How did the journey to work change in Brisbane between 2011 and 2016?

Wed 25 April, 2018

Between 2011 and 2016, Greater Brisbane saw a 2% mode shift towards private motorised transport for journeys to work, the largest such shift of all large Australian cities. Was it to do with where jobs growth happened, or because public transport became less attractive over that time?

This post takes a more detailed look at the spatial changes in private transport mode shares, and then examines the relative impact on spatial variations in jobs growth compared to other factors.

Greater Brisbane main mode shares

Firstly for reference, here are the Brisbane Greater Capital City Statistical Area main mode shares and shifts for 2011 and 2016, measured by place of enumeration and place of work:

2011 2016 Change
Private Place of enumeration 80.0% 81.9% +1.9%
Place of work 79.1% 81.1% +2.0%
Public Place of enumeration 15.1% 13.5% -1.6%
Place of work 15.9% 14.2% -1.7%
Active Place of enumeration 4.9% 4.6% -0.3%
Place of work 5.0% 4.7% -0.3%

More information about main mode definitions and data in general is available at the appendix at the end of this post.

Mode shares and shifts by home location

Here are private transport mode shares by home location for 2006, 2011, and 2016:

(you might need to click on these charts to see them larger and more clearly)

You can see lower private mode shares around the central city and to some extent along the rail lines. In case you are wondering, the Redcliffe Peninsula railway opened in October 2016 – after the August 2016 census.

The changes between years are a little difficult to make out on the map above, so here are the mode shifts to private transport by home location at SA2 level:

Mode shifts to private transport can be seen over most parts of Brisbane, with the biggest being Auchenflower (+6%), Lawnton (+6%), Toowong (+5%), Norman Park (+5%), Strathpine – Brendale (+5%), Keperra (+5%), and Sandgate – Shorncliffe (+5%). Many of the large mode shifts to private transport were actually seen around the train network.

The Redland Islands area had a larger shift to public transport – but keep in mind this will include use of car ferries.

Here’s a map showing the mode split of net new trips by home SA2:

There were a lot of new trips from outer growth areas in the north, west and south, and the vast majority of these trips were by private transport (although the southern growth area of Springfield Lakes, where a rail line opened in 2010, had a relatively high 15% of new trips by public transport). Private transport mode shares of new new trips were also high in middle and most inner suburbs (unlike inner Melbourne).

To sum all that up, here are the changes in trip volumes by main mode and home distance from the CBD:

Private transport dominated most new trips, and there were net declines in public transport trips beyond 2 km from the CBD.

Here’s a look at the main mode split over time, by distance from the CBD:

Brisbane achieved significant mode shift away from private transport between 2006 and 2011, but that was pretty much reversed between 2011 and 2016.

Private transport mode shares dropped in 2011 but pretty much returned to 2006 values in 2016. On average, only the city centre saw a mode shift away from private transport between 2011 and 2016, and that’s only a tiny fraction of the Brisbane’s population.

Mode shares and shifts by work location

Here are workplace private transport mode shares for 2011 and 2016:

(more areas are coloured in 2016 because they reached my minimum density threshold of 4 jobs per hectare at destination zone level for inclusion on the map)

Low private mode share is only really seen around the city centre. Some lower mode share areas further out include St Lucia (UQ campus, 52% in 2016) and Nundah (74%), but most of the suburban jobs are dominated by private transport.

Here are the mode shifts by workplace location:

The biggest mode shifts to private transport were to workplaces in Wooloowin – Lutwyche (+7%), Spring Hill (just north of the CBD, +5%) and Jindalee – Mount Ommaney (+5%). The biggest shifts away from private transport were in Newstead – Bowen Hills (-6%), St Lucia (-4%, which includes the University of Queensland main campus), and West End (-3%).

Notably, the job rich Brisbane CBD had a 2% shift to private transport (with 3,135 more private transport trips in 2016).

Here’s a map of the net new jobs and their main mode splits:

And a zoom in on the inner city to separate the overlapping pie charts:

The SA2 with the biggest jobs growth was “Brisbane City” (covering the CBD) with 4584 new jobs – with 68% of this net increase attributable to private transport. North Lanes – Mango Hill in the northern suburbs was not far behind (4472 new jobs at 96% by private transport), followed by Newstead – Bowen Hills (4266 new jobs at 49% private transport) and Brisbane Airport (4197 new jobs at 95% private transport).

The distribution of jobs growth was not heavily concentrated in central Brisbane – in stark contrast to Melbourne where the central city jobs growth was much more signficant.

