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 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.

本数据集基于空间显性地理信息系统（GIS）框架生成，用于评估马德里街区 (barrio) 层面的交通贫困及其与能源脆弱性的交叉关联。该方法将交通贫困视为多维度现象进行量化构建，涵盖五大核心维度：可达性、可获得性、可负担性、出行时长与出行安全。针对每个维度，研究人员依据现有学术文献与政策导向框架（如SUMI、Clean Cities）确立相关指标，并结合可用数据场景完成适配调整。研究采用公开官方地理空间数据集作为核心数据源，包括公共交通基础设施数据（OpenStreetMap、CRTM）、土地利用数据（CORINE Land Cover 2018）、社会经济指标数据（城市审计 (Urban Audit)、马德里市政厅）以及交通事故数据（马德里市政厅 (Ayuntamiento de Madrid)）。能源相关脆弱性数据取自HABITA-RES项目。所有指标均通过GIS技术完成计算，涵盖空间连接、密度测算、缓冲区分析及栅格聚合等方法。所得指标经归一化处理后，被重新划分为代表交通劣势程度逐级递增的有序类别。最终，将各单一指标进行空间整合以生成综合交通贫困指数，并进一步与能源脆弱性数据相结合，从而识别出兼具双重城市脆弱性的区域。