Population movement across time and space plays a critical role in transportation planning, urban accessibility, and public health analysis. In this study, an anchoring-based mobility modeling framework was developed to characterize temporal and distance-based population-level human mobility patterns using massive aggregated mobility data. Specifically, the framework develops a novel continuous Anchoring Index which quantifies the relative spatial dependence of activity categories on residential (home) versus daytime (work-related) anchors using visit-weighted distance measures. The framework illustrates temporal heterogeneity in mobility behavior by separating activity patterns across weekdays and weekends and across peak and off-peak periods. Distance-based mobility behavior is examined through an OLS regression model capturing category-level and regional determinants of distance from home, while a Multinomial Logit model independently characterizes the temporal determinants of activity category choice across 18 functional destination types without incorporating distance as a predictor. Building on these distance-based metrics, the study portrays mobility flows as category-region networks, which allows structural analysis of connectivity, centrality, and spatial coupling between activities and locations. Instead of focusing on individual-level choice models, the introduced methodology prioritizes interpretability and scalability, making it ideal for population-level analysis. The empirical findings indicate stark differences in anchoring behavior between activity types, including the strong residential anchoring evident for essential services, flexible spatial patterns characteristic of discretionary activities, and pronounced daytime anchoring seen for work-related categories. The results show that both anchoring-based and network-based representations jointly yield a strong yet interpretable framework to understand large human mobility systems as well as their evolution in time, with relevance for accessibility analysis, transport policy, or urban system resilience.
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