AccScience Publishing / IJPS / Online First / DOI: 10.36922/ijps.3297
RESEARCH ARTICLE

Insights from a population grid of South Africa: An applied spatial satellite data analysis

Ewert P.J. Kleynhans1* Clive Egbert Coetzee2
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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
Submitted: 27 March 2024 | Accepted: 3 June 2024 | Published: 14 October 2024
© 2024 by the Author(s). This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution 4.0 International License ( https://creativecommons.org/licenses/by/4.0/ )
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 end, a granular gridded population dataset was derived using satellite image data, and the NASA’s Socioeconomic Data and Applications Center gridded population of the world version 4 population images and datasets were used. Several spatial data models and geostatistical applications were applied to study the spatial characteristics and dynamics of the population of South Africa from 2000 to 2020. Spatial analysis was performed using R-Studio, QGIS, and GeoDa. Among others, the results point to the fact that the South African population is very densely located that population density decreases marginally outward and suggests that the underlying process for the population distribution is stationary. This study proposes that it is indeed possible to reliably and accurately estimate and present gridded population images and datasets using spatial and geostatistical methodologies.

Keywords
Population count
Socioeconomic data and applications center
Spatial data analysis
Spatial randomness
Geostatistical applications
Geographic information system
Funding
The authors acknowledge the support from the World Trade Organization and the National Research Foundation. The findings, views, opinions, and conclusions in this article are those of the authors and should not necessarily be attributed to the funding institutions.
Conflict of interest
The authors declare that they have no competing interests.
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International Journal of Population Studies, Electronic ISSN: 2424-8606 Print ISSN: 2424-8150, Published by AccScience Publishing