Leveraging High‐Frequency Digital Data to Analyze Forced Displacement Dynamics: A Case Study from the Gaza Strip

Abstract The quantification and analysis of forced displacement, driven by political unrest or natural disasters, has become increasingly central to both humanitarian and demographic research. With displaced populations reaching record numbers, there is an urgent need for accurate and timely data on displacement patterns, particularly disaggregated by age and gender. This paper introduces an analytical toolbox designed to leverage the growing diversity of digital trace data that overcomes disruptions of traditional data collection systems during crises, enabling high‐frequency monitoring of forced displacement. The toolbox enhances our understanding of the magnitude, pace, and subpopulation heterogeneity of displacement dynamics. We apply this toolbox to the Gaza Strip following the 2023 Hamas attack. Deriving population estimates using data from Facebook's marketing API in combination with pre‐war population data, we demonstrate how this toolbox facilitates a multifaceted assessment of the consequences of war on population movement, connects mobility patterns to ground events, dissects displacement by gender, and enables cross‐country comparisons. Ultimately, the analysis highlights the unparalleled relative magnitude of forced displacement in the Gaza Strip from October 7, 2023, to May 15, 2024, with up to 70 percent of the population displaced, alongside increasing volatility in population movements as the conflict persists.