Here’s a clearer view of new jobs by workplace distance from the city centre and main mode:

At all distances from the CBD, private transport new trips outnumbered active and public transport new trips (and there was a decline in public transport trips to the very city centre). The vast majority of net new trips were to workplaces more than 4 km from the city centre, and by private transport.

So why was there an overall 2% mode shift to private transport?

The relative lack of jobs growth in the public transport rich city centre is very likely to have contributed to the mode shift to private transport. The vast majority of new jobs were in the suburbs where public transport is significantly less competitive (relative to the CBD).

Others will point to factors that have made public transport less attractive relative to private transport, including problems on the train network, extensive new motorway infrastructure, and public transport fares growing around twice the rate of inflation after 2010.

There was very rapid growth in fares between 2010 and 2015, but then fares were frozen in 2016 and substantially reduced in 2017:

Looking at people working in Greater Brisbane (Greater Capital City Statistical Area), there were 94,055 new private transport commutes, just 246 new public transport commutes, and 2,506 new active transport commutes. So around 97% of net new trips in 2016 were by private transport, much higher than the 2011 baseline private transport mode share of 79% of trips (measured for workplaces in Greater Brisbane), hence the overall 2% mode shift.

Looking at people living in Greater Brisbane, there were 61,557 new private transport commutes, a net reduction of 6,069 public transport commutes, and a net reduction of 54 active transport commutes. Thus every new commute was accounted for by private transport, and further to this there was mode shift away from active and public transport.

So how much of the mode shift can be explained by spatial changes in jobs distribution? If mode shares in each workplace SA2 had not changed between 2011 and 2016 then city level mode shares would be influenced only by spatial variations in jobs growth.

I’ve done the calculations at SA2 geography: if place of work mode shares in Brisbane had not changed between 2011 and 2016 (but volumes had), then the overall private transport mode share would have increased only 1.0% in 2016 (essentially because of higher jobs growth in the suburbs compared to the centre).

Actual private mode share increased by 2.0% (measured by place of work).

So this suggests only half of the mode shift can be explained the spatial variations in jobs growth. The other half will be explained by other factors, particularly changes in the relative attractiveness of modes.

Changes in the relative attractiveness of modes will include public transport service quality, public transport fares, fuel prices, toll prices, and infrastructure provision for private and active transport. Car ownership will undoubtedly be a factor, but I suspect many ownership decisions will be influenced by workplace locations and relative modal attractiveness. Other factors might include changes in real incomes, demographic changes, changes in employment density, and the locations of population growth. I’ll explore the last two in more detail.

What about the relationship between job density and mode share?

You could argue that if general public transport “attractiveness” had not changed, you could still expect a mode shift towards public transport in areas with both high and increasing job density, as car parking might struggle to grow at the same rate as jobs growth (as the land becomes increasingly valuable/scarce). This might particularly be the case in the city centre.

I’ve calculated weighted job density for each SA2 – that is, the average density of destination zones in the SA2, weighted by the number of jobs in each zone (similar to population weighted density, so that large areas within SA2s that house few jobs make little contribution to such scores).

Here’s how weighted job density and workplace private mode share changed in Brisbane for higher density SA2s:

While there is some relationship between job density and private mode share overall, there wasn’t a consistent negative correlation between changes in those values. If there was, you would expect all lines on the chart to be on a similar diagonal orientation (upper left – lower right).

South Brisbane and Upper Mount Gravatt saw increased density but little change in private mode share. Chermside, Auchenflower, and Woolloongabba (which incidentally is at the southern end of the Clem 7 motorway) saw increased job density but also increased private transport mode share (the opposite effect of what you might expect). Spring Hill had only a small drop in job density but a large increase in private mode share.

Newstead – Bowen Hills had the largest shift away from private transport, and also one of the largest increases in job density

You might be wondering how the Brisbane City SA2 (which includes the CBD) can have had an increase in total jobs, but a slight decline in weighted jobs density. It turns out that the 2016 SA2 boundary goes further into the Brisbane River than the 2011 boundary. Here’s a map generated on the ABS website, where blue lines are the 2011 boundaries and red the 2016 boundaries:

If you discounted the increase in area, you might expect a slight increase in job density (about 4% in unweighted average density) to result in a small mode shift away from private transport, quite the opposite of what actually happened. If increasing job density by itself might have pushed a mode shift away from private transport, it appears it was overpowered by factors working in the opposite direction.

The Brisbane City SA2 accounted for 12.5% of Brisbane’s jobs so its mode split impacts more than most on overall city mode shares.

So what might be the stand-alone impact of increased job density in the city centre on private mode share? It’s very hard to quantify. I can certainly look at other city centres, but there will be so many factors at play in those cities that it would be almost impossible to isolate the impact.

