Characterizing the temporally stable structure of community evolution in intra-urban origin-destination networks
Abstract
Intra-urban origin-destination (OD) network communities evolve throughout the day, indicating changing groups of closely connected regions. Under such variation, groups of regions with high consistency of community affiliation characterize the temporally stable structure of the evolution process, supporting comprehending urban dynamics. However, how to quantify this consistency and identify the associated region groups are open questions. In this study, we introduce the consensus OD network to quantify the consistency of community affiliation among regions. Furthermore, the temporally stable community decomposition method is proposed to identify groups of regions with high internal and low external consistency (named “stable groups”), where each group consists of temporally stable cores and attaching peripheries. Wuhan taxi data is used to verify our methods. On the hourly time scale, eleven stable groups containing 82.9 % of regions are identified. This high percentage suggests that dynamic communities can be well organized via cores. Moreover, stable groups are spatially closed and more likely to distribute within a single district and separated by water bodies. Cores exhibit higher point of interest (POI) entropy and more healthcare and shopping services than peripheries. Our methods and empirical findings contribute to some practical issues, such as urban area division, polycentric evaluation and construction, and infectious disease control.
Xiaoyue Xing
PhD Student
2018 - 2024
My research interests include Urban Big Data Analytics, GIScience, Data Mining
