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

Salah, Albert Ali; Pentland, Alex; Lepri, Bruno; Letouzé, Emmanuel; Montjoye, Yves-Alexandre de; Dong, Xiaowen; Dağdelen, Özge; Vinck, Patrick

doi:10.1007/978-3-030-12554-7_1

Cited by 21 publications

(28 citation statements)

References 17 publications

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“…The cities of Istanbul, Ankara and Izmir are the most promising areas to conduct our analysis since they have the larger density of antennas and the larger calling activity made by refugees. This conclusion is also confirmed by the descriptive statistics of the D4R dataset provided in [16]. Moreover, Istanbul's metropolitan area consists of 69 districts [16] with a variety of different characteristics (e.g., different housing costs or job opportunities).…”

Section: Data Selectionmentioning

confidence: 52%

“…This conclusion is also confirmed by the descriptive statistics of the D4R dataset provided in [16]. Moreover, Istanbul's metropolitan area consists of 69 districts [16] with a variety of different characteristics (e.g., different housing costs or job opportunities).…”

Section: Data Selectionmentioning

confidence: 52%

“…In this work we analyze three different CDR datasets [16] provided by Turk Telekom in the context of the research challenge Data4Refugee (D4R). In order to provide the reader with some insights about each D4R dataset, we briefly present each one of them:…”

Section: A Data Descriptionmentioning

confidence: 99%

“…Finally, the number of calls with a callee prefix of type "unknown" (i.e. 3) covers just 0.93% of the whole dataset and they are dropped from our analysis, as they can be considered as noisy data [16]. b) The Coarse Grain Mobility Dataset (GGMD) contains the calls details for a unique group of users throughout the whole year, but with a more coarse spatial granularity, i.e.…”

Section: A Data Descriptionmentioning

confidence: 99%

“…In this work we analyze CDR datasets [16] to unfold the conditions that can contribute to the integration of refugees by studying their calling behavior and mobility. To this end, we employ a novel computational technique aimed at unfolding and matching spatio-temporal patterns, i.e.…”

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confidence: 99%

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Assessing Refugees’ Integration via Spatio-Temporal Similarities of Mobility and Calling Behaviors

Alfeo

Cimino

Lepri

et al. 2019

IEEE Trans. Comput. Soc. Syst.

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In Turkey the increasing tension, due to the presence of 3.4 million Syrian refugees, demands the formulation of effective integration policies. Moreover, their design requires tools aimed at understanding the integration of refugees despite the complexity of this phenomenon.In this work, we propose a set of metrics aimed at providing insights and assessing the integration of Syrians refugees, by analyzing a real-world Call Details Records (CDRs) dataset including calls from refugees and locals in Turkey throughout 2017. Specifically, we exploit the similarity between refugees' and locals' spatial and temporal behaviors, in terms of communication and mobility in order to assess integration dynamics.Together with the already known methods for data analysis, we use a novel computational approach to analyze spatiotemporal patterns: Computational Stigmergy, a bio-inspired scalar and temporal aggregation of samples. Computational Stigmergy associates each sample to a virtual pheromone deposit (mark). Marks in spatiotemporal proximity are aggregated into functional structures called trails, which summarize the spatiotemporal patterns in data and allows computing the similarity between different patterns.According to our results, collective mobility and behavioral similarity with locals have great potential as measures of integration, since they are: (i) correlated with the amount of interaction with locals; (ii) an effective proxy for refugee's economic capacity, thus refugee's potential employment; and (iii) able to capture events that may disrupt the integration phenomena, such as social tensions.

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Section: Data Selectionmentioning

confidence: 52%

Section: Data Selectionmentioning

confidence: 52%

Section: A Data Descriptionmentioning

confidence: 99%

Section: A Data Descriptionmentioning

confidence: 99%

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confidence: 99%

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