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

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.

References

[1] Anselin L. (2019). Spatial Econometrics:Methods and Models. Germany: Springer.

[2] Anselin L., & O’Loughlin J. (1992). Geography of international conflict and cooperation:Spatial dependence and regional context in Africa. In: NewGeopolitics. London: Gordon and Breach, p. 39-75.

[3] Bailey T.C., & Gatrell A.C. (2019). Interactive Spatial Data Analysis. England, UK: Routledge.

[4] Borregaard M., Nachman G., & Hendrichsen D. (2009). Spatial distribution patterns. In: JørgensenS.E., & FathB.D. (Encyclopaediaof Ecology,eds.). Chapter:Spatial Distribution. Oxford: Elsevier, p. 3304-3310.

[5] Brush J.E. (1968). Spatial patterns of population in Indian cities. Geographical Review, 58(3):362-391. https://doi.org/10.2307/212563

[6] Bustos M.F.A., Hall O., Niedomysl T., & Ernstson U. (2020). A pixel level evaluation of five multitemporal global gridded population datasets: A case study in Sweden, 1990-2015. Population and Environment, 42:255-277. https://doi.org/10.1007/s11111-020-00360-8

[7] Calka B., & Bielecka E. (2019). Reliability analysis of Landscan gridded population data. The case study of Poland. International Journal of Geo-Information, 8(5):222. https://doi.org/10.3390/ijgi8050222

[8] Chandna R.C. (2009). Geography of Population, Concepts, Determinants and Pattern. New Delhi: Kalyani Publishers.

[9] Clarke J.I. (1972). Population Geography, 2nd ed. Oxford: Pergamon Press.

[10] Coetzee C.E., & Kleynhans E.P.J. (2018). Investigating city economic convergence in KwaZulu-Natal, South Africa: Integration or isolation. Regional Science, Policy and Practice, 10(3):221-240. https://doi.org/10.1111/rsp3.12140

[11] Coetzee C.E., & Kleynhans E.P.J. (2021). Satellite sensing night-time lights-A South African spatial econometric application. Remote Sensing Applications: Society and Environment, 24(2021), 100650.

[12] Dixon P.M. (2018). Complete Spatial Randomness. Wiley StatsRef:Statistics Reference Online. United States: John Wiley and Sons, p. 1-5. https://doi.org/10.1002/9781118445112.stat08071

[13] Doignon Y., Blöss-Widmer I., Ambrosetti E., & Oliveau S. (2023). Population dynamics in the Mediterranean:A demographic convergence? Germany: Springer. https://doi.org/10.1007/978-3-031-37759-4

[14] Dong N., Yang X., Cai H., & Xu F. (2017). Research on grid size suitability of gridded population distribution in urban area: A case study in urban area of Xuanzhou District, China. PLoS One, 12(1):e0170830. https://doi.org/10.1371/journal.pone.0170830

[15] Duncan O.D. (1957). The measurement of population distribution. Population Studies, 11(1):27-45.

[16] Eurostat, (2024). Merging Statistics and Geospatial Information. Experiences and Observations from National Statistical Authorities, 2012- 2015 Projects. Available from: https://ec.europa.eu/eurostat/documents/7870049/9809882/KS-FT-19-004-EN-N.pdf/aec76d11-967e-4d67-aad2-49c52b264748?t=1559812901000 [2024 May 13]

[17] Getis A., & Ord J.K. (1996). Local spatial statistics: An overview. In: Longley, P., & Batty, M. (eds.). Spatial Analysis: Modelling in a GIS Environment. Cambridge, UK: GeoInformation International, p. 269-85.

[18] Getis A., & Paelinck J.H.P. (2004). An analytical description of spatial patterns. L’Espace Geographique, 2004(4):68-79.

[19] Griffith D.A., & Chun Y. (2017). Spatial Autocorrelation and Spatial Filtering:Gaining Understanding through Theory and Scientific Visualization. Germany: Springer.

[20] Grohmann C.H. (2005). Trend-surface analysis of morphometric parameters: A case-study in southeastern Brazil. Computers and Geosciences, 31:1007-1014. https://doi.org/10.1016/j.cageo.2005.02.011

[21] Hachadoorian L., Gaffin S., & Engelman R. (2011). Projecting a gridded population of the world using ratio methods of trend extrapolation. In: Human Population. Berlin: Springer, p. 13-25.

[22] Hembd J., & Infanger C.L. (1981). An application of trend surface analysis to a rural-urban land market. Land Economics, 57(3):303-322.

[23] Hoover E.M. (1941). Interstate redistribution of population: 1850-1940. Journal of Economic History, 1:199-205.

[24] Hornby W.F., & Jones M. (1980). An Introduction to Population Geography. Cambridge: Cambridge University Press.

[25] Hota R.N. (2014). Trend surface analysis of spatial data. Gondwana Geological Magazine, 29(1 and 2):39-44.

[26] Hugo G. (2002). Changing patterns of population distribution in Australia, Joint special issue. Journal of Population Research and N.Z. Population Review, 2023:1-21.

[27] Hutchinson M.F., & Gessler P.E. (1994). Splines-more than just a smooth interpolator. Geoderma, 62:45-67.

[28] Kyriakidis P.C. (2009). Spatial Point Patterns and Complete Spatial Randomness. Available from: https://www.geog.ucsb.edu/-chris/medrano_geo%20210c/geo%20210c%20-%20spatial%20statistics/lecture%20notes/6_lecpointpatterncsr.pdf [2024 May 13]

[29] Leyk S., Balk D., Jones B., Montgomery M.R., & Engin H. (2019). The heterogeneity and change in the urban structure of metropolitan areas in the United States, 1990-2010. Scientific Data, 6(1):321. https://doi.org/10.1038/s41597-019-0329-6

[30] Liu C., Wang F., & Xu Y. (2018). Habitation environment suitability and population density patterns in China: A regionalization approach. Growth and Change, 50(1):184-200. https://doi.org/10.1111/grow.12283

[31] Long L., & Nucci A. (1997). The hoover index of population concentration: A correction and update. Professional Geographer, 49(4):431-440. https://doi.org/10.1111/0033-0124.00089

[32] Sang M., Jiang J., Huang X., Zhu F., & Wang Q. (2024). Spatial and temporal changes in population distribution and population projection at county level in China. Humanities And Social Sciences Communications, 11:288. https://doi.org/10.1057/s41599-024-02784-1

[33] Scott L.M. (2015). Spatial Pattern, Analysis of. International Encyclopaedia of the Social and Behavioral Sciences. 2nd ed., Vol. 23. Netherlands:Elsevier. https://doi.org/10.1016/B978-0-08-097086-8.72064-2

[34] Small C. (2003). Global Population Distribution and Urban Land Use in Geophysical Parameter Space, Lamont-Doherty Earth Observatory. Columbia: Columbia University, New York.

[35] Socioeconomic Data and Applications Center (SEDAC). (2024). Gridded Population of the World, U.S. National Aeronautics and Space Administration. Available from: https://sedac.ciesin.columbia.edu/data/collection/gpw-v4 [2024 May 13]

[36] Unwin D. (1978). An Introduction to Trend Surface Analysis: Concepts and Techniques in Modern Geography No. 5. Published by Geo Abstracts.

[37] Zhao L., Zhao C., & Huang J. (2022). Spatial dynamics and determinants of population urbanization in the upper reaches of the yellow river. Land, 11:1420. https://doi.org/10.3390/land11091420