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About this book

After the start of the Syrian Civil War in 2011–12, increasing numbers of civilians sought refuge in neighboring countries. By May 2017, Turkey had received over 3 million refugees — the largest refugee population in the world. Some lived in government-run camps near the Syrian border, but many have moved to cities looking for work and better living conditions. They faced problems of integration, income, welfare, employment, health, education, language, social tension, and discrimination. In order to develop sound policies to solve these interlinked problems, a good understanding of refugee dynamics isnecessary.

This book summarizes the most important findings of the Data for Refugees (D4R) Challenge, which was a non-profit project initiated to improve the conditions of the Syrian refugees in Turkey by providing a database for the scientific community to enable research on urgent problems concerning refugees. The database, based on anonymized mobile call detail records (CDRs) of phone calls and SMS messages of one million Turk Telekom customers, indicates the broad activity and mobility patterns of refugees and citizens in Turkey for the year 1 January to 31 December 2017. Over 100 teams from around the globe applied to take part in the challenge, and 61 teams were granted access to the data.

This book describes the challenge, and presents selected and revised project reports on the five major themes: unemployment, health, education, social integration, and safety, respectively. These are complemented by additional invited chapters describing related projects from international governmental organizations, technological infrastructure, as well as ethical aspects. The last chapter includes policy recommendations, based on the lessons learned.

The book will serve as a guideline for creating innovative data-centered collaborations between industry, academia, government, and non-profit humanitarian agencies to deal with complex problems in refugee scenarios. It illustrates the possibilities of big data analytics in coping with refugee crises and humanitarian responses, by showcasing innovative approaches drawing on multiple data sources, information visualization, pattern analysis, and statistical analysis.It will also provide researchers and students working with mobility data with an excellent coverage across data science, economics, sociology, urban computing, education, migration studies, and more.

Table of Contents


Big Data and Refugees


Chapter 1. Introduction to the Data for Refugees Challenge on Mobility of Syrian Refugees in Turkey

The Data for Refugees (D4R) Challenge was a nonprofit 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 was based on anonymized mobile Call Detail Record (CDR) of phone calls and SMS messages of Türk Telekom customers. It indicated broad activity and mobility patterns of refugees and citizens in Turkey for 1 year. The data collection period was from January 1, 2017 to December 31, 2017. The project was initiated by Türk Telekom, in partnership with the Turkish Academic and Research Council (TÜBİTAK) and Boğaziçi University, and in collaboration with several academic and nongovernmental organizations, including UNHCR Turkey, UNICEF, and International Organization for Migration. This chapter describes the Challenge in detail, providing a history of its evolution, as well as a description of the data shared with the participants of the Challenge.
Albert Ali Salah, Alex Pentland, Bruno Lepri, Emmanuel Letouzé, Yves-Alexandre de Montjoye, Xiaowen Dong, Özge Dağdelen, Patrick Vinck

Chapter 2. Call Detail Records to Obtain Estimates of Forcibly Displaced Populations

Call Detail Records have great potential to drive humanitarian action for early warning, monitoring, decision-making, and evaluation. The Data For Development Challenge leveraged mobile phone data for Development in Senegal. We further explored methodologies and protocols to use this data to support humanitarian action for refugees. Obtaining estimates of forcibly displaced population requires not only data analysis but also a solid protocol to ensure privacy and the right outcomes of the project. When no refugee labeled data is available, a framework to identify displaced population is necessary. We present a methodology to analyze mobility that minimizes privacy risks by subtracting mobility patterns of the population until finding those patterns indicative of the displaced population.
David Pastor-Escuredo, Asuka Imai, Miguel Luengo-Oroz, Daniel Macguire

Chapter 3. Mobile Phone Data for Children on the Move: Challenges and Opportunities

Today, 95% of the global population has 2G mobile phone coverage (GSMA 2017) and the number of individuals who own a mobile phone is at an all time high. Mobile phones generate rich data on billions of people across different societal contexts and have in the last decade helped redefine how we do research and build tools to understand society. As such, mobile phone data have the potential to revolutionize how we tackle humanitarian problems, such as many suffered by refugees all over the world (United Nations Secretary-General’s Independent Expert Advisory Group on a Data Revolution for Sustainable Development. A world that counts: Mobilising the data revolution for sustainable development, 2014 [64]). While promising, mobile phone data and the new computational approaches bring both opportunities and challenges (Blumenstock in Estimating economic characteristics with phone data, pp. 72–76, 2018 [9]). Mobile phone traces contain detailed information regarding people’s whereabouts, social life, and even financial standing. Therefore, developing and adopting strategies that open data up to the wider humanitarian and international development community for analysis and research while simultaneously protecting the privacy of individuals are of paramount importance (UNDG 2018). Here we outline the challenging situation of children on the move and actions UNICEF is pushing in helping displaced children and youth globally, and discuss opportunities where mobile phone data can be used. We identify three key challenges: data access, data and algorithmic bias, and operationalization of research, which need to be addressed if mobile phone data are to be successfully applied in humanitarian contexts.
Vedran Sekara, Elisa Omodei, Laura Healy, Jan Beise, Claus Hansen, Danzhen You, Saskia Blume, Manuel Garcia-Herranz

