• Yuya ShibuyaEmail author


In this chapter, the author describes the cases for the study’s analyses, research questions, and methodology to achieve this study’s goal that is to show the potential usefulness of social media data for detecting socio-economic recovery. First, in Sect. 3.1, the author introduces two large-scale disaster cases for the analysis of this study: the Great East Japan Earthquake and Tsunami of 2011 and Hurricane Sandy in 2012. In Sect. 3.2, the research questions and the research flowchart are described. Next, in Sect. 3.3, the data and the methodology regarding socio-economic recovery activities are provided. Lastly, in Sect. 3.4, the author explains the data and the methodology for applying the “people as sensors” approach. In Sect. 3.5, the author briefly summarizes this chapter.


  1. Arai, N., & Meno, F. (2014). Moving processes and conditions of residents under the housing lease program for disaster victims in Sendai City. Journal of Architecture and Planning, 79(700), 1401–1406., (in Japanese).CrossRefGoogle Scholar
  2. Asahi Shimbun. (2011, April 16th). Cyukosya Jyuyo ga Kyuzo [Increasing demand for used cars]., p. 8. (in Japanese).Google Scholar
  3. Asahi Shimbun. (2011, May 30th). Hisaichi no takadai tika jyosyo [land price increase in disaster-impacted areas]., p. 39. (in Japanese).Google Scholar
  4. Barr, J., Cohen, J. P., & Kim, E. (2017). Storm surges, informational shocks, and the price of Urban Real Estate: An application to the case of Hurricane Sandy. Rutgers University, Newark 2017-002, Department of Economics, Rutgers University, Newark., working paper.
  5. Beigi, G, Hu, X., Maciejewski, R., & Liu, H. (2016). An overview of sentiment analysis in social media and its applications in disaster relief (pp. 313–340). In: Pedrycz W., Chen SM. (eds) Sentiment Analysis and Ontology Engineering. Studies in Computational Intelligence, 639. Cham: Springer International Publishing. Scholar
  6. Bin, O., & Landry, C. E. (2013). Changes in implicit flood risk premiums: Empirical evidence from the housing market. Journal of Environmental Economics and Management, 65(3). Scholar
  7. Blake, E. S., Kimberlain, T. B., Berg, R. J., Cangialosi, J. P., & Beven Ii, J. L. (2013). Tropical cyclone report: Hurricane sandy (pp 1–10).
  8. Bloomberg, M. (2013). A stronger, more resilient. New York.
  9. Chatfield, A. T., Scholl, H. J., & Brajawidagda, U. (2014). #Sandy tweets: Citizens’ Co-Production of time-critical information during an unfolding catastrophe. In: Proceedings of 2014 47th Hawaii International Conference on System Sciences (pp. 1947–1957). IEEE.
  10. Haan, M. A., & de Boer, H. W. (2010). Has the internet eliminated regional price differences? Evidence from the used car market. Economist, 158(4), 373–386. Scholar
  11. Hallstrom, D. G., & Smith, V. K. (2005). Market responses to hurricanes. Journal of Environmental Economics and Management, 50(3), 541–561. Scholar
  12. Kearns, J., Pak, S., & Buhayar, N. (2012). Now get ready for a huge economic boost from hurricane sandy.
  13. Kihm, A., & Vance, C. (2016). The determinants of equity transmission between the new and used car markets: A hedonic analysis. Journal of the Operational Research Society, 67(10), 1250–1258. Scholar
  14. Kooreman, P., & Haan, M. A. (2006). Price anomalies in the used car market. De Economist, 154(1), 41–62. Scholar
  15. Lu, X., & Brelsford, C. (2014). Network structure and community evolution on twitter: Human behavior change in response to the 2011. Scientific Reports, 4(6773), 1–11.
  16. Mainichi Shimbun. (2011, May 11th). Higashinihon daishinsai: Hisaichi de cyukosya koto [the great east japan earthquake and tsunami: The prices of used-car increased in the damaged areas]., p. 26 (in Japanese).Google Scholar
  17. Mainichi Shimbun. (2012, September 20th). Kizyun chika [standard price of lands]., p. 27 (in Japanese).Google Scholar
