Wang Sen, Xiao Yu, Huang Qunying, Zhang Chun

Keywords： Social Media; Big Data; Urban Security; Mitigation; Data Mining

Abstract：

Social media data are attracting an increasing number of attention for their high accessibility and effectiveness on indicating urban disasters. Studies and appliances about social media data are focusing on situational awareness and coding, disaster response and relief, damage assessment, etc. Hurricane Sandy, happened in 2012, becomes the second largest cyclone to hit the USA since 1900, which caused catastrophic damage to many areas especially New York City. Based on Twitter and concerning database, the research outlines the temporal and spatial characters of the information by coding schema development, tweet classification and spatial web portal analysis. The logit regression model in the study examines the explanatory power for varying demographic and socioeconomic variables. Miscalculation and error of using big data to reflect real situation are discussed within the scope of mass, material, access, and motivation (MMAM). Result shows that there is statistical significance between tweet number and population as well as landmarks. Demographic factors like education level, age, sex also influence tweet number. This study contributes to previous studies by profiling hurricane Sandy’s impacts using big data mining and analyzing.

Funds：

• [1] WATTS D, CEBRIAN M, ELLIOT M. Public response to alerts and warnings using social media: report of a workshop on current knowledge and research gaps[R]. National Academise Press, 2013.
  [2] HUANG Q, XIAO Y. Geographic situational awareness: mining tweets for disaster preparedness, emergency response, impact, and recovery[J]. ISPRS International Journal of Geo-Information, 2015, 4(3): 1549-1568.
  [3] 张洋, 吕斌, 张纯. 可持续城市防灾减灾与城市规划: 概念与国际经验[M]. 科学出版社, 2012.
  [4] SAGIROGLU S, SINANC D. Big Data: A Review[C] // Collaboration technologies and systems (CTS). 2013 International Conference on. IEEE, 2013: 42-47.
  [5] HRISTIDIS V, CHEN S C, Li T, et al. Survey of data management and analysis in disaster situations[J]. Journal of Systems and Software, 2010, 83(10): 1701-1714.
  [6] City of Los Angeles. Recovery and reconstruction plan. Emergency Operations Organizations, City of Los Angeles, California, 1994.
  [7] Department of Building and Safety. City of Los Angeles zoning code[Z]. (2005). http://www.lacity.org/ladbs/permits/codes.htm.
  [8] WILSON R C. The Loma Prieta Quake: what one city learned[M]. International City Management Association, 1991.
  [9] KUNREUTHER H. Incentives for mitigation investment and more effective risk management: the need for public–private partnerships[J]. Journal of Hazardous Materials, 2001, 86(1): 171-185.
  [10] 胡以志. 灾后重建规划理论与实践：以新奥尔良重建为例，兼论对汶川地震灾后重建的借鉴[J]. 国际城市规划, 2008(4): 66-70.

  [11] 张纯, 张洋, 吕斌. 唐山大地震后重建与恢复的反思：城市规划视角的启示[J]. 城市发展研究, 2012(5): 119-126.
  

