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Stefaan Verhulst and Andrew Young in Stanford Social Innovation Review: The Potential and Practice of Data Collaboratives for Migration

Michelle Winowatan — March 30, 2018

In an article published this week in Stanford Social Innovation Review (SSIR), Stefaan Verhulst and Andrew Young explore the potential of leveraging data held by public, private, and civil institutions to more effectively address challenges and opportunities arising from high levels of global migration. The level of migration has doubled since 2000, with 280 million individuals living in a country other than where they were born, and 66 million people forcibly displaced due to wars and natural disasters. In this article, Verhulst and Young frame migration issue as an information problem and present a case for how cross-sector sharing of data, in the form of data collaboratives, could help fill the informational gap and help provide better understanding on the factors that influence migration, improving response and intervention among public sector, humanitarian, and civil society actors.

In particular, Verhulst and Young explore four potential value propositions of levearging data collaboratives for migration:

Improve situational analysis related to migrant and refugee movements. In various countries around the world, governments and agencies are using satellite imagery and social media data, along with other forms of data, to improve institutional awareness and response to shifting migration patterns. For instance, the satellite imagery company DigitalGlobe entered recently into a data collaborative arrangement with UNHCR to provide “timely, accurate information” related to Sudanese refugees, especially those entering into a large camp across the border in Ethiopia.

Generate new knowledge on drivers of migration, and enable the transfer of existing knowledge across sectors. Researchers around the world have increasingly used web data, including social media data and search query data, to gain a better understanding of the drivers of human behavior. Efforts include social media analysis to help UNICEF understand opinions and behavior related to anti-vaccine activity, and search query analysis to uncover the root causes of suicide in Korea. The sharing and analysis of private-sector web data can create new areas of knowledge that humanitarian organizations and other institutions can put into action.

Inform prediction and forecasting related to migration and refugees. A June 2017 study from the Pew Research Center showed how different search patterns in certain regions—such as Arabic-language searches arising from Turkey, including the keyword “Greece”—correlated with changes in migration flows to Europe. A similar, previous analysis leveraged geo-located Yahoo! search query data to better understand and predict migration flows, especially focusing on the “pendularity”—back-and-forth movements—of migrants. A more systematized (and responsible) approach to cross-sector data sharing and analysis, including but not limited to search query data, could provide these types of predictive insights, and inform more anticipatory and effective responses to shifts in migration.

Enable more targeted impact assessment and evaluation of migration interventions and responses. The SoBigData Exploratory migration studies project, funded by European Union’s Horizon 2020 research and innovation program, is using data collaboration to answer questions related to evaluating migration policy in Europe and migration more generally. With a greater focus on individual experiences, Demal Te Niew project draws on diverse datasets and data journalism to gain insight into the experience of migrants returning to Senegal from Italy, and how they are affected by diverse migration policies and interventions.

Read full article here.

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