  18. McCoy, S. J., & Zhao, X. (2018). A city under water: A geospatial analysis of storm damage, changing risk perceptions, and investment in residential housing. Journal of the Association of Environmental and Resource Economists, 5(2), 301–330. Scholar
  19. McKenzie, R., & Levendis, J. (2010). Flood hazards and urban housing markets: The effects of Katrina on New Orleans. Journal of Real Estate Finance and Economics, 40(1), 62–76. Scholar
  20. Nakabayashi, I. (2016). Saigai fukkou kenkyu no igi to tembou [meanings and prospects of disaster recovery research]. Fukkou, 7(3), 34–41. (in Japanese).Google Scholar
  21. Nikkei Sangyo Shimbun. (2011, May 17th). Cyukosya toroku hisaichi de kyuzou [register of used cars increased in the disaster-striken area]., p. 3. (in Japanese).Google Scholar
  22. Nikkei Shimbun. (2012a, March 11th). Higashinihon daishinsai ichi nen fukkou gan nen youyaku miyagi ken chiji murai yoshihiro shi [one year after the great east japan earth- quake and tsunami: The first year of recovery: Mayor of miyagi prefecture, yasuhiro murai]., p.16. (in Japanese).Google Scholar
  23. Cfe, Nyce, Dumm, R. E., Sirmans, G. S., & Smersh, G. (2015). The capitalization of insurance premiums in house prices. Journal of Risk and Insurance, 82(4), 891–919. Scholar
  24. Prieto, M., Caemmerer, B., & Baltas, G. (2015). Using a hedonic price model to test prospect theory assertions: The asymmetrical and nonlinear effect of reliability on used car prices. Journal of Retailing and Consumer Services, 22(2015), 206–212. Scholar
  25. Racioppi, D. (2014). New homes in demand on Long Beach Island.
  26. Ragini, J. R., Rubesh Anand, P. M., & Bhaskar, V. (2018). Big data analytics for disaster response and recovery through sentiment analysis. International Journal of Information Management, 42(2018), 13–24. Scholar
  27. Rosen, S. (1974). Hedonic prices and implicit markets: Product differentiation in pure competition. Journal of Political Economy, 82(1), 34–55.CrossRefGoogle Scholar
  28. Rubin, C. B. (1985). The community recovery process in the united states after a major natural disaster. International Journal of Mass Emergencies and Disasters, 3(2), 9–28.Google Scholar
  29. Silver, A., & Matthews, L. (2017). The use of Facebook for information seeking, decision support, and self-organization following a significant disaster, 4462. Scholar
  30. Sumiyoshi, Y., Inagaki, K., & Sadohara, S. (2018). Shizen saigai ga fudousan ni ataeru eikyou bunseki [analysis of how a natural disaster influence property price]. In Proceedings of Architectural Institute of Japan Annual Meeting (pp. 885–886), (in Japanese).Google Scholar
  31. Tatsuki, S., & Hayashi, H. (2002). Seven critical element model of life recovery: General linear model analyses of the 2001 Kobe panel survey data. In Proceedings of 2nd Workshop for Comparative Study on Urban Earthquake Disaster Management.Google Scholar
  32. The Cabinet Office. (2012). Annual economic finance report., (in Japanese).
  33. The Tohoku Finance Bureaus. (2017). Zaimu Kyoku Cyosa niyoru Kakuchiiki no Syouhi ni Kansuru Tokutyouteki na Doukou [Reports on Regional trends of consumption]., (in Japanese).
  34. Ukkusuri, S., Zhan, X., Sadri, A., & Ye, Q. (2014). Use of social media data to explore crisis informatics. Transportation Research Record: Journal of the Transportation Research Board, 2459(1), 110–118. Scholar
  35. Williams, S. A., Terras, M. M., & Warwick, C. (2013). What do people study when they study Twitter? Classifying Twitter related academic papers. Journal of Documentation, 69(3), 384–410. Scholar
  36. Yasuda, S., Yukutake, N., & Naoi, M. (2018). The impact of earthquake risk on housing market before and after the Great East Japan earthquake. Keio-IES discussion paper series., (in Japanese).

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.Graduate School of Interdisciplinary Information StudiesUniversity of TokyoTokyoJapan

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