Chapter 4. Coding Boot Camps for Refugees

In 2015, over one million asylum seekers arrived in Europe. With governments and traditional institutions gridlocked in providing refugee support, new actors began to emerge. Several organizations offering coding programs to refugees launched across the globe as way to address and aid in their displacement. European-based boot camps aimed for digital integration, working with refugees to kickstart their new life within the continent. Boot camps based in the Middle East worked with refugees, internally displaced people, and vulnerable youth to provide the skills needed to access digital economy. Regardless of geographical distinction, these boot camps utilized a code education to empower and improve an individual’s livelihood to much success. The research presented was conducted over a period from early 2018 to January 2019.
Jessica Francis

Chapter 5. “Do No Harm” in the Age of Big Data: Data, Ethics, and the Refugees

Leveraging call detail records for humanitarian analysis involves the collection and sharing of a large set of behavioral data, from hundreds of thousands of people. There is a risk that such data could be misused for surveillance and suppression, and there are strong criticisms that have been leveled at efforts involving call detail records. The D4R Challenge is not immune to these criticisms, and during the design and implementation of the challenge, these issues were discussed at length. This chapter outlines these issues and how they were (imperfectly) addressed.
Patrick Vinck, Phuong N. Pham, Albert Ali Salah

Chapter 6. Pioneering Predictive Analytics for Decision-Making in Forced Displacement Contexts

UNHCR has been leading some of the most prominent efforts in research and operational applications of the use of nontraditional sources—including big data—in forced displacement settings. Pioneering the research on predictive analytics for population flow in emergencies, UNHCR created the Winter Cell, a cross-cutting, inter-divisional initiative established to respond to the 2015 Mediterranean refugee crisis. The project identified refugee population flow trends in the routes into Europe, using real-time data about weather conditions and its effects along the routes. Its predecessor, Project Jetson, an applied predictive analytics project, builds on this methodology by estimating the numbers of internally displaced people in Somalia and refugees in the south region of Ethiopia (Dollo Ado) with nontraditional data, including market prices and climate anomalies. This chapter describes the work of UNHCR Innovation in data science research to improve the work of UNHCR in advocacy, emergency preparedness, and operational response.
Christopher Earney, Rebeca Moreno Jimenez

D4R Challenge: Social Integration


Chapter 7. Measuring Fine-Grained Multidimensional Integration Using Mobile Phone Metadata: The Case of Syrian Refugees in Turkey

The current Syrian civil war has led to a mass migration of Syrian refugees into Turkey. As the Syrian conflict has intensified and lengthened, many refugees have faced challenges integrating into their host societies. Here we introduce and evaluate different measures extracted from mobile phone metadata to study integration of refugees along three dimensions: (1) social integration, (2) spatial integration, and (3) economic integration through signatures of employment activity. We use these measures to compare integration across different regions in Turkey and find striking differences both in the distributions of these dimensions and the relations between them. Finally, leveraging the results from two general elections in Turkey in 2015 and 2018, we confirm earlier findings concerning the impact of refugee presence on voting behavior and demonstrate that we can better explain voting behavior by incorporating integration metrics.
Michiel A. Bakker, Daoud A. Piracha, Patricia J. Lu, Keis Bejgo, Mohsen Bahrami, Yan Leng, Jose Balsa-Barreiro, Julie Ricard, Alfredo J. Morales, Vivek K. Singh, Burcin Bozkaya, Selim Balcisoy, Alex Pentland

Chapter 8. Towards an Understanding of Refugee Segregation, Isolation, Homophily and Ultimately Integration in Turkey Using Call Detail Records

