Summary:
Transport poverty, a multifaceted issue, has garnered increasing attention in recent years. This study employs anonymized mobile phone data and GIS techniques to analyze commuting patterns, economic burdens, and spatial distribution of transport poverty. By integrating data from various sources, including mobile phone records and income statistics, the study provides insights into the relationship between transport accessibility, income levels, and social inclusion. This methodology has been used to examine a case study in Madrid's economic area. The findings underscore the importance of accessibility indicators in understanding and addressing transport poverty. Through this bottom-up data processing approach, the study demonstrates the utility of big data analytics in informing evidence-based policy interventions to promote equitable access to transportation services.
Spanish layman's summary:
Este estudio propone una metodología basada en big data que emplea datos móviles y SIG para examinar la pobreza en el transporte. El análisis del caso de estudio en Madrid subraya la relevancia de los indicadores de accesibilidad.
English layman's summary:
Transport poverty, a complex issue, gains attention. This study uses mobile data and GIS to analyze commuting, economics, and spatial aspects. Integrating various data sources, it examines Madrid's case, highlighting the role of accessibility indicators in addressing transport poverty.
Keywords: Transport poverty; Energy poverty; Big data; Indicators; Transport affordability; Transport accessibility.
Registration date: 16/04/2024
IIT-24-111WP
