Profiling Urban Functional Regions With POI and Wi‐Fi Data: A Dual‐Entropy Approach and Demographic Analysis

Understanding urban functional regions (UFRs) is essential for spatial planning and governance. Existing approaches typically rely on static points of interest (POI) or mobility data to delineate functional zones, but they often fail to capture the dynamic coupling between physical space and human activities and overlook population attributes, which are a fundamental parameter underpinning both spatial units in urban planning and human activities. To address this limitation, we propose a Dual‐Information Entropy Model (DIEM) that explicitly synthesizes the structural signals of POI distribution and the dynamic usage intensity of Wi‐Fi mobility records. By further incorporating population portrait data, the proposed framework moves beyond static functional classification toward comprehensive, human‐centric profiling of urban regions. Applied to Beijing's Fourth Ring Road, DIEM significantly outperforms baseline zoning strategies, achieving an Overall Accuracy of 0.896 and a Kappa coefficient of 0.885. More importantly, the results reveal two key patterns: UFRs display temporal elasticity, shifting from concentrated production‐oriented structures on workdays to fragmented consumption and life‐oriented patterns on holidays; and population dynamics exhibit systematic restructuring, with clear variations in age, gender, and educational attainment across functional regions. These findings highlight the value of incorporating demographic dimensions into urban functional analysis and provide new insights for adaptive, human‐centered urban planning.