Page 44 - IJPS-11-2

International Journal of
Population Studies Satellite data analysis of South Africa population grid

a greater degree of spatial distribution than the actual, A series of spatial concentration, autocorrelation, and
as derived from the 2020 GPW (Appendix A5, panel F) randomness analyses indicated that the South African
for South Africa. population is undergoing a significant and increasing

Statistically, the degree of similarity between the population concentration. The derived HI was estimated at
predicted and actual population grids can be estimated 90.4 in 2000 and 90.6 in 2020, while the Moran I statistics
using the spatial correlation function. This is displayed in show a value of 0.67 and 0.68 in 2000 and 2020, respectively.
panels G and H of Appendix A5. The spatial correlation The results further suggest a significant clustering and
function is estimated at 0.83, suggesting a strong degree deviation from CSR, that is, significant spatial clustering of
of similarity between the predicted and actual population the population of South Africa in 2000 and 2020.
grids. Equivalent results were obtained using Gaussian, The trend surface analysis revealed that the higher-
Exponential, and Spherical interpolations. order trend surfaces are more optimal than the lower-
order trend surfaces, for the time periods under analysis.
Based on the presented results (Appendix A5, panels
A-H), it can be concluded that the prediction of population The higher-order trend surfaces suggest that the
population distribution is influenced by local fluctuations
location, distribution, density, and size using GPW images
is highly accurate and dependable when employing and not regional dynamics and point to an apparent
various interpolation techniques. As such, it is possible spatial pattern. This reflects that South African has a very
dense population, with the population density decreasing
to predict or develop a GPW for South Africa post-2020 marginally outwards, suggesting that the underlying
(Appendix A5, panel J) based on the actual 2020 GPW for
South Africa (Appendix A5, panel I). The derived results process for the population distribution is stationary.
yield confidence in the post-2020 GPW for South Africa, The interpolation analysis suggests a prominent level of
proposing a further concentration and density of the accuracy and reliability in predicting population location,
South African population in the future in and around the distribution, density, and size using GPW images with a
existing population centers. The presence and expansion of range of interpolation techniques. As such, it is possible
population corridors linking the main population centers to predict or develop a GPW for South Africa post-2020
in South Africa also seem evident. based on the actual 2020 GPW for South Africa. The
derived results yield confidence in the post-2020 GPW
4. Concluding remarks for South Africa, proposing a further concentration and

The present study explores the reliability and accuracy density of the South African population in the future in
of various spatial mapping methodologies in estimating and around the existing population centers. The presence
and presenting the spatial characteristic and dynamics and expansion of population corridors linking the main
(location, distribution, density, and size) of the population population centers in South Africa also seem evident.
in South Africa. This is of relevance, given the importance Acknowledgments
of accurate population mapping and modeling. The study
utilizes geographical information systems and global None.
positioning systems, revolutionizing the landscape of
methodologies typically adopted in this type of study and Funding
reshaping the modeling of human population distribution The authors acknowledge the support from the World Trade
in both space and time. This study applied several spatial Organization and the National Research Foundation. The
data models and geostatistical applications to study the findings, views, opinions, and conclusions in this article
spatial characteristics or dynamics of the population are those of the authors and should not necessarily be
distribution of the country between 2000 and 2020. attributed to the funding institutions.
This article illustrates how spatial relationships can be Conflict of interest
used to investigate demographic relations and estimate
population characteristics and dynamics, by applying The authors declare that they have no competing interests.
explanatory spatial data analysis and deriving a granular
gridded population dataset. The present work also Author contributions
studied how regions close to each other can influence Conceptualization: Clive Egbert Coetzee
each other’s population characteristics, by using the Formal analysis: All authors
SEDAC’s GPW version 4 images and the SEDAC’s GPW Methodology: Clive Egbert Coetzee
version 4 dataset. Investigation: All authors

Volume 11 Issue 2 (2025) 38 https://doi.org/10.36922/ijps.3297