Webinar | Forced Displacement: A Quantitative Modeling Perspective
Thursday, December 7, 2023 11:00 AM to 12:00 PM EST
Please Note: This event has a new date and time.
The recent and ongoing conflicts including the war in Ukraine together with climate deterioration might lead to a century of unprecedented forced displacement. This webinar will portray the availability of data, and capabilities of quantitative analysis and statistical modeling approaches in providing insights for migration researchers and human rights practitioners to gain a clearer understanding of the magnitude, indicators, and nature of forced displacement phenomena. We will review the characteristics of forced displacement data collected by the United Nations High Commissioner for Refugees (UNHCR) and other organizations. We will also review common strategies for quantitative analyses of these data in order to gain insights on human movement phenomena from global forced displacement trends to country-specific internal forced displacement. The datasets we are interested in consist of counts representing the observed number of individuals moving from a particular location to another location (i.e., dyadic data). Methodologically, we will review common modeling options including Gravity models and regression analysis and potential methodological advancements that may more effectively address the complexities of forced displacement data. We will discuss a case study of the international outflow caused by the ongoing invasion of Ukraine. We also discuss strategies for blending organic data (e.g., data collected from social media, or search engines) with conventional data sources such as official statistics and macroeconomic data in order to better understand human movement. Critically, we highlight the opportunities and challenges of using organic data in modeling and forecasting forced displacement in crisis situations.
Nathan Wycoff is a Data Science Fellow at the Massive Data Institute in the McCourt School of Public Policy at Georgetown University studying forced migration using internet data and machine learning. Previously, he acquired a PhD in Statistics from Virginia Tech with a dissertation on sensitivity analysis for computationally intensive models.
Geraldine Henningsen is a Data Scientist at UNHCR, the UN Refugee Agency, where she specializes in predictive analyses on forcibly displaced populations using machine learning and econometric/statistical approaches to support UNHCR’s anticipatory action efforts. Before joining UNHCR, she worked in academia, conducting quantitative research on climate and energy related topics.
Ali Arab is the Director of Graduate Studies and Associate Professor of Statistics in the Department of Mathematics and Statistics of Georgetown University. His methodological research is in spatio-temporal and spatial statistics, and hierarchical Bayesian modeling. He is interested in applications of statistics in environmental science, epidemiology of infectious diseases, ecology, and human rights problems. His current research is focused on developing methodological tools for studying problems in the intersection of climate change and social/natural phenomena, in particular, bird phenology and climate change, climate and conflict driven forced migration, and climate change and vector-borne diseases. Ali serves as one of the American Statistical Association representatives to the American Association for the Advancement of Science (AAAS) Science and Human Rights Coalition.
Holds a PhD in Social Policy from Brandeis University and has evaluated international development programs for the United Nations, USAID, and the Ford Foundation. She is also Vice President of Global Peace Services USA.
He is Director of Movement Engaged Research Hub, Center for Social Science Research at George Mason University and Associate Professor at the Department of Sociology and Anthropology (GMU).
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