Delineating hierarchical activity space from high-resolution urban mobility flows

2026-03-22

AI Summary

This study addresses the limitations of conventional activity space research, which often relies on static, homogeneous representations of absolute physical space and thus fails to capture the heterogeneity and dynamic responsiveness of human–urban interactions. To overcome this, the authors propose the Hierarchical Activity Region Model (HARM), integrating relational spatial perspectives with hierarchical structure. Leveraging high-resolution taxi trajectory data, the model combines complex network analysis, hierarchical clustering, and spatiotemporal pattern mining to delineate multi-level activity spaces as perceived by diverse population groups. Applied to Manhattan before and after Hurricane Sandy, the approach empirically reveals a pronounced hierarchical organization in urban mobility, characterized by a “hierarchical compression–rebound” response during extreme events. The findings further demonstrate significant inter-group disparities in hierarchical depth and recovery capacity, offering a novel paradigm for resilience- and equity-oriented urban planning.

Technology Category

• Planning, Routing, and Scheduling: Activity and Plan Recognition
• Humans and AI: Planning and Decision Support for Human-Machine Teams
• Reasoning under Uncertainty: Relational Probabilistic Models

Application Category

• User Modeling, Personalization and Recommendation: Practical large-scale studies of user experience
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for heterogeneous, signed, attributed, multi-relational, temporal, higher-order, and annotated Web-related graphs
• Web Mining and Content Analysis: Models for Web evolution

Abstract

Current studies on activity space are limited by the conceptualization of absolute physical space that fails to consider the heterogeneity of relational spaces reconstructed from spatial interactions of human movements between locations and falls short in incorporating the inherent hierarchical property of human mobility. Consequently, these approaches cannot faithfully reflect how people interact with urban spaces through travels. From the lens of relational space, this study proposes the new Hierarchical Activity Region Model (HARM) to derive the space and hierarchical properties of activity spaces perceived by various urban groups. We demonstrate the enhanced validity of our model on travel behavior in Manhattan, New York City, before, during, and after Hurricane Sandy on the basis of taxi data. Empirical results show that intra-urban travel retains clear hierarchical organization, even under disruption of a major weather event. Yet, travel undergoes a compression effect in travel hierarchies, characterized by fewer hierarchical levels and enlarged characteristic scales, followed by a rebound. Clustering the derived hierarchies reveals pronounced heterogeneity that stems from differences in population profiles; some groups sustain deeper structures or recover quickly, while others experience a persistent loss of levels. This study provides valuable insights into the functional hierarchies of urban mobility, which could inform more sustainable, resilient and equitable urban planning. The proposed methodological framework is generic for studying human mobility in broader contexts.

Problem

• activity space
• relational space
• hierarchical mobility
• urban mobility
• human movement

Innovation

• hierarchical activity space
• relational space
• human mobility
• urban resilience
• HARM