Understanding migration patterns and how they change over time has important implications for understanding broader population trends, effectively designing policy and allocating resources. However, data on migration movements are often lacking, and those that do exist are not produced in a timely manner. Social media data offer new opportunities to provide more up-to-date demographic estimates and to complement more traditional data sources. Facebook, for example, can be thought of as a large digital census that is regularly updated. However, its users are not representative of the underlying population. In this paper, we combine data from Facebook’s Marketing API with data from the American Community Survey to generate ‘nowcasts’ — present and near-future predictions — of migrant stocks in the United States. We present a Bayesian hierarchical framework which probabilisitically combines information from the two sources, while accounting and adjusting for different types of biases and error. Combining data sources and modeling strategies enables us to weigh down inconsistencies and extract valuable insights without ignoring existing information.
- 05 November 2018 12:00pm–1:00pm
- Room 519, Chamberlain Building (#35)
Monica Alexander is an Assistant Professor in Statistical Sciences and Sociology at the University of Toronto. Her research focuses on developing statistical methods to help measure disparities in demographic and health outcomes. She received a PhD in Demography and Masters in Statistics from the University of California, Berkeley. Prior to that she received a Masters of Social Research from the ANU and a Bachelor of Science at the University of Tasmania. She has worked on research projects with organizations such as UNICEF, the World Health Organization, the Bill and Melinda Gates Foundation, and the Human Mortality Database.