A geospatial analysis of food insecurity among refugee households in Lebanon using machine learning techniques

Abstract This study integrates geospatial analysis with machine learning to examine the spatial dynamics of food insecurity among Syrian refugees in Lebanon. Using household survey data and novel geospatial indicators from 2018 to 2022 for 22,626 households, we investigate why certain food security measures are effective in specific contexts while others are not. Our findings reveal that geolocational factors play a significant role in shaping food insecurity outcomes, often overshadowing traditional factors like household sociodemographics and living conditions. This suggests a potential shift from labor-intensive socioeconomic survey methods toward more scalable, geospatially driven approaches. The study also highlights considerable variation in food insecurity across locations and subpopulations, raising concerns about the effectiveness of individual measures like the Food Consumption Score (FCS), Household Dietary Diversity Score (HDDS), and Reduced Coping Strategies Index (rCSI) in capturing localized needs. By disaggregating food insecurity dimensions and understanding their spatial distribution, humanitarian and development organizations can better tailor their strategies, directing resources to areas where refugees face the most severe food challenges. From a policy perspective, our insights call for a refined approach that improves the predictive power of food insecurity models, aiding organizations in efficiently targeting interventions.