Demographic and Dwelling Models by Artificial Intelligence: Urban Renewal Opportunities in Spanish Coast

Demographic and Dwelling Models by Artificial Intelligence: Urban Renewal Opportunities in Spanish Coast

Francisco Javier Abarca-Alvarez Francisco Sergio Campos-Sanchez  Rafael Reinoso-Bellido 

Department of Urban and Spatial Planning, University of Granada, Spain

3 September 2018
| Citation



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


[1] Salizzoni, E., Protected Areas confronted by urbanization processes: challenges and operative perspectives. Protected Areas in Europe Challenging Regional and Global Change, eds. T. Hammer, I. Mose, D. Siegrist & N. Weixlbaumer, Oekom, Munich, pp. 47–58, 2016, available at

[2] Gómez-Ordóñez, J.L. & Martínez-Hidalgo, C., The growth of the Mediterranean port cities between 1850 and 1950. Conference Importance of Place-Conference Proceedings Sarajevo. Sarajevo, pp. 597–617, 2015.

[3] Salizzoni, E., Paesaggi Protetti. Laboratori di sperimentazione per il paesaggio costiero euro-mediterrano, Firenze University Press: Firenze, 2012.

[4] Leontidou, L. & Tourkomenis, K., El turismo residencial y la litoralización del Mediterráneo: la migración del norte a las costas meridionales de Europa. Turismo, urbanización y estilos de vida: las nuevas formas de movilidad residencial, eds. T. Mazón, R. Huete & A. Mantecón. Murcia, pp. 37–54, 2009.

[5] Abarca-Alvarez, F.J. & Campos-Sanchez, F.S., El paisaje desde el límite de lo urbano: una utopía necesaria y educadora. Urban NS, 5, pp. 63–78, 2013, available at https://

[6] Salizzoni, E., Conserving biological and cultural diversity along the Latin Arc: The role of Protected Areas. Biocultural diversity in Europe, eds. M. Agnoletti, F. Emanueli, Springer: Dordrecht, pp. 471–85, 2016.

[7] Pérez-Campaña, R., Abarca-Alvarez, F.J. & Talavera-Garcia, R., Centralities in the city border: a method to identify strategic urban-rural interventions. Ri-Vista, 2, pp. 38–53, 2016. Available at

[8] Abarca-Alvarez, F.J., Pérez-Campaña, R. & Talavera-Garcia, R., Identificación de patrones espaciales del borde urbano mediante mapas auto-organizados de la centralidad de la red viaria. Revista Urbano, 36, pp. 18–29, 2017.

[9] Ortega Pérez, N., España: Hacia una nueva política migratoria. Universidad de Granada: Granada, 2003.

[10] Dominguez Mujica, J. & Parreno Castellano J.M., Workers and retirees. The flexible condition of Northern and Western European migrants in Spanish tourist destinations. Boletín de la Asociación Geográficos Españoles, 64, pp. 419–421, 2014.

[11] Italos, C., Akylas, E. & Hadjimitsis, D.G., Effects of tourism and globalization on land cover and the influence on the quality of life of Paphos area in Cyprus. Proceedings of the SPIE, eds. D.G. Hadjimitsis, K. Themistocleous, S. Michaelides & G. Papadavid Papadavid(92290Q), p. 8 2014, available at proceeding.aspx?doi=10.1117/12.2069967

[12] Rapoport, A., Aspectos humanos de la forma urbana. Hacia una confrontación de las ciencias sociales con el diseño de la forma urbana. Gustavo Gili: Barcelona, 381 pp., 1978.

[13] Hines, J.D., Rural gentrification as permanent tourism: the creation of the ‘New’ West archipelago as postindustrial cultural space. Environment and Planning D: Society and Space, 28(3) pp. 509–525, 2010.

[14] Keen, P.G.W., Decision support systems: the next decade. Decision Support Systems, 3(3), pp. 253–265, 1987.

[15] Power, D.J., Sharda, R. & Burstein, F., Decision support systems. Wiley Encyclopedia of Management. Ed. C.L. Cooper, John Wiley & Sons: Chichester, UK, pp. 1–4, 2015.

[16] Silver, M.S., On the design features of decision support systems: The role of system restrictiveness and decisional guidance. Handbook on Decision Support Systems 2: Variations. eds. F.W. Burstein & C. Holsapple. Springer-Verlag: Berlin Heidelberg, pp. 261–91, 2008.

[17] Ritter, H. & Kohonen, T., Self-organizing semantic maps. Biological Cybernetics, 61(4), pp. 241–254, 1989.

