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New Age of Crisis Management with Social Media

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Open Source Geospatial Science for Urban Studies

Abstract

Social Media (SM) Volunteered Geographic Information (VGI) is gradually being used for representing the real-time situation during emergency. This chapter presents the SM-VGI review as a new age contribution to emergency management. The study analyses a series of emergencies during the so-called coup attempt within the boundary of Istanbul on the 15th of July 2016 in terms of spatial clusters in time and textual frequencies within 24 h. The aim of the study is to gain an understanding of the usefulness of geo-referenced Social Media Data (SMD) in monitoring emergencies. Inferences exhibit that SM-VGI can rapidly provide the information in the spatiotemporal context with the proper validations, in this way it has advantages to use during emergencies. In addition, even though geo-referenced data embody the small percent of the total volume of the SMD, it would specify reliable spatial clusters for the events, monitoring with optimized-hot-spot analysis and with the word frequencies of its attributes.

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References

  1. Witt JL (ed) FEMA, partnerships in preparedness—a compendium of exemplary practices in emergency management (2000)

    Google Scholar 

  2. International Federation of Red Cross and Red Crescent Societies: What is a disaster? [cited 2017 02.11.2017]. Available from: https://www.ifrc.org/en/what-we-do/disaster-management/about-disasters/what-is-a-disaster/

  3. CRED: EM-DAT (2016). https://www.emdat.be/

  4. EM-DAT: The International Disaster Database: Frequently asked questions (2009) [cited 2017 30.10.2017]. Available from: https://www.emdat.be/frequently-asked-questions

  5. Alexander, D.: Natural Disasters. Chapman & Hall. Inc., 632 pp (1993)

    Google Scholar 

  6. EM-DAT: General Classification (2009) [cited 2017 30.10.2017]. Available from: https://www.emdat.be/classification

  7. Berren, M.R., Beigel, A., Ghertner, S.: A typology for the classification of disasters. Commun. Ment. Health J. 16(2), 103–111 (1980)

    Article  Google Scholar 

  8. Johnson R (2000) GIS technology for disasters and emergency management. An ESRI white paper (2000)

    Google Scholar 

  9. Houston, J.B., et al.: Social media and disasters: a functional framework for social media use in disaster planning, response, and research. Disasters 39(1), 1–22 (2015)

    Article  Google Scholar 

  10. Eshghi, K., Larson, R.C.: Disasters: lessons from the past 105 years. Disaster Prevent. Manag. Int. J. 17(1), 62–82 (2008)

    Article  Google Scholar 

  11. Green WG: Changes to the disaster database project (2003)

    Google Scholar 

  12. WHO and EHA: Emergency Health Training Programme for Africa (1999)

    Google Scholar 

  13. Cutter, S.L.: GI science, disasters, and emergency management. Trans. GIS 7(4), 439–446 (2003)

    Article  Google Scholar 

  14. Karaman, H., Erden, T.: Net earthquake hazard and elements at risk (NEaR) map creation for city of Istanbul via spatial multi-criteria decision analysis. Nat. Hazards 73(2), 685–709 (2014)

    Article  Google Scholar 

  15. Gulnerman, A.G., Goksel, C., Tezer, A.: Disaster capacity building with a GIS tool of public participation. Fresenius Environ. Bull. 26(1), 237–243 (2017)

    Google Scholar 

  16. FEMA: Disaster planning is up to you (2007). https://www.fema.gov/news-release/2007/03/30/disaster-planning-you.

  17. Schneider, P.J., Schauer, B.A.: HAZUS—its development and its future. Nat. Hazards Rev. 7(2), 40–44 (2006)

    Article  Google Scholar 

  18. HAZTURK: Developing a loss estimation program, HAZTURK based on HAZUS (hazards us) to be used before, during and after a disaster: a case study for Istanbul, in final report to TUBITAK 104Y254 Award. TUBITAK: Ankara, pp 46–117 (2007)

    Google Scholar 

  19. Hancilar, U., et al.: ELER software-a new tool for urban earthquake loss assessment. Nat. Hazards Earth Syst. Sci. 10(12), 2677 (2010)

    Article  Google Scholar 

  20. Erdik, M., et al.: Development of rapid earthquake loss assessment methodologies for Euro-Med region. In: Proceedings of 14th World Conference on Earthquake Engineering (2008)

