The University of Southampton
×
Filter your search:
University of Southampton Institutional Repository

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

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

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.

Decision Trees, Demography, Developing Countries, Geography, Humans, Machine Learning, Population Density, Population Dynamics, Regression Analysis
1742-5689
20170401
Nieves, Jeremiah J.
2b5f2f25-afc0-4585-8531-dc2acc4b3511
Stevens, Forrest R.
7c96c2ef-edac-41a1-be26-c4bc5b3256a6
Gaughan, Andrea E.
395221c6-1091-4657-af7e-bd6cb93dbaf9
Linard, Catherine
231a1de7-72c2-4dc1-bc4e-ea30ed444856
Sorichetta, Alessandro
c80d941b-a3f5-4a6d-9a19-e3eeba84443c
Hornby, Graeme
52fc0227-a0b1-46eb-a08f-ec689c460bf8
Patel, Nirav N.
ffe57351-45aa-4f43-a329-fd8f6cdf24db
Tatem, Andrew J.
6c6de104-a5f9-46e0-bb93-a1a7c980513e
Nieves, Jeremiah J.
2b5f2f25-afc0-4585-8531-dc2acc4b3511
Stevens, Forrest R.
7c96c2ef-edac-41a1-be26-c4bc5b3256a6
Gaughan, Andrea E.
395221c6-1091-4657-af7e-bd6cb93dbaf9
Linard, Catherine
231a1de7-72c2-4dc1-bc4e-ea30ed444856
Sorichetta, Alessandro
c80d941b-a3f5-4a6d-9a19-e3eeba84443c
Hornby, Graeme
52fc0227-a0b1-46eb-a08f-ec689c460bf8
Patel, Nirav N.
ffe57351-45aa-4f43-a329-fd8f6cdf24db
Tatem, Andrew J.
6c6de104-a5f9-46e0-bb93-a1a7c980513e

Nieves, Jeremiah J., Stevens, Forrest R., Gaughan, Andrea E., Linard, Catherine, Sorichetta, Alessandro, Hornby, Graeme, Patel, Nirav N. and Tatem, Andrew J. (2017) Examining the correlates and drivers of human population distributions across low- and middle-income countries. Journal of the Royal Society Interface, 14 (137), 20170401. (doi:10.1098/rsif.2017.0401).

Record type: Article

Abstract

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.

Full text not available from this repository.

More information

Accepted/In Press date: 20 November 2017
e-pub ahead of print date: 13 December 2017
Published date: December 2017
Keywords: Decision Trees, Demography, Developing Countries, Geography, Humans, Machine Learning, Population Density, Population Dynamics, Regression Analysis
Learn more about School of Geography and Environmental Sciences research Learn more about Geography & Environment research

Identifiers

Local EPrints ID: 421802
URI: http://eprints.soton.ac.uk/id/eprint/421802
DOI: doi:10.1098/rsif.2017.0401
ISSN: 1742-5689
PURE UUID: 0f5306ce-9fb8-4344-ad6b-4e47f7292193
ORCID for Alessandro Sorichetta: ORCID iD orcid.org/0000-0002-3576-5826
ORCID for Graeme Hornby: ORCID iD orcid.org/0000-0002-2833-8711
ORCID for Andrew J. Tatem: ORCID iD orcid.org/0000-0002-7270-941X

Catalogue record

Date deposited: 28 Jun 2018 16:30
Last modified: 18 Feb 2021 17:23

Export record

Altmetrics

Contributors

Author: Jeremiah J. Nieves
Author: Forrest R. Stevens
Author: Andrea E. Gaughan
Author: Catherine Linard
Author: Alessandro Sorichetta ORCID iD
Author: Graeme Hornby ORCID iD
Author: Nirav N. Patel
Author: Andrew J. Tatem ORCID iD

University divisions

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Library staff additional information
Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of http://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×