BCC Forecasting the Break: Building Community and Capacity for Large-scale, Data-Intensive Research in Forced Migration Studies View Homepage


Ontology type: schema:MonetaryGrant     


Grant Info

YEARS

2013-2014

FUNDING AMOUNT

271598 USD

ABSTRACT

SMA-1338507 Susan F. Martin Sidney Berkowitz Jeffrey R. Collman Lisa Singh Georgetown University This project will assemble a multidisciplinary community of scholars and practitioners to create community and capacity for a large-scale, data intensive early warning system for detecting forced migration or population displacement. The system will be based on Raptor, a vast unstructured archive at Georgetown University of over 600 million publicly available open-source media articles. Mobilizing vast amounts of open source data will enable discovery of patterns of acute events (triggers) and/or slow-onset processes (trends) in the context of pre-existing stressors. Developing an effective early warning system of population displacement requires collaboration and shared learning between subject matter experts who understand the factors that contribute to forced migration at the macro, meso and micro levels and technical experts who understand how to collect, store, mine and analyze masses of data derived from international, national and local sources. Bringing together social scientists and computer scientists will expose social scientists to new modeling approaches for analyzing their subject matter. At the same time, computer scientists will exploit domain expertise in the social sciences. This expertise will provide insight for the development of beyond state of the art data mining of very large open source data bases for event detection, sequential mining and change detection. Participants will include scholars from many universities and practitioners from relief and migration-oriented organizations. The results of this endeavor will be: 1) methods and algorithms that can serve as a blueprint for integrating computational models into new avenues of social science research, and 2) a community and a plan for improving early warning of forced population displacement through human-computer analysis that address two key societal concerns, population changes and social disparities. Broader Impact Effective early warning of forced population displacement will help in state- and organization-level planning and preparation for such movements, as well as directly aid potential refugees and displaced persons before, during and after their exodus. Planning can lead to action to try to avert mass displacement, preferably by tackling the triggering events and stressors and providing options to those who would otherwise be forced to relocate (e.g., getting food to villages at risk of famine). Earlier warning may also help divert forced migrants from risky modes of movement (e.g., via non-seaworthy boats or across landmine infested borders). Early warning of displacement would enable the pre-positioning of shelter, food, medicines and other supplies in areas that are likely to receive large numbers of refugees and displaced persons. More... »

URL

http://www.nsf.gov/awardsearch/showAward?AWD_ID=1338507&HistoricalAwards=false

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