    Google Scholar 

  21. Kircher, C.A., Whitman, R.V., Holmes, W.T.: HAZUS earthquake loss estimation methods. Nat. Hazards Rev. 7(2), 45–59 (2006)

    Article  Google Scholar 

  22. Elnashai, A.S., et al.: Overview and applications of Maeviz-Hazturk 2007. J. Earthquake Eng. 12(S2), 100–108 (2008)

    Article  Google Scholar 

  23. Goodchild, M.F.: Citizens as sensors: the world of volunteered geography. GeoJournal 69(4), 211–221 (2007)

    Article  Google Scholar 

  24. Wald, D.J., et al., USGS “Did You Feel It?” Internet-based macroseismic intensity maps. 2012, 2012. 54(6).

    Google Scholar 

  25. Clarke, K.C.: Getting started with geographic information systems, vol. 3. Prentice Hall Upper Saddle River, NJ (1997)

    Google Scholar 

  26. Hotosm: Disaster mapping projects (2017) [01.06.2017]. Available from: https://www.hotosm.org/projects/disaster-mapping

  27. Meier, P., Werby, O.: Ushahidi in Haiti: the use of crisis mapping during the 2009 earthquake in Haiti. In: Proceedings of the IADIS International Conference on ICT, Society and Human Beings 2011, Proceedings of the IADIS International Conference e-Democracy, Equity and Social Justice 2011, Part of the IADIS, MCCSIS 2011 (2011)

    Google Scholar 

  28. Hirata, E., et al.: Flooding and inundation collaborative mapping—use of the Crowdmap/Ushahidi platform in the city of Sao Paulo, Brazil. J Flood Risk Manag. (2015)

    Google Scholar 

  29. Sakaki, T., Okazaki, M., Matsuo, Y.: Earthquake shakes Twitter users: real-time event detection by social sensors. In: Proceedings of the 19th International Conference on World wide web. ACM, Raleigh, pp 851–860

    Google Scholar 

  30. Goodchild, M.F.: Citizens as voluntary sensors: spatial data infrastructure in the world of Web 2.0. Int. J. Spat. Data Infrastruct. Res. (2007)

    Google Scholar 

  31. Acar, A., Muraki, Y.: Twitter for crisis communication: Lessons learned from Japan’s tsunami disaster. Int. J. Web Based Communities 7(3), 392–402 (2011)

    Article  Google Scholar 

  32. Iwanaga, I.S.M., et al.: Building an earthquake evacuation ontology from twitter. In: 2011 IEEE International Conference on Granular Computing (GrC) (2011)

    Google Scholar 

  33. Wang, Z., Ye, X., Tsou, M.H.: Spatial, temporal, and content analysis of Twitter for wildfire hazards. Nat. Hazards 83(1), 523–540 (2016)

    Article  Google Scholar 

  34. Issa, E., et al.: Understanding the spatio-temporal characteristics of Twitter data with geotagged and non-geotagged content: two case studies with the topic of flu and Ted (movie). Ann. GIS 23(3), 219–235 (2017)

    Article  Google Scholar 

  35. Ishino, A., et al.: Extracting transportation information and traffic problems from tweets during a disaster. In: Proceedings of IMMM, pp 91–96 (2012)

    Google Scholar 

  36. Statista: Most famous social network sites worldwide as of September 2017, ranked by number of active users (in millions). 2017 [cited 2017 02.11.2017]. Available from: https://www.statista.com/statistics/272014/global-social-networks-ranked-by-number-of-users/

  37. Hecht, B., Shekhar, S.: From GPS and Google Maps to spatial computing. Coursera Inc. (2014)

    Google Scholar 

  38. Turner, A.: Introduction to neogeography (2006). O'Reilly, p. 54

    Google Scholar 

  39. Parker, C.J.: A framework of neogeography. In: The Fundamentals of Human Factors Design for Volunteered Geographic Information. Springer, Berlin, pp. 11–22

    Google Scholar 

  40. Goodchild, M.: Neogeography and the nature of geographic expertise. J. Locat. Based Serv. 3(2), 82–96 (2009)

    Article  Google Scholar 

  41. Elwood, S., Goodchild, M.F., Sui, D.Z.: Researching Volunteered Geographic Information: Spatial Data, Geographic Research, and New Social Practice. Ann. Assoc. Am. Geogr. 102(3), 571–590 (2012)

