Exploiting urban data to address real-world challenges: Enhancing urban mobility for environmental and social well-being

The rapid urbanization of large cities poses significant challenges to residents' well-being, particularly regarding mobility patterns, infrastructure, and environmental pollution. While extensive research has explored the societal impacts of urbanization, effectively identifying, and addressing critical urban areas remains a complex task. This study proposes utilizing data-driven urban approaches to guide decision-making for urban planners, architects, and policymakers by identifying key infrastructure areas crucial for improving mobility and sustainability. Leveraging open data urban repositories, the study aims to develop a data analytics pipeline to identify urban infrastructure points for enhanced, accessible, sustainable, and healthy mobility. This research focuses on understanding urban factors influencing pedestrian and cyclist movement to promote active behaviors, thus enhancing citizens' health and quality of life. The study hypothesizes that the developed methodology, employing a multi-stage data analysis pipeline and clustering algorithms, effectively evaluates walkability and cyclability in urban environments. Using Barcelona as a case study allows for a comprehensive demonstration of the methodology's potential outcomes without compromising general applicability. The data-driven study explores accessibility and mobility variations, addressing issues of affordability and barriers in new micro-mobility solutions, and contributing valuable insights to informed policymaking for global net-zero transitions.