One-line summary
Analytical project combining weather, yield, soil, and geospatial signals for siting-oriented and environmental decision support.
Context
This project addresses infrastructure-facing planning questions where weather variability, location constraints, and environmental signals need to be evaluated in one consistent analytical frame.
The work reflects earlier domain experience and is presented as a transferable analytical structure for decision support rather than a domain-specific agriculture narrative.
What I built
I built a notebook-led analytical workflow that combines weather records, district-level yield series, soil proxies, and geospatial layers into a reproducible pipeline for comparative scoring and scenario-oriented interpretation.
Methods / data
- SQL-style data modeling and transformations in DuckDB
- Python ETL and analysis workflows for feature engineering and quality checks
- DWD weather station data and district-level yield statistics
- Soil and land-cover signals for spatial proxy construction and coverage completion
- Geospatial processing to align heterogeneous layers at district resolution
- Assumption tracking and validation checks for reproducibility
Outputs
- District-level analytical tables for weather-yield-environment signals
- Reproducible notebooks documenting methods and intermediate outputs
- Risk and suitability-oriented map views for comparative interpretation
- Summary metrics that support planning conversations
Why it matters
This project demonstrates a transferable analytical pattern for decision support:
- combine heterogeneous operational and environmental data sources
- define transparent assumptions and metric logic
- maintain reproducible ETL and analysis steps
- produce outputs that can inform siting-oriented and environmental decisions