Modeling shared micromobility as a label propagation process for detecting the overlapping communities
Peng Luo, Chengyu Song, Hao Li, Di Zhu, Fabio Duarte
TL;DR
This work tackles the challenge of uncovering overlapping spatial communities in shared micro-mobility networks by reframing mobility as a geospatial label-propagation process. It introduces Geospatial Interaction Propagation (GIP), which couples a geospatially weighted Speaker-Listener Label Propagation Algorithm (SLPA) with One-Class SVM-based anomaly detection to identify overlapping communities among Census Block Groups. Applied to Washington, D.C.’s e-scooter OD data, GIP achieves higher modularity and substantial efficiency gains over geospatially weighted SLPA implementations, uncovering 86 communities with 41 overlapping CBGs and revealing socio-spatial patterns related to land use, POIs, race, income, and transportation access. The findings offer actionable insights for urban planning and resource allocation in shared-mobility systems, demonstrating that overlapping communities tend to cluster around parks and transit-rich areas, often associated with higher income and White population shares, while highlighting potential inequalities in mobility access. Future work may extend GIP to temporal dynamics and other mobility systems to generalize its applicability.
Abstract
Shared micro-mobility such as e-scooters has gained significant popularity in many cities. However, existing methods for detecting community structures in mobility networks often overlook potential overlaps between communities. In this study, we conceptualize shared micro-mobility in urban spaces as a process of information exchange, where locations are connected through e-scooters, facilitating the interaction and propagation of community affiliations. As a result, similar locations are assigned the same label. Based on this concept, we developed a Geospatial Interaction Propagation model (GIP) by designing a Speaker-Listener Label Propagation Algorithm (SLPA) that accounts for geographic distance decay, incorporating anomaly detection to ensure the derived community structures reflect meaningful spatial patterns. We applied this model to detect overlapping communities within the e-scooter system in Washington, D.C. The results demonstrate that our algorithm outperforms existing model of overlapping community detection in both efficiency and modularity. However, existing methods for detecting community structures in mobility networks often overlook potential overlaps between communities. In this study, we conceptualize shared micro-mobility in urban spaces as a process of information exchange, where locations are connected through e-scooters, facilitating the interaction and propagation of community affiliations. As a result, similar locations are assigned the same label. Based on this concept, we developed a Geospatial Interaction Propagation model (GIP) by designing a Speaker-Listener Label Propagation Algorithm (SLPA) that accounts for geographic distance decay, incorporating anomaly detection to ensure the derived community structures reflect meaningful spatial patterns.