Identification of earthquake disaster hot spots with crowd sourced data

Lecture Notes in Geoinformation and Cartography

Volumn 0, Issue , 2013, Pages 97-119

  Hassanzadeh, Reza ^a   Nedovic Budic, Zorica ^a  

^a UNIVERSITY COLLEGE DUBLIN   (Ireland)

Author keywords

Cross tabulation analysis; Crowd sourced data; Earthquake disaster management; Fuzzy kappa; Hot spot analysis; Real time data; Similarity and dissimilarity

Indexed keywords

BEHAVIORAL RESEARCH; DECISION MAKING; DISASTER PREVENTION; DISASTERS; FUZZY INFERENCE;

CROSS TABULATION ANALYSE; CROWD SOURCED DATA; DISASTER MANAGEMENT; EARTHQUAKE DISASTER; EARTHQUAKE DISASTER MANAGEMENT; FUZZY KAPPA; HOT SPOT ANALYSE; HOTSPOTS; REAL-TIME DATA; SIMILARITY AND DISSIMILARITY;

EARTHQUAKES;

EID: 84925507905     PISSN: 18632246     EISSN: 18632351     Source Type: Book Series    
DOI: 10.1007/978-3-642-33218-0_8     Document Type: Conference Paper

References (36)

1. F. Abel, C. Hauff et al., Twitcident: fighting fire with information from social web streams. in Proceedings of the 21st International Conference Companion on World Wide Web (ACM, Lyon, 2012), pp. 305-308
2. B. Agrios, Thinking Spatially About Crowd Sourcing (ArcWatch e-Magazine, Esri, 2011)
3. American Educational Research Association (AERA), Standards for Educational and Psychological Testing (American Educational Research Association, Washington, 1999)
4. G. Barbier, R. Zafarani et al., Maximizing benefits from crowdsourced data. Comput. Math. Organ. Theory. 18(3),257-279 (2012)
5. N. Budhathoki, B. Bruce et al., Reconceptualizing the role of the user of spatial data infrastructure. GeoJournal 72(3), 149-160 (2008)
6. A. Coburn, R. Spence, Earthquake Protection (Wiley, Hoboken, 2002)
7. C. Corbane, G. Lemoine et al., Relationship between the spatial distribution of SMS messages reporting needs and building damage in 2010 Haiti disaster. Nat. Hazards Earth Syst. Sci. 12, 255-265 (2012)
8. P. Earle, Earthquake twitter. Nat. Geosci. 3(4), 221-222 (2010)
9. A. Fink, The Survey Kit: How to Sample in Surveys (Sage Publications, Thousand Oaks, 2003)
10. J. Fruchterman, Issues with crowdsourced data (2011), http://benetech.blogspot.ie/2011/03/issues-with-crowdsourced-data-part-2.html
11. M. Goodchild, Citizens as sensors: the world of volunteered geography. GeoJournal 69(4), 211-221 (2007)
12. M.F. Goodchild, J.A. Glennon, Crowdsourcing geographic information for disaster response: a research frontier. Int. J. Digit. Earth 3(3), 231-241 (2010)
13. R. Goolsby, Social media as crisis platform: the future of community maps/crisis maps. ACM Trans. Intell. Syst. Technol. 1(1), 1-11 (2010)
14. G. Grünthal, European Macroseismic Scale 1998 (EMS-98). (Cahiers du Centre Européen de Géodynamique et de Séismologie 15, Centre Européen de Géodynamique et de Séismologie, Luxembourg, 1998), p. 99
15. M. Guy, P. Earle et al., in Integration and Dissemination of Citizen Reported and Seismically Derived Earthquake Information via Social Network Technologies Advances in Intelligent Data Analysis IX, vol. 6065, ed. by P. Cohen, N. Adams, M. Berthold (Springer, Berlin, 2010), pp. 42-53
16. A. Hagen Zanker, An improved Fuzzy Kappa statistic that accounts for spatial autocorrelation. Int. J. Geog. Inform. Sci. 23(1), 61-73 (2009)
