A Behavior-Informed and Geo-Context-Aware Home Detection Framework for Mobile Phone Positioning Data

Home location detection is a foundational task in human mobility research and underpins a wide range of data-driven applications. Leveraging mobile phone data, this study develops a behavior-informed and geo-context-aware framework for inferring the home locations of mobile users from their historical visits. Unlike most existing methods that rely on heuristic algorithms or uniform assumptions across devices, our approach captures individual-level behavioral differences in terms of mobility characteristics, such as activity timing, frequency, and spatial range, and accounts for geographic disparities of these behaviors across urban, suburban, and rural areas. The proposed framework first extracts user-specific representative activity locations using a refined density-based clustering algorithm, then classifies users into behaviorally distinct groups, and finally estimates each user's most probable home location for evaluation and downstream applications. Applied to one month of anonymized mobile phone data in Minnesota, USA, our framework achieves higher detection rates and stronger alignment with land use patterns than traditional approaches, particularly for users with sparse or irregular mobility and in less densely populated regions. We further illustrate the practical utility of the proposed framework through two example applications, highlighting its potential to support transportation planning, demographic studies, and broader geospatial applications.