












































AUSTRALIAN POPULATION STUDIES 2019 | Volume 3 | Issue 2 | pages 29-33 
 

© Lock and Pettit 2019. Published under the Creative Commons Attribution-NonCommercial licence 3.0 Australia (CC BY-NC 
3.0 AU). Journal website: www.australianpopulationstudies.org 

Visualising population distribution 
in Australia over time using rapid 
3D web graphics libraries 

Oliver Lock*  University of New South Wales 
Chris Pettit  University of New South Wales 

* Corresponding author. Email: o.lock@unsw.edu.au. Address: City Analytics Lab, Faculty of 
Built Environment. Red Centre West Wing, University of New South Wales. Kensington, NSW 
2033. Australia.  

Paper received 1 May 2019; accepted 29 May 2019; published 18 November 2019 

 

This paper presents an interactive 3D visualisation tool and workflow for exploring current and past 
population data in Australia. The visualisation of small-area population data enables the exploration 
of population dynamics, such as scale, density, movement, age and sociodemographic information. 
Such information is becoming increasingly accessible with the advent of open government data 
(Jetzek et al. 2013), sensor data (Sagl et al. 2012), and more sophisticated computational and 
visualisation tools (Kashnitsky and Schöley 2018; O’Brien and Cheshire 2016; Pettit et al. 2012; Pettit 
et al. 2017). Such tools enable planners, geographers, demographers to understand the current 
structure of cities and how they are changing over time. 

There are a number of challenges in representing small-area population geographies in a user-
friendly way at a large national (or, indeed, international) scale. These geographies consist of 
statistical boundaries which can vary significantly in area. The rendering of density and colour breaks 
can further be difficult, and often can skew the visibility of interconnecting regional cities and 
centres. Due to the complexity of boundary geometries, it can be computationally slow to show 
national datasets in standard GIS applications, and even in web applications they are particularly 
difficult to interpret when zoomed out as a consequence of preserving the legibility of these 
boundaries. 

Several systems have been developed to address the visualisation of detailed population data at a 
large scale by using methods which ameliorate effects of administrative population boundaries. For 
example, work by Smith (2016) discusses this, and creates an interactive 2D population explorer 
using the open release of the European Commission (2019) Global Human Settlement Layer. To 
overcome these challenges this DemoGraphic presents a novel approach (Figure 1) using rapid, open 
source 3D web visualisation libraries to explore how this can be achieved with national-level 
population data in Australia. 

 

D
em

oG
ra

ph
ic

 

 

http://www.australianpopulationstudies.org/
mailto:o.lock@unsw.edu.au


30 Lock & Pettit  Australian Population Studies 3 (2) 2019 

 

 

Figure 1: Australian Population Explorer 

Note: Access the interactive version via https://aus3dpop.city-informatics.com  

WebGL operates across multiple supported browsers (Chrome, Firefox, Safari) with a royalty-free API 
(Application Programming Interface), and creates fast, hardware accelerated 3D graphics (Khronos 
Group 2019). The open-source WebGL-based library DeckGL (Uber 2019) was used due to its 
specialisation in processing very large geographic data sets and rendering them in three dimensions. 
Three dimensional techniques are a sensible choice for displaying population density, which has a 
natural perceptual association with its impact on the height and size of the built environment to 
support that density. 

The method used in this visualisation is known as ‘hexagon binning’. A hexagon bin is a form of 
bivariate histogram which considers both its location and a frequency of values underneath it. The 
process of generating a hexagon bin is as follows: 

• geographic space is transformed into a regular grid of hexagons; 

https://aus3dpop.city-informatics.com/


Australian Population Studies 3 (2) 2019  Lock & Pettit 31 

 

• the number of points (in this instance, population) falling into each hexagon is counted; 

• hexagons with a count above 0 are plotted, with their colour and height varying with the 
density of points underneath. 

There are several reasons why hexagons are useful (Lewin-koh 2011). Hexagons are more efficient 
than covering a plane with squares. They are also visually less biased for displaying densities than 
other regular tessellations with fewer edges (and thus an implied visual directionality). As such, they 
provide a sound medium for communicating population and related urban densities.  

For this visualisation, usual residence census data from the smallest geographic unit for population 
data within the ABS Census data was used, mesh blocks. According to the ABS (2016), mesh blocks 
contain around 30-60 dwellings, the smallest number of dwellings that can be communicated 
without potentially revealing sensitive information. The amount and geometry of mesh blocks has 
changed over time, from the 2006 Census (when originally conceived) when there were 
approximately 314,000 to approximately 347,000 in 2011 and 358,000 in 2016. A challenge lies in 
their evolving geometries as their visual comparison is unclear over time. Another challenge, as 
discussed, is in computing the complex, detailed geometries and trying to represent such granular 
polygons at national scale. 

The resulting visualisation is available through Australian Population Explorer (Lock 2019). The tool is 
rapid and exploratory. Users can toggle between alternate years of the census and see changes in 
population distribution. Further, changes can be made in the coverage of the hexagon to explore 
more densely populated areas. The Lower Percentile toggle can be used to see which areas fall in the 
highest densities of all hexagons generated across the country. 

It can be seen that Australia’s major cities continue to operate as primate cities within their 
respective States/Territories, however, such visualisation also highlights the country’s vast network 
of regional cities and the interconnectedness of urban regions such as in South-East Queensland. By 
increasing the radius of the hexagon to 10km, and around the 95th percentile, users can begin to see 
Australia’s network of smaller cities and how they may connect to one another, which could, for 
example, be useful in considering future, major infrastructure projects. 

