https://github.com/google-research/population-dynamics
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.
examples:
https://geoai-tutorials.gishub.org/PDFM/zillow_home_value/
https://arxiv.org/abs/2411.07207
https://github.com/google-research/population-dynamics
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.
examples:
https://geoai-tutorials.gishub.org/PDFM/zillow_home_value/
https://arxiv.org/abs/2411.07207