Two-step dimensionality reduction of human mobility data: From potential landscapes to spatiotemporal insights

2025-05-27

AI Summary

Human mobility data’s high dimensionality and complexity hinder effective spatiotemporal pattern extraction and often lead to loss of fine-grained location information. To address this, we propose a two-stage dimensionality reduction framework: first, we construct a potential energy landscape from origin–destination (OD) matrices using combinatorial Hodge theory, preserving spatial topology for interpretable local location modeling; second, we apply principal component analysis (PCA) to the landscape to extract dominant spatiotemporal patterns, synergizing topological modeling with statistical dimensionality reduction. This work pioneers the application of combinatorial Hodge theory to mobility analysis, uniquely balancing local structural fidelity with global dynamic characterization. Experiments demonstrate that our method clearly identifies critical mobility patterns—including aggregate decline during pandemics and distinct weekday/holiday behavioral differentiation—yielding interpretable, reusable spatiotemporal insights for urban planning and public health interventions.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Data Mining & Knowledge Management: Mining of Spatial, Temporal or Spatio-Temporal Data
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning

Application Category

• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• Web Mining and Content Analysis: Web data visualization
• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization

Abstract

Understanding the spatiotemporal patterns of human mobility is crucial for addressing societal challenges, such as epidemic control and urban transportation optimization. Despite advancements in data collection, the complexity and scale of mobility data continue to pose significant analytical challenges. Existing methods often result in losing location-specific details and fail to fully capture the intricacies of human movement. This study proposes a two-step dimensionality reduction framework to overcome existing limitations. First, we construct a potential landscape of human flow from origin-destination (OD) matrices using combinatorial Hodge theory, preserving essential spatial and structural information while enabling an intuitive visualization of flow patterns. Second, we apply principal component analysis (PCA) to the potential landscape, systematically identifying major spatiotemporal patterns. By implementing this two-step reduction method, we reveal significant shifts during a pandemic, characterized by an overall declines in mobility and stark contrasts between weekdays and holidays. These findings underscore the effectiveness of our framework in uncovering complex mobility patterns and provide valuable insights into urban planning and public health interventions.

Problem

• Analyzing complex human mobility patterns from large-scale data
• Preserving spatial details in dimensionality reduction of mobility data
• Identifying pandemic-induced shifts in spatiotemporal movement patterns

Innovation

• Two-step dimensionality reduction for mobility data
• Potential landscape construction using Hodge theory
• PCA application to identify spatiotemporal patterns