Abstract
This paper explores the value of the application of Crowd Sourced (CS) data in identification of areas damaged in the aftermath of an earthquake. A survey was conducted to collect CS data based on two stage cluster sampling method from people who experienced the earthquake in Bam city, Iran in 2003. The CS data submission time was considered for data analysis, including continuous, discrete and complete data submission. The CS data reporting on the level of building destruction, the number of fatalities and the number of injuries was used to identify hot spot areas for dispatching response operation teams. To test the value of CS data in identification of hot spots, the results were compared with the Actual Earthquake (AE) data by using of Fuzzy Kappa index, Fuzzy Inference System, and cross tabulation to calculate similarity and dissimilarity, quality and allocation disagreement between them. The similarity and dissimilarity measures indicate that there is a low to moderate similarity between hot spot maps based on the application of CS and those based on the AE data. They suggest that CS data has a moderate potential role in identifying highly damaged areas (hot spots) and low damaged areas (cold spots). The results of this study show that the CS data is better suited for more general determination of hot and cold spot areas than to provide exact locations where the resources could be dispatched. Consequently, we conclude that CS data is useful for decision making process by disaster managers if combined with the other sources of information to allocate the limited resources in affected areas.
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References
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
B. Agrios, Thinking Spatially About Crowd Sourcing (ArcWatch e-Magazine, Esri, 2011)
American Educational Research Association (AERA), Standards for Educational and Psychological Testing (American Educational Research Association, Washington, 1999)
G. Barbier, R. Zafarani et al., Maximizing benefits from crowdsourced data. Comput. Math. Organ. Theory. 18(3) ,257–279 (2012)
N. Budhathoki, B. Bruce et al., Reconceptualizing the role of the user of spatial data infrastructure. GeoJournal 72(3), 149–160 (2008)
A. Coburn, R. Spence, Earthquake Protection (Wiley, Hoboken, 2002)
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)
P. Earle, Earthquake twitter. Nat. Geosci. 3(4), 221–222 (2010)
A. Fink, The Survey Kit: How to Sample in Surveys (Sage Publications, Thousand Oaks, 2003)
J. Fruchterman, Issues with crowdsourced data (2011), http://benetech.blogspot.ie/2011/03/issues-with-crowdsourced-data-part-2.html
M. Goodchild, Citizens as sensors: the world of volunteered geography. GeoJournal 69(4), 211–221 (2007)
M.F. Goodchild, J.A. Glennon, Crowdsourcing geographic information for disaster response: a research frontier. Int. J. Digit. Earth 3(3), 231–241 (2010)
R. Goolsby, Social media as crisis platform: the future of community maps/crisis maps. ACM Trans. Intell. Syst. Technol. 1(1), 1–11 (2010)
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
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
A. Hagen Zanker, An improved Fuzzy Kappa statistic that accounts for spatial autocorrelation. Int. J. Geog. Inform. Sci. 23(1), 61–73 (2009)
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)
Harvard Humanitarian Initiative, Disaster Relief 2.0: The Future of Information Sharing in Humanitarian Emergencies. (Foundation and Vodafone Foundation Technology Partnership, Washington, 2011)
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)
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)
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
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)
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)
P.A.P. Moran, Notes on continuous stochastic phenomena. Biometrika 37, 17–23 (1950)
Open Street Map, Haiti earthquake and OSM (2010), http://www.openstreetmap.org/. Accessed 23 May 2011
J.K. Ord, A. Getis, Local spatial autocorrelation statistics: distributional issues and an application. Geog. Anal. 27, 286–306 (1995)
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)
K. Poser, D. Dransch, Volunteered geographic information for disaster management with application to rapid flood damage estimation. Geomatica 64(1), 89–98 (2010)
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)
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
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
E. Tarantino, B. Figorito, Mapping rural areas with widespread plastic covered vineyards using true color aerial data. Remote. Sens. 4(7), 1913–1928 (2012)
H. Visser, The Map Comparison Kit: Methods, Software and Applications (Research Institute for Knowledge Systems, Bilthoven, 2004)
H. Visser, T.D. Nijs, The map comparison kit. Environ. Model. Softw. 21(3), 346–358 (2006)
D.J. Wald, V. Quitoriano et al., Utilization of the internet for rapid community intensity maps. Seismol. Res. Lett. 70(6), 680–697 (1999)
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)
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The authors greatly appreciate the support provided by the Disaster Management Center of Kerman Municipality (KDMC) in Iran.
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Hassanzadeh, R., Nedovic-Budic, Z. (2013). Identification of Earthquake Disaster Hot Spots with Crowd Sourced Data. In: Zlatanova, S., Peters, R., Dilo, A., Scholten, H. (eds) Intelligent Systems for Crisis Management. Lecture Notes in Geoinformation and Cartography. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33218-0_8
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