With a smaller hex bin it is clearly visible that 2006 and 2011 have greater similarity in population 
structure and pattern than in 2016. This is likely due to a combination of factors related to the new 
digital version of the Census in 2016, including changes in response rates, imputation challenges and 
new address-matching systems (the Address Register), documented by Census Independent 
Assurance Panel (Harding et al. 2017). This representation difference was mitigated by increasing the 
hexagon sizes. By changing size dynamically, it is easier to compare cities over time and highlight 
overarching structures – see for example components B, C and D of Figure 1. 

In summary, the Australian Population Explorer presented here permits the interactive investigation 
of population distribution and trends over time. This contributes to our understanding of effective 
methods to represent detailed small-area information, which can be useful across multiple domains. 
Such applications include planning and delivering of government services, transportation planning, 



32 Lock & Pettit  Australian Population Studies 3 (2) 2019 

 

estimating vulnerable communities in disaster situations, understanding communities for election 
preparation, estimation of population at risk for spread of diseases (Wardrop et al. 2018). 

References 

Australian Bureau of Statistics [ABS] (2018) Population Projections, Australia, 2017 (base) – 2066. 
Catalogue No. 3222. Canberra: ABS.   

Australian Bureau of Statistics [ABS] (2016) Australian Statistical Geography Standard (ASGS): Volume 1 - 
Main Structure and Greater Capital City Statistical Areas, July 2016. Catalogue No. 1270.0.55.001. 
Canberra: ABS  

Harding S, Jackson Pulver L, McDonald P, Morrison P, Trewin D and Voss A (2017) Report on the Quality of 
2016 Census Data. 
http://www.abs.gov.au/websitedbs/d3310114.nsf/Home/Independent+Assurance+Panel 

Chen C, Ma J, Susilo Y, Liu Y and Wang M (2016) The promises of big data and small data for travel 
behavior (aka human mobility) analysis. Transportation Research Part C: Emerging Technologies, 
68, 285–299. https://doi.org/10.1016/j.trc.2016.04.005  

European Commission (2019) GHSL - Global Human Settlement Layer. 
https://ghsl.jrc.ec.europa.eu/index.php. Accessed on 28 March 2019. 

Jetzek T, Avital M and Bjørn-Andersen N (2013) Generating value from open government data. 
Proceedings of the 34th International Conference on Information Systems 1-20. 
http://aisel.aisnet.org/cgi/viewcontent.cgi?article=1181&context=icis2013. 

Kashnitsky I and Schöley J (2018) Regional population structures at a glance. The Lancet 392(10143): 209–
210. https://doi.org/10.1016/S0140-6736(18)31194-2  

Khronos Group (2019) WebGL - OpenGL ES for the Web. https://www.khronos.org/webgl/. Accessed on 
25 June 2019. 

Lewin-Koh N (2011) Hexagon binning: an overview. 
http://cran.rproject.org/web/packages/hexbin/vignettes/hexagon_binning.pdf. Accessed on 28 
March 2019.  

Lock O (2019) Australian Population Explorer. https://aus3dpop.city-informatics.com/. Accessed on 25 
June 2019. 

O’Brien O and Cheshire J (2016) Interactive mapping for large, open demographic data sets using familiar 
geographical features. Journal of Maps 12(4): 676–683. 
https://doi.org/10.1080/17445647.2015.1060183  

Pettit C J, Tanton R and Hunter J (2017) An online platform for conducting spatial-statistical analyses of 
national census data across Australia. Computers, Environment and Urban Systems 63: 68–79. 
https://doi.org/10.1016/j.compenvurbsys.2016.05.008  

Pettit C, Widjaja I, Russo P, Sinnott R, Stimson R and Tomko M (2012) Visualisation Support for Exploring 
Urban Space and Place. In: ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial 
Information Sciences 1: 153–158. https://doi.org/10.5194/isprsannals-I-2-153-2012  

Sagl G, Resch B, Hawelka B and Beinat E (2012) From social sensor data to collective human behaviour 
patterns – analysing and visualising spatio-temporal dynamics in urban environments. GI_Forum 
2012: Geovisualization, Society and Learning 1: 54–63. 
https://doi.org/10.1080/01441647.2019.1616849  

Smith D A (2016) Online interactive thematic mapping: applications and techniques for socio-economic 
research. Computers, Environment and Urban Systems 57: 106-117. 
https://doi.org/10.1016/j.compenvurbsys.2016.01.002 

Uber (2019) deck.gl. https://deck.gl/. Accessed on 24 January 2019.  

http://www.abs.gov.au/websitedbs/d3310114.nsf/Home/Independent+Assurance+Panel
https://doi.org/10.1016/j.trc.2016.04.005
https://ghsl.jrc.ec.europa.eu/index.php
http://aisel.aisnet.org/cgi/viewcontent.cgi?article=1181&context=icis2013
https://doi.org/10.1016/S0140-6736(18)31194-2
https://www.khronos.org/webgl/
http://cran.rproject.org/web/packages/hexbin/vignettes/hexagon_binning.pdf
https://aus3dpop.city-informatics.com/
https://doi.org/10.1080/17445647.2015.1060183
https://doi.org/10.1016/j.compenvurbsys.2016.05.008
https://doi.org/10.5194/isprsannals-I-2-153-2012
https://doi.org/10.1080/01441647.2019.1616849
https://doi.org/10.1016/j.compenvurbsys.2016.01.002
https://deck.gl/


Australian Population Studies 3 (2) 2019  Lock & Pettit 33 

 

Wardrop N A, Jochem W C, Bird T J, Chamberlain H R, Clarke D, Kerr D, Bengtsson L, Juran S, Seaman V 
and Tatem A J (2018) Spatially disaggregated population estimates in the absence of national 
population and housing census data. Proceedings of the National Academy of Sciences 115(14): 
3529–3537. https://doi.org/10.1073/pnas.1715305115 

 

https://doi.org/10.1073/pnas.1715305115

