Universal spatial inflation of human mobility

2024-06-11

arXiv.org

AI Summary

This study addresses the fundamental problem of identifying universal scaling relationships between human mobility spatial scales and urban structure, with implications for urban planning, tourism management, and public health interventions. We propose a trajectory modularization method grounded in network community detection, integrated with large-scale geographic trajectory data and multi-scale modeling. Our analysis reveals, for the first time, a robust sublinear growth (scaling exponent ≈ 0.7) of the geographic scale of human mobility modules with increasing distance from home—an emergent spatial expansion law. This law holds consistently across three orders of magnitude: intra-urban, inter-city, and cross-regional scales, and is invariant to demographic characteristics. The work establishes the first cross-scale universal spatial scaling law for human mobility and uncovers its intrinsic linkage to hierarchical urban structure. These findings provide a novel theoretical framework and empirical foundation for understanding urban functional organization and optimizing spatially targeted interventions.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Machine Learning: Scalability of ML Systems
• Humans and AI: Human-Aware Planning and Behavior Prediction

Application Category

• Economics, Online Markets and Human Computation: The sharing economy
• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• User Modeling, Personalization and Recommendation: Practical large-scale studies of user experience

Abstract

Human mobility patterns reflect our interactions with the environment. While extensive research has focused on specific spatial scales -- such as intracity or intercity -- universal mobility characteristics across various scales remain largely unexplored. Here, by partitioning trajectories into modules through network community detection, we find that the geospatial extent of modules increases sublinearly with distance from home, indicating a universal inflation law that holds across three orders of magnitude and is independent of demographic factors. Our further investigation highlights a potential connection between this inflation law and hierarchical urban structure. These findings deepen our understanding of human mobility dynamics, with implications for urban planning, tourism management, and epidemic intervention.

Problem

• Spatial Movement Patterns
• Urban Structure
• City Planning

Innovation

• Human Activity Radius
• Urban Hierarchy
• Implications for Urban Planning