Open data tools, indicator catalogues, and analytical platforms for climate, environmental, and geospatial public health research.
"Advancing geographically precise public health through climate, environmental, and geospatial intelligence."
Place Alert Labs (PALs) is an initiative advancing geographically precise public health through climate, environmental, and geospatial intelligence. Over the past decade, PALs has generated research, data resources, technologies, and analytical platforms to better understand how climate and the environment influence health — and how this knowledge can be translated into more targeted and equitable public health action.
This is the open PALs portal, where we make our data, technologies, and platforms openly available to support research, collaboration, and innovation.
Our work includes tools for the precise geocoding of individual patient records and for characterising environmental exposures such as air pollution, heat, precipitation, and flooding. We have also developed an Africa-wide open dataset of pollution proxies for settings where ground monitoring data are limited, and in Zimbabwe our geocoding programme has produced copyrighted technologies and materials, including the Zimbabwe Address Database (ZAD).
Through work across several countries in sub-Saharan Africa, including sentinel research in The Gambia, Kenya, and Mozambique under the PRECISE project, we have collected personal exposure data to validate environmental exposure proxies and strengthen exposure science in African settings.
Through the HE2AT Centre, we have also characterised more than 50 environmental exposures for over 80,000 individual women, helping to build a data ecosystem that can catalyse the generation of urgently needed epidemiological evidence on climate and environmental influences on health.
Supported by funding from the US National Institutes of Health (NIH), Google, Grand Challenges Canada, UKRI, and others, PALs is building an ambitious foundation for scaling environmental health models, tools, and mentorship to support a more geographically precise public health.
Comprehensive documentation of climate and environmental exposure variables for understanding heat-health impacts across Sub-Saharan Africa. Covers 35+ variables across 9 domains — temperature, air quality, thermal comfort indices, soil, vegetation, urbanisation, and more.
A curated geospatial indicator catalogue mapping the physical and social determinants of maternal health outcomes across three PRECISE study sites. Includes an AI research assistant for querying the participant dataset directly.
Personal environmental exposure assessment — GPS trajectory analytics, wearable sensor data, and indoor/outdoor context classification for environmental epidemiology research.
Satellite-derived proxy exposure indicators — surface heat, air pollution (NO₂, PM₂.₅), vegetation, built-up intensity, and night-time lights across custom study areas. For settings without ground-based monitoring.
AI-assisted harmonisation of heterogeneous climate and health datasets — variable mapping, unit conversion, and schema unification for resource-constrained research settings.
Point-and-extract environmental data for any location and time window. Upload coordinates, select NDVI, LST, rainfall, elevation, soil, and more — download analysis-ready results via Google Earth Engine.
Africa-wide H3 hexagon map of road network density derived from OpenStreetMap — a continent-scale spatial proxy for traffic-related air pollution exposure at ~5 km² resolution.
Straight-line distance to the nearest highway and major road for every H3 cell in Africa — pollution exposure proxy and road accessibility measure with population-weighted admin choropleth.
Request a personal API token for programmatic access to the PALS Lab research database — authenticated queries against the PRECISE Big Table from the Hub, notebooks, or any HTTP client.
Fast in-process analytical access to the PRECISE Big Table via DuckDB. Receive a Python or R connection snippet ready for your PALSlab notebook — designed for large queries across ~3.2M records.
The shared analytical environment for the PALS Lab research group — Python and R kernels, pre-loaded libraries, shared data directories, and direct database connectivity for collaborative analysis.
Interested in collaborating, accessing data, or learning more about our research? Reach out to the Place Alert Labs team — we'll get back to you directly.