Why are younger adults more likely to use public transport? (an exploration of mode shares by age – part 1)

Sat 19 September, 2020

Young adults are much more likely to use public transport (PT) than older adults.

Is it because younger adults are more likely to live and/or work near the city centre? Is it because they are more likely to live near train stations? Is it because they tend to live in higher density areas with better public transport? Is it because they are less likely to own a car? Is it because they are less likely to own a driver’s licence? Is it because they are less likely to be parents? Is it to do with their income? Is it related to how many of them are recent immigrants? Or is it a generational thing?

The answers are not as simple as you might expect. This is the first post in a series that aims to understand what influences mode choice across different ages. I’ll focus on (pre-COVID19) data about general travel and journeys to work in my home city of Melbourne, but I suspect the patterns will be similar in comparable cities.

About the data (boring but important)

My largest data source is the 2016 ABS census of population and housing which provides detailed demographic data about residents, captures the modes used for journeys to work, but doesn’t record travel for any other purposes and only covers a single day in August. There’s data on the travel choices of millions of people, and so it is possible to disaggregate data by several dimensions before you run into problems with small counts.

For general travel mode shares my data source is the Victoria Integrated Survey of Travel and Activity (VISTA) which is Victoria’s household travel survey, recording the transport and activity of a representative sample of Melbourne and Geelong residents across the whole year. The data set is smaller than the census (being a survey), but also contains rich demographic information and covers all travel purposes by people of all ages. I have used aggregated data over the survey years 2012-2018 to form a larger sample, so any underlying trends in behaviour over that period will be averaged out.

For this analysis I am filtering my data to either Greater Melbourne (for 2011 and 2016 census data) or otherwise the 31 local government areas (LGAs) that make up metropolitan Melbourne (all are entirely inside Greater Melbourne except Yarra Ranges).

All of this data was collected before the COVID19 pandemic, and of course travel patterns may well not return to similar patterns once the pandemic is over.

Consistent with elsewhere on this blog, I attribute

  • any journey involving a train, tram, bus, or ferry as a public transport journey (even if other modes were also used, including private transport),
  • a journey only involving walking and cycling as an active transport journey, and
  • any other journey as a private transport journey (mostly being car journeys).

This post mostly focuses on public transport – which I will often abbreviate to PT.

While the data sets I am using only identify sex as male or female, I want to acknowledge that not all people fit into binary classifications.

How does PT mode share vary by age and sex for general travel?

Here’s a chart showing public transport mode shares by age and gender using VISTA data for all travel purposes:

Public transport mode share peaked in the 15-19 age group – essentially around the later years of secondary school and early years of tertiary education or working life where people have more independence, may need to travel longer distances to get to school or university, and are too young and/or cannot afford private transport.

Public transport mode share then fell away with age, though the profile by gender is slightly different (some of this may be noise in the survey). Women under the age of 30 were more likely to use PT, but then they became less likely to use PT after age 30 – perhaps after the arrival of children.

Children under 10 years were least likely to use public transport, and there was only a small increase in public transport use amongst women aged over 65. Public transport use dropped considerably for those aged 85-89, and there wasn’t sufficient sample to confidently calculate mode shares for any older age groups.

How does PT mode share vary by age and sex for journeys to work?

Here is a chart of public transport mode share of journeys to work in Greater Melbourne by age and sex (using census data):

The chart shows women were much more likely to use public transport to get to work than men, particularly for young adults but also those in their 60s. Overall PT mode share was 17.7% for males and 20.3% for females. PT mode share peaked for females at age 26, and for males at age 24.

So what might explain the variations across age and gender? In this first post I’m going to explore the how mode share varies by home and work distance from the CBD.

Travelling to the city centre

We know that travel to/from the central city is much more likely to involve public transport, so here are general travel PT mode shares dis-aggregated by whether or not the trip was to/from the City of Melbourne (local government area):

For travel to/from the City of Melbourne, PT mode shares peaked around 50% for workers in their early 20s, and generally fell away with age, with females showing a higher PT mode share in all age groups (mode shares are only shown for ages 20-64 due to small samples of trips in other age groups).

For travel not to/from the City of Melbourne, PT mode shares peaked for teenagers and was very low for all other age groups, with only slightly higher mode shares for those in their 20s, early 30s, and early 80s.

With census journey to work data, we can increase the resolution to 2 year age-bands and dis-aggregate work destinations by distance bands from the CBD. The darker line of each colour is for females, the lighter for males.

Public transport mode share was much higher for workplaces near the CBD, and then declined with workplace distance from the CBD.

But within each workplace distance band from the CBD there was also a generally declining PT mode share with age, flattening out somewhat for ages above 45. While there was a difference between genders at all workplace distance bands, it was generally smaller than the overall mode share difference between genders for journeys to work.

How can these lines have quite a different curve shape to overall PT mode share by age/sex? Well, here is a chart showing the proportion of Greater Melbourne workers at every age who worked within 4 kms – and within 10 kms – of the Melbourne CBD:

Young adults were much more likely to work closer to the CBD than older adults, and women even more so (although they are not actually a majority of workers close to the CBD).

Teenagers were least likely to work in the City of Melbourne, which likely reflects their lack of qualifications for high-skill jobs that tend to locate in the central city.

The curves for men and women peaked at different ages, with younger adult women more likely to work in the City of Melbourne than younger adult men, which then flipped for ages 38+. This isn’t because of stay-at-home mums because the data only counts people who travelled to work.

Here’s another look at that data – showing the distribution of work locations from the CBD for age bands. Around 40% of young adult workers worked within 6 km of the CBD:

And flipping that, workplaces closer to the CBD have a higher proportion of younger adults:

Public transport mode shares for general travel (in the VISTA survey) were related to both age and trip destination distance from the CBD, with those in their 50s least likely to use PT for destinations within 5 km of the CBD:

So a major explanation why younger adults were more likely to use public transport in their journey to work is that they were more likely to work in the central city. However, when you control for travel proximity to the CBD there is still a significant relationship between PT mode share and age – other factors must be at play.

