In September, Chris Goodman, Senior Google Cloud Engineer at NGIS, presented at the Google Earth Engine Partner Summit on how Data Commons can make trusted environmental and public health datasets easier to combine, query, and act on.

Data Commons is an open-source initiative from Google that organises the world’s public data into a unified knowledge graph. By aligning datasets from different catalogues to a shared Schema.org vocabulary, Data Commons makes public statistics discoverable and comparable for researchers, policymakers, journalists, and AI agents.

Key capabilities of the platform include:

• Automatic visualisations, including timeline charts, scatter plots, and choropleth maps.
• A natural-language query interface powered by Gemini, alongside public statistical grounding in Google Search.
• REST APIs for downloading data and reports, plus a Model Context Protocol (MCP) server for AI agent workflows.
• A customisable web interface that organisations can adapt for domain-specific applications.

Unifying health, demographic, and environmental data across Australia

 Through the AusEnHealth initiative led by the Queensland University of Technology (QUT) in collaboration with FrontierSI, Google, and research partners, NGIS is bringing Australian health, demographic, and environmental datasets together into a single platform for the first time:

• Health data: Sourced from state and federal health agencies. Because health records are highly sensitive, each dataset undergoes strict ethics approval, de-identification, and spatial aggregation before area-level health indicators are added to the platform.
• Demographic data: Sourced primarily from the Australian Bureau of Statistics (ABS), including census demographics, occupation profiles, housing characteristics, and socioeconomic indices (SEIFA).
• Environmental data: Derived from satellite observations processed in Google Earth Engine alongside climate and monitoring records, capturing land surface temperature, tree canopy cover, and air quality indicators such as PM2.5.

By harmonising these three streams across standard Australian geographic boundaries, AusEnHealth lets researchers and local governments query across domains that previously lived in separate silos.

From satellite observations to local climate-health interventions

Extreme heat is one of the clearest examples of why cross-domain data matters. Severe heatwaves bring extended periods of unusually high daytime and overnight temperatures. When overnight minimums stay high, buildings and the human body cannot cool down, compounding heat stress. Yet the true toll of heatwaves often goes under-reported because excess mortality statistics lag the event, making heat less visible in the news than sudden disasters like bushfires or floods.

Heat risk also varies sharply from suburb to suburb. Older residents, people with pre-existing conditions, and outdoor workers face higher exposure, while housing quality, air conditioning access, and urban tree canopy determine how well different neighbourhoods cope.

Using AusEnHealth, researchers and local councils can overlay satellite heat and canopy metrics with ABS demographic vulnerability and health outcomes to build local risk profiles. For example:

• Targeted urban cooling: Large trees cool both the area beneath their canopy and the surrounding precinct. Showing where low canopy cover, high land surface temperatures, and vulnerable populations intersect helps local councils build an evidence-based business case for tree planting and built-environment upgrades.
• Cleaner air in populated corridors: Combining air quality data with cardiovascular and respiratory health indicators helps transport and urban planners quantify the public health payoff of cleaner public transport, electric vehicle adoption, and walkable urban design.

To learn more about the project, visit ausenhealth.com.au or explore the open-source platform at datacommons.org.