{"url":"/dataset/pdfm-embeddings","name":"PDFM Embeddings","full_name":"Population Dynamics Foundation Model Embeddings","description_markdown":"PDFM Embeddings are condensed vector representations designed to encapsulate the complex, multidimensional interactions among human behaviors, environmental factors, and local contexts at specific locations. These embeddings capture patterns in aggregated data such as search trends, busyness trends, and environmental conditions (maps, air quality, temperature), providing a rich, location-specific snapshot of how populations engage with their surroundings. Aggregated over space and time, these embeddings ensure privacy while enabling nuanced spatial analysis and prediction for applications ranging from public health to socioeconomic modeling.","description_withheld":null,"homepage":"https://github.com/google-research/population-dynamics","introduced_date":"2024-11-14","introduced_date_note":null,"introduced_by":{"paper":"/paper/general-geospatial-inference-with-a","title":"General Geospatial Inference with a Population Dynamics Foundation Model","first_author":"Mohit Agarwal","url":null},"license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"modalities":[{"name":"Tabular","url":"/datasets/modality/tabular"}],"tasks":[{"name":"geo-localization","url":"/task/geo-localization","datasets_with_task":"/datasets/task/geo-localization"}],"languages":[{"name":"English","url":"/datasets/language/english"}],"variants":["PDFM Embeddings"],"data_loaders":[],"num_papers_in_archive":1,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[],"papers_with_a_benchmark_row":[],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":0,"samples_harvested":0,"samples_ran":0,"samples_unverified":0,"pointer_only_for_licence":0,"papers_with_no_sample_that_ran":0,"note":"the per-paper counts above, summed; not a rate"},"papers_note":"The archive never published its papers-using-dataset list; these are papers with a leaderboard row on this dataset's benchmarks."}