I’m curious – is the fact that younger adults (particularly women) were more likely to work in the city centre related to their…

Educational qualifications

Well, younger adults turn out to have the highest educational qualifications of any age group, with those in their early 30s generally being the most qualified (as at 2016):

Note: Supplementary codes includes people with no educational attainment.

Furthermore, younger women are generally more qualified than younger men, which could explain why a higher proportion of younger women work in the City of Melbourne, and therefore have a higher public transport mode share overall.

[As an aside: I find that chart fascinating – there’s been a generational shift in educational attainment which will continue to work it’s way up the age brackets in the decades ahead, resulting in an increasingly skilled workforce over time. Part of this will be skilled migration, part may be temporary migrants (eg international postgraduate students), and another part presumably reflects greater access to higher education in recent decades.]

Looking to the future perhaps this cohort of highly educated young adults will continue to work in the inner city as they age, along with younger skilled graduates, leading to more centralisation of employment in Melbourne as we become more of a “knowledge economy”? Or maybe the recent mass working-from-home experience of highly skilled workers during the COVID-19 pandemic will see more workers based in the central city but travelling to their workplace less often.

But back to the how education levels impact work location and mode choice…

People with higher educational attainment are more likely to work closer to the CBD:

The chart shows around half of workers with postgraduate degrees worked within 4 km of the CBD, whereas those who didn’t complete secondary education were much more likely to work in the suburbs.

We know that workplace distance from the CBD impacts PT mode shares, but does varying educational qualifications explain the differences in mode share between ages?

The following animated chart shows how PT mode shares for journeys to work vary by age for people with the same level of educational qualifications and working the same distance from the Melbourne CBD. I have animated the chart across workplace distance from the CBD bands.

If you watch and study the chart, you’ll see that there is a relationship between age and PT mode share for most combinations of educational attainment and workplace distance from the CBD. That is, age is significant in itself, or there is some other explanation for mode share difference by ages.

You’ll also see that public transport mode shares were generally higher for higher levels of educational attainment, with postgraduate degrees mostly showing the highest public transport mode share.

Here’s an alternative, non-animated view of that data. It’s a matrix of mini line charts showing PT mode share by age, for each combination of workplace distance from the CBD and highest level of educational attainment. You could call it a matrix of worms. The light horizontal line within each matrix box represents a 50% PT mode share, and the colours give you a rough sense of age (refer legend). I don’t expect you to be able read the mode share values for any age band on any line, but it does show PT mode shares falls with rising age for all education levels and workplace distances from the CBD (except some further out where PT mode share is just very low for all ages).

Here is yet another view: the relationship between PT mode share, workplace distance from the CBD, and highest level of educational attainment. I have roughly sorted the education levels by PT mode share, rather than ordering by level of qualification.

PT mode shares were not directly proportional to education levels, but I suspect this will be partly related to occupations – eg Certificate III and IV qualifications often related to trades where driving to non-centralised work sites is a more convenient option.

Those with postgraduate degrees generally showed the highest public transport mode share at each distance interval.

So we’ve explored work distance from the CBD, but what about…

Home distance from CBD

Younger adults were more likely to live closer to the Melbourne CBD compared to other age groups:

Public transport service quality is generally higher closer to the CBD, so does the fact that younger adults were more likely to live closer to the city explain their higher PT mode shares?

The following chart shows how public and active transport journey to work mode shares vary by home distance from the Melbourne CBD:

Public transport mode shares show a relationship with both home distance from the CBD and age – with mode shares peaking for ages 20-39, and dropping with older age bands. I’ve plotted active transport mode shares as well (for interest), which shows teenage workers much more likely to get to work by active transport – which makes sense as many of them will be below driving age and/or unable to afford private transport. Curiously those aged 70-79 who live in the suburbs are slightly more likely to walk to work.

Okay, but people who live closer to the CBD are more likely to work closer to the CBD, as the following chart shows:

Or another way of looking at it:

While the distance bands vary on each axis (more intervals for work distances from the CBD), you can see a very common scenario is that people’s work is a very similar distance from the CBD as their home. That is, they work relatively locally (for more on this, see Introducing a census journey to work origin-destination explorer, with Melbourne examples)

The following chart looks at mode shares for those who worked within 2 km of the CBD:

PT mode shares for these commuters were relatively high and flat for workers who live more 5 km from the CBD (those closer are more likely to use active transport – as per the bottom half of the chart). PT mode shares rose slightly with distance from the CBD for home distances 25-40 km from the CBD. But there was still a difference between age bands, with younger adults more likely to have used PT to get to work, regardless of how far from the CBD they lived.

So are there still PT mode share differences by age if you control for both home and work distance from the CBD?

Home AND work distance from the CBD

Firstly, here are PT mode shares for journeys to work by home and work distance from the CBD, animated over age bands:

Technical notes: the chart only shows mode shares where at least 200 people fell within the age and distance bands – which is quite a low threshold so there is a little noise – so please try not to get distracted by small differences in numbers. For teenagers and those aged 60-69, many combinations failed this threshold so are left blank.

The chart shows that work distance from the CBD is a very strong driver of mode shares at all age bands. Home distance from the CBD is much weaker driver of PT mode share – and only really significant for those living within 5 km of the CBD, and those under 40 years within 15 km of the CBD.

If the animation is hard to follow, here’s another matrix-of-worms chart. It shows PT mode share by age band – for every combination of home and work distance from the CBD.

The thin horizontal lines within each square of the matrix represent 50% PT mode share. While you cannot read off the PT mode shares for any age and distance combination, you can see that within each pane PT mode share either generally fell with increasing age, or were very low for all ages. That is to say, that home and workplace distance from the CBD doesn’t fully explain the relationship between age and PT mode shares for journeys to work. Other factors must be at play.

The above chart makes it hard to compare mode shares for the different work distances from the CBD, so here is a transposed version with work distances as rows and home distances as columns:

There’s not a lot of difference between home distance bands for each work distance band, except for younger adults living closer to the CBD and working in the city centre. This confirms the earlier finding that work distance from the CBD is a much stronger determinate in PT mode shares.

