Grid-Based Semantic Enrichment of Human Mobility Data for Urban Spatial Patterns Profiling: Hotspot and Service Gap Area Detection in Kota Yogyakarta, Indonesia

Abstract

Understanding urban spatial patterns from human mobility data requires semantic context beyond raw trajectory analysis. Existing approaches face three key limitations: grid size selection relies on empirical judgment rather than systematic evaluation, semantic enrichment assigns a single dominant label without quantifying degrees of dominance, and hotspot detection relies solely on demand-side density without considering urban facility supply. This study proposes a grid-based semantic enrichment framework for urban spatial pattern detection, comprising four components: a multi-criteria grid-size evaluation framework, confidence-score-based semantic enrichment, supply-demand synthesis for hotspot identification, and service gap identification. The framework was applied to Kota Yogyakarta, Indonesia, using over 4.6 million trajectory points from 130,395 mobile users. A grid size of 500 m was identified as optimal across ten evaluated resolutions. Supply-demand synthesis was applied to 342 grid cells. Results show that only 10.5% of cells qualify as full hotspots, concentrated in the central-eastern urban area and dominated by functionally mixed zones. Unmet demand is the primary service gap type, covering 24% of the study area, more than four times the proportion of underutilized supply (5.6%), suggesting the presence of informal activity not captured in spatial datasets. These findings demonstrate that the proposed framework provides a systematic methodology for extracting meaningful urban insights from large-scale human mobility data, with practical implications for evidence-based urban planning in mid-sized Indonesian cities.