Proof capsule
Public Data / Poverty & EPH
Research-grade public data product
A public-data analysis project combining Argentine household survey microdata, socioeconomic modeling, maps, and reproducible analytical outputs.
Problem
Public poverty and socioeconomic data are difficult to explore when raw survey microdata, geographic units, indicators, notebooks, and visual outputs are disconnected. Analysts and citizens need reproducible paths from official/public datasets to interpretable maps, charts, and summaries.
What I built
A poverty and socioeconomic analysis pipeline for Argentina using household survey microdata, statistical analysis, geospatial processing, predictive modeling, and public-facing outputs such as maps, charts, and tables.
What this proves
- Apply research-grade analytical judgment to public socioeconomic data rather than only moving JSON artifacts.
- Build reproducible paths from survey microdata to indicators, geospatial analysis, and human-facing outputs.
- Translate economic research into inspectable maps, charts, tables, and documentation for public audiences.
Key principles
- Official/public inputs should be traceable to the indicators and outputs they produce.
- Notebooks and analysis artifacts need a clear path into maps, charts, tables, and documentation.
- Limitations and demo reliability should be stated explicitly rather than hidden behind broad claims.