SBIR-STTR Award

The increasing incidence of population migration due to human-caused crises, such as wars or civil strife, or from environmental causes, such as droughts, fires, and other natural disasters, can have significant impacts on DoD interests with implications for humanitarian assistance/disaster relief (HA/DR) operations and stabilization efforts. These changing population dynamics can exacerbate already fragile states and have important security implications for United States forces involved in Civil Military Operations and Civil Affairs in the migration-affected regions. Being able to more accurately forecast and predict migration patterns—leveraging a diverse set of open-source datasets—will allow policymakers to more effectively prepare regions for change by taking into consideration the impacts on infrastructure and on human and social capital. In response to these challenges, scientific efforts across academia and the private sector have led to creation of large amounts of data relevant for understanding population migration. The sheer volume and complexity of this often unstructured and disparate information, however, make the data difficult to use without a comprehensive, flexible, and extensible modeling framework. Many of these efforts to model population dynamics are siloed, making it difficult to share insights, techniques, and datasets to facilitate rapid knowledge sharing and prototyping efforts. This makes it difficult for newly developed datasets, tools, and models to communicate across disciplines working with similar data or on similar problems. To address these problems, Aptima proposes to develop Project Consolidate, a collaborative migration modeling tool that (1) learns the context of research activities that share commonalities (e.g., regions, datasets, variables, models) and (2) utilizes innovative methods to generate recommendations to researchers. These recommendations will support rapid prototyping and facilitate more robust modeling efforts to improve research for the population migration community. Aptima is partnering with the College of William and Mary (W&M), creator of the AidData platform and GeoQuery data processing and querying technology, and Sand Hill Geographic, a leading developer of technologies in the geographic information system (GIS) domain. Project Consolidate will create innovative methods for modeling to improve collaboration on population migration modeling efforts. It will also increase the cadence of predictive research analyses to more accurately predict population migration impacts. The Project Consolidate system will accomplish these goals by (1) reducing data processing barriers for analyses through GeoQuery, (2) intelligently tagging the contents to support archiving and querying capabilities, (3) facilitating predictive modeling and transparency, and (4) increasing collaboration between researchers through recommendations of relevant content and other users.

Global migration reached a staggering record of over 280 million migrants in 2020, a number that has almost quadrupled over the past 60 years. The share of the world’s population that are migrants has also markedly increased, now nearing 4% of the global population. As these numbers continue to rise, it is increasingly unsustainable for researchers in the population migration modeling (PMM) community to continue to operate in siloed efforts that lead to either inefficient rework or poor knowledge sharing. Project Consolidate will address these challenges by designing and building a collaboration-focused and AI-enabled recommender system that allows PMM researchers to discover, build, and share models more efficiently and effectively. It will build on top of a suite of mature technologies, such as Aptima’s Seek and AidData’s GeoQuery platforms, to combine automated structured and unstructured annotation of content relevant for PMM (e.g., publications, datasets, surveys), and provide project-oriented recommendations of resources to individual researchers and teams through an intuitive interface designed with PMM users in mind. The resulting Phase II prototype system will be implemented in a manner that increases the likelihood of transition to government and industry use through robust sustainability and accreditation considerations.