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Monitoring Urban Waterlogging Disaster Using Social Sensors

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The Semantic Web and Web Science (CSWS 2014)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 480))

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Abstract

Nowadays, urban waterlogging has been one of the most serious global urban hazards in some big cities in the world especially in Chinese cities. While, existing methods fail to cover all locations and forecast the waterlogging trend. Meanwhile, the past one decade has witnessed an astounding outburst in the number of online social media services. For example, when a rainstorm occurs, people make a large number of tweets related to the rainstorm, which enables detection of urban waterlogging promptly, simply by analyzing the tweets. In this paper, we present a semantic method that can monitor urban waterlogging using social sensors. Currently, we use ontology and fuzzy reasoning to analyze waterlogging locations and its severity and build Apps to monitor and forecast waterlogging in more than ten cities in China. With this method, people can easily monitor all the possible urban waterlogging locations with severity and trend, which may reduce the possibility of traffic congestion in a rainstorm.

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Notes

  1. 1.

    http://weibo.com/

  2. 2.

    Scan the two-dimensional code to download Apps http://www.cheosgrid.cn:8000/.

    figure a
    figure b
  3. 3.

    http://lbsyun.baidu.com/

  4. 4.

    http://www.wechat.com/

References

  1. Abdelhaq, H., Sengstock, C., Gertz, M.: EvenTweet: online localized event detection from twitter. Proc. VLDB Endowment 6(12), 1326–1329 (2013)

    Article  Google Scholar 

  2. Agichtein, E., Castillo, C., Donato, D., Gionis, A., Mishne, G.: Finding high-quality content in social media. In: WSDM ’08: Proceedings of the 2008 International Conference on Web Search and Data Mining, pp. 183–194 (2008)

    Google Scholar 

  3. Albakour, M., Macdonald, C., Ounis, L., et al.: Identifying local events by using microblogs as social sensors. In: Proceedings of the 10th Conference on Open Research Areas in Information Retrieval, pp. 173–180. le centre de hautes etudes internationales d’informatique documentaire (2013)

    Google Scholar 

  4. Backstrom, L., Kleinberg, J., Kumar, R., Novak, J.: Spatial variation in search engine queries. In: Proceedings of the 17th International Conference on World Wide Web, pp. 357–366. ACM (2008)

    Google Scholar 

  5. Barbier, G., Liu, H.: Data mining in social media. In: Aggarwal, C.C. (ed.) Social Network Data Analytics, pp. 327–352. Springer, New York (2011)

    Chapter  Google Scholar 

  6. Becker, H., Naaman, M., Gravano, L.: Learning similarity metrics for event identification in social media. In: WSDM ’10: Proceedings of the third ACM international conference on Web search and data mining, pp. 291–300 (2010)

    Google Scholar 

  7. Becker, H., Naaman, M., Gravano, L.: Identifying content for planned events across social media sites. In: WSDM ’12: Proceedings of the fifth ACM international conference on Web search and data mining, pp. 533–542 (2012)

    Google Scholar 

  8. Chen, L., Zhang, C., Wilson, C.: Tweeting under pressure: analyzing trending topics and evolving word choice on sina weibo. In: Proceedings of the First ACM Conference on Online Social Networks, pp. 89–100. ACM (2013)

    Google Scholar 

  9. Christakis, N.A., Fowler, J.H.: Social Network Sensors for Early Detection of Contagious Outbreaks (2010)

    Google Scholar 

  10. Crooks, A., Croitoru, A., Stefanidis, A., Radzikowski, J.: #Earthquake: Twitter as a distributed sensor system. Trans. GIS 17(1), 124–147 (2013)

    Article  Google Scholar 

  11. Jin, X., Wang, C., Luo, J., Yu, X., Han, J.: LikeMiner: a system for mining the power of ‘like’ in social media networks. In: KDD ’11 Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 753–756 (2011)

    Google Scholar 

  12. Kryvasheyeu, Y., Chen, H., Moro, E., van Hentenryck, P., Cebrian, M.: Performance of social network sensors during hurricane sandy (2014). arXiv:1402.2482

  13. Li, C.: Ecohydrology and good urban design for urban storm water-logging in Beijing, China. Ecohydrology & Hydrobiology 12(4), 287–300 (2012)

    Article  Google Scholar 

  14. Lingad, J., Karimi, S., Yin, J.: Location extraction from disaster-related microblogs. In: Proceedings of the 22nd International Conference on World Wide Web Companion, pp. 1017–1020. International World Wide Web Conferences Steering Committee (2013)

    Google Scholar 

  15. Liu, Y., Alexandrova, T., Nakajima, T.: Using stranger as sensors: temporal and geo-sensitive question answering via social media. In: Proceedings of the 22nd International Conference on World Wide Web, pp. 803–814. International World Wide Web Conferences Steering Committee (2013)

    Google Scholar 

  16. Majid, A., Chen, I., Chen, G., Mirza, H.T., Hussain, I., Woodward, J.: A context-aware personalized travel recommendation system based on geotagged social media data mining. Int. J. Geogr. Inf. Sci. 27(4), 662–684 (2013)

    Article  Google Scholar 

  17. 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, pp. 851–860. ACM (2010)

    Google Scholar 

  18. Sakaki, T., Toriumi, F., Uchiyama, K., Matsuo, Y., Shinoda, K., Kazama, K., Kurihara, K., Noda, I.: The possibility of social media analysis for disaster management. In: Humanitarian Technology Conference (R10-HTC), 2013 IEEE Region 10, pp. 238–243. IEEE (2013)

    Google Scholar 

  19. Schade, S., Díaz, L., Ostermann, F., Spinsanti, L., Luraschi, G., Cox, S., Nuñez, M., De Longueville, B.: Citizen-based sensing of crisis events: sensor web enablement for volunteered geographic information. Appl. Geomatics 5(1), 3–18 (2013)

    Article  Google Scholar 

  20. Schröter, K., Kreibich, H., Merz, B.: Rapid flood loss estimation for large scale floods in germany. In: EGU General Assembly Conference Abstracts, vol. 15, p. 8798 (2013)

    Google Scholar 

  21. Tang, J., Wang, M., Hua, X.-S., Chua, T.-S.: Social media mining and search. Multimedia Tools Appl. 56(1), 1–7 (2011)

    Article  Google Scholar 

  22. 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. ACM (2010)

    Google Scholar 

  23. Xu, G., Li, L.: Social Media Mining and Social Network Analysis: Emerging Research (2013)

    Google Scholar 

  24. Yadav, K., Chakraborty, D., Soubam, S., Prathapaneni, N., Nandakumar, V., Naik, V., Rajamani, N., Subramaniam, L.V., Mehta, S., De, P.: Human sensors: Case-study of open-ended community sensing in developing regions. In: 2013 IEEE International Conference on Pervasive Computing and Communications Workshops (PERCOM Workshops), pp. 389–392. IEEE (2013)

    Google Scholar 

  25. Yan, R.: Large-scale social media mining in facebook. In: WSM ’11 Proceedings of the 3rd ACM SIGMM International Workshop on Social Media, pp. 21–22 (2011)

    Google Scholar 

  26. Yang, C.C., Yang, H., Tang, X., Jiang, L.: Identifying implicit relationships between social media users to support social commerce. In: ICEC ’12 Proceedings of the 14th Annual International Conference on Electronic Commerce, pp. 41–47 (2012)

    Google Scholar 

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Acknowledgement

This work is funded by LY13F020005 of NSF of Zhejiang, NSFC61070156, YB2013120143 of Huawei and Fundamental Research Funds for the Central Universities.

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Authors and Affiliations

  1. Department of Computer Science, Zhejiang University, Hangzhou, China

    Ningyu Zhang, Guozhou Zheng, Huajun Chen, Xi Chen & Jiaoyan Chen

Authors
  1. Ningyu Zhang
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  2. Guozhou Zheng
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  3. Huajun Chen
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  4. Xi Chen
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  5. Jiaoyan Chen
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Corresponding author

Correspondence to Guozhou Zheng .

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Editors and Affiliations

  1. Peking University, Beijing, China

    Dongyan Zhao

  2. Guangdong University of Foreign Studies, Guangzhou, China

    Jianfeng Du

  3. East China University, Shanghai, China

    Haofen Wang

  4. Southeast University, Nanjing, China

    Peng Wang

  5. Wuhan University, Wuhan, China

    Donghong Ji

  6. The University of Aberdeen, Aberdeen, United Kingdom

    Jeff Z. Pan

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Zhang, N., Zheng, G., Chen, H., Chen, X., Chen, J. (2014). Monitoring Urban Waterlogging Disaster Using Social Sensors. In: Zhao, D., Du, J., Wang, H., Wang, P., Ji, D., Pan, J. (eds) The Semantic Web and Web Science. CSWS 2014. Communications in Computer and Information Science, vol 480. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-45495-4_20

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  • DOI: https://doi.org/10.1007/978-3-662-45495-4_20

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-662-45494-7

  • Online ISBN: 978-3-662-45495-4

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