Project in brief
A weather, yield, soil, and geospatial analysis project for risk-aware siting and environmental review.
Context
This project addresses planning questions where weather variability, location constraints, and environmental signals need to be compared in one analytical frame.
The work comes from an agriculture-facing problem, but I present it here as a transferable pattern: define the geography, join the public data carefully, and keep the screening assumptions visible.
What I built
I built a notebook-led workflow that combines weather records, district-level yield series, soil proxies, and geospatial layers into a pipeline for comparative scoring and scenario 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
- Notebooks documenting methods, checks, and intermediate outputs
- Risk and suitability-oriented map views for comparative interpretation
- Summary metrics for planning conversations and follow-up review
Why it matters
This project demonstrates a transferable analytical pattern:
- combine heterogeneous operational and environmental data sources
- define transparent assumptions and metric logic
- maintain ETL and analysis steps that can be rerun
- produce maps and summary tables that inform siting-oriented environmental review