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Exodus 2.0: crowdsourcing geographical and social trails of mass migration

机译:Exodus 2.0:众包的地理和社会径流

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The exodus of displaced populations is a recurring historical phenomenon, and the ongoing Syrian humanitarian crisis is its latest incarnation. During such mass migration events, information is an essential commodity. Of particular importance is geographical (e.g., pathways and refugee camps) and social (e.g., refugee activities and networking) information. Traditionally, such information had been produced and disseminated by authorities, but a new paradigm is emerging: Web 2.0 and mobile computing technologies enable the involved stakeholder communities to produce, access, and consume migration-related information. The purpose of this article is to put forward a new typology for understanding the factors around migration and to examine the potential of crowd-generated dataespecially open data and volunteered geographic informationto study such events. Using the recent wave of migration to Europe from the Middle East and northern Africa as a case study, we examine how migration-related information can be dynamically mined and analyzed to study the migrants' pathways from their home countries to their destination sites, as well as the conditions and activities that evolve during the migration process. These new data sources can provide a deeper and more fine-grained understanding of the migration process, often in real-time, and often through the eyes of the communities affected by it. Nevertheless, this also raises significant methodological and technical challenges for their future use associated with potential biases, data quality issues, and data processing.
机译:流离失所的人口的出源是一种经常性的历史现象,持续的叙利亚人道主义危机是其最新的化身。在此类大众迁移事件中,信息是必不可少的商品。特别重要的是地理(例如,途径和难民营)和社会(例如,难民活动和网络)信息。传统上,这些信息由当局产生和传播,但新的范例是新兴的:Web 2.0和移动计算技术使涉及的利益相关方社区能够生成,访问和消耗与迁移相关的信息。本文的目的是提出了一种新的类型,以了解迁移周围的因素,并审查人群生成的数据专业开放数据和志愿地理信息的潜力研究。利用最近从中东和北非的欧洲迁移到欧洲的案例研究,我们研究了与迁移相关信息如何动态开采和分析,以研究来自他们家乡的移民途径,以及他们的目的地网站作为在迁移过程中发展的条件和活动。这些新的数据源可以提供更深入而更细粒度的了解迁移过程,通常是实时的,并且通常通过受其影响的社区的眼睛。尽管如此,这也提高了与潜在偏见,数据质量问题和数据处理相关的未来使用的重要方法论和技术挑战。

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