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GeoInformatica

, Volume 20, Issue 2, pp 327–349 | Cite as

Exploring cell tower data dumps for supervised learning-based point-of-interest prediction (industrial paper)

  • Ran Wang
  • Chi-Yin ChowEmail author
  • Yan Lyu
  • Victor C. S. Lee
  • Sarana Nutanong
  • Yanhua Li
  • Mingxuan Yuan
Article

Abstract

Exploring massive mobile data for location-based services becomes one of the key challenges in mobile data mining. In this paper, we investigate a problem of finding a correlation between the collective behavior of mobile users and the distribution of points of interest (POIs) in a city. Specifically, we use large-scale cell tower data dumps collected from cell towers and POIs extracted from a popular social network service, Weibo. Our objective is to make use of the data from these two different types of sources to build a model for predicting the POI densities of different regions in the covered area. An application domain that may benefit from our research is a business recommendation application, where a prediction result can be used as a recommendation for opening a new store/branch. The crux of our contribution is the method of representing the collective behavior of mobile users as a histogram of connection counts over a period of time in each region. This representation ultimately enables us to apply a supervised learning algorithm to our problem in order to train a POI prediction model using the POI data set as the ground truth. We studied 12 state-of-the-art classification and regression algorithms; experimental results demonstrate the feasibility and effectiveness of the proposed method.

Keywords

Spatio-temporal data analysis Classification Regression Cell tower data dumps Point-of-interest prediction 

Notes

Acknowledgments

R. Wang and C.-Y. Chow were partially supported by a research grant (CityU Project No. 9231131). S. Nutanong was partially supported by a CityU research grant (CityU Project No. 7200387). This work was also supported by the National Natural Science Foundation of China under the Grant 61402460.

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Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  • Ran Wang
    • 1
  • Chi-Yin Chow
    • 1
    Email author
  • Yan Lyu
    • 1
  • Victor C. S. Lee
    • 1
  • Sarana Nutanong
    • 1
  • Yanhua Li
    • 2
  • Mingxuan Yuan
    • 3
  1. 1.Department of Computer ScienceCity University of Hong KongKowloonHong Kong
  2. 2.Department of Computer ScienceWorcester Polytechnic Institute (WPI)WorcesterUSA
  3. 3.Huawei Noah’s Ark LabShatinHong Kong

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