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.
Explore environmental indicators and their spatial distribution across PRECISE study sites. Built on the PRECISE Big Table dataset, the dashboard surfaces patterns in climate, vegetation, and pollution for rapid exploratory analysis.
The shared analytical environment for the PALS Lab research group. Python and R kernels, pre-loaded libraries, shared data directories, and direct database connectivity — everything in one place for collaborative analysis and scripting.
Spatiotemporal Personal Exposure Characterization & TRAjectory Analyzer. An open-source platform for personal environmental exposure assessment, integrating GPS trajectory analytics, wearable sensor data, and indoor/outdoor context classification to generate reproducible, time-weighted exposure profiles for environmental epidemiology and urban health research.
An open-source toolkit for harmonising heterogeneous climate and health datasets at scale. AI-assisted workflows handle variable mapping, unit conversion, and schema unification — purpose-built for resource-constrained settings where data quality and interoperability are critical.
An Africa-wide H3 hexagon map of road network density (RND) derived from OpenStreetMap. RND — total road length per unit area — provides a continent-scale spatial proxy for traffic-related air pollution exposure. Explore at ~5 km² resolution with country-level filtering and summary statistics.
Request a personal API token for programmatic access to the PALS Lab research database. Tokens enable authenticated queries against the PRECISE Big Table and linked datasets — ready to use from the Hub, notebooks, or any HTTP client.
Request direct access to the PRECISE Big Table via DuckDB — a fast in-process analytical database. Instantly receive a Python or R connection snippet ready to paste into your PALSlab Hub notebook. Designed for large analytical queries across ~2.4M records.
Point-and-extract environmental data for any location and time window. Upload a CSV or shapefile, select from NDVI, land surface temperature, rainfall, elevation, soil properties, and more — then download analysis-ready results. Powered by Google Earth Engine.
Translates CMIP6 climate projections into city-level neonatal mortality estimates across Africa. Three evidence-based pathways: direct neonatal hyperthermia, heat-induced preterm birth, and excess stillbirths. Model mitigation interventions (canopy, cool roofs) and explore escalation trajectories to 2100.