    Article  Google Scholar 

  42. Gulnerman, A.G., Gengec, N.E., Karaman, H.: Review of public Tweets over Turkey within a pre-determined time. ISPRS-International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, pp. 153–159

    Google Scholar 

  43. Schroeder, P.: Criteria for the design of a GIS/2. specialists’ meeting for NCGIA Initiative 19. GIS and Society, Summer (1996)

    Google Scholar 

  44. Anderson, L.T.: Guidelines for preparing urban plans (1995)

    Google Scholar 

  45. Sieber, R.: Public participation geographic information systems: a literature review and framework. Ann Assoc Am Geogr 96(3), 491–507 (2006)

    Article  Google Scholar 

  46. Gulnerman, A.G., Karaman, H.: PPGIS case studies comparison and future questioning. In: Proceedings—15th International Conference on Computational Science and Its Applications, ICCSA 2015 (2015)

    Google Scholar 

  47. Ball, J.: Towards a methodology for mapping ‘regions for sustainability’ using PPGIS. Prog. Plann. 58(2), 81–140 (2002)

    Article  Google Scholar 

  48. Muller, J.P., et al.: EU-FP7-IMARS: Analysis of Mars multi-resolution images using auto-coregistration, data mining and crowd source techniques: processed results—a first look. In: International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences—ISPRS Archives (2016)

    Google Scholar 

  49. Wardlaw, J., Jackson, B.: Rights lab: slavery from space. [cited 2017 18.10.2017]. Available from: https://www.zooniverse.org/projects/ezzjcw/rights-lab-slavery-from-space

  50. Wardlaw, J., et al.: Mars in motion [cited 2017 18.10.2017]. Available from: https://www.zooniverse.org/projects/imarsnottingham/mars-in-motion

  51. Scistarters: Project Finder (2017) [cited 2017 18.10.2017]. Available from: https://scistarter.com/finder

  52. Meier, P.: Crisis mapping in action: how open source software and global volunteer networks are changing the world, one map at a time. J. Map Geogr. Libr. 8(2), 89–100 (2012)

    Article  Google Scholar 

  53. Zooniverse: People—powered research 2017 [cited 2017 18.10.2017]. Available from: https://www.zooniverse.org/

  54. Scistarters: Science we can do together (2017). Available from: https://scistarter.com/

  55. Ushahidi: Read the crowd (2017) [cited 2017 18.10.2017]. Available from: https://www.ushahidi.com/

  56. OSM: Open street map about (2016) [cited 2015 25 November]. Available from: https://www.openstreetmap.org/about

  57. Valenzuela, S., Arriagada, A., Scherman, A.: The social media basis of youth protest behavior: the case of Chile. J Commun 62(2), 299–314 (2012)

    Article  Google Scholar 

  58. Tufekci, Z., Wilson, C.: Social media and the decision to participate in political protest: observations from Tahrir Square. J. Commun. 62(2), 363–379 (2012)

    Article  Google Scholar 

  59. Lim, M.: Clicks, cabs, and coffee houses: social media and oppositional movements in Egypt, 2004–2011. J. Commun. 62(2), 231–248 (2012)

    Article  Google Scholar 

  60. Haciyakupoglu, G., Zhang, W.: Social media and trust during the Gezi protests in Turkey. J. Comput.-Med. Commun. 20(4), 450–466 (2015)

    Google Scholar 

  61. Tulloch, D.L.: Is VGI participation? From vernal pools to video games. GeoJournal 72(3–4), 161–171 (2008)

    Article  Google Scholar 

  62. Hall, G.B., et al.: Community-based production of geographic information using open source software and Web 2.0. Int. J Geogr. Inf. Sci. 24(5), 761–781 (2010)

    Google Scholar 

  63. Moffitt, J.: Tweet Metadata Timeline (2017) [cited 2017 26.10.2017]. Available from: https://support.gnip.com/articles/tweet-timeline.html

  64. Gross, D., Hanna, J.: Facebook introduces check-in feature (2010) [cited 2017 01.11.2017]. Available from: https://edition.cnn.com/2010/TECH/social.media/08/18/facebook.location/index.html

