards. However, less attention has been given to the underlying choices researchers make when creating these indices. With the aim to contribute to understanding the issues surrounding social vulnerability indices, this research will analyze and compare two popular methods for social vulnerability mapping: CDC SVI and SoVI®, using San Francisco, California, U.S.A. as a case study. To do so, this research focuses on the impact of each model’s unique components: the type of social vulnerability each model exhibits and the overall usability of each model.... (More)
- As interest in social vulnerability to hazards grows, more indices are formulated for identifying and mapping population groups that may experience differential consequences from natural hazards. However, less attention has been given to the underlying choices researchers make when creating these indices. With the aim to contribute to understanding the issues surrounding social vulnerability indices, this research will analyze and compare two popular methods for social vulnerability mapping: CDC SVI and SoVI®, using San Francisco, California, U.S.A. as a case study. To do so, this research focuses on the impact of each model’s unique components: the type of social vulnerability each model exhibits and the overall usability of each model. Using Pearson correlation analysis to assess the association of age dependency variables, the two models, different geographic scales and statistical choices, it is clear that index variable selection has the biggest impact on index results. Geographic units within San Francisco that have the largest difference between the two models, when classified, are analyzed to understand what underlying variables the models use to represent social vulnerability to create different results. Results show that CDC SVI better represents a socioeconomic related social vulnerability, while SoVI® focuses on old age related social vulnerability. Furthermore, a SWOC analysis is employed to understand which model works best for an organization internally vis a vis ease of use and time and cost and externally, regarding the type of social vulnerability they intend to reduce. Findings suggest that for internal use, CDC SVI is easier to use, but for external use, the organization should consider the variables that compose each index to understand what kind of social vulnerability they aim to reduce. (Less)
- Popular Abstract
- A Comparative Analysis of Two Social Vulnerability Indices
With the aim to contribute to understanding the issues surrounding social vulnerability indices, this research analyzes and compares two popular methods for social vulnerability mapping: CDC SVI and SoVI®, using San Francisco, California, U.S.A. as a case study. As interest in social vulnerability to natural hazards, like earthquakes and tsunamis grows, more indices are formulated for identifying and mapping population groups that may experience differential consequences from natural hazards. However, less attention has been given to the underlying choices researchers make when creating these indices.
This research focuses on social vulnerability assessments, a means to... (More) - A Comparative Analysis of Two Social Vulnerability Indices
With the aim to contribute to understanding the issues surrounding social vulnerability indices, this research analyzes and compares two popular methods for social vulnerability mapping: CDC SVI and SoVI®, using San Francisco, California, U.S.A. as a case study. As interest in social vulnerability to natural hazards, like earthquakes and tsunamis grows, more indices are formulated for identifying and mapping population groups that may experience differential consequences from natural hazards. However, less attention has been given to the underlying choices researchers make when creating these indices.
This research focuses on social vulnerability assessments, a means to quantify and visually represent social vulnerability, providing specific evidence to direct resources for reducing the effect that hazards have on society. These two indices use population survey (U.S. Census) data for geographic units (average population of 4,000), transforming the data to a number representative of the degree of social vulnerability. Variables, like “% of people 65 and over” or “% of people without a vehicle”, compile the two indices’ data to represent different aspects of social vulnerability. Social vulnerability models can be helpful to governments and organizations working to reduce the effects of hazards, but if there are many indices that exhibit varying results, which should be used? This research addresses the problem of what index to use by looking at quantitative and qualitative aspects of each model.
To analyze and compare the models, this research focuses on the impact of each model’s unique components: the type of social vulnerability each model exhibits and the overall usability of each model. By using Pearson correlation analysis to assess the association of (1) age variables, (2) the two indices in their basic form, (3) different geographic scales the models are created at and (4) statistical choices transforming the data, it is clear that index variable selection has the biggest impact on overall index results. Geographic units with the largest difference between the two models, when classified, are analyzed to understand what underlying variables the models use to represent social vulnerability to create different results. Results show that CDC SVI better represents a socioeconomic related social vulnerability, while SoVI® focuses on old-age related social vulnerability. Finally, a SWOC analysis is employed to understand which model works best for an organization internally vis a vis ease of use and time and cost and externally, regarding the type of social vulnerability they intend to reduce. Findings suggest that for internal use, CDC SVI is easier to use, but for external use, the organization should consider the variables that compose each index to understand what kind of social vulnerability they aim to reduce.
