# Construcci\'on de un Mapa de Vulnerabilidad Sanitaria en Argentina a partir de datos p\'ublicos

## TL;DR

This paper details the creation of a health vulnerability map for Argentina using public data, employing statistical and machine learning methods to assess population access to health services at a granular level.

## Contribution

It introduces a methodology combining statistical analysis and autoencoders to produce a detailed health vulnerability map from open data sources.

## Key findings

- Development of a census tract-level vulnerability map
- Application of PCA, MCA, and autoencoders for data analysis
- Quantification of population access to health benefits

## Abstract

This document is intended to present in detail the processing criteria and the analysis techniques used for the production of the Vulnerability Map Sanitary based on the use of public and open data sources. The paper makes use of statistical analysis techniques (MCA, PCA, etc.) and machine learning (autoencoders) for the processing and analysis of information. The final product is a map at the census track level that seeks to quantify the population's access to basic health benefits.