RÉSUMÉ
Human migratory decisions are driven by a wide range of factors, including economic and environmental conditions, conflict, and evolving social dynamics. These factors are reflected in disparate data sources, including household surveys, satellite imagery, and even news and social media. Here, we present a deep learning-based data fusion technique integrating satellite and census data to estimate migratory flows from Mexico to the United States. We leverage a three-stage approach, in which we (1) construct a matrix-based representation of socioeconomic information for each municipality in Mexico, (2) implement a convolutional neural network with both satellite imagery and the constructed socioeconomic matrix, and (3) use the output vectors of information to estimate migratory flows. We find that this approach outperforms alternatives by approximately 10% (r 2), suggesting multi-modal data fusion provides a valuable pathway forward for modeling migratory processes.
RÉSUMÉ
We present the geoBoundaries Global Administrative Database (geoBoundaries): an online, open license resource of the geographic boundaries of political administrative divisions (i.e., state, county). Contrasted to other resources geoBoundaries (1) provides detailed information on the legal open license for every boundary in the repository, and (2) focuses on provisioning highly precise boundary data to support accurate, replicable scientific inquiry. Further, all data is released in a structured form, allowing for the integration of geoBoundaries with large-scale computational workflows. Our database has records for every country around the world, with up to 5 levels of administrative hierarchy. The database is accessible at http://www.geoboundaries.org, and a static version is archived on the Harvard Dataverse.
