CoPFun: an urban co-occurrence pattern mining scheme based on regional function discovery

CoPFun: an urban co-occurrence pattern mining scheme based on regional function discovery World Wide Web https://doi.org/10.1007/s11280-018-0578-x CoPFun: an urban co-occurrence pattern mining scheme based on regional function discovery 1 1 2 Xiangjie Kong · Menglin Li · Jianxin Li · 1 3 1 Kaiqi Tian · Xiping Hu · Feng Xia Received: 31 October 2017 / Revised: 7 March 2018 / Accepted: 24 April 2018 © Springer Science+Business Media, LLC, part of Springer Nature 2018 Abstract Analysis of mobile big data enables smart cities from aspects of traffic pat- tern, human mobility, air quality, and so on. Co-occurrence pattern in human mobility has been proposed in recent years and sparked high attentions of academia and industry. Co- occurrence pattern has shown enormous values in aspects of urban planning, business, and social applications, such as shopping mall promotion strategy making, and contagious dis- ease spreading. What’s more, human mobility has strong relation with regional functions, because each urban region owns a major function to offer specialized services for city’s operations and such location-based services attract massive passenger flow, which is exactly the essence of urban human mobility pattern. Therefore, in this paper, we put forward a co- occurrence pattern mining scheme (CoPFun) based on regional function discovery utilizing various mobile data. First, http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png World Wide Web Springer Journals

CoPFun: an urban co-occurrence pattern mining scheme based on regional function discovery

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Publisher
Springer US
Copyright
Copyright © 2018 by Springer Science+Business Media, LLC, part of Springer Nature
Subject
Computer Science; Information Systems Applications (incl.Internet); Database Management; Operating Systems
ISSN
1386-145X
eISSN
1573-1413
D.O.I.
10.1007/s11280-018-0578-x
Publisher site
See Article on Publisher Site

Abstract

World Wide Web https://doi.org/10.1007/s11280-018-0578-x CoPFun: an urban co-occurrence pattern mining scheme based on regional function discovery 1 1 2 Xiangjie Kong · Menglin Li · Jianxin Li · 1 3 1 Kaiqi Tian · Xiping Hu · Feng Xia Received: 31 October 2017 / Revised: 7 March 2018 / Accepted: 24 April 2018 © Springer Science+Business Media, LLC, part of Springer Nature 2018 Abstract Analysis of mobile big data enables smart cities from aspects of traffic pat- tern, human mobility, air quality, and so on. Co-occurrence pattern in human mobility has been proposed in recent years and sparked high attentions of academia and industry. Co- occurrence pattern has shown enormous values in aspects of urban planning, business, and social applications, such as shopping mall promotion strategy making, and contagious dis- ease spreading. What’s more, human mobility has strong relation with regional functions, because each urban region owns a major function to offer specialized services for city’s operations and such location-based services attract massive passenger flow, which is exactly the essence of urban human mobility pattern. Therefore, in this paper, we put forward a co- occurrence pattern mining scheme (CoPFun) based on regional function discovery utilizing various mobile data. First,

Journal

World Wide WebSpringer Journals

Published: May 28, 2018

References

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