[18] Kohonen, T., The self-organizing map. Neurocomputing, 21(1–3), pp. 1–6, 1998.

[19] Spielman, S.E. & Thill, J.C., Social area analysis, data mining, and GIS. Computers, Environment and Urban Systems, 32(2), pp. 110–122, 2008.

[20] Hamaina, R., Leduc, T. & Moreau, G., Towards urban fabrics characterization based on buildings footprints. Bridging the Geographic Information Sciences, ed. J. Gensel, pp. 231–248, 2012.

[21] Salah, M., Trinder, J. & Shaker, A., Evaluation of the self-organizing map classifier for building detection from lidar data and multispectral aerial images. Journal of Spatial Science, 54(2), pp. 15–34, 2009.

[22] Agarwal, P. & Skupin, A., Self-organising Maps: Applications in Geographic Information Science. Wiley, pp. 2008, 2015.

[23] Zhang, J., Shi, H. & Zhang, Y., Self-organizing map methodology and google maps services for geographical epidemiology mapping. DICTA 2009 - Digital Image Computing: Techniques and Applications, pp. 229–235, 2009.

[24] Weiss, S.M. & Indurkhya, N. Predictive Data Mining: A Practical Guide, Morgan Kaufmann, San Francisco, 1998.

[25] Faggiano, L., de Zwart, D., García-Berthou, E., Lek, S. & Gevrey, M. Patterning ecological risk of pesticide contamination at the river basin scale. Science of The Total Environment, 408(11), pp. 2319–2326, 2010.

[26] Wu, P.K. & Hsiao, T.C., Factor knowledge mining using the techniques of AI neural networks and self-organizing map. International Journal of Distributed Sensor Networks, 2015, pp. 1–19, 2015.

[27] Wasserstein, R.L. & Lazar, N.A., The ASA’s statement on p-values: context, process, and purpose. The American Statistician, 70(2), pp. 129–133, 2016.

[28] Coe, R. & Merino, C., Magnitud del efecto: Una guía para investigadores y usuarios. Revista de Psicología, 21(1), pp. 147–177, 2003.

[29] Cohen, J., Statistical Power Analysis for the Behavioral Sciences. Lawrence Erlbaum Associates, Publishers, 590 pp, 1998.

[30] Bação, F., Lobo, V. & Painho, M. The self-organizing map and it’s variants as tools for geodemographical data analysis: the case of Lisbon’s Metropolitan Area. Computers & Geosciences, 31, pp. 155–63, 1995.

[31] Bação, F., Lobo, V. & Painho, M., Self-organizing maps as substitutes for k-means clustering. Computer Science, 3516, pp. 476–483, 2005.

[32] Takatsuka, M., An application of the Self-Organizing Map and interactive 3-D visualization to geospatial data. Proceedings of the 6th International Conference on GeoComputation, Brisbane, pp. 24–26, 2001.

[33] Abarca-Alvarez, F.J. & Osuna-Pérez, F. Cartografías semánticas mediante redes neuronales: los mapas auto-organizados (SOM) como representación de patrones y campos. EGA Revista Expresión Gráfica Arquitectónica, 18(22), 154–163, 2013.

[34] Yan. J. & Thill, J.C., Visual data mining in spatial interaction analysis with self-organizing maps. Environment and Planning B: Planning and Design, 36(3), pp. 466–486, 2009.

[35] Kauko, T., Using the self-organising map to identify regularities across country-specific housing-market contexts. Environment and Planning B: Planning and Design, 32(1), pp. 89–110, 2005.

[36] Abarca-Alvarez, F.J., Campos-Sánchez, F.S. & Osuna-Pérez, F. Taxonomía de las inmigraciones turísticas de Andalucía basada en las cualidades de sus asentamientos urbanos. In: Congreso Migraciones Contemporáneas, Territorio y Urbanismo. Cartagena, pp. 301–315, 2015.

[37] Basara, H.G. & Yuan, M., Community health assessment using self-organizing maps and geographic information systems. International Journal of Health Geographics, 7, p. 67, 2008.

[38] Hatzichristos, T., Delineation of demographic regions with GIS and computational intelligence. Environment and Planning B: Planning and Design, 31(1), pp. 39–49, 2004.

[39] Kaski, S. & Kohonen, T., Exploratory data analysis by the self-organizing map: structures of welfare and poverty in the world. Neural Networks in Financial Engineering. Proceedings of the Third International Conference on Neural Networks in the Capital Markets, pp. 498–507, 1996. Available from: summary?doi=

[40] Streich, B. Stadtplanung in der Wissensgesellschaft Ein Handbuch. VS Verlag für Sozialwissenschaften, 2005.