So in summary, younger adults are more likely to live and work closer to the CBD, and that is likely related to them generally having higher educational qualifications. While these factors generally lead to higher public transport use, we’ve found they don’t fully explain why younger adults have higher public transport mode shares.

Further posts in this series will look at other demographic factors that may explain these differences. Read on to part two.


Mapping Melbourne’s journeys to work

Mon 24 June, 2019

The unwritten rules of mapping data include avoiding too much data and clutter, and not using too much colour. This blog often violates those rules, and when it comes to visualising journeys to work, I think we can learn a lot about cities with somewhat cluttered colourful animated maps.

This post maps journeys to work in Melbourne, using data from the 2016 census. I will look at which types of home-work pairs have different public, private and active transport mode shares and volumes.

Although this post will focus on Melbourne, I will include a brief comparison to Sydney at the end.

Where are public transport journeys to work in Melbourne?

First I need to explain the maps you are about to see.

So that I can show mode shares, I’ve grouped journeys between SA2s (which are roughly the size of a suburb). Lines are drawn from the population centroid of the home SA2 (thin end) to the employment centroid of the work SA2 (thicker end). Centroids are calculated as the weighted average location residents/jobs in each SA2 (using mesh block / destination zone data). This generally works okay for urban areas, but be aware that actual trips will be distributed across SA2s, and some SA2s on the urban fringe are quite large.

The thickness of each line at the work end is roughly proportional to the number of journeys by the mode of interest between the home-work pair (refer legends), but it’s difficult to use a scale that is meaningful for smaller volumes. Unfortunately there’s only so much you can do on a 2-D chart.

I’ve not drawn lines where there are fewer than 50 journeys in total (all modes), or where there were no journeys of the mode that is the subject of the map. This threshold of 50 isn’t perfect either as SA2s are not consistently sized within and between cities, so larger SA2s are more likely to generate lines on the map.

To try to help deal with the clutter, I’ve made the lines somewhat transparent, and also animated the map to highlight trips with different mode share intervals. For frames showing all lines, the lines with highest mode share are drawn on top.

So here is an animated map showing public transport journeys to work in Melbourne, by different mode share ranges and overall:

Technical note: I have included journeys to work that are internal to an SA2. Usually these appear as simple circles, but sometimes they appear as small teardrops where the population and employment centroids are sufficiently far apart.

You can see that the highest PT shares and largest PT volumes are for journeys to the central city, and generally from SA2s connected to Melbourne CBD by train (including many outer suburbs).

As the animation moves to highlight lower PT mode share ranges, the lines become a little less radial, a little shorter on average, and the lowest (non-zero) PT mode shares are mostly for suburban trips.

A notable exception is journeys to Port Melbourne Industrial SA2 (also known as Fishermans Bend), which is located at the junction of two major motorways and is remote from rapid public transport (it does however have a couple of high frequency bus lines from the CBD).

The lowest PT mode shares are seen for trips around the outer suburbs. The maps above unfortunately aren’t very good at differentiating small volumes. The following animated map shows public transport journeys with a filter progressively applied to remove lines with small numbers of public transport journeys (refer blue text in title):

You can see that most of the outer suburban lines quickly disappear as they have very small volumes. Inter-suburban lines with more than 50 public transport journeys go to centres including Dandenong, Clayton, Box Hill, and Heidelberg.

Here’s another animation that builds up the map starting with low public transport mode share lines, and then progressively adds lines with higher public transport mode shares:

As an aside, here is a chart showing journeys to work by straight line distance (between SA2 centroids), public transport mode share, work distance from the CBD and home-work volume:

The black dots represent journeys to the inner 5km of the city, and you can see public transport has a high mode share of longer trips. Public transport mode share falls away for shorter journeys to the inner city as people are more likely to use active transport. A dot on the top left of the curve is 8,874 journeys from Docklands to Melbourne – which benefits from the free tram zone and the distances can be 1-2 km. Most of the longer journeys with low public transport mode share are to workplaces remote from the CBD (coloured dots).

Another way to deal with the clutter of overlapping lines around the CBD is to progressively remove lines to workplaces in and around the CBD. Here is another animated map that does exactly so that you can better see journeys in the nearby inner and middle suburbs.

As you strip away the CBD and inner suburbs, you lose most lines with high public transport mode shares and volumes. However some high public transport mode share lines remain, including the following outbound journeys:

  • Melbourne (CBD) to Melbourne Airport: 72% of 64 journeys
  • Melbourne (CBD) to Box Hill: 66% of 76 journeys
  • Melbourne (CBD) to Clayton: 57% of 82 journeys
  • South Yarra – East to Clayton: 57% of 173 journeys

Just keep in mind that these are all very small volumes compared to total journeys in Melbourne.

You might have noticed on the western edge of the map some yellow and orange lines from the Wyndham area (south-west Melbourne) that go off the map towards the south west. These journeys go to Geelong.

Here’s a map showing journeys around Geelong and between Geelong and Greater Melbourne (journeys entirely within Greater Melbourne excluded):

You can see very high public transport mode shares for journeys from the Geelong and Bellarine region to the Melbourne CBD and Docklands (and fairly large volumes), but no lines to Southbank, East Melbourne, Parkville or Carlton – all more remote from Southern Cross Station, the city terminus for regional trains.

(The other purple lines to the CBD are from Ballarat, Bacchus Marsh, Daylesford, Woodend, Kyneton, Castlemaine, Kilmore-Broadford and Warragul, with at least 60 journeys each.)

You can also see those orange and yellow lines from the Wyndham area to central Geelong, being mode shares of 20-40%. The Geelong train line provides frequent services between Tarneit, Wyndham Vale, and Geelong, and has proved reasonably popular with commuters to Geelong (frequency was significantly upgraded in June 2015 with the opening of the Regional Rail Link, just 14 months before the census of August 2016).