This work was completed in partnership with the San Francisco Department of Emergency Management in California, USA. The organization has requested information on socially vulnerable neighbourhoods to understand where the greatest need is for resources and where communities with limited capability to prepare for, respond to, and recover from a disaster are located. They do have, and continue to develop, outreach mechanisms to communities in need through trusted community based organizations and other government organizations (ibid). Social vulnerability maps can be used before a disaster to build relationships with and strengthen capacity of community-based organizations and individuals, so populations can better interact with the formal emergency management system, limiting disaster related consequences.
Furthermore, social vulnerability mapping will create a basis for SFDEM to lobby for grants and funding specific to community needs. SFDEM is within a large emergency management system, including state and national systems, so being able to advocate is important. As their mandate suggests, SFDEM coordinates and communicates with other organizations. It is through all of these activities that social vulnerability mapping can be used to dictate funds, services and coordinate with organizations in the most in-need communities.
The main takeaway from this research is the importance of studying variables composing a social vulnerability index. It is important to find out why certain areas are considered, or not considered, socially vulnerable. Without examining variables, governments and organizations assume that projects to reduce risk will have the same effectiveness across the socioeconomic spectrum. Users of social vulnerability indices should analyze chosen variables to understand what kind of social vulnerability may exist in the context, and to understand if the variables are appropriate for the specific kind of risk reduction initiative.
Abbreviations
CDC SVI (Center for Disease Control Social Vulnerability Index): Created by the United States Center for Disease Control, Agency for Toxic Substances and Disease Registry (ATSDR) to save lives and identify populations that need more resources to improve the effectiveness of disaster preparedness, mitigation, response and recovery
SoVI® (Social Vulnerability Index): The purpose of SoVI® is to quantify social vulnerability to environmental hazards in the U.S. When mapped, the results show where there is uneven capacity for disaster risk reduction, and pinpoints areas where policy and resources for disaster risk management would be most useful (Hazards & Vulnerability Research Institute, 2013). This well-used method has evolved over time to account for new findings in research (ibid). The most recent 2017 model, by Cutter and Emrich, is used for the purpose of this study. (Less)
Please use this url to cite or link to this publication:
http://lup.lub.lu.se/student-papers/record/8928519
- author
- Tarling, Hannah Andrea LU
- supervisor
-
- Per Becker LU
- organization
- alternative title
- A Comparative Analysis of Two Social Vulnerability Indices
- course
- VBRM15 20171
- year
- 2017
- type
- H2 - Master's Degree (Two Years)
- subject
- keywords
- social vulnerability index, Social vulnerability, indices, SoVI®, CDC SVI, social vulnerability in San Francisco, California, comparative analysis
- language
- English
- id
- 8928519
- date added to LUP
- 2017-11-21 09:19:10
- date last changed
- 2017-12-11 07:02:57
@misc{8928519,
abstract = {{As interest in social vulnerability to hazards grows, more indices are formulated for identifying and mapping population groups that may experience differential consequences from natural hazards. However, less attention has been given to the underlying choices researchers make when creating these indices. With the aim to contribute to understanding the issues surrounding social vulnerability indices, this research will analyze and compare two popular methods for social vulnerability mapping: CDC SVI and SoVI®, using San Francisco, California, U.S.A. as a case study. To do so, this research focuses on the impact of each model’s unique components: the type of social vulnerability each model exhibits and the overall usability of each model. Using Pearson correlation analysis to assess the association of age dependency variables, the two models, different geographic scales and statistical choices, it is clear that index variable selection has the biggest impact on index results. Geographic units within San Francisco that have the largest difference between the two models, when classified, are analyzed to understand what underlying variables the models use to represent social vulnerability to create different results. Results show that CDC SVI better represents a socioeconomic related social vulnerability, while SoVI® focuses on old age related social vulnerability. Furthermore, a SWOC analysis is employed to understand which model works best for an organization internally vis a vis ease of use and time and cost and externally, regarding the type of social vulnerability they intend to reduce. Findings suggest that for internal use, CDC SVI is easier to use, but for external use, the organization should consider the variables that compose each index to understand what kind of social vulnerability they aim to reduce.}},
author = {{Tarling, Hannah Andrea}},
language = {{eng}},
note = {{Student Paper}},
title = {{Comparative Analysis of Social Vulnerability Indices: CDC’s SVI and SoVI®}},
year = {{2017}},
}