Population Dynamics Insights (PDI) provides a rich, location-specific snapshot
of how populations engage with their surroundings. It captures this complex
relationship in an embeddings dataset that can be integrated into your
existing geospatial and predictive models.

PDI embeddings are generated by Google's proprietary model trained on a
geo-indexed dataset of rich aggregated information from Google Search trends,
and Google Maps' busyness visitation patterns, points of interest, weather and
air quality data. Delivered as a 330-dimensional embeddings dataset in BigQuery,
PDI enables organizations to understand the "DNA" of any location, fill critical
data gaps, and model human-environment interactions globally with unprecedented
precision and zero manual feature engineering. These embeddings ensure privacy
while enabling nuanced spatial analysis and prediction for applications ranging
from public health to socioeconomic modeling.

## About PDI embeddings

Population dynamics embeddings are generated using a purpose-built machine
learning model, trained on a rich set of features and converted into a condensed
vector representation. These embeddings are trained on:

- Aggregated Search Trends: Captures the most popular and representative search patterns for a location to inform human behavior and interests.
- Aggregated Maps Point of Interest: Captures the types of businesses, establishments and landmarks present.
- Aggregated Busyness: Captures foot traffic visitation patterns to indicate density and frequency of human presence.
- Aggregated Weather and Air Quality: Captures the weather and air quality statistics and patterns.

These features are aggregated at [S2
cell](https://s2geometry.io/) level 12 to
generate localized, context-aware embeddings that preserve privacy.

### Combine with related Google Maps Platform analytics datasets

While PDI provides machine-learning embeddings that capture Google Search
trends, the built environment and human activity patterns, and environmental
context (such as weather and air quality), you might also need exact Point of
Interest (POI) counts, competitor locations, or brand density. To model
relationships between commercial presence and local surroundings in BigQuery,
pair PDI with
[Places Insights](https://developers.google.com/maps/documentation/placesinsights/overview.md.txt).