Air quality analytics & forecasting

Air measurements existed only as raw numbers per sensor, with gaps and drift. Nobody could see what they meant for a specific street.

• ✦Time-series pipeline with gap filling, outlier detection and sensor calibration
• ✦Spatial aggregation of readings to neighbourhood level
• ✦Weather data joined in for context and short-term forecasting
• ✦Open-data dashboard readable without a data background

Policy staff can see, per address, how the air has changed and what is expected next.