Next Article in Journal
Development of a CityGML Application Domain Extension for Simulating the Building Construction Process
Next Article in Special Issue
A Multi-Mode PDR Perception and Positioning System Assisted by Map Matching and Particle Filtering
Previous Article in Journal
Clustering Complex Trajectories Based on Topologic Similarity and Spatial Proximity: A Case Study of the Mesoscale Ocean Eddies in the South China Sea
Article

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://0-doi-org.brum.beds.ac.uk/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
Show Figures

Graphical abstract

MDPI and ACS Style

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. https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi8120575

AMA Style

Abarca-Alvarez FJ, Reinoso-Bellido R, Campos-Sánchez FS. Decision Model for Predicting Social Vulnerability Using Artificial Intelligence. ISPRS International Journal of Geo-Information. 2019; 8(12):575. https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi8120575

Chicago/Turabian Style

Abarca-Alvarez, Francisco J., Rafael Reinoso-Bellido, and Francisco S. Campos-Sánchez 2019. "Decision Model for Predicting Social Vulnerability Using Artificial Intelligence" ISPRS International Journal of Geo-Information 8, no. 12: 575. https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi8120575

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

Article Access Map by Country/Region

1
Back to TopTop