17. A. Hagen-Zanker, B. Straatman et al., Further developments of a fuzzy set map comparison approach. Int. J. Geog. Inform. Sci. 19(7), 769-785 (2005)
18. Harvard Humanitarian Initiative, Disaster Relief 2.0: The Future of Information Sharing in Humanitarian Emergencies. (Foundation and Vodafone Foundation Technology Partnership, Washington, 2011)
19. P.F. Kuo, X. Zeng, et al., Guidelines for Choosing Hot-Spot Analysis Tools Based on Characteristics, Network Restrictions, and Time Distributions. in The 91st Annual Meeting of the Transportation Research Board, Washington, 2011
20. S.B. Liu, L. Palen, The new cartographers: crisis map mashups and the emergence of neogeographic practice. Cartography Geog. Inform. Sci. 37, 69-90 (2010)
21. A.M. MacEachren, A. Jaiswal, et al., SensePlace2: Geotwitter analytics support for situation awareness. in IEEE Conference on Visual Analytics Science and Technology, Providence, 2011
22. P. Meier, R. Munro, The unprecedented role of SMS in disaster response: learning from Haiti. SAIS Rev 30(2), 91-103 (Johns Hopkins university press, 2010)
23. T. Milo, Crowd-based data sourcing. in 7th International Workshop of Databases in Networked Information Systems, Aiuz Wakamatsu, Japan, ed. by S. Kikuchi, A. Madaan, S. Sachdeva, S. Bhalla (Spring, Heidelberg, 2011)
24. P.A.P. Moran, Notes on continuous stochastic phenomena. Biometrika 37, 17-23 (1950)
25. Open Street Map, Haiti earthquake and OSM (2010), http://www.openstreetmap.org/. Accessed 23 May 2011
26. J.K. Ord, A. Getis, Local spatial autocorrelation statistics: distributional issues and an application. Geog. Anal. 27, 286-306 (1995)
27. J.R.G. Pontius, M. Millones, Death to kappa: birth of a quantity disagreement and allocation disagreement for accuracy assessment. Int. J. Remote. Sens. 32(15), 4407-4429 (2011)
28. K. Poser, D. Dransch, Volunteered geographic information for disaster management with application to rapid flood damage estimation. Geomatica 64(1), 89-98 (2010)
29. C. Power, A. Simms et al., Hierarchical fuzzy pattern matching for the regional comparison of land use maps. Int. J. Geog. Inform. Sci 15, 77-100 (2001)
30. K.-F. Richter, S. Winter, in Citizens as Database: Conscious Ubiquity in Data Collection Advances in Spatial and Temporal Databases, vol. 6849, ed. by. D. Pfoser, Y. Tao, K. Mouratidis et al. (Springer, Berlin, 2011), pp. 445-448
31. Statistical Centre of Iran (SCI),A Report of Bam Earthquake Impact on the Population and Building. (Information and Publication Office of Statistical Centre of Iran (SCI), Programming and Planning Organization of Iran, Iran, 2004), p. 251
32. E. Tarantino, B. Figorito, Mapping rural areas with widespread plastic covered vineyards using true color aerial data. Remote. Sens. 4(7), 1913-1928 (2012)
33. H. Visser, The Map Comparison Kit: Methods, Software and Applications (Research Institute for Knowledge Systems, Bilthoven, 2004)
34. H. Visser, T.D. Nijs, The map comparison kit. Environ. Model. Softw. 21(3), 346-358 (2006)
35. D.J. Wald, V. Quitoriano et al., Utilization of the internet for rapid community intensity maps. Seismol. Res. Lett. 70(6), 680-697 (1999)
36. M. Zook, G. Mark et al., Volunteered geographic information and crowdsourcing disaster relief: a case study of the Haitian earthquake. World Med. Health Policy 2(2), 7-33 (2010)