  [12] MANYIKA J, CHUI M, BROWN B, et al. Big data: the next frontier for innovation, competition, and productivity[J]. Analytics, 2011.
  [13] SAVAGE M, DEVINE F, CUNNINGHAM N, et al. A new model of social class? findings from the BBC’s Great British class survey experiment[J]. Sociology, 2013, 47(2): 219-250.
  [14] TAPIA A H, LALONE N, KIM H W. Run amok: group crowd participation in identifying the Bomb and Bomber from the Boston Marathon Bombing[C]. Proceedings of the 11th ISCRAM, 2014.
  [15] MEIER P. How crisis mapping saved lives in Haiti[OL]. National Geographic, 2013. http://voices.nationalgeographic.org/2012/07/02/crisismapping-haiti/.
  [16] HOUSTON J B, HAWTHORNE J, PERREAULT M F, et al. Social media and disasters: a functional framework for social media use in disaster planning, response, and research[J]. Disasters, 2015, 39(1): 1-22.
  [17] LINDSAY B R. Social media and disasters: current uses, future options, and policy considerations[J]. Disasters, 2014, 39(1): 1-22.
  [18] CAMERON M A, POWER R, ROBINSON B, et al. Emergency situation awareness from Twitter for crisis management[C]. Proceedings of the 21st International Conference Companion on World Wide Web. ACM, 2012: 695-698.
  [19] SUTTON J, PALEN L, SHKLOVSKI I. Backchannels on the front lines: emergent uses of social media in the 2007 Southern California Wildfires[C]. Proceedings of the 5th International ISCRAM Conference. Washington DC, 2008: 624-632.
  [20] VIEWEG S E. Situational awareness in mass emergency: a behavioral and linguistic analysis of microblogged communications[J]. Proguest LIC, 2012: 300.
  [21] SAKAKI T, OKAZAKI M, MATSUO Y. Earthquake shakes Twitter users: real-time event detection by social sensors[C]. Proceedings of the 19th international conference on World wide web. ACM, 2010: 851-860. 
  [22] SIGNORINI A, SEGRE A M, POLGREEN P M. The use of Twitter to track levels of disease activity and public concern in the US during the influenza A H1N1 pandemic[J]. PLos One, 2011, 6(5): e19467.
  [23] KENT J D, CAPELLO JR H T. Spatial patterns and demographic indicators of effective social media content during the Horsethief Canyon Fire of 2012[J]. Cartography and Geographic Information Science, 2013, 40(2): 78-89. ttp://journals.plos.org/plosone/article?id=10.1371/journal.pone.0019467.
  [24] MANDEL B, CULOTTA A, BOULAHANIS J, et al. A demographic analysis of online sentiment during Hurricane Irene[C]. Proceedings of the Second Workshop on Language in Social Media. Association for Computational Linguistics, 2012: 27-36. http://cs.iit.edu/~culotta/pubs/mandel12demo.pdf.
  [25] KUMAR S, BARBIER G, ABBASI M A, et al. Tweettracker: an analysis tool for humanitarian and disaster relief[C]. ICWSM, 2011. http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.639.4272&rep=rep1&type=pdf.
  [26] GAO Huiji, BARBIER Geoffrey, GOOLSBY Rebecca. Harnessing the crowdsourcing power of social media for disaster relief[J]. IEEE Intelligent Systems 26, 2011(3): 10-14. http://ieeexplore.ieee.org/document/5898447/?reload=true.
  [27] ASHKTORAB Z A, BROWN C B, NANDI M C, et al. Tweedr: mining twitter to inform disaster response. ISCRAM, 2014. http://cs.iit.edu/~culotta/pubs/ashktorab14tweedr.pdf.
  [28] PUROHIT H, CASTILLO C, DIAZ F, et al. Emergency-relief Coordination on Social Media: Automatically Matching Resource Requests and Offers[J]. First Monday, 2013: 19. http://firstmonday.org/ojs/index.php/fm/article/view/4848.
  [29] LONGUEVILLE B D, LURASCHI G, SMITS P, et al. Citizens as sensors for natural hazards: a VGI integration workflow[J]. Geomatica, 2010, 64: 41-59. http://www.isprs.org/proceedings/XXXVIII/4-W13/ID_02.pdf.
  [30] SCHNEBELE E, CERVONE G. Improving remote sensing flood assessment using volunteered geographical data[J]. Natural Hazards & Earth System Sciences, 2013, 13(3): 669-677.
  [31] SCHNEBELE E, OXENDINE C, CERVONE G, et al. Using non-authoritative sources during emergencies in urban areas[M] // Computational approaches for urban environments. Springer International Publishing, 2015: 337-361.
  [32] XIAO Y, HUANG Q, WU K. Understanding social media data for disaster management[J]. Natural Hazards, 2015, 79(3): 1663-1679.
  [33] LINDA G, HOLLOWAY C. Hurricane Sandy after action: report and recommendations to Mayor Michael R. Bloomberg[R]. The City of New York, New York, NY 36, 2013.
  [34] OH O, KWON K H, RAO H R. An exploration of social media in extreme events: rumor theory and twitter during the Haiti Earthquake 2010[C]. International Conference on Information Systems, Icis 2010, Saint Louis, Missouri, Usa, December. DBLP, 2010: 231.