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.

Since the middle of the last century post-industrial cities around the world have been losing population and shrinking due to the decline of their structural growth models, showing important socioeconomic transformations. This is a negative phenomenon but one that cities can benefit from. The aim of this work is to verify what type of measures against urban decline would be most suitable if applied to a specific case study. To do this, international cases of shrinking cities where successful measures were already carried out facing decline: (i) are collected, (ii) are classified based on several influencing criteria, and (iii) are grouped under similar alternatives against the decline. Measures and criteria focused on achieving sustainability are emphasized. Alternatives are then prioritised using an Analytic Hierarchy Process designed at several hierarchical levels. The results are discussed based on the construction of sustainable future scenarios according to the optimal alternatives regarding the case study, improving the model validity. The work evidences that environmental and low-cost measures encouraging the economy and increasing the quality of life, regardless of the city size-population range where they were performed, may be the most replicable. Future research lines on the integration of the method together with other decision-support systems and techniques are provided.

Resumenos Sistemas de Información Geográfica (SIG) han sido ampliamente utilizados para el almacenamiento y gestión de la información territorial, mostrándose especialmente útiles para el análisis y para la verificación de hipótesis previamente formuladas y con componentes espaciales relevantes. Existen metodologías heurísticas que en contextos como los actuales, de sobre-abundancia de datos, permiten evidenciar sus coherencias, sin requerir necesariamente hipótesis o formulaciones previas para generar conocimiento. Se propone el uso combinado de (i) técnicas procedentes de la Inteligencia Artificial, como son las Redes Neuronales Artificiales (ANN) del tipo Mapa Auto-organizado (SOM), que han demostrado ser muy eficaces y robustas clasificando y caracterizando perfiles en los datos; integradas con (ii) técnicas de Machine Learning como son los árboles de decisión, singularmente funcionales en la creación de modelos predictivos e interpretables para formular hipótesis explicativas de los perfiles anteriores a partir de otras variables diferenciadas. La investigación plantea combinar SIG, SOM y árboles de decisión para la construcción de modelos explicativos de los perfiles demográficos y sociales de Andalucía, a partir de datos de bajo coste sobre la dimensión residencial. Se verifica la viabilidad de tales modelos predictivos y su alto valor para la comprensión y para la toma de decisiones sobre tales territorios.Palabras clave: árbol de decisión SIG, DSS, mapa auto-organizado. Abstract:Geographic Information Systems (GIS) have been widely used for the storage and management of territorial information, being especially useful for the analysis and verification of previously formulated hypotheses and coexisting with relevant spatial components. There are heuristic methodologies that, in contexts such as the present one, of data over-abundance, allow showing their coherence, not necessarily requiring hypotheses or previous formulations to generate knowledge. The combined use of (i) Artificial Intelligence techniques such as the Artificial Neural Network (ANN), namely the Self-Organized Maps (SOM), is proposed. They are very effective and robust by classifying and characterizing profiles in the data. They interact with (ii) machine learning techniques such as decision trees, which are singularly functional in the creation of predictive and interpretable models, with the intention of formulating explanatory hypotheses of the previous profiles, working with other different variables. The research proposes the combination of GIS, SOM and decision trees for the construction of explanatory models of the demographic and social profiles of Andalusia, based on low cost data on the residential dimension. The feasibility of such predictive models and their great value for understanding and as decision support on such territories are evaluated satisfactorily.

No abstract

La importancia del espacio de habitación trasciende cuestiones básicas de tipo funcional o estético para influir, además, en el comportamiento y en la actitud de las personas que lo habitan, así como en su carácter o en su ánimo. Consiste en un valor o suerte de capital fuertemente ligado a otros capitales, como el fijo y el social, entre otros. Se discute de forma crítica a través de los enfoques de diversos expertos en la materia, mediante varios pasos: significado, composición, materialización, escala, uso, potencial, rentabilidad y legitimación. Puede asumir funciones urbanas de integración, conexión cualificada y acumulación de rentas multinivel, lo que lo convierte en un factor clave de resiliencia y sostenibilidad urbana, útil a la planificación y a la agenda política.

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 residential 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.

La gentrificación no siempre es detectada por la sociedad, la política y la planificación a tiempo de interpretar sus dinámicas y de llevar a cabo intervenciones que mitiguen sus efectos adversos. Sus implicaciones son tan importantes en la fisionomía social de las ciudades, que será relevante toda herramienta que permita pronosticar o evidenciar cualquier tipo de señal de la gentrificación. La investigación trata de evaluar la viabilidad de la detección de ámbitos vinculados a procesos de gentrificación, incipientes o asentados, mediante el uso de fuentes de información comunes en las ciudades, como son los censos de viviendas. Para ello se propone el uso de metodologías de extracción de información basadas en técnicas de minería de datos procedentes de las ciencias de la Inteligencia Artificial. La metodología se evalúa experimentalmente en un territorio complejo y extenso, la costa mediterránea peninsular española. Los resultados permiten identificar un perfil urbano que incluye todas las barriadas a las que el estado del arte atribuye gentrificación, resultando la proporción de viviendas en alquiler determinante. Se concluye que la metodología propuesta es útil para evidenciar territorios con señales similares a los entornos urbanos con gentrificación, permitiendo la detección temprana de procesos semejantes en otros ámbitos.