Validating the Voice of the Crowd During Disasters

  • John Noel C. VictorinoEmail author
  • Maria Regina Justina E. Estuar
  • Alfredo Mahar Francisco A. Lagmay
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9708)


Since the late 1990 s, the intensity of tropical cyclones have increased over time, causing massive flooding and landslides in thePhilippines. Nationwide Operational Assessment of Hazards or Project NOAH was put in place as a responsive program for disaster prevention and mitigation. Part of the solution was to set up and FloodPatrol which provided the public with a web and mobile phone based application for reporting flood height. This paper addresses the problem of providing an interactive and visual method of validating crowdsourced flood reports for the purpose of helping frontline responders and decision makers in disaster management. The approach involves finding the neighborhood of the crowdsourced flood report and weather station data based on their geospatial proximity and time record. A report is classified as correct if it falls within the obtained confidence interval of the crowdsourced flood report neighborhood. The neighborhood of crowdsourced flood reports are correlated with weather station data, which serves as the ground truth in the validation process. Use cases are presented to provide examples of automatic validation. The results of this study is beneficial to disaster management coordinators, first-line responders, government unit officials and citizens. The system provides an interactive approach in validating reports from the crowd, aside from providing an avenue to report flood events in an area. Overall, this contributes to the study of how crowdsourced reports are verified and validated.


Crowdsourcing Validation Verification Disaster informatics 



Acknowledgment is given to Philippine Council for Industry, Energy and Emerging Technologies Research and Development (PCIEERD), Department of Science and Technology (DOST), for funding this research; Project NOAH, Ateneo Java Wireless Competency Center and the Ateneo Social Computing Science Laboratory for providing the needed assistance and support for this paper.


  1. 1.
    Andrews, L.: A Template for the Nearest Neighbor Problem (2001)., Accessed 29 March 2016
  2. 2.
    Bentley, J.L.: Survey of techniques for Fixed radius near neighbor searching. Technicalreport, Stanford Linear Accelerator Center, California, USA (1975)Google Scholar
  3. 3.
    Brown, S.: The Philippines Is the Most Storm-Exposed Country on Earth (2013)., Accessed 29 March 2016
  4. 4.
    Chazelle, B.: An improved algorithm for the fixed-radius neighbor problem. Inf. Process. Lett. 16(4), 193–198 (1983)MathSciNetCrossRefGoogle Scholar
  5. 5.
    Degrossi, L.C., de Albuquerque, J.P., Fava, M.C., Mendiondo, E.M.: Flood Citizen Observatory: a crowdsourcing-based approach for flood risk management in Brazil. In: Reformat, M. (ed.) The 26th International Conference on Software Engineering and Knowledge Engineering, Hyatt Regency, Vancouver, BC, Canada, July 1–3, 2013, pp. 570–575. Knowledge Systems Institute Graduate School (2014)Google Scholar
  6. 6.
    Duncan, A., Hogarth, P., Paringit, E., Lagmay, A.: Sharing UK LIDAR and flood mapping experience with the Philippines. In: International Conference on Flood Resilience: Experiences in Asia and Europe, pp. 73–75Google Scholar
  7. 7.
    Gao, H., Barbier, G., Goolsby, R., Zeng, D.: Harnessing the crowdsourcing power of social media for disaster relief. Technical report, DTIC Document (2011)Google Scholar
  8. 8.
    Goolsby, R.: Social media as crisis platform: The future of community maps/crisis maps. ACM Trans. Intell. Syst. Technol. 1(1), 7:1–7:11 (2010). CrossRefGoogle Scholar
  9. 9.
    Howe, J.: Crowdsourcing: Why the Power of the Crowd Is Driving the Future of Business, 1st edn. Crown Publishing Group, New York, NY, USA (2008)Google Scholar
  10. 10.
    Lagmay, A.: Disseminating near-real time hazards information and flood maps in the philippines through web-gis. Proj. NOAH Open File Rep. 1, 21–36 (2012)Google Scholar
  11. 11.
    McDougall, K.: Using volunteered information to map the queensland floods. In: Proceedings of the 2011 Surveying and Spatial Sciences Conference: Innovation in Action: Working Smarter (SSSC 2011), pp. 13–23. Surveying and Spatial Sciences Institute (2011)Google Scholar
  12. 12.
    Schlieder, C., Yanenko, O.: Spatio-temporal Proximity and Social Distance: A Confirmation Framework for Social Reporting. In: Proceedings of the 2Nd ACM SIGSPATIAL International Workshop on Location Based Social Networks, pp. 60–67. LBSN 2010, NY, USA (2010).
  13. 13.
    Surowiecki, J.: The wisdom of crowds: Why the many are smarter than the few and how collective wisdom shapes business. Economies, Soc. Nations xxi, 296 (2004)Google Scholar
  14. 14.
    Virola, R.: Statistically Speaking.. Climate Change - Will the Poor Suffer More? (2009)., Accessed 29 March 2016
  15. 15.
    Virola, R.: Statistically Speaking.. The Devastation of Ondoy and Pepeng! (2009)., Accessed 29 March 2016
  16. 16.
    Whiteman, H.: Philippines gets more than its share of disasters (2014)., Accessed 29 March 2016
  17. 17.
    Zook, M., Graham, M., Shelton, T., Gorman, S.: Volunteered geographic information and crowdsourcing disaster relief: a case study of the Haitian earthquake. World Med. Health Policy 2(2), 7–33 (2010)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • John Noel C. Victorino
    • 1
    Email author
  • Maria Regina Justina E. Estuar
    • 1
  • Alfredo Mahar Francisco A. Lagmay
    • 2
  1. 1.Ateneo Social Computing Science Laboratory, Department of Information Systems and Computer ScienceAteneo de Manila UniversityQuezon CityPhilippines
  2. 2.DOST Project NOAH, National Institute of Geological SciencesUniversity of the PhilippinesQuezon CityPhilippines

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