  65. Power, R., Robinson, B., Wise, C.: Using Crowd Sourced Content to Help Manage Emergency Events, in Social Media for Government Services. Springer, Berlin, pp. 247–270 (2015)

    Google Scholar 

  66. Gelernter, J., Mushegian, N.: Geo-parsing messages from microtext. Trans. GIS 15(6), 753–773 (2011)

    Article  Google Scholar 

  67. Gelernter, J., Wu, G.: High performance mining of social media data. In Proceedings of the 1st Conference of the Extreme Science and Engineering Discovery Environment: Bridging from the eXtreme to the Campus and beyond. ACM (2012)

    Google Scholar 

  68. Gelernter, J., Balaji, S.: An algorithm for local geoparsing of microtext. GeoInformatica 17(4), 635–667 (2013)

    Article  Google Scholar 

  69. Leetaru, K., et al.: Mapping the global Twitter heartbeat: the geography of Twitter (2013)

    Google Scholar 

  70. Landwehr, P.M., et al.: Using tweets to support disaster planning, warning and response. Saf. Sci. 90, 33–47 (2016)

    Article  Google Scholar 

  71. Utani, A., Mizumoto, T., Okumura, T.: How geeks responded to a catastrophic disaster of a high-tech country: rapid development of counter-disaster systems for the great east Japan earthquake of March 2011. In: Proceedings of the Special Workshop on Internet and Disasters. ACM (2011)

    Google Scholar 

  72. Kudou, T., mecab. GitHub repository (2013)

    Google Scholar 

  73. Ao, J., Zhang, P., Cao, Y.: Estimating the locations of emergency events from Twitter streams. Procedia Comput. Sci. 31, 731–739 (2014)

    Article  Google Scholar 

  74. Yin, J., et al.: Using social media to enhance emergency situation awareness. IEEE Intell. Syst. 27(6), 52–59 (2012)

    Article  Google Scholar 

  75. Castillo, C., Mendoza, M., Poblete, B.: Predicting information credibility in time-sensitive social media. Internet Res. 23(5), 560–588 (2013)

    Article  Google Scholar 

  76. Poorazizi, M.E., Hunter, A.J., Steiniger, S.: A volunteered geographic information framework to enable bottom-up disaster management platforms. ISPRS Int. J. Geo-Inf. 4(3), 1389–1422 (2015)

    Article  Google Scholar 

  77. Crooks, A., et al.: # Earthquake: Twitter as a distributed sensor system. Trans. GIS 17(1), 124–147 (2013)

    Article  MathSciNet  Google Scholar 

  78. Rosser, J.F., Leibovici, D.G., Jackson, M.J.: Rapid flood inundation mapping using social media, remote sensing and topographic data. Nat. Hazards 87(1), 103–120 (2017)

    Article  Google Scholar 

  79. Lin, Y.R., Margolin, D.: The ripple of fear, sympathy and solidarity during the Boston bombings. EPJ Data Sci. 3(1), 1–28 (2014)

    Article  Google Scholar 

  80. Terpstra, T., et al.: Towards a Realtime Twitter Analysis During Crises for Operational Crisis Management. Simon Fraser University (2012)

    Google Scholar 

  81. Hara, Y.: Behaviour analysis using tweet data and geo-tag data in a natural disaster. Transp. Res. Procedia 11, 399–412 (2015)

    Article  Google Scholar 

  82. Statista: Most popular social networks worldwide as of April 2018, ranked by number of active users (in millions). 2018 [cited 2018 03.05.2018]. Available from: https://www.statista.com/statistics/272014/global-social-networks-ranked-by-number-of-users/

  83. Statista: Active social network penetration in selected countries as of January 2018 (2018). Available from: https://www.statista.com/statistics/282846/regular-social-networking-usage-penetration-worldwide-by-country/

  84. Statista: Global digital population as of April 2018 (in millions) (2018) [cited 2018 03.05.2018]. Available from: https://www.statista.com/statistics/617136/digital-population-worldwide/

  85. Social Media Data Stewardship: Social Media Research Toolkit (2017) [cited 2017 22.10.2017]. Available from: https://socialmediadata.org/social-media-research-toolkit/

