Density and Intensity-Based Spatiotemporal Clustering with Fixed Distance and Time Radius

  • Aragats AmirkhanyanEmail author
  • Christoph Meinel
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10595)


Nowadays, social networks produce a huge amount of spatial and spatiotemporal data that provide interesting knowledge. This knowledge can be discovered by clustering algorithms and the result of that can be used for different applications. One of such applications is the geospatial event detection based on data from social networks. Many of such detection methods rely on clustering algorithms that should provide clusters with the high level of density in space and intensity in time. Meanwhile, traditional clustering methods are not always practical for spatial and spatiotemporal data because of the specific of such data. Therefore, in this paper, we present the density and intensity-based spatiotemporal clustering algorithm with fixed distance and time radius. This approach produces the clusters that have the density-based center in space and intensity-based center in time. In the paper, we provide the description of the method from the perspective of 2 aspects: spatial and temporal. We complete the paper with the full description of the algorithm methods and detailed explanation of the pseudo code.


Clustering Spatiotemporal clustering Spatial data Spatiotemporal data Analysis Location-based social networks 


  1. 1.
    Distance between geo coordinates.
  2. 2.
    Geocluster: Server-side clustering for mapping in Drupal based on Geohash.
  3. 3.
  4. 4.
  5. 5.
    Google’s recommendations for clustering geodata.
  6. 6.
    Ahern, S., Naaman, M., Nair, R., Yang, J.H.I.: World explorer: visualizing aggregate data from unstructured text in geo-referenced collections. In: Proceedings of the 7th ACM/IEEE-CS Joint Conference on Digital Libraries, JCDL 2007, pp. 1–10. ACM, New York, (2007). doi: 10.1145/1255175.1255177
  7. 7.
    Amirkhanyan, A., Cheng, F., Meinel, C.: Real-time clustering of massive geodata for online maps to improve visual analysis. In: 2015 11th International Conference on Innovations in Information Technology (IIT), pp. 308–313, November 2015Google Scholar
  8. 8.
    Amirkhanyan, A., Meinel, C.: Visualization and analysis of public social geodata to provide situational awareness. In: 2016 Eighth International Conference on Advanced Computational Intelligence (ICACI), pp. 68–73, February 2016Google Scholar
  9. 9.
    Amirkhanyan, A., Meinel, C.: Analysis of data from the twitter account of the berlin police for public safety awareness. In: Proceedings of the 2017 IEEE 21st International Conference on Computer Supported Cooperative Work in Design (CSCWD 2017), April 2017Google Scholar
  10. 10.
    Amirkhanyan, A., Meinel, C.: Analysis of the value of public geotagged data from twitter from the perspective of providing situational awareness. In: Dwivedi, Y.K., et al. (eds.) I3E 2016. LNCS, vol. 9844, pp. 545–556. Springer, Cham (2016). doi: 10.1007/978-3-319-45234-0_48 CrossRefGoogle Scholar
  11. 11.
    Ankerst, M., Breunig, M.M., Kriegel, H.P., Sander, J.: Optics: Ordering points to identify the clustering structure, pp. 49–60. ACM Press (1999)Google Scholar
  12. 12.
    Birant, D., Kut, A.: St-dbscan: an algorithm for clustering spatial-temporal data. Data Knowl. Eng. 60(1), 208–221 (2007). doi: 10.1016/j.datak.2006.01.013 CrossRefGoogle Scholar
  13. 13.
    De Longueville, B., Smith, R.S., Luraschi, G.: “Omg, from here, i can see the flames!’: a use case of mining location based social networks to acquire spatio-temporal data on forest fires. In: Proceedings of the 2009 International Workshop on Location Based Social Networks, LBSN 2009, pp. 73–80. ACM, New York (2009). doi: 10.1145/1629890.1629907
  14. 14.
    Duan, L., Xiong, D., Lee, J., Guo, F.: A local density based spatial clustering algorithm with noise. In: IEEE International Conference on Systems, Man and Cybernetics, SMC 2006, vol. 5, pp. 4061–4066, October 2006Google Scholar
  15. 15.
    Ester, M., Kriegel, H.P., Sander, J., Xu, X.: A density-based algorithm for discovering clusters in large spatial databases with noise, pp. 226–231. AAAI Press (1996)Google Scholar
  16. 16.
    Kisilevich, S., Mansmann, F., Nanni, M., Rinzivillo, S.: Spatio-temporal clustering. In: Maimon, O., Rokach, L. (eds.) Data Mining and Knowledge Discovery Handbook, pp. 855–874. Springer, Boston (2010). doi: 10.1007/978-0-387-09823-4_44 CrossRefGoogle Scholar
  17. 17.
    Parimala, M., Lopez, D., Senthilkumar, N.: A survey on density based clustering algorithms for mining large spatial databases (2011).
  18. 18.
    Ram, A., Jalal, S., Jalal, A.S., Kumar, M.: A density based algorithm for discovering density varied clusters in large spatial databasesGoogle Scholar
  19. 19.
    Sakai, A., Tamura, K., Kitakami, H.: A new density-based spatial clustering algorithm for extracting attractive local regions in georeferenced documents. In: Proceedings of the International MultiConference of Engineers and Computer Scientists, IMECS 2014, pp. 360–365. Newswood Limited, Hong Kong (2014).
  20. 20.
    Sakai, T., Tamura, K., Kitakami, H.: Density-based adaptive spatial clustering algorithm for identifying local high-density areas in georeferenced documents. In: 2014 IEEE International Conference on Systems, Man and Cybernetics (SMC), pp. 513–518, October 2014Google Scholar
  21. 21.
    Sakaki, T., Okazaki, M., Matsuo, Y.: Earthquake shakes twitter users: real-time event detection by social sensors. In: Proceedings of the 19th International Conference on World Wide Web, WWW 2010, pp. 851–860. ACM, New York (2010). doi: 10.1145/1772690.1772777
  22. 22.
    Singh, S.: Spatial temporal analysis of social media data.
  23. 23.
    Tamura, K., Ichimura, T.: Density-based spatiotemporal clustering algorithm for extracting bursty areas from georeferenced documents. In: 2013 IEEE International Conference on Systems, Man, and Cybernetics (SMC), pp. 2079–2084, October 2013Google Scholar
  24. 24.
    Walther, M., Kaisser, M.: Geo-spatial event detection in the twitter stream. In: Serdyukov, P., Braslavski, P., Kuznetsov, S.O., Kamps, J., Rüger, S., Agichtein, E., Segalovich, I., Yilmaz, E. (eds.) ECIR 2013. LNCS, vol. 7814, pp. 356–367. Springer, Heidelberg (2013). doi: 10.1007/978-3-642-36973-5_30 CrossRefGoogle Scholar

Copyright information

© IFIP International Federation for Information Processing 2017

Authors and Affiliations

  1. 1.Hasso Plattner Institute (HPI), University of PotsdamPotsdamGermany

Personalised recommendations