However, public transport mode shares for travel within Greater Geelong are very small – even for SA2 that are connected by trains. This might reflect Geelong train station being on the edge of its CBD, relatively cheap parking in central Geelong, limited stopping frequency at some stations (many at 40 minute base pattern), and/or limited walk-up population catchments at several of Geelong’s suburban train stations.

Does public transport have significant mode share for cross-suburban journeys to work?

To search for cross-suburban journeys with relatively high public transport mode shares, here is a map that only shows lines with public transport mode shares above 20% between homes and workplaces both more than 5 km from the CBD (yes, those are arbitrary thresholds):

Of these journeys, the highest mode shares are for journeys from the inner northern suburbs to St Kilda and Hawthorn. There’s also a 49% mode share from Footscray to Maribyrnong (connected by frequent trams and buses).

The tear drop to the north of the city is 114 people who used PT from Coburg to Brunswick (connected by two tram routes and one train route).

Most of the other links on this map are fairly well aligned with train, tram, or SmartBus routes, suggesting high quality services are required to attract significant mode shares.

But these trips are a tiny fraction of journeys to work around Melbourne. In fact 3.0% of journeys to work in Melbourne were by public transport to workplaces more than 5 km from the CBD. The same statistic for Sydney is more than double this, at 7.3%.

What about private transport journeys?

Firstly, here’s a map showing private transport mode shares and volumes, building up the map starting with low private mode share lines.

The links with lowest private transport mode shares are very radial as you might expect (pretty much the inverse of the public transport maps). Progressively less radial lines get added to the map before there is a big bang when the final private transport mode share band of 95-100% gets added, with large volumes of outer suburban trips.

For completeness, here’s an animation that highlights each mode share range individually.

There are some other interesting stories in this data. The following map shows private transport mode share of journeys to work, excluding workplaces up to 10 km from the CBD to remove some clutter.

If you look carefully you’ll see that there is a much lower density of trips that cross the Yarra River (which runs just south of Heidelberg and Eltham). There are limited bridge crossings, and this is probably inhibiting people considering such journeys.

The construction of the North East Link motorway will add considerable cross-Yarra road capacity, and I suspect it may induce more private transport journeys to work across the Yarra River (although tolls will be a disincentive).

What about active transport journeys?

Next is a map for active transport journeys, but this time I’ve progressively added a filter for the number of active transport journeys, as most of the lines on the full chart are for very small volumes.

As soon as the filter reaches a minimum of 50 active journeys most of the lines between SA2s in the middle and outer suburbs disappear. Note that journeys between SA2s are not necessarily long, they might just be a short trip over the boundary.

Then at minimum 200 journeys you can only see central city journeys plus intra-SA2 journeys in relatively dense centres such as Hawthorn, Heidelberg, Box Hill, Clayton, Frankston, Mornington, Footscray, and St Kilda. The large volume in the south of the map that hangs around is Hastings – Somers, where 882 used active transport (probably mostly walking to work on the HMAS Cerebus navy base).

Active transport journeys are mostly much shorter than private and public transport journeys – as you might expect as most people will only walk or ride a bicycle so far. But there are people who said they made very long active transport journeys to work – the map shows some journeys from Point Nepean, Torquay, Ballarat, Daylesford, and Castlemaine to Melbourne. That’s some keen cyclists, incredible runners, people who changed jobs in the week of the census (the census asks for work location the prior week, and modes used on census day), and/or people who didn’t fill in their census forms accurately. The volumes of these trips are very small (mostly less than 5).

That map is very congested around the central city, so here is a map zoomed into the inner suburbs and this time animated by building up the map starting with high active transport mode share lines.

The highest active transport mode shares are for travel within Southbank and from Carlton to Parkville, followed by journeys to places like the CBD, Docklands, South Yarra, South Melbourne, Carlton, Fitzroy, Parkville, and Carlton.

Then you see a lot of trips added from the inner northern suburbs, which are connected to the central city by dare-I-say “above average” cycling infrastructure across some relatively flat terrain. In particular, a thick red line on the map is for 471 active transport journeys from Brunswick to Melbourne (CBD) with a mode share of 17%. A second thick red line is Richmond to Melbourne (CBD) being 589 journeys with 16% active mode share.

Another way of summarising mode shares by work and home distance from the CBD

I’ve experimented with another visualisation approach to overcome the clutter issues. The next charts have home distances from the CBD on the Y axis, work distances from the CBD on the X axis, bubble size representing number of journeys, and colour showing mode shares. I’m drawing smaller journey volumes on top, and I’ve used some transparency to help a little with the clutter.

Firstly here is public transport (animated to show each mode share range individually):

The chart is roughly a V-shape with many trips on the left edge and along a diagonal (mostly representing intra-SA2 journeys), then with several vertical stripes being major suburban employment destinations (including Dandenong at 31 km, Clayton at 19 km, and Frankston at 40 km). Trips above the diagonal are roughly inbound, while trips below the diagonal are roughly outbound.

Some observations:

  • The diagonal line (mostly local journeys) has very low public transport mode shares (sometimes zero).
  • Higher PT mode shares are only seen on the far left and bottom left hand corner of the chart. Some outliers include Richmond to Box Hill (32%), Clayton to Malvern East (32%), and South Yarra – East to Clayton (57%).
  • PT mode shares of 80+% are only seen for journeys to the CBD from home SA2s at least 11 km out (with one exception of Melbourne CBD to St Kilda with 80% PT share).
  • Home-work pairs with zero public transport journeys are scattered around the middle and outer suburbs, most being longer distance journeys (home and work at different distances from the CBD).

Here’s the same chart for private transport:

The lowest private transport shares are seen for journeys to the CBD. The diagonal has many mode shares in the 80-90% range.

And here is active transport:

The highest active transport mode shares are seen in the central city area, followed by the diagonal mostly representing local journeys (with generally higher shares closer to the CBD). Some notable outliers include local trips within Clayton (1,298 active trips / 46% active mode share), Box Hill (914 / 40%), Hastings – Somers (1,762 / 27%), Flinders (240 / 24%), Glen Waverley – West (308 / 21%), and Mentone (226 / 23%).