But as a rough stab, had Brisbane City SA2’s private mode share increased from 29.0% to 29.5% (instead of 30.6%), and all other things were the same, then the overall Brisbane private mode share would have been 0.14% lower.

While the actual impact is uncertain, it would only increase the influence of the “other factors” that are responsible for at least half of the 2% mode share towards private transport.

And what about the spatial distribution of population growth?

All other things being equal, if population growth had disproportionately occurred in places with high private transport mode share (eg the middle and outer suburbs), you might expect a mode shift to private transport. However I don’t think this was significant in Brisbane as there has also been inner city population growth.

Indeed, if the home-based private transport mode share of each SA2 had not changed between 2011 and 2016 (but population numbers had), then the overall Brisbane private mode share (by place of enumeration) would have increased only 0.1% (rather than 1.9%). So the overall mode shift doesn’t seem to have a lot to do with where population growth happened.

So what are these effects other cities? I’ll cover that in an upcoming post.

Appendix: about the data

Here’s how I have defined “main mode”:

Private (motorised) transport any journey to work involving car, motorcycle, taxi, truck and/or “other”, but not involving any mode of public transport (train, tram, bus, or ferry)
Public transport any journey involving train, bus, tram, or ferry (journeys could also involve private or active transport modes)
Active tranport journeys by walking or cycling only

I have extracted data from the ABS census for 2006, 2011, and 2016 for areas within the 2011 boundary of the Brisbane Significant Urban Area. The detailed maps are at the smallest available geography – Census Collector Districts (CD) for 2006 and Statistical Area Level 1 (SA1) for 2011 and 2016 for home locations, and Destination Zones (DZ) for workplaces in 2011 and 2016 (detailed workplace data is not readily available for 2006 for most cities). I’ve aggregated this data for distance from city centre calculations (filtered by 2011 Significant Urban Area boundaries), which means the small randomisations will have amplified slightly.

In 2011, a significant number of jobs were not assigned to a destination zone:

  • 3.8% of jobs were assigned to an SA2 but not a DZ – I’ve imputed these proportionately to the DZs in their SA2 based on modal volumes reported for each DZ (for want of something better).
  • 18,540 Queensland jobs (0.9%) were only known to be somewhere in Greater Brisbane.
  • 115,011 jobs (5.8%) were only known to be somewhere in Queensland (hopefully mostly outside Greater Brisbane!).

These special purpose codes are not present in the 2016 data – presumably the ABS did a much better job of coding jobs to DZs. It means that the volumes in 2011 may be slightly understated, and so growth between 2011 and 2016 might be slightly overstated.

I’ve also extracted the data at SA2 (Statistical Area Level 2) based on 2016 boundaries for the purposes of calculating mode shifts and changes in trip volumes at SA2 level (to avoid aggregating small random adjustments ABS applies). However this wasn’t possible for jobs where 2011 SA2s were split into smaller SA2s in 2016 – because some 2011 jobs were assigned an SA2 but not a DZ, so we cannot map those to a specific 2016 SA2 (I aggregated imputed DZ numbers to 2016 SA2 boundaries instead).

I also extracted data at the Brisbane Greater Capital City Statistical Area level, as noted (the boundary did not change between 2011 and 2016).

I have not counted jobs that were reported to have no fixed address in my place of work analysis. I’ve also excluded people who worked at home, did not go to work on census day, or did not provide information about their mode(s) of travel. These workers are also excluded from job density calculations.

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Introducing a census journey to work origin-destination explorer, with Melbourne examples

Sun 28 January, 2018

The Australian census provides incredibly rich data about journeys to work, with every journey classified by origin, destination, and mode(s) of transport. So you can ask questions such as “where did workers living in X commute to and how many used public transport?” or “where did workers in Y commute from and what percentage used private transport?”, or “What percentage of people in each home location work in the central city?”.

It’s very possible to answer these questions with census data, but near-impossible to produce an atlas of maps that would answer most questions.

But thanks to new data visualisation platforms, it’s now possible to build interactive tools that allow exploration of the data. I’ve built one in Tableau Public, using both 2011 and 2016 census data for all of Australia at the SA2 geography level (SA2s are roughly suburb sized). This means you can look at each census year, as well and the changes between 2011 and 2016.

I’m going to talk through what I’ve built with plenty of interesting examples from my home city Melbourne.

I hope you find exploring the data as fascinating and useful as I do. I also hope this tool makes it easier to inform transport discussions with evidence.

Also, a warning that this is a longer post, so get comfortable.