In this chapter, we contribute a methodological framework for measuring integration through the lens of spatial and social segregation using CDR data. We illustrate the application of this framework using the datasets provided by Türk Telekom. Integration is one of the main durable solutions to refugee crises recognized by the UN High Commissioner for Refugees (UNHCR). It is a complex and gradual legal, economic, social and cultural process that burdens both the settling population, and the receiving society. Successful integration requires actions from a variety of stakeholders (including different levels of government, NGOs, welfare service providers, etc.), which can make evaluating the outcomes of targeted programmes and policies extremely difficult. While these generally differ from country to country, UNHCR recognizes a need for standardized indicators that can be used to compare integration across countries and regions, and to assess the success of various efforts. Here, we show how segregation, isolation and homophily can be measured by deriving population estimates from CDRs, and how the evolution of refugees’ communication patterns and mobility traces can provide initial insights into their social integration.
Jeremy Boy, David Pastor-Escuredo, Daniel Macguire, Rebeca Moreno Jimenez, Miguel Luengo-Oroz

Chapter 9. Using Call Data and Stigmergic Similarity to Assess the Integration of Syrian Refugees in Turkey

By absorbing more than 3.4 million Syrians, Turkey has shown remarkable resilience. But the host community tensions toward these newcomers is rising. Thus, the formulation of effective integration policies is needed. However, assessing the effectiveness of such policies demands tools able to measure the integration of refugees despite the complexity of such phenomena. In this work, we propose a set of metrics aimed at providing insights and assessing the integration of Syrians refugees, by analyzing the CDR dataset of the challenge. Specifically, we aim at assessing the integration of refugees, by exploiting the similarity between refugees and locals in terms of calling behavior and mobility, considering different spatial and temporal features. Together with the already known methods for data analysis, in this work we use a novel computational approach to analyze users’ mobility: computational stigmergy, a bio-inspired scalar and temporal aggregation of samples. Computational stigmergy associates each sample to a virtual pheromone deposit (mark) defined in a multidimensional space and characterized by evaporation over time. Marks in spatiotemporal proximity are aggregated into functional structures called trails. A stigmergic trail summarizes the spatiotemporal dynamics in data and allows to compute the stigmergic similarity between them.
Antonio Luca Alfeo, Mario G. C. A. Cimino, Bruno Lepri, Gigliola Vaglini

Chapter 10. Integration of Syrian Refugees: Insights from D4R, Media Events and Housing Market Data

We explore various means of quantifying integration using two of the D4R Challenge datasets. We propose various integration indices and discuss their output. We combine the data from the D4R Challenge with data from the GDELT Project and with data on transactions on the housing market in Turkey. We also describe research directions to be undertaken in the future using the D4R data.
Simone Bertoli, Paolo Cintia, Fosca Giannotti, Etienne Madinier, Caglar Ozden, Michael Packard, Dino Pedreschi, Hillel Rapoport, Alina Sîrbu, Biagio Speciale

Chapter 11. Quantified Understanding of Syrian Refugee Integration in Turkey

Turkey hosts over 3.5 million Syrian refugees. How they integrate into local communities significantly impacts the stability of the host country. In this project, we use mobile users’ Call-Detail Records (CDR) and Point-Of-Interest (POI) data to infer users’ mobility and activity patterns in order to investigate the level of integration. Using these data, we compare the spatial patterns of refugees against those of citizens. We observe a few patterns that set refugees apart, e.g., smaller travel distances, fewer high-expense activities, and separate home locations from the locals. We also establish a metric based on a citizen-refugee classifier to quantify the degree of integration. We are able to rank 11 densely populated cities, and notice that the level of integration varies from city to city. For example, Gaziantep serves as an example of a well-integrated city, whereas Sanliurfa appears to be poorly integrated.
Wangsu Hu, Ran He, Jin Cao, Lisa Zhang, Huseyin Uzunalioglu, Ahmet Akyamac, Chitra Phadke

Chapter 12. Syrian Refugee Integration in Turkey: Evidence from Call Detail Records

Over the past 7 years, the needs of the three and a half million Syrian refugees have shifted from emergency response to programs focused on their integration. Using D4R call detail records (CDRs), this chapter focuses on questions derived from the relevant academic literature and explores whether and how local context and service provision affect refugee integration. Unlike existing studies, we address multiple factors in a single analysis, accounting for potential confoundedness between different factors that might otherwise bias results. Our analysis supplements D4R with an array of original data sources related to refugee integration and service provision and employs linear regression and regularization techniques. We find that social integration is affected by multiple socioeconomic, welfare, and geography-related factors such as economic activity, availability of health facilities and charity foundations, network centrality, and district location. In terms of mobility, long-term over-time movement of refugees appears to be motivated by the availability of scarce welfare resources such as health clinics, as well as economic activity and the availability of religious facilities in a district. Our results suggest that policy-makers concerned with social integration of refugees must readily take into account the role of service provision in that process.
Tugba Bozcaga, Fotini Christia, Elizabeth Harwood, Constantinos Daskalakis, Christos Papademetriou

