Spatio-temporal patterns of urban demand: Statistical insights on hotspots and reallocation

Urban activity systems exhibit recurring spatio-temporal patterns, yet disentangling temporal reallocation from spatial persistence remains a key challenge for urban monitoring and mobility analysis. This paper introduces a reproducible statistical framework based on canonical hotspots – spatial regions identified from historical data and held fixed throughout the analysis – to provide stable references for comparing urban dynamics across days. Using a cleaned analytical sample of Manhattan, NY, taxi pickup records covering the period 2009–2015, we represent daily activity as normalized temporal profiles and spatial allocations over a fixed hotspot structure. Information-theoretic distances, permutation-based inference, and SIMPER-inspired contribution analyses are used to quantify temporal reallocation, spatial divergence, and economic dominance patterns. Results show that weekday variation is driven primarily by temporal reallocation. Spatial differences, although statistically significant, are highly concentrated: six operationally critical hotspots account for more than 82% of the observed weekday–weekend spatial divergence. Furthermore, spatial persistence is found to be a poor proxy for economic relevance. Revenue-weighted centroids diverge systematically from trip-based centroids, indicating that economic dominance emerges from transient spatio-temporal alignments rather than from persistent demand concentrations alone. Beyond the empirical findings, the framework provides a mechanism for separating enduring spatial organization from recurrent temporal redistribution. The results suggest that urban activity systems exhibit a layered structure in which stable spatial patterns coexist with systematic temporal reallocation. By combining stable spatial references with distributional analysis, the proposed framework provides a reproducible basis for comparing urban activity systems and characterizing spatio-temporal heterogeneity in urban mobility data.