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

The findings reveal that geolocational factors play a significant role in shaping food insecurity outcomes, often overshadowing traditional factors like household sociodemographics and living conditions, and suggests a potential shift from labor-intensive socioeconomic survey methods toward more scalable, geospatially driven approaches.