Machine learning can guide food security efforts when primary data are not available

Abstract

haracterizing the socio-economic status of populations and providing reliable and up-to-date estimates of who the most vulnerable are, how many they are, where they live and why they are vulnerable is essential for governments and humanitarian organizations to make informed and timely decisions on implementation of humanitarian assistance policies and programmes 1 . These data are traditionally collected through face-to-face surveys. However, these are expensive, time-consuming and, in certain areas, not possible to perform due to conflict, disease, insecurity or remoteness. Therefore, during the past few years, researchers have begun to investigate the potential of non-traditional data and new computational methods to estimate vulnerabilities and socio-economic characteristics when primary data are not available. In these studies, mobile phone data 2 , satellite imagery 3 , a combination of both 4,5 , mobile money transaction records 6 , geolocated Wikipedia articles 7 or tweets 8 and social media advertising data 9 have been used in combination with state-of-the-art machine learning methods to provide reliable estimates of poverty at different spatial resolutions for several sub-Saharan African countries and southern and southeastern Asian ones.

On the forecastability of food insecurity

“…In this context, our study represents an initial step towards the application of forecasting approaches to food insecurity at a high spatial and temporal granularity. Our results confirm that nowcasting or one-step-ahead forecasting are feasible, as reported in recent studies 36 , 38 , but long-term forecasts are challenging and strongly conditioned by data availability.…”

“…Okori and collaborators first proposed to use machine learning models to predict whether a household is in famine or not from household socioeconomic and agricultural production characteristics 33,34 . The effort of predicting levels of insufficient food consumption has been tackled in the context of Malawi, in a study where the authors built a model trained on 2011 data to estimate the situation in 2013 35 , and more recently in a work proposing a model to nowcast sub-national levels of insufficient food consumption on a global scale 36 . Both studies propose methods to predict the current situation when primary data is not available, but they do not address the challenge of making projections for the future.…”

Highlighting the role of agriculture and geospatial technology in food security and sustainable development goals

“…“Machine learning,” and “GIS” keywords clearly fall within this third quadrant and indicate that application of machine learning, GIS, and remote sensing present huge space for research which is apparently verifiable by the visibility of papers lately since 2019 in the research works (Ahn et al, 2022; Hossain et al, 2019; Martini et al, 2022; Sood & Singh, 2021; Westerveld et al, 2021; Zhou et al, 2022). Martini et al, (2022) suggest that in the coming decades computer and agricultural scientists will come together to help overcome food security‐related issues.…”

Accelerating and enhancing the generation of socioeconomic data to inform forced displacement policy and response

“…This would involve an iterative examination of the utility of the outputs given their spatial resolution, asking what level of spatial disaggregation would be necessary to usefully inform operations related to forced displacement. Testing this operational relevance could also provide an opportunity to apply one of the innovative features of Hunger Map Live: the ability to show which input variables drive predictions (Martini et al, 2022). This could be used, for example, to highlight to UNHCR operations whether a sudden estimated increase in food insecurity in a particular place is driven by a displacement event (that UNHCR is likely to be aware of and already responding to) rather than an environmental change (that UNHCR may not be); the former was the case for a Hunger Map Live estimated increase in food insecurity in Malakal in South Sudan at the time of the workshop on 1 December 2022.…”