Before building weather features, I wanted to see where the DWD stations fall inside the NUTS-3 districts. Notebook 2.1 maps those stations and counts how much support each district has.
A district with few stations needs different handling from one with dense coverage. Checking that now gives me a way to carry the gap into later analysis as an explicit limitation.
Technical lane: Data Evaluation Business lane: Product & DeliveryValidation intent.
I check coverage before feature engineering so I can identify districts with weak station support and explain how that affects the assumptions.
- Data source
- DWD CDC station metadata
- Spatial join
- Point-in-polygon assignment to NUTS-3 districts
- Output class
- District coverage quality labels and counts
Evaluate DWD Stations at NUTS-3
Spatially join station points to NUTS-3 polygons and quantify districts with zero or low station coverage.
Key output
The notebook joins DWD CDC station points to district polygons and produces coverage counts and quality labels. You can use that table to find districts with zero or low station coverage before working with their weather features.
- Quality gate principleI keep weak station coverage in the data quality metadata so it remains visible beside the later model outputs.
What I am watching
I want to keep those labels attached when I aggregate weather features and interpret the risk scores. A ranking is easier to judge when you can also see how much station data supports it.
Open notebook source
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