How does Sydney compare to Melbourne?

Here is a chart with private transport mode share maps for both Melbourne and Sydney, animated in tandem to progressively add higher mode share journeys.

You can see that Sydney has a lot more trips at lower private transport mode shares, and that workplaces outside the city centre start to show up earlier in the animation in Sydney – being the dense transit-orientated suburban employment clusters that are largely unique to Sydney (see: Suburban employment clusters and the journey to work in Australian cities).

If time permits, I may do similar analysis for Sydney and other cities in future posts.


How radial are journeys to work in Australian cities?

Fri 14 June, 2019

In almost every city, hordes of people commute towards the city centre in the morning and back away from the city in the evening. This largely radial travel causes plenty of congestion on road and public transport networks.

But only a fraction of commuters in each city actually work in the CBD. So just how radial are journeys to work? How does it vary between cities? And how does it vary by mode of transport?

This post takes a detailed look at journey to work data from the ABS 2016 Census for Melbourne, Sydney, and to a less extent Brisbane, Perth, Adelaide and Canberra. I’ve included some visualisations for Melbourne and Sydney that I hope you will find interesting.

How to measure radialness?

I’m measuring radialness by the difference in degrees between the bearing of the journey to work, and a direct line from the home to the CBD of the city. I’m calling this the “off-radial angle”.

So an off-radial angle of 0° means the journey to work headed directly towards the CBD. However that doesn’t mean the workplace was the CBD, it might be have been short of the CBD or even on the opposite side of the CBD.

Similarly, an off-radial angle of 180° means the journey to work headed directly away from the CBD. And a value of 90° means that the trip was “orbital” relative to the CBD (a Melbourne example would be a journey from Box Hill that headed either north or south). And then there are all the angles in between.

To deal with data on literally millions of journeys to work, I’ve grouped journeys by home and work SA2 (SA2s are roughly the size of a suburb), and my bearing calculations are based on the residential centroid of the home SA2 and the employment centroid of the work SA2.

So it is certainly not precise analysis, but I’ve also grouped off-radial angles into 10 degree intervals, and I’m mostly looking for general trends and patterns.

So how radial are trips in Melbourne and Sydney?

Here’s a chart showing the proportion of 2016 journeys to work at different off-radial angle intervals:

Technical note: As per all my posts, I’ve designated a main mode for journeys to work: any journey involving public transport is classed as “Public”, any journey not involving motorised transport is classed as “Active”, and any other journey is classed as “Private”.

In both cities over 30% of journeys to work were what you might call “very radial” – within 10 degrees of perfectly radial. It was slightly higher in Melbourne.

You can also see that public transport trips are even more radial, particularly in Melbourne. In fact, around two-thirds of public transport journeys to work in 2016 had a destination within 2 km of the CBD.

Melbourne’s “mass transit” system (mostly trains and trams) is very radial, so you might be wondering why public transport accounts for less than half of those very radial journeys (41% in fact).

Here are Melbourne’s “very radial” journeys broken down by workplace distance from the Melbourne CBD:

very-radial-trips-by-mode-distance-from-cbd

Public transport dominates very radial journeys to workplaces within 2 km of the centre of the CBD, but is a minority mode for workplaces at all other distances. Many of these highly radial journeys might not line up with a transit line towards the city, and/or there could well be free parking at those suburban workplaces that make driving all too easy. I will explore this more shortly.

Sydney however had higher public transport mode shares for less radial journeys to work. I think this can be explained by Sydney’s large and dense suburban employment clusters that achieve relatively high public transport mode shares (see: Suburban employment clusters and the journey to work in Australian cities), the less radial nature of Sydney’s train network, and generally higher levels of public transport service provision, particularly in inner and middle suburbs.

Visualising radialness on maps

To visualise journeys to work it is necessary to simplify things a little so maps don’t get completely cluttered. On the following maps I show journey to work volumes between SA2s where there are at least 50 journeys for which the mode is known. The lines between home and work SA2s get thicker at the work end, and the thickness is proportional to the volume (although it’s hard to get a scale that works for all scenarios).

First up is an animated map that shows journeys to work coloured by private transport mode share, with each frame showing a different interval of off-radial angle (plus one very cluttered view with all trips):

(click/tap to enlarge maps)

I’ve had to use a lot of transparency so you have a chance at making out overlapping lines, but unfortunately that makes individual lines a little harder to see, particularly for the larger off-radial angles.

You can see a large number of near-radial journeys, and then a smattering of journeys at other off-radial angles, with some large volumes across the middle suburbs at particular angles.

The frame showing very radial trips was rather cluttered, so here is an map showing only those trips, animated to strip out workplaces in the CBD and surrounds so you can see the other journeys:

Private transport mode shares of very radial trips are only very low for trips to the central city. When the central city jobs are stripped out, you see mostly high private transport mode shares. Some relative exceptions to this include journeys to places like Box Hill, Hawthorn, and Footscray. More on that in a future post.

Here are the same maps for Sydney:

Across both of these maps you can find Sydney’s suburban employment clusters which have relatively low private transport mode shares. I explore this, and many other interesting ways to visualise journeys to work on maps in another post.

What about other Australian cities?

To compare several cities on one chart, I need some summary statistics. I’ve settled on two measures that are relatively easy to calculate – namely the average off-radial angle, and the percent of journeys that are very radial (up to 10°).

The ACT (Canberra) actually has the most radial journeys to work of these six cities, despite it being something of a polycentric city. Adelaide has the next most radial journeys to work, but there’s not a lot of difference in the largest four cities, despite Sydney being much more a polycentric city than the others. Note the two metrics do not correlate strongly – summary statistics are always problematic!

Here are those radialness measures again, but broken down by main mode:

Sydney now looks the least radial of the cities on most measures and modes, particularly by public transport.

