Reclassifying streets based on fine-grained functions

Street classifications have long been dominated by car-centric models, or alternatively, land-uses. This exploratory study introduces a fine-grained, function-based alternative using raw Point of Interest (POI) data. Using a modularity algorithm applied to a topological network of POI classes and street segments, the study identifies distinct street profiles across ten European cities. The results reveal several stable profiles, based on non-major functions, with “restaurant” and “recycling” profiles emerging as the most consistently observed types. This approach offers a data-driven tool to support human-centred street design and provides practical insights for more responsive urban planning. • Proposes a fine-grained street typology using raw POI data from OpenStreetMap • Applies modularity-based clustering to identify functional street profiles • Uncovers consistent non-major street types like “recycling” type across ten European cities • Offers a reproducible, data-driven tool for human-centred street planning