Today at Cloud Next we’re introducing new environment datasets, part of Google Earth AI, now available in Experimental in Google Maps Platform. These datasets complement our suite of Environment APIs and provide air quality, pollen and weather insights. This enables you to go beyond the API’s real-time data and unlock environmental insights through hyper-local, high-resolution historical data, seamlessly integrated with BigQuery.
These datasets are designed to help organizations transform complex environmental data into actionable insights for proactive planning, improved health outcomes, and robust business resilience. For example, healthcare providers can predict patient surges by modeling air quality and pollen levels, and pharmaceutical companies can correlate different health-related metrics such as symptom aggravation to aggregated historical data and authoritative health standards.
The precision of these datasets are derived from Google’s proprietary AI models, the blending of vast geospatial data sources such as government reference monitoring stations and commercial sensor networks, and ongoing validation to ensure reliable historical and predictive insights.
Weather insights
The weather insights dataset provides access to approximately five years of high-resolution historical weather data, capturing precise details down to 0.1 degrees globally and approximately 4 kilometers in the US and Europe, on an hourly basis. By blending this comprehensive weather record with your operational data in BigQuery, you can analyze past patterns to predict demand, optimize delivery routes, and strategically plan energy or travel projects. Using these historical insights to build a proactive strategy, and applying the right actions in real time by combining it with the Weather API forecast, you can improve operational efficiency and stay resilient across industries like retail, logistics, travel, energy, insurance and mobility. For example, travel companies can forecast seasonal weather booking trends to optimize travel packages and marketing campaigns, based on seasonal environmental patterns.
Precipitation data visualization in BigQuery
Air quality and pollen insights
Using the air quality and pollen insights datasets, you can now access years of historical data to pinpoint the concentration of specific pollutants and pollen types down to a 500-meter or 1-kilometer grid, providing the high data accuracy necessary to eliminate local "blind spots." Imagine correlating these precise environmental patterns directly with patient records or optimizing retail inventory based on upcoming pollen waves. By effortlessly integrating this validated environmental context with your own proprietary data in BigQuery, you can move beyond reactive decision making toward deeper health impact analysis and proactive care management.
Visualizing the median PM2.5 levels in Manhattan on a specific day
Ready to explore how air quality, pollen and weather historical data can help you achieve new levels of precision and foresight? Sign up for early access and to learn more.
