Dynamic Estimates of Displacement in Disaster Regions: A Policy-driven framework triangulating data
Elisabetta Pietrostefani, Matt Mason, Rodgers Iradukunda, Hong Tran-Jones, Iryna Loktieva, Francisco Rowe

TL;DR
This paper proposes a policy-driven framework that combines traditional and digital data sources to improve real-time, high-resolution displacement monitoring in disaster regions, enhancing humanitarian response capabilities.
Contribution
It introduces a structured triangulation approach integrating multiple data streams, validated against established datasets, to produce more accurate and responsive displacement estimates.
Findings
Triangulated data approaches provide real-time, high-resolution displacement insights.
Digital trace data enhances the accuracy and responsiveness of displacement monitoring.
Benchmarking confirms the validity and scalability of the proposed framework.
Abstract
While traditional data systems remain fundamental to humanitarian response, they often lack the real-time responsiveness and spatial precision needed to capture increasingly complex patterns of displacement. Internal displacement reached an unprecedented 83.4 million people by the end of 2024, underscoring the urgent need for innovative, data driven approaches to monitor and understand population movements. This report examines how integrating traditional data sources with emerging digital trace data, such as mobile phone GPS and social media activity, can enhance the accuracy, responsiveness, and granularity of displacement monitoring. Drawing on lessons from recent crises, including the escalation of the war in Ukraine and the 2022 floods in Pakistan, the report presents a structured pilot effort that tests the triangulation of multiple data streams to produce more robust and reliable…
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Taxonomy
TopicsHuman Mobility and Location-Based Analysis · Facility Location and Emergency Management · Geographic Information Systems Studies
