Name
Abstract | ||
|---|---|---|
Understanding social activities in Urban Activity Centers can benefit both urban authorities and citizens. Traditionally, monitoring large social activities usually incurs significant costs of human labor and time. Fortunately, with the recent booming of urban open data, a wide variety of human digital footprints have become openly accessible, providing us with new opportunities to understand the social dynamics in the cities. In this paper, we resort to urban open data from bike sharing systems, and propose a two-phase framework to identify social activities in Urban Activity Centers based on bike sharing open data. More specifically, we first detect bike usage anomalies from the bike trip data, and then identify the potential social activities from the detected anomalies using a proposed heuristic method by considering both spatial and temporal constraints. We evaluate our framework based on the large-scale real-world dataset collected from the bike sharing system of Washington, D.C. The results show that our framework can efficiently identify social activities in different types of Urban Activity Centers and outperforms the baseline approach. In particular, our framework can identify 89% of the social activities in the major Urban Activity Centers of Washington, D.C. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1109/UIC-ATC-ScalCom-CBDCom-IoP.2015.43 | 2015 IEEE 12th Intl Conf on Ubiquitous Intelligence and Computing and 2015 IEEE 12th Intl Conf on Autonomic and Trusted Computing and 2015 IEEE 15th Intl Conf on Scalable Computing and Communications and Its Associated Workshops (UIC-ATC-ScalCom) |
Keywords | Field | DocType |
Urban Open Data,Bike Sharing System,Urban Activity Center | Data science,Open data,Heuristic,Computer security,Computer science,Computer network,Public transport,Global Positioning System,Social dynamics,Government | Conference |
ISBN | Citations | PageRank |
978-1-4673-7212-1 | 2 | 0.37 |
References | Authors | |
8 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Longbiao Chen | 1 | 17 | 3.32 |
| Dingqi Yang | 2 | 542 | 28.79 |
| Jérémie Jakubowicz | 3 | 222 | 17.95 |
| Gang Pan | 4 | 1501 | 123.57 |
| Daqing Zhang | 5 | 3619 | 217.31 |
| Shijian Li | 6 | 1155 | 69.34 |