Abstract Summary
Understanding people's attitudes towards mobility and daily activity patterns is crucial for designing effective transport policies to build more liveable and resilient cities. Sustainable mobility, a pivotal factor in reducing urban emissions, requires a comprehensive knowledge of the multiple factors influencing people's mobility behaviours. Ensuring a just and inclusive transition necessitates carefully assessing these factors, particularly in light of existing inequalities in access to opportunities that disproportionately impact socioeconomically challenged communities. In urban policymaking, the neighbourhood assumes a central role in implementing impactful strategies. Current urban planning models, such as the 15-minute city or Barcelona's superblocks, exemplify the growing momentum in prioritising local access and promoting sustainable mobility and liveability. However, emphasising the significance of neighbourhoods does not imply viewing them in isolation; instead, it underscores their importance as the highest spatial resolution and, in some cases, the smallest administrative unit conducive to effective policymaking. Our project seeks to provide a highly detailed characterisation of neighbourhoods across the Netherlands, focusing on the mobility attitudes of their inhabitants. The first step in our methodology involves developing a geodemographic profile for each neighbourhood --which we equate to Postcode 6 (PC6) boundaries, the smallest geographical unit in the country-- using data from the Centraal Bureau voor de Statistiek (CBS). When PC6 data is unavailable, we disaggregate higher-level data into PC6. Next, we quantify built-environment characteristics by assessing access to basic amenities (e.g., supermarkets, schools, healthcare facilities, green spaces) and conducting a morphometric analysis to quantify urban form (the spatial distribution of buildings, blocks, and streets). These elements enable us to evaluate the physical components influencing different mobility behaviours. Once demographic and built environment characteristics are established, we extract mobility patterns using sequence analysis and machine learning techniques from The Netherlands' annual travel survey (ODiN). These patterns are then assigned to each neighbourhood based on complementary data extracted from ODiN. Our work aims to enhance our comprehension of people's mobility and activity patterns. By integrating spatial distribution of mobility behaviours with geodemographic and built environment analyses, we seek to offer a more holistic understanding of potential drivers behind varying attitudes towards mobility. Crucially, our work strives to unveil potential inequalities in access to opportunities and modes of transport that may be impacting disadvantaged communities nationwide.
