The relationships between a population of people, their health outcomes, and their local contexts can be very complex. Nevertheless, developing an understanding of these population dynamics can be crucial for addressing complex social problems, such as disease, economic security, disaster response, and much more. Despite the importance, however, accurate predictions for these population dynamics have been elusive for decades and remain a challenge for researchers, policymakers, and businesses.
Traditional approaches to understanding population dynamics tend to rely on data from censuses, surveys, or satellite imagery. While valuable, these types of data each have their own unique shortcomings. Censuses, though comprehensive, are infrequent and expensive; surveys can offer localized insights, but often lack scale and generalizability; and satellite imagery provides a broad overview, but lacks granular detail on human activity. In an effort to mitigate some of these shortcomings, over the years Google has designed, built, and shared a wealth of datasets that offer unique insights into population behavior, including Google Search Trends, COVID-19 Community Mobility Reports, and Access to Emergency Obstetrics Care.
In continued pursuit of this objective, today we are pleased to introduce a novel geospatial foundation model, built on aggregated data to preserve privacy, which we describe in “General Geospatial Inference with a Population Dynamics Foundation Model”. We designed the model (referred to as PDFM) so users could easily fine-tune it to a wide variety of downstream tasks. We are also releasing a dataset of unique location embeddings derived from the PDFM and code recipes users can employ to enhance their existing geospatial models. The dataset and code recipes aim to provide insights that can be applied to machine learning (ML) problems that rely on an understanding of populations and the characteristics of their local environments. They are easily adapted to many data science questions, enabling a more holistic and nuanced understanding of population dynamics around the world.