About the data (boring but important)

The census asks people which modes they used in the journey to work, and the data is encoded for up to three modes.

I’ve extracted a count of the number of trips between all SA2s within each state, by “main mode” for both 2011 and 2016. I’ve aggregated all responses into one of the following “main mode” categories:

  • Private (motorised) transport only – any journey involving car, truck, motorbike or taxi, but no modes of public transport, or people who only responded with “other”. Around 89% of journeys in this category were simply “car as driver”.
  • Walking/cycling only (or “active transport”) – journeys by walking or cycling only.
  • Public transport – any journey involving any public transport mode (train, tram, bus, and/or ferry). These journeys might also involve private motorised transport and/or cycling.

There are 466,597 rows of data all up – so you will need to be a little patient while Tableau prepares charts for you.

Things to note:

  • I’ve had to extract each state separately to stop the number of possible origin-destination combinations getting too large. This means that interstate journeys to work are not included in the data. I have however combined New South Wales (NSW) and the small Australian Capital Territory (ACT), as many people commute between Queanbeyan (NSW) and Canberra (ACT). Apologies to other areas near state borders!
  • When you ask the ABS for the number of people meeting certain criteria, the answer will never be 1 or 2. The ABS randomly adjust small numbers to protect privacy, and it’s not a good idea to add up lots of small randomly adjusted figures. That’s another reason why I haven’t gone smaller than SA2 geography and why I’ve aggregated mode combinations to just three modal categories. You will still see counts of 3 or 4, which need to be treated with caution.
  • Not all SA2s are the same size in terms of residential population, and particularly in terms of working population. The biggest source of commuters for a work area might simply be an SA2 with a larger total residential population.
  • The ABS change the SA2 boundaries between censuses. With each census some SA2s are split into smaller SA2s, particularly in fast growing areas. If you want to compare 2011 and 2016 figures, it is necessary to aggregate the 2016 data to 2011 boundaries, which the tool does where required. Some visualisation pages will give you the option of aggregating 2016 data to 2011 boundaries to make it easier to compare 2011 and 2016 data.
  • I’ve only counted journeys where the origin, destination and mode are known. Anyone who didn’t go to work on census day, didn’t state their mode(s) of travel, or didn’t state a fixed land-based work location are excluded.
  • Assigning “other” only trips as private transport might not be perfect, as it might include non-motorised modes like skateboards and foot scooters. It will also count air travel, and it’s arguable whether that is private or public transport (it’s certainly not low-carbon transport). However, overall numbers are quite small – 0.81% of all journeys with a stated mode in Australia.

Mode share maps to/from a location

First up, you can produce maps showing the main mode share of commuters from all home SA2 for a particular work SA2, or all workplaces for a particular home SA2.

Here is a map of private transport mode shares for journeys to work from Point Cook North:

Private transport dominates most middle and outer work destinations (even local trips), with many at 100%. Lower shares are evident for central city destinations, although Southbank next to the CBD is relatively high at 65%, and 100% of commuters who travelled to Fishermans Bend did so by private transport.

You can also look at it the other way around. Here’s private transport mode share for commutes to Parkville (just north of the CBD):

There was a low private transport mode share from the city centre and Brunswick to the north, roughly 40-50% mode shares from the south-eastern suburbs (accessible by train), but very high mode shares from the middle and outer suburbs to the north and west (public transport access more difficult). The new Metro Tunnel could make a dent in these mode shares, with a new train station in Parkville.

Here is a map of private transport only mode share for journeys to the “Melbourne” SA2 (which represents the Melbourne CBD):

Private transport (only) mode shares were lower than 30% for most areas, as public and active transport options are generally cheaper and more convenient for travel to the CBD. However you can see corridors with higher private transport mode share, including Kew – Bulleen – Doncaster – Warrandyte, and Keilor East – Keilor – Greenvale (around Melbourne Airport). These corridors are more remote from heavy rail lines. Other patches of higher private mode share include Rowville – Lysterfield, Altona North, and Point Cook East (including Sanctuary Lakes).

A high private transport mode share does not necessary mean a flood of private vehicles are coming from these areas. Kinglake is the rich orange area in the north-east of the above map, and according the 2016 census, 57% of people commuted to the Melbourne CBD by private transport only. Except that 57% is actually just 23 out of just 40 people making that commute – which is pretty small number in whole scheme of things.

Which leads me to…

Journey volume and mode split maps

These maps show the volume (size of pie) and mode split for journeys from/to a selected SA2.

The following map shows the volume and mode split of journeys to the “Melbourne” SA2 in 2016:

As I discussed in a recent post, not many people actually commute from the outer suburbs to the central city. Indeed, only 767 people commuted from Rowville to the Melbourne CBD in 2016, which is less than one train full.

