Emergency Situation Awareness During Natural Disasters Using Density-Based Adaptive Spatiotemporal Clustering

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9052)


With the increase in the popularity of social media as well as the emergence of easy-to-use geo-mobile applications on smartphones, a huge amount of geo-annotated data is posted on social media sites. To enhance emergency situation awareness, these geo-annotated data are expected to be used in a new medium. In particular, geotagged tweets on Twitter are used by local governments to determine the situation accurately during natural disasters. Geotagged tweets are referred to as georeferenced documents; they include not only a short text message but also the posting time and location. In this paper, we propose a new spatiotemporal analysis method for emergency situation awareness during natural disasters using \((\epsilon ,\tau )\)-density-based adaptive spatiotemporal clustering. Such clustering can identify bursty local areas by using adaptive spatiotemporal clustering criteria considering local spatiotemporal densities. Extracting \((\epsilon ,\tau )\)-density-based adaptive spatiotemporal clusters allows the proposed method to analyze emergency situations such as natural disasters in real time. The experimental results showed that the proposed method can analyze emergency situations related to the weather in Japan more sensitively compared with our previous method.


Emergency situation awareness Density-based spatiotemporal clustering Natural disaster Geotagged tweet Naive bayes classifier 



This work was supported by JSPS KAKENHI Grant Number 26330139 and Hiroshima City University Grant for Special Academic Research (General Studies).


  1. 1.
    Aramaki, E., Maskawa, S., Morita, M.: Twitter catches the flu: Detecting influenza epidemics using twitter. In: Proceedings of the Conference on EMNLP 2011, pp. 1568–1576 (2011)Google Scholar
  2. 2.
    Avvenuti, M., Cresci, S., Marchetti, A., Meletti, C., Tesconi, M.: Ears (earthquake alert and report system): A real time decision support system for earthquake crisis management. In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1749–1758 (2014)Google Scholar
  3. 3.
    Ester, M., Kriegel, H.P., Sander, J., Xu, X.: A density-based algorithm for discovering clusters in large spatial databases with noise. In: Second International Conference on Knowledge Discovery and Data Mining, pp. 226–231 (1996)Google Scholar
  4. 4.
    Hui, C., Tyshchuk, Y., Wallace, W.A., Magdon-Ismail, M., Goldberg, M.: Information cascades in social media in response to a crisis: a preliminary model and a case study. In: Proceedings of the 21st International Conference Companion on WWW, pp. 653–656 (2012)Google Scholar
  5. 5.
    Hwang, M.H., Wang, S., Cao, G., Padmanabhan, A., Zhang, Z.: Spatiotemporal transformation of social media geostreams: a case study of twitter for flu risk analysis. In: Proceedings of the 4th ACM SIGSPATIAL IWGS, pp. 12–21 (2013)Google Scholar
  6. 6.
    Kamath, K.Y., Caverlee, J., Lee, K., Cheng, Z.: Spatio-temporal dynamics of online memes: a study of geo-tagged tweets. In: Proceedings of the 22nd International Conference on WWW, pp. 667–678 (2013)Google Scholar
  7. 7.
    Kim, K.S., Lee, R., Zettsu, K.: mTrend: discovery of topic movements on geo-microblogging messages. In: Proceedings of the 19th ACM SIGSPATIAL International Conference on Advances in GIS, pp. 529–532 (2011)Google Scholar
  8. 8.
    Kreiner, K., Immonen, A., Suominen, H.: Crisis management knowledge from social media. In: Proceedings of the 18th ADCS, pp. 105–108 (2013)Google Scholar
  9. 9.
    Kumar, A., Jiang, M., Fang, Y.: Where not to go?: detecting road hazards using twitter. In: Proceedings of the 37th International ACM SIGIR Conference on Research & Development in Information Retrieval, pp. 1223–1226 (2014)Google Scholar
  10. 10.
    Mendoza, M., Poblete, B., Castillo, C.: Twitter under crisis: can we trust what we rt? In: Proceedings of the First Workshop on SOMA, pp. 71–79 (2010)Google Scholar
  11. 11.
    Naaman, M.: Geographic information from georeferenced social media data. SIGSPATIAL Special 3(2), 54–61 (2011)CrossRefGoogle Scholar
  12. 12.
    Sakai, T., Tamura, K.: Identifying bursty areas of emergency topics in geotagged tweets using density-based spatiotemporal clustering algorithm. Proceedings of the IWCIA 2014, 95–100 (2014)Google Scholar
  13. 13.
    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 WWW, pp. 851–860 (2010)Google Scholar
  14. 14.
    Sander, J., Ester, M., Kriegel, H.P., Xu, X.: Density-based clustering in spatial databases: the algorithm gdbscan and its applications. Data Min. Knowl. Disc. 2(2), 169–194 (1998)CrossRefGoogle Scholar
  15. 15.
    Thom, D., Bosch, H., Koch, S., Worner, M., Ertl, T.: Spatiotemporal anomaly detection through visual analysis of geolocated twitter messages. In: Pacific Visualization Symposium (PacificVis), 2012 , pp. 41–48. IEEE (2012)Google Scholar
  16. 16.
    Vieweg, S., Hughes, A.L., Starbird, K., Palen, L.: Microblogging during two natural hazards events: what twitter may contribute to situational awareness. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 1079–1088 (2010)Google Scholar
  17. 17.
    Yin, J., Lampert, A., Cameron, M., Robinson, B., Power, R.: Using social media to enhance emergency situation awareness. IEEE Intell. Syst. 27(6), 52–59 (2012)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Tatsuhiro Sakai
    • 1
  • Keiichi Tamura
    • 1
  • Hajime Kitakami
    • 1
  1. 1.Graduate School of Information SciencesHiroshima City UniversityHiroshimaJapan

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