Spatial dataset for assessing transport poverty and energy vulnerability in Madrid (Spain) [Dataset]
The dataset was generated using a spatially explicit GIS-based framework for assessing transport poverty and its intersection with energy vulnerability at the neighbourhood (barrio) level in Madrid. The methodology is based on the operationalization of transport poverty as a multidimensional phenome...
| Autores: | , , , |
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| Tipo de recurso: | conjunto de datos |
| Fecha de publicación: | 2026 |
| País: | España |
| Institución: | Consejo Superior de Investigaciones Científicas (CSIC) |
| Repositorio: | DIGITAL.CSIC. Repositorio Institucional del CSIC |
| OAI Identifier: | oai:dnet:digitalcsic_::0f8cda0b7e82e132687817dfd7e802b5 |
| Acceso en línea: | http://hdl.handle.net/10261/429554 |
| Access Level: | acceso abierto |
| Palabra clave: | Fuel poverty Double energy vulnerability Mobility Spatial analysis Social exclusion http://metadata.un.org/sdg/7 http://metadata.un.org/sdg/12 http://metadata.un.org/sdg/11 http://metadata.un.org/sdg/10 Ensure access to affordable, reliable, sustainable and modern energy for all Reduce inequality within and among countries Make cities and human settlements inclusive, safe, resilient and sustainable Ensure sustainable consumption and production patterns |
| Sumario: | The dataset was generated using a spatially explicit GIS-based framework for assessing transport poverty and its intersection with energy vulnerability at the neighbourhood (barrio) level in Madrid. The methodology is based on the operationalization of transport poverty as a multidimensional phenomenon, structured across five key dimensions: accessibility, availability, affordability, travel time, and safety. For each dimension, indicators were defined based on existing literature and policy-oriented frameworks (e.g., SUMI, Clean Cities), and adapted to the available data context. Open and official geospatial datasets were used as primary data sources, including public transport infrastructure (OpenStreetMap, CRTM), land use data (CORINE Land Cover 2018), socio-economic indicators (Urban Audit, Madrid City Council), and traffic accident data (Ayuntamiento de Madrid). Energy-related vulnerability data were incorporated from the HABITA-RES project. Each indicator was computed using GIS techniques such as spatial joins, density calculations, buffer analysis, and raster-based aggregation. The resulting indicators were normalized and reclassified into ordinal categories representing increasing levels of transport disadvantage. Finally, individual indicators were spatially combined to produce composite transport poverty indices, and further integrated with energy vulnerability data to identify areas of overlapping urban vulnerability. |
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