  86. Gentry, J.: R Based Twitter Client (2016)

    Google Scholar 

  87. Cribbin, T.B., Julie, Brooker, P., Basnayake, H.: The Chorus Project Tweet Catcher (2015)

    Google Scholar 

  88. KNIME: KNIME Twitter Nodes (2014)

    Google Scholar 

  89. Carto: Twitter Data Analysis With a Pulse (2016). Available from: https://carto.com/connectors/twitter-maps/

  90. Social Media Research Foundation. NodeXL (2017) [cited 2017 22.10.2017]. Available from: https://www.smrfoundation.org/nodexl/

  91. Gengec, N.E.: Geo Tweets Downloader (2015). github. p. Spatial Tweets Downloader

    Google Scholar 

  92. Yamamoto, Y.: Twitter4j (2015)

    Google Scholar 

  93. Twitter: REST APIs (2015)

    Google Scholar 

  94. Twitter: The Streaming APIs (2015)

    Google Scholar 

  95. PostgreSQL: Homepage (2017). Available from: https://www.postgresql.org/

  96. PostGIS: About PostGIS (2017). Available from: https://postgis.net/

  97. Bruns, A., Liang, Y.E.: Tools and methods for capturing Twitter data during natural disasters. First Monday 17(4) (2012)

    Google Scholar 

  98. Zi, C., et al.: Who is tweeting on Twitter: human, bot, or cyborg? In: Proceedings of the 26th Annual Computer Security Applications Conference 978-1-4503-0133-6. ACM, Austin, pp 21–30 (2010)

    Google Scholar 

  99. R Core Team: R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. (2013). http://www.R-project.org/

  100. RStudio: Take control of your R code (2016) [cited 2017 06.11.2017]. Available from: https://www.rstudio.com/products/rstudio/

  101. R Special Interest Group on Databases. Kirill Müller, H.W.: DBI: R Database Interface (2016)

    Google Scholar 

  102. Conway, J., et al.: RPostgreSQL : R interface to the PostgreSQL database system (2016)

    Google Scholar 

  103. Basille, M., Bucklin, D.: rpostgis: R Interface to a ‘PostGIS’ Database (2017)

    Google Scholar 

  104. Feinerer, I.: tm: Text Mining Package (2015)

    Google Scholar 

  105. Aksoy, A., Ozturk, T.: trstop (2016) GitHub

    Google Scholar 

  106. Sadi, E.S.: Metin Madenciliği (Text Mining). In: Bilgisayar Kavramları (2014)

    Google Scholar 

  107. Cakir, U., Mehmet, Güldamlasioglu, S.: Text mining analysis in turkish language using big data tools. In: 2016 IEEE 40th Annual Computer Software and Applications Conference (2016)

    Google Scholar 

  108. Hearst, M.A.: Untangling text data mining. In: Proceedings of the 37th annual meeting of the Association for Computational Linguistics on Computational Linguistics. 1999. Association for Computational Linguistics

    Google Scholar 

  109. Hearst, M.: What is text mining. SIMS, UC Berkeley (2003)

    Google Scholar 

  110. Bouchet-Valat, M.: SanowballC: Snowball stemmers based on the C libstemmer UTF-8 library (2014)

    Google Scholar 

  111. Evren, K.C.: SnowballC (2007)

    Google Scholar 

  112. Stanford NLP Group: Stemming and lemmatization (2008) [cited 2017]. Available from: https://nlp.stanford.edu/IR-book/html/htmledition/stemming-and-lemmatization-1.html

  113. Feinerer, I.: Introduction to the tm Package Text Mining in R (2017)

    Google Scholar 

  114. ESRI: Optimized Hot Spot Analysis (2016). Available from: https://desktop.arcgis.com/en/arcmap/10.3/tools/spatial-statistics-toolbox/optimized-hot-spot-analysis.htm

  115. Cubukcu, K.M.: Planlamada ve coğrafyada temel istatistik ve mekansal istatistik (2015) Nobel

    Google Scholar 

  116. ESRI: Hot spot analysis (Getis-Ord Gi*) (2016). Available from: https://desktop.arcgis.com/en/arcmap/10.3/tools/spatial-statistics-toolbox/hot-spot-analysis.htm

