











































AUSTRALIAN POPULATION STUDIES 2020 | Volume 4 | Issue 1 | pages 70-72 
 

© Kimpton 2020. Published under the Creative Commons Attribution-NonCommercial licence 3.0 Australia (CC BY-NC 3.0 
AU). Journal website: www.australianpopulationstudies.org 

Visualising Australia’s older 
population using grid maps 

Anthony Kimpton*  The University of Queensland 

* Email: a.kimpton@uq.edu.au. Address: Queensland Centre for Population Research, School 
of Earth and Environmental Sciences, Chamberlain Building, The University of Queensland, St 
Lucia, Qld 4072, Australia. 

Paper received 17 April 2020; accepted 28 April 2020; published 25 May 2020 

 

Introduction 

In 2016, 15.7 percent of Australians were aged 65 or over (3.7 million out of a total population of 
23.4 million; Australian Bureau of Statistics 2016). This national age structure is already without 
precedent while projections suggest this could reach 22 percent by 2057 (8.8 million; Australian 
Institute of Health and Welfare 2018). As such, these projections suggest that it will become 
increasingly challenging for all tiers of Australian government to fund the infrastructure and support 
services critical for the health and wellbeing of older Australians as they grow at a faster rate than 
the working age population (Parliamentary Budget Office 2019). There are four critical dimensions 
when examining population age structures, namely: (1) numbers; (2) characteristics and values;      
(3) proportions of the population in particular age groups; and (4) spatial distribution (Hugo 2003). In 
this DemoGraphic I focus on the latter two dimensions to identify where Australians aged 65 and 
over are most spatially concentrated. 

Data and methods 

I draw on the Australian Bureau of Statistics (ABS) 2016 Census of Population and Housing data in 
Statistical Area Level Two (SA2) neighbourhood units released as open data from their DataPacks 
web page (ABS 2016). The ABS designed SA2s to approximate functional communities that are 
connected through regular social and economic interaction, although Australian population density is 
low within the interior but relatively high towards the coast, with SA2s ranging in area between 49 
hectares and 51 million hectares. 

Given this variability in area, it has remained a challenge to visualise population structures across 
Australia. I have therefore generated a gridded population that is typically featured in dasymetric 
population maps (Silva et al. 2013; Li et al. 2016). Specifically, I converted the population into points 
located at random locations across all SA2 polygons, aggregated these points to a decimal degree-
spaced grid, calculated the gridded proportion of Australians aged 65 or over, and then plotted these 
values using proportional circles. These gridded proportion circles are useful because they reveal the 
geographic spread of national age structure and normalise the area containing populations to a grid, 
thus ensuring that there are no overlapping symbols or polygons that are too small to read. In 
addition, I have introduced marginal histograms (Lambrechts 2019) to this map to reveal the multi-

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http://www.australianpopulationstudies.org/
mailto:a.kimpton@uq.edu.au


Australian Population Studies 4 (1) 2020  Kimpton 71 

 

modal distribution of the Australian age structure along lines of latitude and longitude, thus enabling 
a unique spatial examination of Australia’s older population. 

Key features 

The DemoGraphic (Figure 1) depicts clear spatial patterning. For instance, the symbols scaled 
according to population structure reveal that there are areas without symbols, indicating that fewer 
than 1 percent of the gridded population are aged 65 or older. Symbols larger than the 30 percent 
symbol featured within the legend reveal that there are ageing enclaves throughout Australia where 
the population aged 65 or older is more than twice the national average of 15 percent, such as the 32 
percent circle representing approximately 25,000 Australians residing along the Fleurieu Peninsula, 
which is located south of the city of Adelaide. This spatial pattern highlights the regional dimension 
of ageing and echo other work that has outlined this pressing issue for regional communities 
(Houghton & Vonthethoff 2017). Lastly, the marginal histograms that reveal the proportion of the 
population that is aged 65 or older for a given latitude or longitude reveals that these concentrations 
can range from 5 to 23 percent across the surface of Australia. 

 

Figure 1: A grid map and marginal histograms of the proportion of Australia’s population aged 65 and older 

Source: Calculated by the author using data extracted from the 2016 census using data from the Australian Bureau of 
Statistics Census DataPacks (ABS 2016) 



72 Kimpton  Australian Population Studies 4 (1) 2020 

 

Supplementary Material 

The bespoke script developed to read and prepare the ABS data and Figure 1 is available from the 
author’s online repository at https://rpubs.com/AnthonyKimpton/.  

Acknowledgements 

This DemoGraphic is developed through a project funded by the Australian Research Council Linkage 
Project grant LP160100031 with additional support from the Queensland Department of Transport 
and Main Roads as industry partner. The analysis and interpretations are solely those of the author 
and do not necessarily reflect the views and opinions of the Department or any of its employees. 

References 

Australian Bureau of Statistics (2016) Census DataPacks. 
https://datapacks.censusdata.abs.gov.au/datapacks/. Accessed on 15 April 2020. 

Australian Institute of Health and Welfare (2018) Older Australia at a glance. 
https://www.aihw.gov.au/reports/older-people/older-australia-at-a-glance. Accessed on 15 April 
2020. 

Houghton K and Vonthethoff B (2017) Ageing and work in regional Australia: pathways for accelerating 
economic growth. http://www.regionalaustralia.org.au/home/ageing-work-regional-australia/. 
Accessed on 15 April 2020. 

Hugo G (2003) Australia's ageing population: some challenges for planners. Australian Planner 40(2): 109-
118. 

Li S, Juhász-Horváth L, Harrison P A, Pintér L and Rounsevell M D A (2016) Population and age structure in 
Hungary: a residential preference and age dependency approach to disaggregate census data. 
Journal of Maps 12(sup1): 560-569. 

Lambrechts M (2019) How to make a grid map with histograms in R with ggplot. Flowing- Data. 
https://flowingdata.com/2019/12/16/grid-map-histogram-ggplot/. Accessed on 15 April 2020. 

Parliamentary Budget Office (2019) Australia’s Ageing Population: Understanding the Fiscal Impacts Over 
the Next Decade. Report No. 02/2019. Canberra: Parliamentary Budget Office. 

Silva B, Javier Gallego F, and Lavalle C (2013) A high-resolution population grid map for Europe. Journal of 
Maps 9(1): 16-28. 

 

https://rpubs.com/AnthonyKimpton/
https://datapacks.censusdata.abs.gov.au/datapacks/
https://www.aihw.gov.au/reports/older-people/older-australia-at-a-glance
http://www.regionalaustralia.org.au/home/ageing-work-regional-australia/
https://flowingdata.com/2019/12/16/grid-map-histogram-ggplot/

