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A Web-Based System with Spatial Clustering to Observe the Changes of Emergency Distribution Using Social Big Data

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Behavior Engineering and Applications

Abstract

Understanding the changes of emergency distribution is an important step in the response to disaster. There are various emergency-related big data available on Internet; however it requires a complex system to use big data for emergency observation. In this study, we propose a Web-based system with spatial clustering to enable the observation to the changes of emergency distribution using social big data. Based upon the widely available Web technology, the proposed system is designed in three components, the social big data scrubbing, spatial big data clustering, and visualizing the changes of emergency distribution. And we applied the observations on two emergency incidents using the Twitter data, one is the Kumamoto earthquake 2016, and the other is the New York Hurricane Sandy 2012.

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References

  1. Block R (2007) Software review: Scanning for clusters in space and time a tutorial review of satscan. Social Science Computer Review 25(2):272–278

    Article  Google Scholar 

  2. Doan S, Vo BKH, Collier N (2011) An analysis of twitter messages in the 2011 tohoku earthquake. In: International Conference on Electronic Healthcare, Springer, pp 58–66

    Google Scholar 

  3. Lin NP, Chang CI, Chueh HE, Chen HJ, Hao WH, et al (2008) A deflected grid-based algorithm for clustering analysis. WSEAS Transactions on Computers 7(4):125–132

    Google Scholar 

  4. Singh SS, Chauhan N (2011) K-means v/s k-medoids: A comparative study. In: National Conference on Recent Trends in Engineering & Technology, vol 13

    Google Scholar 

  5. Wang J, Wu Y, Yen N, Guo S, Cheng Z (2016) Big data analytics for emergency communication networks: A survey. IEEE Communications Surveys Tutorials PP(99):1–1, DOI 10.1109/COMST.2016.2540004

    Google Scholar 

  6. Xu X, Ester M, Kriegel HP, Sander J (1998) A distribution-based clustering algorithm for mining in large spatial databases. In: Data Engineering, 1998. Proceedings., 14th International Conference on, IEEE, pp 324–331

    Google Scholar 

  7. Zhong L, Takano K, Ji Y, Yamada S (2016) Big data based service area estimation for mobile communications during natural disasters. In: 2016 30th International Conference on Advanced Information Networking and Applications Workshops (WAINA), pp 687–692, DOI 10.1109/WAINA.2016.146

    Google Scholar 

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Acknowledgements

This research was supported by JST-NSF joint funding, Strategic International Collaborative Research Program, SICORP, entitled “Dynamic Evolution of Smartphone-Based Emergency Communications Network”, from 2015 to 2018.

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Correspondence to Yilang Wu .

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Wu, Y., Wang, J. (2018). A Web-Based System with Spatial Clustering to Observe the Changes of Emergency Distribution Using Social Big Data. In: Wong, R., Chi, CH., Hung, P. (eds) Behavior Engineering and Applications. International Series on Computer Entertainment and Media Technology. Springer, Cham. https://doi.org/10.1007/978-3-319-76430-6_10

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  • DOI: https://doi.org/10.1007/978-3-319-76430-6_10

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-76429-0

  • Online ISBN: 978-3-319-76430-6

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