Feeding the Invisible: Food Security in Intermediate Cities with Python

In many countries, food insecurity is not only a social problem but also a data problem. In Colombia, key monitoring systems have lost continuity, leaving critical information gaps for public decision-making. This talk presents the development of a Python prototype to build a monitoring and prediction system for food insecurity risk in intermediate cities, using only open data. From a reproducible pipeline, multiple data science components are integrated: ingestion and processing of food price data (SIPSA), time series models for price forecasting (including classical approaches and machine learning like XGBoost), household segmentation through clustering from socioeconomic surveys, construction of a composite index relating income, prices, and vulnerability, and development of a decision support system (DSS) prototype. Attendees will take away a replicable approach for building complex indicators, strategies for working with imperfect open data, ideas for integrating models, socioeconomic data, and visualization in a single system, and a real example of applying Python in public policy and territorial development.