A monthly sub-national Harmonized Food Insecurity Dataset for comprehensive analysis and predictive modeling

Area of Science:

• Global food security analysis
• Humanitarian data science
• Predictive modeling for food crises

Background:

• Food insecurity measurement is complex and lacks comprehensive global data.
• Timely and accurate data are crucial for anticipating, monitoring, and mitigating food crises.
• Existing data sources are fragmented, hindering unified analysis.

Purpose of the Study:

• Introduce the Harmonized Food Insecurity Dataset (HFID) as an open-source resource.
• Consolidate key global food insecurity indicators into a unified dataset.
• Enhance the analysis and prediction of global food insecurity.

Main Methods:

• Consolidated data from Integrated Food Security Phase Classification (IPC)/Cadre Harmonisé (CH), Famine Early Warning Systems Network (FEWS NET), World Food Program's (WFP) Food Consumption Score (FCS), and reduced Coping Strategy Index (rCSI).
• Utilized a common reference system for administrative units for spatial consistency.
• Ensured monthly updates for comprehensive temporal coverage.

Main Results:

• The HFID provides a unified, open-source resource for analyzing food insecurity.
• The dataset offers comprehensive spatial and temporal coverage where data permits.
• Highlights global data disparities and gaps in food insecurity monitoring.

Conclusions:

• The HFID is a vital tool for food insecurity experts and humanitarian agencies.
• Facilitates a unified approach to analyzing food insecurity conditions globally.
• Empowers the scientific community to develop predictive models for enhanced food crisis prevention.