Browse by author
Lookup NU author(s): Dr Diana Maria Contreras Mojica
Full text for this publication is not currently held within this repository. Alternative links are provided below where available.
Cities are systems composed of infrastructure and densely built spaces inhabited by population. The provision of sustainable urban infrastructure is a challenge for urban planners, decision-makers and engineers due to the rise in urban population. Moreover, the performance of the systems that compose the city is stressed by the occurrence of natural hazards. Infrastructure is composed of spatially distributed and interdependent networks connected by multiple physical links. These networks are at risk of interruptions in case of a natural hazard, which subsequently impacts the economy and social conditions of the affected area. Currently, there are several methods for the estimation of social vulnerability, but they do not integrate interdependencies with the critical infrastructure. The infrastructure of a city is not only compounded by physical structures, but also by the population that they serve and the interactions between them. The nature of this interaction can increase or decrease the vulnerability of the system. Nevertheless, current social vulnerability indexes do not include the dependence of population from the critical infrastructure. The aim of our research is to identify the spatial variables, indicators and indexes to characterize the social vulnerability to natural hazards and critical infrastructure dependence in urban environments. We use variables and indicators such as population density, living space per person, spatial autocorrelation, spatial association, spatial clustering, coverage, distance, the degree of resilience represented by redundancy (alternative sources of services), preparedness (training) and resilience of population. Based on these spatial variables and indicators, we will develop a spatial model that integrates socio-economic vulnerability and dependency. Initially, we will test our methodology in one critical facility using Geographically Weighted Regression (GWR). These spatial variables and indicators will allow us to develop a spatial index of interdependency to identify bottom-up resilience actions oriented to reduce the socio-economic vulnerability generated by this interdependency.
Author(s): Contreras D, Aguirre P, Molinos M, Chamorro A
Editor(s): Sandholz, S; Fekete, Alexander
Publication type: Conference Proceedings (inc. Abstract)
Publication status: Submitted
Conference Name: Deutscher Kongress für Geographie
Year of Conference: 2019
Series Title: Urban-Rural Infrastructure Interdependencies - Flows of people, services and disaster risk