The Australian Capital Territory (Canberra) has highly radial private and active journeys to work, but much less-radial public transport journeys than most other cities. This probably reflects Canberra’s relatively low cost parking (easy to drive to the inner city), but also that the public transport bus network is orientated around the suburban town centres that contain decent quantities of jobs.

Adelaide has the most radial journeys to work when it comes to active and public transport.

What about other types of travel?

In a future post, I’ll look at the radialness of general travel around Melbourne using household travel survey data (VISTA), and answer some questions I’ve been pondering for a while. Is general travel around cities significantly less radial than journeys to work? Is weekend travel less radial than weekday travel?

Follow the blog on twitter or become an email subscriber (see top-right of this page) to get alerted when that comes out.


Are Australian cities sprawling with low-density car-dependent suburbs?

Wed 30 January, 2019

Many people talk about urban growth in Australian cities being car-dependent low-density suburban sprawl. But how true is that in more recent times? Are new greenfield density targets making a difference? Are cities growing around their rapid public transport networks? And how do growth areas compare to established areas at a similar distance out from city centres?

This post takes a look at what census data can tell us about outer urban growth areas in terms of population density, motor vehicle ownership, distance from train/busway stations, and journey to work mode shares.

How much of city population growth is in outer areas?

Firstly a recap, here is the percentage of annual population growth in each city that has occurred in “outer” areas (defined by groupings of SA3s around the edges of cities – refer my previous post for maps showing outer areas) for Greater Capital City Statistical Areas.

Sydney has had less than a third of its population growth in outer areas since around 2003, while Perth has mostly had the highest outer growth percentage (since 1996), and more recently pretty much all population growth in Perth has been on the fringe. You can see how the other cities sit in between.

However, not all of this “outer” population growth was in urban growth on the fringe. For that we need to distinguish between urban growth and infill development, even in “outer” areas. So we really need a better definition of outer growth areas.

How to define outer urban growth areas

I have built groupings of SA1s (Statistical Area Level 1) that try to represent outer urban greenfield residential development. SA1s are the smallest census geographic areas (average population 400) for which all census data variables are available.

I’ve selected 2016 SA1s that meet all of the following criteria:

  • Brand new SA1 or significant population growth: The 2016 SA1 is new and cannot be matched to a 2011 SA1 (by location/size and/or ABS correspondences), or if it can be matched, the population at least doubled between 2011 and 2016. Brand new SA1s are very common in urban growth areas as new SA1s are created to avoid oversized SA1s on last census boundaries (except this doesn’t always happen – more on that shortly).
  • In an SA2 with significant population growth: The SA2 (Statistical Area Level 2 – roughly suburb sized with typically 3,000 to 25,000 residents) that contains the SA1 had population growth of at least 1000 people between 2011 and 2016 (based on 2016 boundaries). That is, the general area is seeing population growth, not just one or two SA1s.
  • Are on – or close to – the urban fringe. I’ve filtered out particular SA2s that I’ve judged to be contain all or mostly in-fill development rather than greenfield development, or that are largely surrounded by existing urban areas and are not close to the urban fringe. I’ll be the first to admit that some of the inclusions/exclusions are a little arbitrary.

The criteria aren’t perfect, but it seems to work pretty well when I inspect the data. I’m calling these “Growth SA1s” or outer urban growth in this post.

For urban centres, I’m using Significant Urban Area 2016 boundaries (rather than Greater Capital City boundaries), and I’ve bundled Yanchep with Perth, Melton with Melbourne, and the Sunshine Coast and Gold Coast with Brisbane to form South East Queensland (SEQ).

Where are these outer urban growth areas?

What follows are maps for each city with the density of these growth SA1s shown by colour.

Melbourne’s northern and western growth areas:

Technical note: The maps do not show non-growth SA1s with fewer than 5 people per hectare, or “growth SA1s” with fewer than 1/hectare, although these SA1s are including in later analysis.

And the south of Melbourne:

Note: not shown on these Melbourne maps are isolated tiny growth SA1s in Rosebud and Mooroolbark.

Here are Sydney’s growth SA1s – all in the western suburbs:

Next up South East Queensland, starting in the north with the Sunshine Coast:

Northern Brisbane:

Outer urban growth is scattered in southern Brisbane and northern Gold Coast:

Gold Coast – Tweed Heads:

Perth’s northern and eastern growth areas:

Perth’s southern growth areas:

Note: Canning Vale East is an inclusion you could debate – the previous land use of the growth SA1s appear to have been rural based on satellite imagery.

Northern Adelaide:

Southern Adelaide:

And finally Canberra:

So how much of each cities’ population growth has been in outer growth areas?

Here’s a breakdown of the population growth for my six urban areas:

Over the five-year period, outer urban growth areas accounted for 19% of Sydney’s population growth, 43% of Melbourne’s, 37% of SEQ’s, 60% of Perth’s, 27% of Adelaide’s and 69% of Canberra’s.

Technical note: These “outer urban growth” figures are different to the chart at the top of this post which had a coarser definition of “outer” and used Greater Capital City boundaries. Some of my “outer urban growth” areas actually don’t quality as “outer” in the coarser definition, and I’ve also excluded several “outer” SA2s from “outer urban growth” where I’ve deemed the growth to be mostly infill. Hence the differences.

In case you are wondering, it’s not easy to create a longer-term time-series analysis about the proportion of population growth in “outer urban growth” areas because the classification of SA2s would have to change on a year-by-year basis which would be messy and somewhat arbitrary.

A challenge for density analysis: some SA1s are over-sized

You might have noticed some SA1s in the maps above are very large and show a low average density of 1-5 persons per hectare (I’ve coloured them in a light cyan). Many of these SA1s had thousands of residents in 2016, which is way more than the ABS guideline of 200 to 800 residents. Unfortunately what seems to have happened for 2011 and 2016 in some cities is that the ABS did not create enough SA1s to account for new urban areas. Some Melbourne SA1s had a population over 4000 in 2016. Many of these SA1s contain a combination or urban and rural land use, so their calculated density is rather misleading.

