Decision-Making Tool for the Selection of Priority Areas for Building Rehabilitation in Barcelona

Abstract

The promotion of rehabilitation is an urgent necessity in today’s consolidated cities, both due to the need to update their buildings to achieve habitability and safety standards that are required nowadays, as well as to stop the deterioration of buildings in vulnerable environments, where paradoxically the obtainment of economic resources to invest in building maintenance and upgrade is scarcer. Decision making on the delimitation of areas in which the need to invest is higher is extremely complex and often relies on large secondary data studies that are contrasted with local stakeholders’ intuition and knowledge on the ground. Usually, rehabilitation aids are directed to relatively large areas, where a certain need may be found. However, these areas are often excessively wide and specific needs that would require special focus can be diluted in the whole. The current trend of area-based and site-specific rehabilitation programs calls for precise and focused data studies and methodologies. The research presented here provides a methodology for the selection of priority areas to promote rehabilitation in the context of Barcelona’s vulnerable neighborhoods. The selection methodology combines primary and secondary data with a very high level of disaggregation that identifies where the needs are greatest, and it also provides a tool that is still based on primary disaggregated data for the delimitation of areas. The results obtained highlight specific priority areas such as parts of the Raval, Carmel and Besòs-Maresme neighborhoods within larger zones that had been previously defined as vulnerable. The proposed methodology seeks to provide tools to foster evidence-based decision making, thus improving both the understanding of reality and its spatial distribution through data mining techniques and data visualization.

Keywords

• public rehabilitation policies
• housing renewal
• area-based rehabilitation
• urban regeneration
• data-based decision making
• data mining