  117. Red Hen Systems, I.: MapCalcTM Learner-Academic software

    Google Scholar 

  118. Hürriyet: Dakika dakika darbe girişimi - 15–16 Temmuz 2016 (2016). Available from: https://www.hurriyet.com.tr/dakika-dakika-darbe-girisimi-15-16-temmuz-2016-40149409

  119. NTV: 15 Temmuz gecesi ve sonrasında neler yaşandı (2016). Available from: https://www.ntv.com.tr/galeri/turkiye/15-temmuz-gecesi-ve-sonrasinda-neler-yasandi,fBIiAr1pu0WkrhUBXdZykA/ClgQwqBRtEaLnVzxTJdIXw

  120. Gulnerman, A.G., Karaman, H.: Social media spatial monitor of coup attempt in the republic of Turkey. In: 2017 17th International Conference on Computational Science and Its Applications (ICCSA). IEEE (2017)

    Google Scholar 

  121. Boccardo, P.: New perspectives in emergency mapping. Eur J Remote Sens 46(1), 571–582 (2013)

    Article  Google Scholar 

  122. Khorram, Y.: As Sandy pounded NYC, fire department worker was a Twitter lifeline (2012) [cited 2017 06.11.2017]. Available from: https://edition.cnn.com/2012/11/01/tech/social-media/twitter-fdny/

  123. Lee, K., et al.: Spatio-temporal provenance: Identifying location information from unstructured text. In: 2013 IEEE International Conference on Pervasive Computing and Communications Workshops (PERCOM Workshops). IEEE (2013)

    Google Scholar 

  124. Mocnik, F.B., Mobasheri, A., Zipf, A.: Open source data mining infrastructure for exploring and analysing OpenStreetMap. Open Geospat Data, Softw Standards 3(1), 7 (2018)

    Article  Google Scholar 

  125. Fonte, C., et al.: VGI quality control. ISPRS Geospatial week 2015, pp 317–324 (2015)

    Google Scholar 

  126. Haklay, M.: How good is volunteered geographical information? A comparative study of OpenStreetMap and ordnance survey datasets. Environ. Plann. B: Plann. Des. 37(4), 682–703 (2010)

    Article  Google Scholar 

  127. Abbasi, M.-A., Liu, H.: Measuring user credibility in social media. In: International Conference on Social Computing, Behavioral-Cultural Modeling, and Prediction. Springer, Berlin (2013)

    Google Scholar 

  128. Lenormand, M., et al.: Tweets on the road. PloS one 9(8), e105407 (2014)

    Google Scholar 

  129. D’Andrea, E., et al.: Real-time detection of traffic from twitter stream analysis. IEEE Trans. Intell. Transp. Syst. 16(4), 2269–2283 (2015)

    Article  Google Scholar 

  130. Hasby, M., Khodra, M.L.: Optimal path finding based on traffic information extraction from Twitter. In: 2013 International Conference on ICT for Smart Society (ICISS). IEEE (2013)

    Google Scholar 

  131. Bassolas, A., et al.: Touristic site attractiveness seen through Twitter. arXiv preprint arXiv:1601.07741 (2016)

    Google Scholar 

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Acknowledgements

This work is supported by the Scientific and Technological Research Council of Turkey (TUBITAK-2214/A Grant Program), grant number 1059B141600822, and Istanbul Technical University Scientific Research Projects Funding Program (ITUBAP-40569), grant number MDK-2017-40569.

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Authors and Affiliations

  1. Department of Geomatics Engineering, Faculty of Civil Engineering, Istanbul Technical University, 34469, Istanbul, Turkey

    Ayse Giz Gulnerman & Himmet Karaman

  2. Centre for Advanced Spatial Analysis, University College London, London, WC16BT, UK

    Anahid Basiri

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  1. Ayse Giz Gulnerman
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Correspondence to Ayse Giz Gulnerman .

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  1. GIScience Research Group, Heidelberg University, Heidelberg, Germany

    Amin Mobasheri

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Gulnerman, A.G., Karaman, H., Basiri, A. (2021). New Age of Crisis Management with Social Media. In: Mobasheri, A. (eds) Open Source Geospatial Science for Urban Studies. Lecture Notes in Intelligent Transportation and Infrastructure. Springer, Cham. https://doi.org/10.1007/978-3-030-58232-6_8

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