Data for Refugees: The D4R Challenge on Mobility of Syrian Refugees in Turkey

The Data for Refugees (D4R) Challenge is a non-profit challenge initiated to improve the conditions of the Syrian refugees in Turkey by providing a special database to scientific community for enabling research on urgent problems concerning refugees, including health, education, unemployment, safety, and social integration. The collected database is based on anonymised mobile Call Detail Record (CDR) of phone calls and SMS messages from one million Turk Telekom customers. It indicates broad activity and mobility patterns of refugees and citizens in Turkey for one year. The data collection period is from 1 January 2017 to 31 December 2017. The project is initiated by Turk Telekom, in partnership with the Turkish Academic and Research Council (TUBITAK) and Bogazici University, and in collaboration with several academic and non-governmental organizations, including UNHCR Turkey, UNICEF, and International Organization for Migration.

[1]  Mirco Musolesi,et al.  Disease Containment Strategies based on Mobility and Information Dissemination , 2015, Scientific Reports.

[2]  Albert Ali Salah,et al.  Countrywide arrhythmia: emergency event detection using mobile phone data , 2016, EPJ Data Science.

[3]  L. Taylor.  No place to hide? The ethics and analytics of tracking mobility using mobile phone data , 2016 .

[4]  Franco Zambonelli,et al.  Re-identification and information fusion between anonymized CDR and social network data , 2015, Journal of Ambient Intelligence and Humanized Computing.

[5]  Zbigniew Smoreda,et al.  D4D-Senegal: The Second Mobile Phone Data for Development Challenge , 2014, ArXiv.

[6]  O. Gemikonakli,et al.  Towards connecting people, locations and real-world events in a cellular network , 2015, Telematics Informatics.

[7]  Christelle Scharff,et al.  Human mobility during religious festivals and its implications on public health in Senegal: A mobile dataset analysis , 2015, 2015 IEEE Global Humanitarian Technology Conference (GHTC).

[8]  Etienne Huens,et al.  Data for Development: the D4D Challenge on Mobile Phone Data , 2012, ArXiv.

[9]  Neeti Pokhriyal,et al.  Combining disparate data sources for improved poverty prediction and mapping , 2017, Proceedings of the National Academy of Sciences.

[10]  Rein Ahas,et al.  Ethnic Differences in Activity Spaces: A Study of Out-of-Home Nonemployment Activities with Mobile Phone Data , 2014 .

[11]  Pierluigi Mancarella,et al.  Using Mobile Phone Data for Electricity Infrastructure Planning , 2015, ArXiv.

[12]  Vincent D. Blondel,et al.  A survey of results on mobile phone datasets analysis , 2015, EPJ Data Science.

[13]  David E. Millard,et al.  A Literature Survey and Classifications on Data Deanonymisation , 2015, CRiSIS.

[14]  Nicky McCreesh,et al.  Migration statistics relevant for malaria transmission in Senegal derived from mobile phone data and used in an agent-based migration model. , 2016, Geospatial health.

[15]  Daniel Carlos dos Santos Machado.  Analyzing Geospatial patterns of syrian refugee flows in southeastern Turkey by use of remote sensing and complementary data , 2015 .

[16]  Sébastien Gambs,et al.  De-anonymization Attack on Geolocated Data , 2013, 2013 12th IEEE International Conference on Trust, Security and Privacy in Computing and Communications.

[17]  Brian M. Tomaszewski,et al.  Geographic Information Systems (Gis) for Disaster Management , 2014 .

[18]  Renato Casagrandi,et al.  Big-data-driven modeling unveils country-wide drivers of endemic schistosomiasis , 2017, Scientific Reports.