Abstract
Education is a human right, and equal access to education is important for achieving sustainable development. Measuring socioeconomic development, especially the changes to education inequality, can help educators, practitioners, and policymakers with decision- and policy-making. This article presents an approach that combines population distribution, human settlements, and nighttime light (NTL) data to assess and explore development and education inequality trajectories at national levels across multiple time periods using latent growth models (LGMs). Results show that countries and regions with initially low human development levels tend to have higher levels of associated education inequality and uneven distribution of urban population. Additionally, the initial status of human development can be used to explain the linear growth rate of education inequality, but the association between trajectories becomes less significant as time increases.
Highlights
Assessing our socioeconomic development in a frequent, rapid, and accurate manner is important for achieving the United Nations’ Sustainable Development Goals (SDGs) on various national and global scales [1]
This paper presents an approach that combines multi-source data to assess changes in trajectories of human development and education inequality at a national level from 1990 to 2010
We analyzed the trajectories of human development, urban population distribution, and education inequality using multi-source data on multiple spatiotemporal scales
Summary
Assessing our socioeconomic development in a frequent, rapid, and accurate manner is important for achieving the United Nations’ Sustainable Development Goals (SDGs) on various national and global scales [1]. We use DMSP NTL data to estimate human development [21] and assess the associations of growth patterns with education inequality. Light Development Index (NLDI) based on DMSP NTL data and LandScan population density data to measure human development. Similar to the NLDI, the SLC is calculated based on the cumulative proportion of land use and the cumulative proportion of land These studies show that there is great potential for scientists to utilize geospatial data to monitor the allocation of resources, the distribution of population, and the different levels of development on various spatiotemporal scales. This research utilizes multi-source data to evaluate human development levels and the uneven distribution of the urban population on various spatiotemporal scales to explore development trajectories and patterns of human development and education inequality.
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