Chapter 13. Assessing Refugees’ Onward Mobility with Mobile Phone Data—A Case Study of (Syrian) Refugees in Turkey

Secondary or onward mobility of refugees can pose considerable challenges for targeted and timely humanitarian assistance, and for long-term integration. There is very little systematic knowledge of the onward migration of refugees after their initial flight to a country of reception in general, and specifically in Turkey. In this chapter, we describe how the analysis of mobile phone Call Details Records can help to better understand spatio-temporal patterns of refugees’ onwards mobility. The analysis reveals some clear, large-scale mobility patterns (from South to North, from East to West, from Centre to the Coast, to large urban areas), and also some temporal patterns, but also shows that human mobility is complex and accordingly requires more advanced analytical tools. We conclude that it might be worth of re-framing registration policies for refugees, given the highly mobile share of refugee population, and the important role that this mobility probably plays for livelihoods.
Harald Sterly, Benjamin Etzold, Lars Wirkus, Patrick Sakdapolrak, Jacob Schewe, Carl-Friedrich Schleussner, Benjamin Hennig

Chapter 14. Segregation and Sentiment: Estimating Refugee Segregation and Its Effects Using Digital Trace Data

In light of the ongoing events of the Syrian Civil War, many governments have shifted the focus of their hospitality efforts from providing temporary shelter to sustaining this new long-term population. In Turkey, a heightened focus has been placed on the encouragement of integration of Syrian refugees into Turkish culture, through the dismantling of Syrian refugee-only schools in Turkey and attempts to grant refugees permanent citizenship, among other strategies. Most of the existing literature on the integration and assimilation of Syrian refugees in Turkey has taken the form of surveys assessing the degree to which Syrian refugees feel they are part of Turkish culture and the way Turkish natives view the refugee population. Our analysis leverages call detail record data, made available by the Data for Refugees (D4R) Challenge, to assess how communication and segregation vary between Turkish natives and Syrian refugees over time and space. In addition, we test how communication and segregation vary with measures of hostility from Turkish natives using data from the social media platform Twitter. We find that measures of segregation vary significantly over time and space. We also find that measures of intergroup communication positively correlate with measures of public sentiment toward refugees. Attempts to address the concerns of Turkish natives in order to minimize the traction of online hate movements may help to improve the integration process.
Neal Marquez, Kiran Garimella, Ott Toomet, Ingmar G. Weber, Emilio Zagheni

Chapter 15. Measuring and Mitigating Behavioural Segregation as an Optimisation Problem: The Case of Syrian Refugees in Turkey

Turkey hosts the largest population of Syrian refugees of any country in the world. As options to return home or settle in other countries remain limited, long-term integration of the refugee population into Turkish society is a major policy objective. Using a large dataset of mobile phone records provided by one of Turkey’s largest mobile phone service operators, Türk Telekom, in the frame of the Data for Refugees project, we define, analyse and optimise inter-group integration as it relates to the communication patterns of two segregated populations: refugees living in Turkey and the local Turkish population, respectively. To achieve this, working with call records and SMS origins and destinations between and among both populations, we develop an extensible, statistically solid and reliable framework to measure the differences between the communication patterns of two groups. Using this new framework, we identify the districts of the Istanbul province where the variation between the ways the two populations communicate is largest. Finally, to show the potential of our framework, we provide and estimate the costs of some recommendations on how to target public and private investments to incentivise refugees to live outside of established refugee enclaves, increasing inter-group contact and integration.
Daniel Rhoads, Javier Borge-Holthoefer, Albert Solé-Ribalta

D4R Challenge: Labor, Education, Health, Safety


Chapter 16. Seasonal Labor Migration Among Syrian Refugees and Urban Deep Map for Integration in Turkey

This chapter provides an overview of the data analysis and visualization steered under “An Urban Deep Map for Integration in Turkey” (UDMIT) project, which uses mobile call data records of Syrian refugees under temporary protection provided by Data for Refugees: The D4R Challenge on Mobility of Syrian Refugees in Turkey (Salah et al. Data for refugees: the D4R challenge on mobility of Syrian refugees in Turkey (2018) [25]). First, to examine Syrian refugees’ temporal and spatial dimensions of mobility, the chapter concentrates on their interprovincial migration patterns within Turkey. Based on an analysis of these patterns, the study offers insights on the potential motivations for regular and seasonal interprovincial mobility, especially regarding access to services and employment opportunities in the formal and informal labor market. The findings are also complemented by policy recommendations on how the D4R data can be of use to central and local authorities on providing occupational health and safety services and on improving refugees’ access to information. Second, the current study delivers a web-based deep mapping platform that allows generating and reporting a visual representation of refugee population densities and mobility across Turkey on a real-time basis. The interface enables examining the spatio-temporal D4R data at three scales (country, province, and district level) together with other layers of data, including (a) demographic information at the province and district levels, (b) service providers (nongovernmental organizations, schools and healthcare services), (c) media analytics, and (d) public discussion. Within the scope of this limited study, the deep mapping platform has been developed as an early-version prototype to demonstrate the potential of opening the data to the use of experts and public with a multilayered, visual, and interactive tool.
Sedef Turper Alışık, Damla Bayraktar Aksel, Asım Evren Yantaç, İlker Kayi, Sibel Salman, Ahmet İçduygu, Damla Çay, Lemi Baruh, Ivon Bensason

Chapter 17. Refugees in Undeclared Employment—A Case Study in Turkey

Exploitation of vulnerable groups such as refugees for cheap labour is a notorious phenomenon in Turkey. Up to 2017, only 1.3% of around 3 million Syrian refugees registered in Turkey have been granted a work permit, leaving the overwhelming majority dependent on undeclared employment with all its negative implications: high-risk jobs, pay below minimum wage and lack of access to social security. Mobile phone metadata allows for a detailed view on commuting routines and migration, possibly unearthing employment situations which are not captured otherwise. This study proposes a methodological framework for detecting fine-granular socio-economic occurrences in situations where little training data are available. As a proof of concept, the study applies the methodology to identify potentially undeclared employment among refugees in Turkey by analyzing seasonal migration and commuting patterns in two specific cases: during the late-summer hazelnut harvest in the province of Ordu and at the construction site of the Istanbul Airport. The study finds clear indication for work-related migration and commuting patterns among refugees hinting at undeclared employment. The proposed framework therefore provides an analytical instrument to make targeted interventions such as controls more effective by detecting small areas where undeclared work likely takes place.
Fabian Bruckschen, Till Koebe, Melina Ludolph, Maria Francesca Marino, Timo Schmid

Chapter 18. The Use of Big Mobile Data to Gain Multilayered Insights for Syrian Refugee Crisis

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.
Özgün Ozan Kılıç, Mehmet Ali Akyol, Oğuz Işık, Banu Günel Kılıç, Arsev Umur Aydınoğlu, Elif Surer, Hafize Şebnem Düzgün, Sibel Kalaycıoğlu, Tuğba Taşkaya-Temizel

Chapter 19. Improve Education Opportunities for Better Integration of Syrian Refugees in Turkey

The integration of Syrian refugees in the Turkish society is crucial for the long-term well-being of both populations. Education is one of the most important element to integrate Syrians’ children and prevent a “lost generation”. In this project, we investigate two main aspects related to refugees’ education: “How to improve Syrians’ access to schooling?” and “What is the impact of Syrians’ schooling on Turkish society?” The analysis presented in the paper provides quantitative elements to analyze and optimize education resources with respect to refugees’ and natives’ needs, supporting the claim that education plays a key role in improving integration in the society.
Marco Mamei, Seyit Mümin Cilasun, Marco Lippi, Francesca Pancotto, Semih Tümen

Chapter 20. Optimizing the Access to Healthcare Services in Dense Refugee Hosting Urban Areas: A Case for Istanbul

With over 3.5 million refugees, Turkey continues to host the world’s largest refugee population. This introduced several challenges in many areas including access to healthcare system. Refugees have legal rights to free healthcare services in Turkey’s public hospitals. With the aim of increasing healthcare access for refugees, we looked at where the lack of infrastructure is felt the most. Our study attempts to address these problems by assessing whether Migrant Health Centers’ locations are optimal. The aim of this study is to improve refugees’ access to healthcare services in Istanbul by improving the locations of health facilities available to them. We used call data records provided by Turk Telekom.
M. Tarik Altuncu, Ayse Seyyide Kaptaner, Nur Sevencan

Chapter 21. Characterizing the Mobile Phone Use Patterns of Refugee-Hosting Provinces in Turkey

