Next Article in Journal
Testing Analytical Frameworks in Transdisciplinary Research for Sustainable Development
Previous Article in Journal
Sustainability of Low Carbon City Initiatives in China: A Comprehensive Literature Review
Previous Article in Special Issue
Spatial Accessibility to Hospitals Based on Web Mapping API: An Empirical Study in Kaifeng, China
Open AccessArticle

Computational Bottom-Up Vulnerability Indicator for Low-Income Flood-Prone Urban Areas

1
Faculty of Engineering and Architecture, Institución Universitaria Colegio Mayor de Antioquia, Medellín 050034, Colombia
2
Department of Geosciences and Environment, Faculty of Mines, Universidad Nacional de Colombia, Medellín 050034, Colombia
3
Disasters Risk Management Program, Faculty of Engineering and Architecture, Institución Universitaria Colegio Mayor de Antioquia, Medellín 050034, Colombia
*
Author to whom correspondence should be addressed.
Sustainability 2019, 11(16), 4341; https://doi.org/10.3390/su11164341
Received: 31 May 2019 / Revised: 5 August 2019 / Accepted: 6 August 2019 / Published: 11 August 2019
  |  
PDF [23734 KB, uploaded 13 August 2019]
  |  

Abstract

This study presents the implementation of a methodology for the formulation of a vulnerability indicator for low-income urban territories in flood-prone areas, for two flood types: Sudden and slow. The methodology developed a computational assessment tool based on the Multiple Correspondence Analysis and the framework for vulnerability analysis in sustainable science. This approach uses participatory mapping and on-site data. The data collection was easily implemented with free software tools to facilitate its use in low-income urban territories. The method combines the evaluation of experts using the of the traditional approach for the qualification of the variables of vulnerability in its three components (exposure, susceptibility, and resilience), and incorporates a computational method of the correspondence analysis family to formulate the indicators of vulnerability. The results showed that the multiple correspondence analysis is useful for the identification of the most representative variables in the vulnerability assessment, used for the construction of spatial disaggregated vulnerability indicators and therefore the development of vulnerability maps that will help in the short term in disaster risk management, urban planning, and infrastructure protection. In addition, the variables of the susceptibility component are the most representative regardless of the type of flooding, followed by the variables of the exposure component, for sudden flood-prone territories, and the resilience component for slow flood-prone territories. Our findings and the computational tool can facilitate the prioritization of improvement projects and flood risk management on a household, neighborhood, and municipal level. View Full-Text
Keywords: multiple correspondence analysis; indicator; vulnerability; disaster risk reduction; data-driven methods to monitor and assess progress towards sustainable development goals multiple correspondence analysis; indicator; vulnerability; disaster risk reduction; data-driven methods to monitor and assess progress towards sustainable development goals
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
SciFeed

Share & Cite This Article

MDPI and ACS Style

Rodríguez-Gaviria, E.M.; Ochoa-Osorio, S.; Builes-Jaramillo, A.; Botero-Fernández, V. Computational Bottom-Up Vulnerability Indicator for Low-Income Flood-Prone Urban Areas. Sustainability 2019, 11, 4341.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Sustainability EISSN 2071-1050 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top