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The Use of Big Mobile Data to Gain Multilayered Insights for Syrian Refugee Crisis

  • Özgün Ozan Kılıç
  • Mehmet Ali Akyol
  • Oğuz Işık
  • Banu Günel Kılıç
  • Arsev Umur Aydınoğlu
  • Elif Surer
  • Hafize Şebnem Düzgün
  • Sibel Kalaycıoğlu
  • Tuğba Taşkaya-TemizelEmail author
Chapter

Abstract

This study aims to shed light on various aspects of refugees’ lives in Turkey using mobile call data records of Türk Telekom, enriched with numerous local data sets. To achieve this, we made use of several statistical and data mining techniques in addition to a novel methodology to find home and work-time anchors of mobile phone users we developed. Our results showed that refugees are highly mobile as a survival strategy—a significant number of whom work as seasonal workers. Most prefer to live in relatively low-status neighborhoods, close to city transport links and fellow refugees. The ones living in these neighborhoods appear to be introverts, living in a closed neighborhood. However, the middle- and upper-class refugees appear to be the opposite. Fatih, İstanbul was found as an important hub for refugees. Finally, the officially registered refugee numbers do not reflect the real refugee population in Turkey. Due to their high mobility, refugees lag behind in keeping up-to-date information about their residential address, resulting in a significant discrepancy between the official numbers and the real numbers. We think that policymakers can benefit from the proposed methods in this study to develop real-time solutions for the well-being of refugees.

Notes

Acknowledgements

We would like to thank Türk Telekomünikasyon A.Ş. for the one-year anonymized mobile communication data they provided within the D4R Challenge.

