Examining the correlates and drivers of human population distributions across low- and middle-income countries

• Published in: Journal of the Royal Society interface Vol. 14; no. 137; p. 20170401
• Main Authors: Nieves, Jeremiah J., Stevens, Forrest R., Gaughan, Andrea E., Linard, Catherine, Sorichetta, Alessandro, Hornby, Graeme, Patel, Nirav N., Tatem, Andrew J.
• Format: Journal Article
• Language: English
• Published: England The Royal Society 01.12.2017; The Royal Society Publishing
• Subjects: Census; Continents; Dasymetric; Decision Trees; Demography; Developing Countries; Disaggregation; Economic models; Geography; Human populations; Humans; Income; Learning algorithms; Life Sciences–Earth Science interface; Low income groups; Machine Learning; Mapping; Population; Population Density; Population distribution; Population Dynamics; Population growth; Poverty; Random Forests; Regression Analysis; Spatial discrimination; Spatial distribution; Topography; random forests; disaggregation; mapping; dasymetric; census; population
• ISSN: 1742-5689; 1742-5662
• DOI: 10.1098/rsif.2017.0401

Geographical factors have influenced the distributions and densities of global human population distributions for centuries. Climatic regimes have made some regions more habitable than others, harsh topography has discouraged human settlement, and transport links have encouraged population growth. A better understanding of these types of relationships enables both improved mapping of population distributions today and modelling of future scenarios. However, few comprehensive studies of the relationships between population spatial distributions and the range of drivers and correlates that exist have been undertaken at all, much less at high spatial resolutions, and particularly across the low- and middle-income countries. Here, we quantify the relative importance of multiple types of drivers and covariates in explaining observed population densities across 32 low- and middle-income countries over four continents using machine-learning approaches. We find that, while relationships between population densities and geographical factors show some variation between regions, they are generally remarkably consistent, pointing to universal drivers of human population distribution. Here, we find that a set of geographical features relating to the built environment, ecology and topography consistently explain the majority of variability in population distributions at fine spatial scales across the low- and middle-income regions of the world.