We use coarse-grained mobile phone data from a large Turkish mobile phone provider and cross-reference this data with social media data and a qualitatively composed violent events list to explore the integration of refugees in Turkey. The data provides grounds for fruitful future research. It suggests that border communities with the refugee-sending country have much different communications patterns than non-border communities. Additionally, proximity to refugee camps may increase negative sentiment on social media toward refugees, which we suggest may be a proxy for understanding “compassion fatigue.” These findings provide directions for future research on integration.
Erika Frydenlund, Meltem Yilmaz Şener, Ross Gore, Christine Boshuijzen-van Burken, Engin Bozdag, Christa de Kock

Chapter 22. Refugee Mobility: Evidence from Phone Data in Turkey

Our research report employs the D4R data and combines it with several other sources to study one of the multiple aspects of integration of refugees, namely the mobility of refugees across provinces in Turkey. In particular, we employ a standard gravity model to empirically estimate a series of determinants of refugee movements. These include the standard determinants such as province characteristics, distances across provinces, levels of income, network effects as well as some refugee-specific determinants such as the presence of refugee camps and the intensity of phone call interaction among refugees. Importantly, we explore the effect of certain categories of news events, notably protests, violence, and asylum grants. Considering news as an indicator of policy implemented at the provincial level, we gain a better understanding as to how policy can facilitate refugee mobility and thus enhance integration. To benchmark our findings, we estimate the same model for the mobility of individuals with a non-refugee status.
Michel Beine, Luisito Bertinelli, Rana Cömertpay, Anastasia Litina, Jean-François Maystadt, Benteng Zou



Chapter 23. Leveraging Open Algorithms (OPAL) for the Safe, Ethical, and Scalable Use of Private Sector Data in Crisis Contexts

The Open Algorithms (OPAL) is an approach where a dedicated server installed within a data holding institution receives queries from the outside world and responds with aggregated and anonymized indicators, thus providing insights into the raw data that reside inside the institution without jeopardizing these assets. This chapter discusses how in the future the Open Algorithms (OPAL) system or an equivalent mechanism could allow using data collected and controlled by private companies that are shared through data challenges such as the D4R in a scalable, ethical manner.
Emmanuel Letouzé

Chapter 24. The Potential and Practice of Data Collaboratives for Migration

Migration—including but not limited to forced migration—is one of the greatest concerns of the twenty-first century, and is only likely to grow in importance over the coming years. Yet our understanding of the current situation, the causes, and consequences of population movements and what solutions work remain limited. In this piece, we argue that this lack of understanding is part of shortcoming that could, at least in part, be addressed through the targeted analysis of datasets dispersed across stakeholders in governments, the private sector, and civil society. Data collaboratives, an emerging form of public–private partnership that allows for collaboration across sectors and actors, have the potential to break down data siloes to the end of improving our understanding of the drivers of migration and facilitating better decision-making by those active in the space. By taking three recommended steps—mapping and documenting data collaboratives, identifying and nurturing “data stewards,” and developing data responsibility frameworks—actors in the migration field could unlock the value of data held across sectors and improve the lives of migrants, refugees and those affected by the movement of populations across borders.
Stefaan G. Verhulst, Andrew Young

Chapter 25. Policy Implications of the D4R Challenge

The Data for Refugees (D4R) Challenge resulted in many insights related to the movement patterns of the Syrian refugees within Turkey. In this chapter, we summarize some of the important findings, and suggest policy recommendations for the main areas of the challenge. These recommendations are sometimes broad suggestions, as the policy interventions involve many factors that are difficult to take into account. We give examples of such issues to help policy-makers.
Albert Ali Salah, M. Tarık Altuncu, Selim Balcisoy, Erika Frydenlund, Marco Mamei, Mehmet Ali Akyol, Kerem Yavuz Arslanlı, Ivon Bensason, Christine Boshuijzen-van Burken, Paolo Bosetti, Jeremy Boy, Tugba Bozcaga, Seyit Mümin Cilasun, Oğuz Işık, Sibel Kalaycıoğlu, Ayse Seyyide Kaptaner, Ilker Kayi, Özgün Ozan Kılıç, Berat Kjamili, Huseyin Kucukali, Aaron Martin, Marco Lippi, Francesca Pancotto, Daniel Rhoads, Nur Sevencan, Ervin Sezgin, Albert Solé-Ribalta, Harald Sterly, Elif Surer, Tuğba Taşkaya Temizel, Semih Tümen, Ismail Uluturk


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