References

  1. 1.
    Alemanno A (2018) Big data for good: unlocking privately-held data to the benefit of the many. Eur J Risk Regul 9(2):183–191CrossRefGoogle Scholar
  2. 2.
    Ahas R, Silm S, Järv O, Saluveer E, Tirui M (2010) Using mobile positioning data to model locations meaningful to users of mobile phones. J Urban Technol 17(1):3–27CrossRefGoogle Scholar
  3. 3.
    Asik GA (2017) Turkey badly needs a long-term plan for Syrian refugees. Harvard Business Review. https://hbr.org/2017/04/turkey-badly-needs-a-long-term-plan-for-syrian-refugees. Accessed 14 September 2018
  4. 4.
    Balkan B, Tumen S (2016) Immigration and prices: quasi-experimental evidence from Syrian refugees in Turkey. J Popul Econ 29(3):657–686CrossRefGoogle Scholar
  5. 5.
    Chandy R, Hassan M, Mukherji P (2017) Big data for good: insights from emerging markets. J Product Innov Manag 34(5):703–713CrossRefGoogle Scholar
  6. 6.
    Cagaptay S, Menekse B (2014) The impact of Syria’s refugees on Southern Turkey. Policy Focus 130. Washington, DC: The Washington Institute For Near East Policy. http://www.washingtoninstitute.org/uploads/Documents/pubs/PolicyFocus130_Cagaptay_Revised3s.pdf. Accessed 31 Aug 2018
  7. 7.
    Dedeoğlu S (2016) Yoksulluk nöbetinden yoksulların rekabetine: Türkiye’de mevsimlik tarımsal üretimde yabancı göçmen işçiler mevcut durum raporu. http://www.ka.org.tr/TumYayinlar. Accessed 12 Sept 2018
  8. 8.
    Directorate General of Migration Management of Turkey (DGMM) (2017) Hangi ilde ne kadar Suriyeli var? İşte il il liste. http://www.bik.gov.tr/hangi-ilde-ne-kadar-suriyeli-var-iste-il-il-liste/. Accessed 31 Aug 2018
  9. 9.
    Eraydın G (2017) Migration, settlement and daily life patterns of Syrian urban refugees through time geography: a case of Önder neighborhood. Unpublished PhD Thesis, Middle East Technical UniversityGoogle Scholar
  10. 10.
    Furno A, Fiore M, Stanica R, Ziemlicki C, Smoreda Z (2017) A tale of ten cities: characterizing signatures of mobile traffic in urban areas. IEEE Trans Mobile Comput 16(10):2682–2696CrossRefGoogle Scholar
  11. 11.
    Graells-Garrido E, Peredo O, García J (2016) Sensing urban patterns with antenna mappings: the case of Santiago. Chile Sens 16(7):1098CrossRefGoogle Scholar
  12. 12.
    Hartigan JA (1975) Clustering algorithms. NY, USA, New YorkzbMATHGoogle Scholar
  13. 13.
    Hürriyet Emlak (2018) Online real estate ads. https://www.hurriyetemlak.com. Accessed 8 Aug 2018
  14. 14.
    Isaacman S, Becker R, Cáceres R., Kobourov S., Martonosi M, Rowland J, Varshavsky A (2011) Identifying important places in people’s lives from cellular network data. In: International conference on pervasive computing. Springer, Berlin, Heidelberg, pp 133–151Google Scholar
  15. 15.
    Işık O, Ataç E (2011) Yoksulluğa dair: bildiklerimiz, az bildiklerimiz, bilmediklerimiz. Birikim 269(268):66–86Google Scholar
  16. 16.
    İçduygu A (2015) Syrian refugees in Turkey: the long road ahead. Migration Policy Institute, Washington DCGoogle Scholar
  17. 17.
    Isikkaya AD (2016) Housing policies in Turkey: evolution of TOKI (Governmental Mass Housing Administration) as an urban design tool. J Civil Eng Archit 10:316–326Google Scholar
  18. 18.
    Jiang S, Ferreira J, González MC (2017) Activity-based human mobility patterns inferred from mobile phone data: a case study of Singapore. IEEE Trans Big Data 3(2):208–219CrossRefGoogle Scholar
  19. 19.
    Kalkınma Atölyesi (2013) Mevsimlik gezici tarım işçiliği izleme: mevcut durum haritası (2012–2013) http://www.ka.org.tr/TumYayinlar. Accessed 12 Sept 2018
  20. 20.
    Kaya A (2017) Istanbul as a space of cultural affinity for Syrian refugees: “Istanbul is safe despite everything!” Southeast Eur 41(3):333–358CrossRefGoogle Scholar
  21. 21.
    Kondo Y, Salibian-Barrera M, Zamar R (2016) RSKC: an R package for a robust and sparse k-means clustering algorithm. J Stat Softw 72(5):1–26CrossRefGoogle Scholar
  22. 22.
    Kohonen T (1998) The self-organizing map. Neurocomputing 21(1–3):1–6CrossRefGoogle Scholar
  23. 23.
    National Academies of Sciences, Engineering, and Medicine (NASEM) (2015) The integration of immigrants into American society. The National Academies Press, Washington, DC.  https://doi.org/10.17226/21746
  24. 24.
    Nurmi P, Koolwaaij J (2006) Identifying meaningful locations. In: Mobile and ubiquitous systems: networking & services, 2006 third annual international conference, pp 1–8. IEEE, San JoseGoogle Scholar
  25. 25.
    ORSAM (Ortadoğu Stratejik Araştırmalar Merkezi) (2014) Suriye’ye komşu ülkelerde Suriyeli mültecilerin durumu: bulgular, sonuçlar ve öneriler. http://www.madde14.org/images/e/e5/OrsamSuriyeKomsu2014.pdf. Accessed 31 Aug 2018
  26. 26.
    Prime Minister’s Office (2017) Memorandum Circular n. 2017/6. Official Gazette of Turkey, 30043Google Scholar
  27. 27.
    Salah AA, Pentland A, Lepri B, Letouzé E, Vinck P, de Montjoye YA, Dong X, Dağdelen Ö (2018) Data for Refugees: The D4R Challenge on mobility of Syrian refugees in Turkey. arXiv preprint arXiv:1807.00523
  28. 28.
    Soto V, Frias-Martinez V, Virseda J, Frias-Martinez E (2011) Prediction of socioeconomic levels using cell phone records. In: International conference on user Modeling, adaptation, and personalization. Springer, Berlin, Heidelberg, pp 377–388CrossRefGoogle Scholar
  29. 29.
    Stock I, Aslan M, Paul J, Volmer V, Faist T (2016) Beyond humanitarianism: addressing the urban, self-settled refugees in Turkey. COMCAD, BielefeldGoogle Scholar
  30. 30.
    Türk Telekom (2018) Data for refugees. http://d4r.turktelekom.com.tr/. Accessed 31 Aug 2018
  31. 31.
    United Nations High Commissioner for Refugees (UNHCR) (2017) Global trends 2017: forced displacement in 2017. http://www.unhcr.org/5943e8a34.pdf. Accessed 31 Aug 2018
  32. 32.
    Witten DM, Tibshirani R (2010) A framework for feature selection in clustering. J Am Stat Assoc 105(490):713–726MathSciNetCrossRefGoogle Scholar
  33. 33.
    World Bank (2015) Turkey’s response to the Syrian refugee crisis and the road ahead. World Bank, Washington DCGoogle Scholar
  34. 34.
    Yang P, Zhu T, Wan X, Wang X (2014) Identifying significant places using multi-day call detail records. In: 2014 IEEE 26th international conference on tools with artificial intelligence (ICTAI), pp 360–366. IEEE, LimassolGoogle Scholar
  35. 35.
    Zhao Z, Shaw SL, Xu Y, Lu F, Chen J, Yin L (2016) Understanding the bias of call detail records in human mobility research. Int J Geograph Inf Sci 30(9):1738–1762CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Özgün Ozan Kılıç
    • 1
  • Mehmet Ali Akyol
    • 1
  • Oğuz Işık
    • 1
  • Banu Günel Kılıç
    • 1
  • Arsev Umur Aydınoğlu
    • 1
  • Elif Surer
    • 1
  • Hafize Şebnem Düzgün
    • 2
  • Sibel Kalaycıoğlu
    • 1
  • Tuğba Taşkaya-Temizel
    • 1
    Email author
  1. 1.Middle East Technical UniversityÇankayaTurkey
  2. 2.Colorado School of MinesGoldenUSA

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