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

Edna M. Rodríguez-Gaviria, Sol Ochoa-Osorio, Alejandro Builes-Jaramillo and Verónica Botero-Fernández
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Edna M. Rodríguez-Gaviria: Faculty of Engineering and Architecture, Institución Universitaria Colegio Mayor de Antioquia, Medellín 050034, Colombia
Sol Ochoa-Osorio: Disasters Risk Management Program, Faculty of Engineering and Architecture, Institución Universitaria Colegio Mayor de Antioquia, Medellín 050034, Colombia
Alejandro Builes-Jaramillo: Faculty of Engineering and Architecture, Institución Universitaria Colegio Mayor de Antioquia, Medellín 050034, Colombia
Verónica Botero-Fernández: Department of Geosciences and Environment, Faculty of Mines, Universidad Nacional de Colombia, Medellín 050034, Colombia

Sustainability, 2019, vol. 11, issue 16, 1-19

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.

Keywords: multiple correspondence analysis; indicator; vulnerability; disaster risk reduction; data-driven methods to monitor and assess progress towards sustainable development goals (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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