1. Crowdsourcing Information for Enhanced Disaster Situation Awareness and Emergency Preparedness and Response Edward T.-H Chu*, S. W. Chen** and Jane W. S. Liu** *National Yunlin University of Science and Technology **Institute of Information Science, Academia Sinica Open Data and Information for a Changing Planet B2 Critical Information and Communication Technologies for Disaster-Preparedness and Response Chair: Jan-Ming Ho

2. Motivation CROSS System Data Fusion and Validation Survey Conclusion Outline 2

3. 3 Disaster Surveillance System Functionalities of disaster surveillance system  Collect real-time information  Estimate boundaries of threatened areas Sensor networks play an important role in collecting data  Camera surveillance network  Wireless sensor network

4. 4 Limitation of Sensor Networks In major disasters  Sensors can be easily damaged  Official rescue resources are limited Collecting information in major disasters is difficult ？

5. 5 Social network services have been playing an role in collecting disaster information  Ushahidi, Sahana, Facebook, Twitter, Google Map Growing Trend in Disaster Management 5

6. 6 Limitations Existing social network based disaster management systems are limited in  coordinating volunteers efficiently  redundant tasks may be performed repeatedly Some places may be visited repeatedly while others may not be visited at all  Cannot provide a comprehensive view  Prolong the response time of exploring the whole disaster area

7. Contributions A crowdsourcing support system for disaster surveillance (CROSS) Cross plans routes for the volunteers to explore threatened areas A prototype including an emergency APP and sever services – Demo video 7

8. CROSS System 8 Crowd Command Center Path Threaten Area Location

9. Major components of CROSS Broadcast manager – Post a volunteer requirement Path planning manager – Determine a route for each volunteer Crowdsourced map manager – Integrate the messages received from volunteers and display it on a map 9

10. Broadcast Manager 10 (Broadcast volunteering opportunities)

11. Broadcast Manager (cont.) 11 (Report volunteer identity) (Display the locations of volunteers) smart phone view server view

12. Path Planning Manager 12 (The route of a volunteer) (The routes of volunteers) server view smart phone view

13. Crowdsourced Map Manager 13 (The crowdsourced map ) (Emergency call)

14. Research Issues Recruitment: Where to find appropriate volunteers to perform tasks? Incentives: How to motivate volunteers? Participants selection: How to dispatch volunteers to different areas? Path planning: How to determine a route for a volunteer? Quality control: How to validate the quality of reports? Presentation: How to present fused information to end users? 14

15. Open Self-Adjusting Everyone can report, read and modify reports Ushahidi: a platform which allows users to crowdsource disaster information [1][2] – verification buttons are used to correct reports 15

16. Voting and Ranking Techniques Voting techniques are proven to be successful in determining the trustworthiness of messages – YouTube & Facebook Each report has its own set of comments Registered users can provide feedback and grade reports [3-5] 16

17. 17

18. 18 Trust Associated with Group Membership [5] Red: reliable (official responders) Yellow: medium (registered volunteers) Green: low (unknown crowd)

19. 19 Trust Determined by Supplemental Material [5-7]

20. System Guided Crowdsourcing 20 The result reported by the greatest number of workers is referred as the final result Control group approach [8]Majority decision approach [8] The result is evaluated by many workers. A crowd rating is used to represent its quality

21. Demo video A demo video can be found on the following link: http://youtu.be/xCddMzGKyfohttp://youtu.be/xCddMzGKyfo 21

22. Conclusion Social network services have been playing an important role in colleting disaster information We developed CROSS to crowdsource disaster information We surveyed several methods of validating disaster information – Voting techniques are widely used We are developing a framework for fusing human sensor and physical sensor data 22

23. References (1/2) [1] Huiji Gao and Geoffrey Barbier and Rebecca Goolsby, “Harnessing the crowdsourcing power of social media for disaster relief,” IEEE Computer, 2011, IEEE Intelligent Systems, vol. 26, no. 3, pp. 10-14, May-June 2011. [2] Michael F. Goodchild* and J. Alan Glennon, “Crowdsourcing geographic information for disaster response: a research frontier,” International Journal of Digital Earth, Vol. 3, No. 3, September 2010. [3] Andrea H. Tapia, Kartikeya Bajpai, Jim Jansen, John Yen and Lee Giles, “Seeking the Trustworthy Tweet: Can Microblogged Data Fit the Information Needs of Disaster Response and Humanitarian Relief Organizations,” The 8th International Conference on Information Systems for Crisis Response and Management (ISCRAM), May 2011. [4] Geoffrey Barbier, Reza Zafarani, Huiji Gao, Gabriel Fung and Huan Liu, “Maximizing Benefits from Crowdsourcing Data,” Computational and Mathematical Organization Theory, vol. 18, no. 3, pp. 257-279, Sep. 2012. [5] Alfred C. Weaver, Joseph P. Boyle, and Liliya I. Besaleva, “Applications and Trust Issues when Crowdsourcing a Crisis,” in Proc. 21th IEEE Int. ICCCN, 2012, pp. 1-5. [6] Matthias Stevens and Ellie D’Hondt, “Crowdsourcing of Pollution Data using Smartphones,” Proc. UbiComp ’10, Sep. 2010. 23

24. 24 References (2/2) [7] Nik Bessis, Eleana Asimakopoulou, Tim French, Peter Norrington and Fatos Xhafa, “The Big Picture, from Grids and Clouds to Crowds: A Data Collective Computational Intelligence Case Proposal for Managing Disasters,” Proc. P2P, Parallel, Grid, Cloud and Internet Computing Conf. (3PGCIC,10), pp. 351-356, Nov. 2010. [8] Matthias Hirth, Tobias Hoßfeld and Phuoc Tran-Gia, “Cost-Optimal Validation Mechanisms and Cheat-Detection for Crowdsourcing Platforms,” Proc. 5 th IEEE Int. IMIS, 2011, pp. 316-321.

25. 25 Thank You!