IJBTT

Spatial Mining of Urban Emergency Events using Crowdsourcing
	© 2020 by IJCTT Journal
	Volume-68 Issue-3
	Year of Publication : 2020
	Authors : Sahana H R, Dr. M V VijayaKumar
	DOI :  10.14445/22312803/IJCTT-V68I3P104

How to Cite?

Sahana H R, Dr. M V VijayaKumar, "Spatial Mining of Urban Emergency Events using Crowdsourcing," International Journal of Computer Trends and Technology, vol. 68, no. 3, pp. 17-21, 2020. Crossref, 10.14445/22312803/IJCTT-V68I3P104

Abstract
With the advancements of the knowledge or information communication technologies, it`s critical to enhance the efficiency and accuracy of emergency management systems through modern processing techniques. The literature has witnessed the tremendous technical advancements in Sensor Networks, Internet/Web of Things, cloud computing, Mobile/Embedded computing, Spatial/Temporal processing, Big Data and these technologies have provided new opportunities and solutions to emergency management. The GIS (Geographic Information System) models and simulation capabilities are wont to exercise response and recovery plans during non-disaster times. They assist the decision-makers understand near real-time possibilities during an occasion. Here the authors have proposed the Spatial Mining of Urban Emergency Events using Crowdsourcing. At First, the basic definitions of the proposed method are given and secondly, the positive samples are selected to mining the spatial information of urban emergency events. Next, the location and GIS information are extracted from positive samples. The important spatial information is decided supported address and GIS information.

Keywords
Crowdsourcing, Geographic Information System, social media, urban computing.

Reference
[1] Goodchild, M.F. Citizens as Voluntary Sensors: Spatial Data Infrastructure in the World of Web 2.0. International Journal of Spatial Data Infrastructures Research 2, (2007), 24-32.
[2] (Business Models for the Next Generation of Software. http://www.oreillynet.com/pub/a/oreilly/tim/news/2005/09/30/what-is-web-20.html (2005).
[3] T. Sakaki, M. Okazaki, and Y. Matsuo. Earthquake Shakes Twitter Users: Real-time Event Detection by Social Sensors. In Proceedings of the 19th international World Wide Web conference, pp. 851-860, 2010.
[4] V. Krishnamurthy and H. Vincentb Poor. A Tutorial on Interactive Sensing in Social Networks. IEEE Transactions on Computational Social Systems, early access, 2014.
[5] Field K and O’Brien J. Cartoblography: Experiments in using and organizing the spatial context of micro-blogging. Transactions in GIS, 15: 5–23, 2010.
[6] Stefanidis A, Crooks A, and Radzikowski J. Harvesting ambient geospatial information from social media feeds. GeoJournal, 77, 2012.
[7] Earle P, Guy M, Buckmaster R, Ostrum C, Horvath S, and Vaughan A. OMG earthquake! Can Twitter improve earthquake response? Seismological Research Letters,81:246–51, 2010
[8] Mummidi, L. and Krumm, J. Discovering points of interest from users` map annotations. GeoJournal, 72(3-4):215-227, 2008.
[9] Crandall, D., Backstrom, L., Huttenlocher, D., and Kleinberg, J. Mapping the World`s Photos. In Proceedings of the 18th International World Wide Web Conference, 761-761, 2009.
[10] Pultar, E., Raubal, M., and Goodchild, M.F. GEDMWA:Geospatial Exploratory Data Mining Web Agent. In Proceedings of the 16th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, 2008.
[11] M. Trusov, V. Bodapati, and E. Bucklin. Determining influential users in internet social networks. J.Market.Res., 47:643–658, 2010.
[12] T. Sakaki, M. Okazaki, and Y. Matsuo. Tweet Analysis for Real-time Event Detection and Earthquake Reporting System Development. IEEE Transactions on Knowledge and Data Engineering, 25(4):919-931, 2013.
[13] A. Crooks, A. Croitoru, A. Stefanidis, and J. Radzikowski. Earthquake: Twitter as a Distributed Sensor System. Transaction in GIS, pp. 1-26, 2012.
[14] B. Longueville, R. Smith, and G. Luraschi. OMG, from here I can see the flames, a use case of mining location based social networks to acquire spatio-temporal data on forest fires. In Proceedings of the International Workshop on Location-Based Social Networks, pp. 73-80, 2009.
[15] X. Jin, S. Spangler, R. Ma, and J. Han. Topic Initiator Detection on the World Wide Web. In Proceedings of the 19th international World Wide Web conference, pp. 481-490, 2010.
[16] Abdul Razaque, ?Survey of Crowdsensing: Architecture, Classification and Security Challenges?, SSRG International Journal of Electronics and Communication Engineering Volume 6 Issue –10 Oct 2019
[17] Z. Xu, et al. Knowle: a Semantic Link Network based System for Organizing Large Scale Online News Events. Future Generation Computer Systems, 43-44:40-50, 2015.
[18] L. Wang, J. Tao, et al. G-Hadoop: MapReduce across distributed data centers for data-intensive computing. Future Generation Computer Systems, 29(3):739-750, 2013.
[19] Z. Xu et al. Semantic based representing and organizing surveillance big data using video structural description technology. The Journal of Systems and Software, 102 ?217-225?2015.
[20] Z. Xu, X. Luo, S. Zhang, X. Wei, L. Mei, and C. Hu. Mining Temporal Explicit and Implicit Semantic Relations between Entities using Web Search Engines. Future Generation Computer Systems, 37:468-477, 2014.
[21] C. Hu, Z. Xu, et al. Semantic Link Network based Model for Organizing Multimedia Big Data. IEEE Transactions on Emerging Topics in Computing, 2(3):376-387.
[22] X. Luo, Z. Xu, J. Yu, and X. Chen. Building Association Link Network for Semantic Link on Web Resources. IEEE transactions on automation science and engineering, 8(3):482-494, 2011.