TY - GEN
T1 - Discovery of booming and decaying Point-Of-Interest with human mobility data
AU - Lu, Xinjiang
AU - Yi, Fei
AU - Yu, Zhiwen
AU - Guo, Bin
AU - Du, He
N1 - Publisher Copyright:
© 2017 Association for Computing Machinery.
PY - 2017/9/11
Y1 - 2017/9/11
N2 - By observing the Point-Of-Interests (POIs) in cities over time, we can find that some of them disappear, new ones emerge and most of them just keep alive. In this paper, we name such evolutionary process of POI as POI lifetime and the stages of POI lifetime as POI lifetime status. Specifically, when we find a POI appears/disappears by observing the online maps over time, we may say that a POI is in booming/decaying lifetime status. How to discover the booming/decaying POIs is a challenging but valuable task. To address this problem, we propose a framework leveraging the human mobility data to discover the booming and decaying POIs in urban areas. We conduct the experiments on real-world data sets. The results demonstrate that the ensemble classifier can discover the booming/decaying POIs effectively, and the human mobility factors extracted are indicative for discovering the POI lifetime status.
AB - By observing the Point-Of-Interests (POIs) in cities over time, we can find that some of them disappear, new ones emerge and most of them just keep alive. In this paper, we name such evolutionary process of POI as POI lifetime and the stages of POI lifetime as POI lifetime status. Specifically, when we find a POI appears/disappears by observing the online maps over time, we may say that a POI is in booming/decaying lifetime status. How to discover the booming/decaying POIs is a challenging but valuable task. To address this problem, we propose a framework leveraging the human mobility data to discover the booming and decaying POIs in urban areas. We conduct the experiments on real-world data sets. The results demonstrate that the ensemble classifier can discover the booming/decaying POIs effectively, and the human mobility factors extracted are indicative for discovering the POI lifetime status.
KW - Human mobility
KW - Point of interest
KW - Urban computing
UR - http://www.scopus.com/inward/record.url?scp=85030872605&partnerID=8YFLogxK
U2 - 10.1145/3123024.3123146
DO - 10.1145/3123024.3123146
M3 - 会议稿件
AN - SCOPUS:85030872605
T3 - UbiComp/ISWC 2017 - Adjunct Proceedings of the 2017 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2017 ACM International Symposium on Wearable Computers
SP - 137
EP - 140
BT - UbiComp/ISWC 2017 - Adjunct Proceedings of the 2017 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2017 ACM International Symposium on Wearable Computers
PB - Association for Computing Machinery, Inc
T2 - 2017 ACM International Joint Conference on Pervasive and Ubiquitous Computing and ACM International Symposium on Wearable Computers, UbiComp/ISWC 2017
Y2 - 11 September 2017 through 15 September 2017
ER -