# A geospatial hybrid platform to support public policy-making and monitoring for community-based food management and security in the context of global climate change: A study protocol

## TL;DR

This paper outlines a geospatial platform to help Latin American communities address food insecurity by integrating climate data and policy tools.

## Contribution

The novel contribution is a multidisciplinary geospatial platform for monitoring and policymaking to enhance food security amid climate change.

## Key findings

- The platform integrates transnational data and predictive models to assess climate vulnerability.
- An open data platform with a dashboard and virtual assistant is being developed for FNS monitoring.
- The project has been awarded the ALSEA prize and emphasizes collaboration across sectors.

## Abstract

In Latin America, Food and Nutritional Insecurity (FNI) is a challenge, particularly in households that receive social assistance programs, where food scarcity affects up to 50% of these households. Environmental degradation and climate change are significant contributors to FNI, underscoring the importance of ongoing monitoring. In this paper, we present the roadmap for a higher-impact project to support Food and Nutrition Security (FNS) policymaking in Latin America. The ultimate goal is to improve FNS in communities affected by climate change through the development of an interactive platform that evidences the identification of variables, a transnational data acquisition program, the development of predictive models, and the assessment of climate vulnerability in the region. Additionally, an open data platform, together with the dashboard and a virtual assistant, is being developed for monitoring FNS indicators in Latin America. The project was awarded the ALSEA prize and addresses technological and regional challenges through a multidisciplinary and international team. Effective coordination between space agencies, academia, government, and the productive sector is required to ensure that project results are usable and add value at the local level. The Supporting Evidence for the Proposed Approach presented in this paper are promising and pave the way for future developments that extend not only the geographic scope but also the dimensional analysis.