Detecting Hot Spots Using the Data Field Method

  • Zhenyu Wu
  • Jiaying ChenEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 905)


With the developments of the mobile devices and Internet of things, the location data have recorded amount of information about people activities. Mining the hot spots from the location-based data and studying the changing patterns of hot spots are useful to the early warnings of the disasters, traffic jams and crimes. Current researches on hot spots detections ignore the temporal factors. In this paper, the data field method is used to describe the interactions of spots, and the temporal factors are incorporated into the data field method. Furthermore, a hot spots detection method is proposed. Finally, the heat map is used to illustrate the effectiveness of the proposed method based on an open dataset.


Data field Hot spots Location Based Service 



This work is supported by National Natural Science Foundation of China (No. 61502246), NUPTSF (No. NY215019).


  1. 1.
    Lockhart, J.W., Weiss, G.M., Xue, J.C., Gallagher, S.T., Grosner, A.B., Pulickal, T.T.: Design considerations for the WISDM smart phone-based sensor mining architecture. In: Proceedings of the Fifth International Workshop on Knowledge Discovery from Sensor Data, SensorKDD 2011, San Diego, CA, USA, pp. 25–33 (2011)Google Scholar
  2. 2.
    Guo, B., Wang, W., Yu, Z.W., Wang, Y., Yen, N.Y., Huang, R., Zhou, X.G.: Mobile crowd sensing and computing: the review of an emerging human-powered sensing paradigm. ACM Comput. Surv. 48(1), 7–31 (2015)CrossRefGoogle Scholar
  3. 3.
    Heipke, C.: Crowdsourcing geospatial data. J. Photogramm. Remote. Sens. 65(6), 550–557 (2010)CrossRefGoogle Scholar
  4. 4.
    Hu, Q.W., Wang, M.: Urban hotspot and commercial area exploration with check-in data. Acta Geod. Cartogr. Sin. 43(3), 314–321 (2014)Google Scholar
  5. 5.
    Gudmundsson, J., Kreveld, M.V., Staals, F.: Algorithms for hotspot computation on trajectory data. In: Proceedings of the 21st ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, SIGSPATIAL 2013, Orlando, FL, USA, pp. 134–143 (2013)Google Scholar
  6. 6.
    Hwang, S.: Extending spatial hot spot detection techniques to temporal dimensions. In: Proceedings of the 4th ISPRS Workshop on Dynamic and Multi-dimensional GIS (2005)Google Scholar
  7. 7.
    Grubesic, T.H., Murray, A.T.: Detecting hot spots using cluster analysis and GIS. In: Proceedings of the 5th ISPRS Workshop (2001)Google Scholar
  8. 8.
    Tripathi, J.P.: Algorithm for detection of hot spots of traffic through analysis of GPS data. In: Computer Science and Engineering Department, THAPAR University (2010)Google Scholar
  9. 9.
    Rodrigues, P.P., Lopes, L.: Distributed clustering of streaming sensors: a general approachGoogle Scholar
  10. 10.
    Zhang, D., Sun, L., Li, B., Chen, C., Pan, G., Li, S.J., Wu, Z.H.: Understanding taxi service strategies from taxi GPS traces. In: IEEE Transactions on Intelligent Transportation Systems (2014)Google Scholar
  11. 11.
    Tork, H.F., Gama, J.: An eigenvector-based hotspot detection. In: Proceedings of 16th Portuguese Conference on Artificial Intelligence, Acores, Portugal, pp. 290–301 (2013)Google Scholar
  12. 12.
    Ashbrook, D., Starner, T.: Using GPS to learn significant locations and predict movement across multiple users. Pers. Ubiquitous Comput. 7(5), 275–286 (2003)CrossRefGoogle Scholar
  13. 13.
    Zhou, C.Q., Frankowski, D., Ludford, P., Shekhar, S., Terveen, L.: Discovering personally meaningful places: an interactive clustering approach. ACM Trans. Inf. Syst. 25(3) (2007)CrossRefGoogle Scholar
  14. 14.
    Liao, L., Patterson, D.J., Fox, D., Kautz, H.: Building personal map from GPS data. In: Progress in Convergence: Technologies for Human Wellbeing, vol. 1093, no. 1, pp. 249–265. Academy of Sciences, New York (2006)CrossRefGoogle Scholar
  15. 15.
    Witayangkurn, A., Horanont, T., Sekimoto, Y., Shibasaki, R.: Anomalous event detection on large scale GPS data from mobile phones using hidden markov model and cloud platform. In: Adjunct Proceedings of the 2013 ACM Conference on Pervasive and Ubiquitous Computing Adjunct Publication, UbiComp 2013, Zurich, Switzerland, pp. 1219–1228 (2013)Google Scholar
  16. 16.
    Pawling, A., Yan, P., Candia, J.: Anomaly detection in streaming sensor data. Intell. Tech. Warehous. Min. Sens. Netw. Data, 99–117 (2008)Google Scholar
  17. 17.
    Liao, Z., Yang, S., Liang, J.: Detection of abnormal crowd distribution. In: IEEE/ACM International Conference on Green Computing and Communications, pp. 600–604 (2010)Google Scholar
  18. 18.
    Silva, J.A., Faria, E.R., Barros, R.C., Hruschka, E.R., de Carvalho, A.C.P.L.F., Gama, J.: Data stream clustering: a survey. ACM Comput. Surv. 46(1) (2016)CrossRefGoogle Scholar
  19. 19.
    Li, D.Y., Du, Y.: Artificial Intelligence with Uncertainty, 2nd edn. CRC Press, Boca Raton (2015)zbMATHGoogle Scholar
  20. 20.
    Piorkowski, M., Sarafijanovic-Djukic, N., Grossglauser, M.: A parsimonious model of mobile partitioned networks with clustering. In: The First International Conference on Communication Systems and Networks, Bangalore, India (2009)Google Scholar

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© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.School of Internet of ThingsNanjing University of Posts and TelecommunicationsNanjingChina

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