Identifying and Characterising Higher Order Interactions in Mobility Networks Using Hypergraphs
This work addresses the problem of understanding complex human mobility patterns for applications in urban planning and public health, representing an incremental advancement by extending existing methods to capture group interactions.
The paper tackled the limitation of traditional mobility models that only capture pairwise interactions by proposing co-visitation hypergraphs to model higher-order relationships among locations from trajectory data, demonstrating its effectiveness in analyzing city-scale patterns and detecting shifts during disruptions like extreme weather events.
Understanding human mobility is essential for applications ranging from urban planning to public health. Traditional mobility models such as flow networks and colocation matrices capture only pairwise interactions between discrete locations, overlooking higher-order relationships among locations (i.e., mobility flow among two or more locations). To address this, we propose co-visitation hypergraphs, a model that leverages temporal observation windows to extract group interactions between locations from individual mobility trajectory data. Using frequent pattern mining, our approach constructs hypergraphs that capture dynamic mobility behaviors across different spatial and temporal scales. We validate our method on a publicly available mobility dataset and demonstrate its effectiveness in analyzing city-scale mobility patterns, detecting shifts during external disruptions such as extreme weather events, and examining how a location's connectivity (degree) relates to the number of points of interest (POIs) within it. Our results demonstrate that our hypergraph-based mobility analysis framework is a valuable tool with potential applications in diverse fields such as public health, disaster resilience, and urban planning.