Spatial machine-learning analysis of child indicators

ended

Horizon Europe seeks to achieve the UN’s Sustainable Development Goals (SDGs). The WHO–UNICEF–Lancet Commission suggests placing children at the center of the SDGs, because children are among the world’s most vulnerable and marginalized population. Child indicators calculated with surveys are limited because surveys can be outdated and do not cover all the regions of a country. In this context, the objective of the project is to develop a methodology to overcome this limitation and identify vulnerable young children in countries with outdated, incomplete, and low-quality survey information. The methodology is based on a spatial machine-learning analysis of child indicators and integrates 3 disciplines: child development, spatial analysis of satellite images, and machine-learning. Satellite images and households’ simulations will complement the missing or outdated information of surveys, and machine learning will identify children that could be left behind during the development process due to the intersectionality of gender with biological diversities, ethnicity, socio-economic status, and geographical location. The project will create new tools for monitoring the progress towards the SDGs and will help to formulate targeted interventions that increase the social impact and reduce the economic costs of poverty-reduction and development programs. If the project is funded, Dr. Rolando Gonzales Martinez will carry out the fellowship at the University of Groningen, under the supervision of Prof. Dr. Hinke Haisma and with support from Prof. Dr. Dimitris Ballas. A short visit to UNICEF is planned as a secondment for Dr. Gonzales Martinez, so he can work with UNICEF on improving the policy impact of the project. The transfer of knowledge between the research fellow and the host organization, the teaching activities, and the short visit to UNICEF will increase the career prospects and employability of Dr. Gonzales Martinez within and outside academia.