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The Spanish Mediterranean coast has undergone intense urban development in recent decades. It has often focused on building a property patrimony based more on real estate, business expectations and consuming resources than on its actual use. Similarly, its functionality and need to adapt to social needs and the requirements of the certain demographic profiles of its time have largely been ignored. the purpose of this study is to shed light on the Spanish Mediterranean coast’s existing residential models and the relationship with the local demographic reality of users. Its aim is to be part of a Decision Support System which focuses on urban regeneration and functional recovery. this study uses heuristic methodologies to demonstrate the coherence of an abundance of open access data. Such methodologies do not necessarily require specific hypotheses or formulations to generate useful knowledge. The 2011 Population and Housing Census (INE) is used as a knowledge source, on which data mining techniques based on Artificial Intelligence techniques are applied. We specifically use Self-Organising Maps (SOM) through Artificial Neural Networks (ANN), subsequently mapping the results through a Geographic Information System (GIS). these techniques permit an exploration of the different residen- tial profiles in this territory. Each profile exposes very different levels of sustainability and resilience, identifying the groups or social collectives that singularly inhabit them, which are at times authentic drivers of the maintenance and growth of these models. to the extent that they are linked to demographic profiles, the knowledge obtained in this study is evidence of the different residential profiles’ territorial location, and highlights the opportunities and weaknesses of urban regeneration.
Artificial Neural Network (ANN), Coastal Areas, Decision Support System (DSS), Dwelling, Geographic Information System (GIS), Self-Organising Maps (SOM), Urban Renewal
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