Small-area population densities and counts were estimated for Australian census collection districts (CDs), using Landsat TM imagery. A number of mathematical and statistical refinements to previously reported methods were explored. The robustness of these techniques as a practical methodology for population estimation was investigated and evaluated using a primary image for model development and training, and a second image for validation. Correlations of up to 0.92 in the training set and up to 0.86 in the validation set were obtained between census and remote sensing estimates of CD population density, with median proportional errors of 17.4% and 18.4%, respectively. Total urban populations were estimated with errors of +1% and-3%, respectively. These results indicate a moderate level of accuracy and a substantial degree of robustness. Accuracy was greatest in suburban areas of intermediate population density. There was a general tendency towards attenuation in all models tested, with high densities being under-estimated and low densities being over-estimated. It is concluded that the level of accuracy obtainable with this methodology is limited by heterogeneity within the individual CDs, particularly large rural CDs, and that further improvements are in principle unlikely using the aggregated approach. An alternative statistical approach is foreshadowed.
Estimating census district populations from satellite imagery: Some approaches and limitations
Reprints and Corporate Permissions
Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?
To request a reprint or corporate permissions for this article, please click on the relevant link below:
Academic Permissions
Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?
Obtain permissions instantly via Rightslink by clicking on the button below:
If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.
Related Research Data
Related research
People also read lists articles that other readers of this article have read.
Recommended articles lists articles that we recommend and is powered by our AI driven recommendation engine.
Cited by lists all citing articles based on Crossref citations.
Articles with the Crossref icon will open in a new tab.