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

Motivation CROSS System Data Fusion and Validation Survey Conclusion Outline 2

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 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 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 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

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

CROSS System 8 Crowd Command Center Path Threaten Area Location

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

Broadcast Manager 10 (Broadcast volunteering opportunities)

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

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

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

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

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

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

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

19 Trust Determined by Supplemental Material [5-7]

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

Demo video A demo video can be found on the following link: 21

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

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 , May-June [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 [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 [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 , Sep [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 [6] Matthias Stevens and Ellie D’Hondt, “Crowdsourcing of Pollution Data using Smartphones,” Proc. UbiComp ’10, Sep

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 , Nov [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

25 Thank You!