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Decision Model for Predicting Social Vulnerability Using Artificial Intelligence

1
Department of Urban and Spatial Planning, University of Granada, 18071 Granada, Spain
2
Higher Technical School of Architecture, University of Granada, 18071 Granada, Spain
*
Author to whom correspondence should be addressed.
ISPRS Int. J. Geo-Inf. 2019, 8(12), 575; https://doi.org/10.3390/ijgi8120575
Received: 17 September 2019 / Revised: 30 November 2019 / Accepted: 9 December 2019 / Published: 11 December 2019
(This article belongs to the Special Issue Human Dynamics Research in the Age of Smart and Intelligent Systems)
Social vulnerability, from a socio-environmental point of view, focuses on the identification of disadvantaged or vulnerable groups and the conditions and dynamics of the environments in which they live. To understand this issue, it is important to identify the factors that explain the difficulty of facing situations with a social disadvantage. Due to its complexity and multidimensionality, it is not always easy to point out the social groups and urban areas affected. This research aimed to assess the connection between certain dimensions of social vulnerability and its urban and dwelling context as a fundamental framework in which it occurs using a decision model useful for the planning of social and urban actions. For this purpose, a holistic approximation was carried out on the census and demographic data commonly used in this type of study, proposing the construction of (i) a knowledge model based on Artificial Neural Networks (Self-Organizing Map), with which a demographic profile is identified and characterized whose indicators point to a presence of social vulnerability, and (ii) a predictive model of such a profile based on rules from dwelling variables constructed by conditional inference trees. These models, in combination with Geographic Information Systems, make a decision model feasible for the prediction of social vulnerability based on housing information. View Full-Text
Keywords: social vulnerability; predictive models; urban model; dwelling; decision model; artificial neural network; self-organizing maps; decision trees social vulnerability; predictive models; urban model; dwelling; decision model; artificial neural network; self-organizing maps; decision trees
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Abarca-Alvarez, F.J.; Reinoso-Bellido, R.; Campos-Sánchez, F.S. Decision Model for Predicting Social Vulnerability Using Artificial Intelligence. ISPRS Int. J. Geo-Inf. 2019, 8, 575.

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