Investigating spatiotemporal patterns for comprehensive accessibility of service facilities by location-based service data in Nanjing (2016–2022)

Abstract The annual spatiotemporal variation for the comprehensive accessibility of service facilities (CASF), expressing the dynamic development of urban service, has not been routinely monitored adequately (such as multi-type facility interactions, policy-driven dynamics, and persistent inequities). To address this gap, we propose three key innovations: (1) Utility-improved accessibility model: A novel framework integrating 15 facility types through entropy-weighted utility indices, overcoming the single-type limitation of traditional models. This model dynamically couples facility diversity (point of interest density), population demand (mobile big data), and time-decayed travel impedance (route planning application programming interface), enabling multi-dimensional CASF evaluation. (2) Integrated spatiotemporal methodology: We synergistically combine four methods, standard deviational ellipse (directional trends), space-time cube (3D continuity), emerging hotspot analysis (cluster evolution), and time series clustering (trajectory phases) to capture multi-year interactions between policy-driven urban expansion and accessibility dynamics. (3) Policy-ready findings: Our analysis of Nanjing (2016–2022) reveals that CASF exhibits multi-center agglomeration (centroid shift toward Jiangbei New District) with divergent trajectories persistent hotspots in historic cores (Gulou District) vs stagnant peripheries (northern Qixia). These patterns directly align with Nanjing’s 2021–2035 master plan. The framework provides actionable insights for policymakers to prioritize investments in intensifying hotspots (e.g., Jiangbei’s commercial hubs), address systemic deprivation in persistent cold spots (e.g., Qixia’s underserved areas), and monitor phased impacts of transportation projects through CASF trajectory clusters.