Using multi-source data to evaluate walkability in urban and rural Hong Kong: A multidimensional framework

Promoting walkability is essential for sustainable urban development, but existing large-scale walkability evaluation frameworks pay less attention to rural areas and overlook comprehensive multidimensional evaluations. This study introduces a refined, data-driven framework for assessing walkability across urban and rural areas in Hong Kong, incorporating three dimensions: convenience, safety, and comfort, using 13 indicators derived from 10 multi-source datasets, including remote sensing, point of interest, traffic, and government open data. We applied the model to assess over 400,000 walkway sections in Hong Kong's urban and rural built-up areas. The results reveal significant spatial heterogeneity in walkability. Urban areas, particularly newer New Towns, demonstrate superior walkability compared to rural regions, driven by better pedestrian infrastructure. However, substantial inequalities persist both within urban neighborhoods and between urban and rural areas. The analysis emphasizes the importance of integrated, multidimensional frameworks and highlights specific areas where targeted interventions are urgently needed. Validated through perceived walkability surveys and pedestrian counts from street view images, our model demonstrates high reliability and alignment with real pedestrian experiences. This study provides actionable insights to enhance equitable and sustainable pedestrian environments in densely populated metropolitan contexts. • Developed an integrated, multidimensional walkability framework tailored for urban and rural areas in Hong Kong. • Mapped the walkability score across the built-up area of Hong Kong based on pedestrian network. • Assessed over 400,000 walkway segments, revealing significant urban-rural and intra-urban walkability disparities. • Highlighted the urgent need for targeted interventions to address pedestrian infrastructure gaps, particularly in underserved rural neighborhoods.