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 Syrian refugees, by analyzing a real-world call detail record (CDR) 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 spatio-temporal patterns: Computational stigmergy, a bio-inspired scalar and temporal aggregation of samples. Computational stigmergy associates each sample with a virtual pheromone deposit (mark). Marks in spatiotemporal proximity are aggregated into functional structures called trails, which summarize the spatiotemporal patterns in data and allow 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: 1) correlated with the amount of interaction with locals; 2) an effective proxy for refugee's economic capacity, and thus refugee's potential employment; and 3) able to capture events that may disrupt the integration phenomena, such as social tension. © 2019 IEEE.

segregation mobility unemployment Computational stigmergy social integration Refugee

Social integrations unemployment refugee Spatiotemporal patterns Segregation (metallography) Stigmergy Computational approach Carrier mobility integration Behavioral similarities Amount of interaction