Characteristics of POI Dynamic Changes over Time and the Impacts of a Major Disruption: A Case Study of Changzhou, China

Point-of-interest (POI) data are widely used as urban big data to sense the functional organization of urban land and associated socioeconomic activities. Yet, POI-based studies often treat these records as temporally stable, even though different POI categories vary in turnover and responses to external shocks, affecting the comparability of urban sensing over time. Using POI datasets for Changzhou, China, from 2016, 2019, 2022, and 2025, this study examines category-specific changes before, during, and after the COVID-19 disruption. The results identify three patterns: government-driven POIs (government and residential) show high stability and retention; government–market hybrid POIs (financial and educational) exhibit moderate stability; and market-driven POIs (catering and shopping) have low stability. Across periods, change intensity is inversely related to stability. During the pandemic, turnover increased across all categories, retention declined markedly, and most replacements occurred within the same category, indicating functional continuity despite entity-level change. Although the overall spatial framework of urban functions remained broadly stable, new hotspots emerged during the pandemic and some persisted into the recovery period. These findings demonstrate that POI-based urban sensing is temporally contingent and that category-specific dynamics should be considered when interpreting urban land-use functions and supporting land-use planning under disruptive events.