Street view diagnostics of inclusion and street quality gaps to guide governance in Tokyo

Cities worldwide struggle to advance Sustainable Development Goal (SDG 11) because street-level built environments often lag inclusion-relevant needs, while city-scale indicators rarely detect these micro-scale mismatches. A street-scale diagnostic is developed by contrasting a Street-Space Quality (SQ) index derived from street-view imagery with a census-based Social Inclusion (SI) proxy capturing socio-demographic composition heterogeneity. Metrics are computed on 100 m grids, aggregated to 1-km units, and compared through an SI–SQ rank difference (RD) to classify three mismatch types: Type I (TI; SI ≫ SQ), Type II (TII; SI ≈ SQ), and Type III (TIII; SI ≪ SQ). From 2015 to 2024, Tokyo’s SQ reconfigures toward higher vitality but lower openness—sidewalk and façade permeability rise while greenness and sky view stagnate—whereas SI strengthens in the core and western sub-centers but weakens along the bayside. RD maps show TI expanding east/bayside and TIII shrinking yet persisting in the northwest, indicating asymmetric needs for SI-led versus SQ-led interventions; policy incentives concentrate in core districts. Beyond Tokyo, the RD-based workflow provides a potentially transferable diagnostic template, contingent on comparable small-area socio-demographic tabulations, sufficient and temporally consistent street-view coverage, and local calibration/validation of the vision model and indicator mappings.