I’m designating any SA1s with more than 1000 residents and larger than 100 hectares as “oversized”, and I’ve exclude these from some density analysis below. Here’s a chart showing the proportion of outer growth area populations that are in oversized SA1s:

You can see it is a substantial problem in Sydney, Melbourne, Perth and South East Queensland, but miraculously not a problem at all in Adelaide or Canberra (I’m sure someone in ABS could explain why this is so!).

If you are interested, in 2011 it was a bigger problem in Melbourne, and only Canberra was fully clean.

So how dense are outer urban growth areas?

Firstly, I am excluding over-sized SA1s from this analysis for the reasons just mentioned.

Secondly, all cities will also have growth areas that were partially developed at the time of the census (ie some lots with occupied houses and other lots empty) so the densities measured here may be understated of the likely fully built-out density of these SA1s. That said, those areas perhaps are more likely to be in over-sized SA1s, but it’s hard to be sure. So keep this in mind when looking at growth area densities.

You can see dramatic differences, with Sydney, Canberra, and Melbourne showing higher densities, and South East Queensland with much lower densities. As we saw on the maps above, South East Queensland’s outer growth areas are very dispersed, so perhaps more of them are growing slowly and more of them are partially built-out? It’s hard to be sure.

But perhaps what is most remarkable is that Canberra had the highest densities in outer urban growth areas of any city – nothing like what you might consider suburban sprawl. Here’s what was 144.5 people per hectare in 2016 in Wright on Canberra’s new western growth front looks like:

(pic from Google Streetview, dated December 2016)

The densest SA1 in Sydney’s growth areas was 101 persons/ha. Nothing like this was seen in other cities.

Canberra’s outer growth areas are actually, on average, denser than the rest of Canberra (on a population weighted density measure):

The same was also true by a slim margin in both Perth and Adelaide, but they have relatively “suburban” densities for both growth and established areas. The growth areas of Sydney and Melbourne are more dense than Perth and Adelaide, but not compared to the rest of these cities as a whole. That’s probably got to do a lot with the large cities having dense inner suburbs.

So perhaps it is better to compare the urban growth areas with established areas a similar distance from city centres, which the following chart does (I’ve filtered out 5 km distance intervals without growth areas of at least 2000 population, and apologies for rather squashed Canberra label):

Technical note: for South East Queensland I’ve measured distances from the Brisbane CBD.

Outer growth areas were much more dense than the rest of each city at most distances from the city centre, except in Sydney.

One issue with the above chart is that different distance intervals have different populations – for example only 2,815 people were in growth SA1s at a distance of 45-50 km from the Perth CBD (just above my threshold of 2000), so the low population density of that interval is not hugely significant.

To get around that issue, I’ve calculated the overall population weighted density of non-growth SA1s that are within these 5 km distance intervals from the CBD (including all of SEQ beyond 15 km from the CBD). The following chart compares those calculations with the population weighted density of the growth areas overall:

This shows that urban growth areas are on average more dense than other parts of the city at similar distance from the CBD, except in South East Queensland. And remember, many of the growth SA1s will be partially built out, so their expected density is understated.

Are outer urban growth areas near rapid public transport?

The next chart shows the proportion of growth SA1 population by distance from the nearest train or busway station:

Technical notes: Distances are measured from the centroid of each SA1 to a point location defined for each station (sourced from August 2016 GTFS feeds). For oversized SA1s these distances might be a little longer than reality for the average resident. I haven’t excluded oversized SA1s because I want to see the population alignment of growth areas overall. Canberra excluded due to lack of separated rapid transit.

What sticks out clearly is that just over half the of the population in Perth’s outer growth areas was more than 5 km from a station in 2016. That is to say Perth has had the least alignment of outer urban growth areas and rapid public transport networks of all five cities. I’m not sure many urban planners would recommend such a strategy.

However, Perth’s MetroNet program appears to be trying to rectify this with new lines and stations proposed near urban growth areas such as Yanchep, Canning Vale East, Ellenbrook, Byford, and Karnup (Golden Bay). It will however take some time to get to them all built and open.

South East Queensland was second to Perth in terms of urban growth remote from stations, with a lot of the growth scattered rather than concentrated around rail corridors. I haven’t included the Gold Coast light rail in my proximity calculation – it runs at an average speed of 27 km/h (which is slower than most train networks) and doesn’t serve outer urban growth areas.

Sydney and Adelaide had the highest proximity of growth areas to stations.

Around half of Melbourne’s growth SA1s that were more than 5km from a train station were in Mernda and Doreen, a corridor in which a rail extension opened in 2018. Many of the rest are not in the current designated growth corridors, or are where future train stations are planned. Melbourne’s current designated urban growth corridors are fairly well aligned to its train network. From a transport perspective this is arguably a better kind of sprawl than what Perth has been experiencing.

Adelaide’s outer growth areas more than 5 km from a station were in Mount Barker (satellite town to the east) and Aldinga (on the far south coast of Adelaide).

Are the outer urban growth areas better aligned to rapid public transport stations than non-growth areas at the same distance from city centres? Here’s the chart as above but with an extra column for non-growth areas within the same distance intervals from the CBD (as before).

The populations of urban growth areas are less likely to be within a couple of kilometres of a station (most of that land probably has long-established urban development), but curiously in Adelaide and South East Queensland the urban growth areas are more likely to be within 5 kilometres of a station than the non-growth areas, suggesting better rapid public transport alignment than older urban growth areas. Older urban areas in other cities are more closely aligned to stations, particularly in Perth.

As an interesting aside, here’s a breakdown over the last three censuses of population by distance from train/busway stations (operational in 2016 – so it overstates 2006 and 2011 slightly):

You can really see how Perth has had much population growth remote from its rapid public transport network, which probably goes some way to explaining the overall 1.2% journey to work mode shift towards private transport between 2011 and 2016.

So how did people in these outer growth areas get to work?

Technical note: The figures here for “private transport” are for journeys involving only private transport modes – i.e. they exclude journeys involving both private and public transport (eg car+train).

