Refugee Watch: An Open-Source Framework for Climate-Resilient Humanitarian Analytics

Lisa Y.W. Tang, Wai Tang

Keywords: earth observation, humanitarian analytics, time series forecasting, forced displacement, climate resilience, open-source framework, low-resource settings, Global South

TL;DR: Refugee Watch: earth observation data with statistical and deep autoregressive forecasting for non-technical users

Abstract: The global refugee crisis presents complex challenges where displacement intersects with climate vulnerability, leaving millions without access to basic necessities and undermining agricultural self-sufficiency. Climate-related extremes further exacerbate the challenges of self-sufficiency through on-site agricultural activities. To address this pressing issue, we introduce \emph{Refugee Watch}, a proof-of-concept educational framework designed to facilitate climate resilience research. This reproducible tool enables seamless integration of real-time dataset updates, as well as accessible deep learning and statistical forecasting via \href{https://tcw1470-refugee-watch.share.connect.posit.cloud/}{Posit Cloud}. Its source code is shared at our \href{https://anonymous.4open.science/r/refugee-watch/README.md}{GitHub}. This educational platform aims to provide researchers, social scientists, and policymakers with a crucial entry point for visualizing climate data and informing evidence-based strategies to support refugees adapting to climate change. By leveraging this tool, stakeholders can develop effective interventions to mitigate the impacts of climate extremes on vulnerable populations.

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Submission Number: 108