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.
Highlights
A flood is that event that, due to the precipitation, swell, tide or failure of some hydraulic structure, causes an increase in the level of the free surface of the water of the rivers or of the sea, generating invasion or penetration of water in places where there is usually none and, generally, damage to the population, agriculture, livestock and infrastructure [1]
According to the World Health Organization (WHO) [6], floods can potentially increase the transmission of waterborne diseases such as typhoid fever, cholera, leptospirosis, and hepatitis A; vector-borne diseases such as malaria, dengue and dengue hemorrhagic fever, yellow fever and West Nile fever, not counting the effects on mental health [7]
We evaluated exposure, susceptibility, and capacity, adapting the framework proposed by [22] and we used multiple correspondence analysis (MCA) to identify and classify the most important variables regarding vulnerability in order to calculate a particular indicator for each case study
Summary
A flood is that event that, due to the precipitation, swell, tide or failure of some hydraulic structure, causes an increase in the level of the free surface of the water of the rivers or of the sea, generating invasion or penetration of water in places where there is usually none and, generally, damage to the population, agriculture, livestock and infrastructure [1]. We evaluated exposure, susceptibility, and capacity, adapting the framework proposed by [22] and we used MCA to identify and classify the most important variables regarding vulnerability in order to calculate a particular indicator for each case study. Two urban areas were evaluated, both presenting slow-onset flooding: The La Victoria and San Miguel neighborhoods (Figure 6) The communities from these two neighborhoods are socioSeucsotaninoambiliictyal2l0y19,v1u1l,n43e4r1able populations, living in households with unsatisfied basic needs (U7BoNf 1), displaced by violence, and with high rates of unemployment or work in informal activities. The variables representing the three components of vulnerability [22]—(i) exposure to a hazard event (E), (ii) susceptibility (S), and (iii) resilience (R)—were selected from the literature [14,70,71,72,73,74], refined, and qualified in workshops with 13 experts in the fields of disaster risk reduction, geology, civil engineering, environmental engineering, and social sciences. A distribution by quartiles was applied to the IV to classify each household by order of prioritization, with green colors (Group 1) third quartile, orange (Group 2) the mean and median, and red (Group 3) first quartile, to produce vulnerability classification maps by households that, according to the homogeneity of the results, was generalized to block scale
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