Unfortunately all the pie charts in the inner city tend to overlap, while the pie charts in the outer suburbs are tiny. Here’s a zoomed in map for the inner suburbs with a lot less overlap:

You can see large green wedges in the inner city, where walking or cycling to the CBD is practical. You can also see that almost everywhere the blue wedges (public transport) are much larger than the red (private transport).

What does stand out more in this map is Kew – where 716 people travelled to the Melbourne CBD by private transport (highest of any SA2) – with a relatively high 41% mode share for a location so close to the city, despite it being connected to the CBD by four frequent tram and bus lines. Kew is also a quite wealthy area, so perhaps parking costs do not trouble such commuters (maybe employers are paying?). Other home SA2s with high volumes and relatively high private mode shares are Essendon – Alberfeldie (521 journeys, 28% private mode share), Brighton (493, 33%), Keilor East (419, 41%), Toorak (404, 35%) and Balwyn North (396, 35%). Most of these are wealthy suburbs, with the notable exception of Keilor East, which does not have a nearby train station.

Here is the same for Parkville:

The home areas with significant numbers of Parkville commuters are in the inner northern suburbs, and active and public transport were the dominant mode share for these trips. While 92% of commuters from Burnside Heights to Parkville were by private transport, there were only 35 such trips. The overall private transport mode share for Parkville as a destination was 50%.

Here is the same type of map for Fishermans Bend (Port Melbourne Industrial), which is just south-west of the CBD:

Private transport dominates mode share, and you can see a slight bias towards the western suburbs. Which means a lot of cars driving over the Westgate Bridge.

Around 30,000 people travelled to work in Clayton in Melbourne’s south-east. Here’s a map showing the origins of those commutes:

Almost half of the workers who both live and work in Clayton walked or cycled (only) to work, of which I suspect many work at Monash University. The public transport mode shares are higher towards the north-west, particularly around the Dandenong train line that connects to Clayton. Very few people put themselves through the pain of commuting from Melbourne’s western and northern suburbs to Clayton.

Over 60,000 people commuted to Dandenong in 2016, which includes the large Dandenong South industrial area. Here are the volumes and mode splits for where they came from:

You can see a significant proportion of the workforce lived to the south-east, and much less to the north and west. You can also see private transport dominates travel from all directions (despite there being two train lines through the Dandenong activity centre, and a north-south SmartBus route through the industrial area).

Here‘s a look at people who commuted to work at Melbourne Airport:

You can see that airport workers predominantly came from the nearby suburbs, and the vast majority commuted by private transport. The most common home locations of airport workers include Sunbury South (543), Gladstone Park – Westmeadows (411), and Greenvale – Bulla (351 – note Greenvale has a much higher population than Bulla).

The largest public transport volume actually came from the CBD (48 out of 67 commuters, which is a 72% mode share), probably using staff discount tickets on SkyBus. The biggest trip growth 2011 to 2016 was from Craigieburn – Mickelham: 367 more trips of which 355 were by private transport only.

The data can also be filtered to only show a particular main mode. For example, here is a map of the origins for private transport trips to the Melbourne CBD (ie who drives to work in the CBD):

Which can also be shown as a sorted bar chart:

The most common sources of private transport trips to the CBD were generally very wealthy suburbs, where many people are probably untroubled by the cost of car parking (they can easily afford it, or someone else is paying). However bear in mind that not all SA2s have the same population so larger SA2s will be higher on the list (all other things being equal).

This data can also be viewed the other way around. Here are the volumes and mode splits of journeys from Point Cook South in 2016. The Melbourne CBD was the biggest destination (994 journeys) with 69% public transport mode share followed by Docklands (342 journeys) with 64% public transport mode share.

Here is yet another way to look at this data, which is particularly relevant for the central city…

Percentage of commuters who travel to selected workplace SA2s

Here is a map showing the proportion of commuters in each home SA2 who work in the Melbourne, Southbank or Docklands SA2s (the tool allows selection of up to three workplace SA2s):

There are some interesting patterns in this map. Generally the percentage of people commuting to central Melbourne declined with distance from the CBD. There are however some outlier SA2s that had relatively high percentages of people travelling to central Melbourne, despite being some distance from the city centre.

In fact, here is a chart showing distance from the CBD, and the percentage of commuters travelling to the central city:

Tableau has labelled some of the points, but not all (interact with the data in Tableau to explore more). The outliers above the curve are generally west or north of the city, with Point Cook South being the most significant outlier. Further from the city, the commuter towns of Macedon, