Page 36 - IJPS-11-2

International Journal of

Population Studies

RESEARCH ARTICLE
Insights from a population grid of South Africa:

An applied spatial satellite data analysis

Ewert P.J. Kleynhans * and Clive Egbert Coetzee 2
1
1 Department of Economics, School of Economic Sciences, North-West University, Potchefstroom,
South Africa
2 Department of Economics (Mil), Faculty of Military Science, Stellenbosch University, Saldanha,
South Africa

Abstract
The present study explores the reliability and accuracy of various spatial mapping
methodologies in estimating and presenting the spatial characteristic and dynamics
(location, distribution, density, and size) of the population in South Africa. As a basic
underlying concept, the study first explores spatial heterogeneity, that is, that every
location is related to every other location, and those nearby are related stronger.
This study, therefore, illustrates the spatial relationships between locations and
the spatial pattern of the population in South Africa. Analyzing the spatial images
determines the extent of such influence and the nature of the spatial patterns. To this
*Corresponding author: end, a granular gridded population dataset was derived using satellite image data,
Ewert Kleynhans and the NASA’s Socioeconomic Data and Applications Center gridded population
(ewert.kleynhans@nwu.ac.za) of the world version 4 population images and datasets were used. Several spatial
Citation: Kleynhans, E.P.J. & data models and geostatistical applications were applied to study the spatial
Coetzee, C.E. (2025). Insights from characteristics and dynamics of the population of South Africa from 2000 to 2020.
a population grid of South Africa:
An applied spatial satellite data Spatial analysis was performed using R-Studio, QGIS, and GeoDa. Among others, the
analysis. International Journal of results point to the fact that the South African population is very densely located that
Population Studies, 11(2): 30-42. population density decreases marginally outward and suggests that the underlying
https://doi.org/10.36922/ijps.3297
process for the population distribution is stationary. This study proposes that it is
Received: March 27, 2024 indeed possible to reliably and accurately estimate and present gridded population
1st revised: March 30, 2024 images and datasets using spatial and geostatistical methodologies.
2nd revised: April 30, 2024
Accepted: June 3, 2024 Keywords: Population count; Socioeconomic data and applications center; Spatial data
analysis; Spatial randomness; Geostatistical applications; Geographic information system
Published online: October 14,
2024
Copyright: © 2024 Author(s).
This is an Open-Access article 1. Introduction
distributed under the terms of the
Creative Commons Attribution Understanding a country’s population’s spatial characteristics and dynamics requires
License, permitting distribution,
and reproduction in any medium, detailed knowledge and understanding of its spatial location, distribution, density, and
provided the original work is size. Over the past 25 years, there has been a notable rise in the utilization of gridded
properly cited. population images and datasets in research, with the most significant increase occurring
Publisher’s Note: AccScience in the last decade (Bustos et al., 2020). Gridded population images and datasets can
Publishing remains neutral with be helpful, for example, to identify and map populated and unpopulated places, that
regard to jurisdictional claims in
published maps and institutional is, population distribution. It can further be used in estimation of population size and
affiliations. density in specific locations, as well as in modeling and projections.

Volume 11 Issue 2 (2025) 30 https://doi.org/10.36922/ijps.3297