Decision Model for Predicting Social Vulnerability Using Artificial Intelligence
| Autor: | Francisco Sergio Campos-Sánchez, Rafael Reinoso-Bellido, Francisco Javier Abarca-Álvarez |
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| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
Artificial neural network
Geographic information system Social Vulnerability urban model Computer science Decision trees Geography Planning and Development 0211 other engineering and technologies Decision tree Context (language use) self-organizing maps Urban model 02 engineering and technology 010501 environmental sciences 01 natural sciences Social group Predictive models dwelling Decision model Earth and Planetary Sciences (miscellaneous) Computers in Earth Sciences Decision-making health care economics and organizations 0105 earth and related environmental sciences Self-Organizing Maps decision trees business.industry 021107 urban & regional planning Dwellings predictive models Data science Identification (information) decision model business Social vulnerability social vulnerability artificial neural network |
| Zdroj: | ISPRS International Journal of Geo-Information Volume 8 Issue 12 Digibug. Repositorio Institucional de la Universidad de Granada Consejo Superior de Investigaciones Científicas (CSIC) |
| Popis: | The APC was funded by their authors. 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. This research was funded by the University of Granada, grant number PP2016-PIP09 |
| Databáze: | OpenAIRE |
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