While private transport (mostly car driver only journeys) dominated journeys to work from almost all growth areas, Melbourne and Sydney were the only cities to see significant numbers of residents in outer growth areas with private transport mode shares below 80%.

South East Queensland’s outer urban growth areas were the most reliant on private transport to get to work, with an overall private transport mode share of 93%, followed by Adelaide on 92%, Canberra on 91%, Perth on 90%, Melbourne on 86%, and Sydney on 81%.

Here’s how the growth area mode shares compare to other areas a similar distance from city centres (note: the Y-axis is not zero-based):

Significantly, the growth areas of Sydney and Melbourne had lower private transport mode shares of journeys to work than other parts of the city a similar distance out – even though they are generally further away from train or busway stations (as we saw above)! That’s not to say they didn’t drive themselves to a train station to get to work.

Similar to population density, here is a summary of growth areas compared to other areas in the same distance interval from the CBD:

There’s really not a huge amount of difference within cities. Sydney’s growth areas had a mode share 1.5% lower than non-growth areas, while Canberra’s growth areas had a mode share 2.5% higher.

What are motor vehicle ownership rates like in the outer growth areas?

My preferred measure is household motor vehicles per persons aged 18-84 (roughly people of driving age).

Motor vehicle ownership rates are generally very high across the growth areas – with the notable exceptions of Melbourne and Canberra where around a quarter of the growth area population had a motor vehicle ownership rate of less than 80 (although that is still pretty high!). (I explored this in more detail in an earlier post on Melbourne)

South East Queensland, Perth, and Adelaide outer urban growth areas had the highest motor vehicle ownership rates. Perth’s urban growth areas overall averaged 96.7 motor vehicles per persons aged 18-84 – pretty close to saturation.

How does motor vehicle ownership compare to established areas a similar distance from the city centre? The following chart compares motor vehicle ownership between urban growth and other areas at the same distance from the CBD (note: the Y-axis is not zero-based):

Motor vehicle ownership tends to increase with distance from the CBD, and in Sydney and South East Queensland the growth areas have higher ownership compared to non-growth areas. But the opposite is true in Melbourne, Perth and Canberra.

The population at each distance interval varies considerably, so here is a summary of the data across all distance intervals that have growth SA1s for each city:

The growth areas of Melbourne, Perth and Canberra had slightly lower motor vehicle ownership than other areas a similar distance from the city, while the opposite was true in other cities. That said, motor vehicle ownership rates are very high across all cities.

 

Finally, I’ll look at the relationships between these measures for growth areas (see another post for analysis for whole cities).

How does motor vehicle ownership relate to distance from stations?

Technical note: for scatter plots I’ve filtered out SA1s with less than 50 population as they are more likely to have outlier results (one person can change a measure by 2% or more).

Lower rates of motor vehicle ownership are generally only found close to train/busway stations (and are dominated by Melbourne examples), but close proximity to a station does not guarantee lower rates of motor vehicle ownership. Quite a few Adelaide SA1s are found the top middle part of the chart – these are all in Mount Barker which has frequent peak period express buses to the Adelaide CBD operating along the South East Freeway – which is similar to rapid transit although without a dedicated right of way.

How do journey to work mode shares relate to distance from stations?

Here’s a scatter plot of private transport mode shares of journeys to work and distance from train/busway station:

This shows that lower private transport mode shares are only generally seen within proximity of train or busway stations, and areas remote from stations are very likely to have high private transport mode shares. But also that proximity to a station does not guarantee lower private transport mode shares of journeys to work (particularly in SEQ).

Technical aside: You might have noticed that almost no SA1s report 99% private mode share. How can that be? The ABS make random adjustments to small figures to avoid identification of individuals which means you never see counts of 1 and 2 in their data. To get a mode share of 99% you’d need at least 300 journeys to work with “3” being non-private (or a similar but larger ratio). Very few SA1s have 300+ journeys to work, and even for over-sized SA1s, they are very unlikely to have only 3 or 4 non-private journeys to work. A mode share of 100% is much easier because you can get that no matter the total number of journeys.

How does population density relate to distances from train/busway stations?

Densities above 45 persons/ha were mostly only found within 5 km of stations, and almost entirely in Sydney and Melbourne. The highest densities were very close to train stations in Sydney. In the middle area of the chart you can see quite a few Perth SA1s that are around 30-40 persons/ha but remote from stations. These are all in the Ellenbrook area of Perth’s north-east, generating a lot of car traffic.

How does motor vehicle ownership relate to private transport mode shares of journeys work to work?

For interest, here is the relationship as a scatter plot:

There is certainly a relationship, but it’s not strong (r-squared = 0.22). Other factors are at play.

Conclusions

  • Perth and Canberra are seeing most of their population growth on the fringe, with Sydney, Adelaide, Melbourne, and South East Queensland seeing most of their population growth in established areas.
  • Growth areas in Sydney, Melbourne, and Canberra have higher than traditional urban densities, indeed Sydney and Canberra have a few very high density greenfield developments. Perth, Adelaide, and particularly South East Queensland have urban growth at relatively low densities. In fact, SEQ is the only major urban centre where growth areas are measured as less dense than non-growth areas at similar distances from the CBD.
  • Perth’s urban growth areas are largely remote from rapid transit stations, and this is likely contributing directly to very high and increasing rates of motor vehicle ownership and private transport mode shares. Melbourne’s current urban growth corridors are closely aligned to train stations (thanks to the opening of the Mernda line), and this is also largely true of Sydney and Adelaide.
  • Almost all outer urban growth areas had high rates of motor vehicle ownership. Overall, Melbourne, Perth, and Canberra’s outer urban growth areas had slightly lower rates of motor vehicle ownership compared to other areas at the same distance from the CBD. Only Sydney, Melbourne and Canberra have some growth areas with lower motor vehicle ownership and/or lower private transport mode shares of journeys to work – and these were all close to train or busway stations.

I hope you’ve found this at least half as interesting as I have.

For a similar and more detailed analysis around these topics, see this excellent 2013 BITRE research report on changes between 2001 and 2006.