A monthly sub-national Harmonized Food Insecurity Dataset for comprehensive analysis and predictive modeling
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866917890959081472 |
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| author | Machefer, Mélissande Ronco, Michele Thomas, Anne-Claire Assouline, Michael Rabier, Melanie Corbane, Christina Rembold, Felix |
| author_facet | Machefer, Mélissande Ronco, Michele Thomas, Anne-Claire Assouline, Michael Rabier, Melanie Corbane, Christina Rembold, Felix |
| contents | Food security is a complex, multidimensional concept challenging to measure comprehensively. Effective anticipation, monitoring, and mitigation of food crises require timely and comprehensive global data. This paper introduces the Harmonized Food Insecurity Dataset (HFID), an open-source resource consolidating four key data sources: the Integrated Food Security Phase Classification (IPC)/Cadre Harmonisé (CH) phases, the Famine Early Warning Systems Network (FEWS NET) IPC-compatible phases, and the World Food Program's (WFP) Food Consumption Score (FCS) and reduced Coping Strategy Index (rCSI). Updated monthly and using a common reference system for administrative units, the HFID offers extensive spatial and temporal coverage. It serves as a vital tool for food security experts and humanitarian agencies, providing a unified resource for analyzing food security conditions and highlighting global data disparities. The scientific community can also leverage the HFID to develop data-driven predictive models, enhancing the capacity to forecast and prevent future food crises. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_06076 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A monthly sub-national Harmonized Food Insecurity Dataset for comprehensive analysis and predictive modeling Machefer, Mélissande Ronco, Michele Thomas, Anne-Claire Assouline, Michael Rabier, Melanie Corbane, Christina Rembold, Felix Machine Learning Food security is a complex, multidimensional concept challenging to measure comprehensively. Effective anticipation, monitoring, and mitigation of food crises require timely and comprehensive global data. This paper introduces the Harmonized Food Insecurity Dataset (HFID), an open-source resource consolidating four key data sources: the Integrated Food Security Phase Classification (IPC)/Cadre Harmonisé (CH) phases, the Famine Early Warning Systems Network (FEWS NET) IPC-compatible phases, and the World Food Program's (WFP) Food Consumption Score (FCS) and reduced Coping Strategy Index (rCSI). Updated monthly and using a common reference system for administrative units, the HFID offers extensive spatial and temporal coverage. It serves as a vital tool for food security experts and humanitarian agencies, providing a unified resource for analyzing food security conditions and highlighting global data disparities. The scientific community can also leverage the HFID to develop data-driven predictive models, enhancing the capacity to forecast and prevent future food crises. |
| title | A monthly sub-national Harmonized Food Insecurity Dataset for comprehensive analysis and predictive modeling |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2501.06076 |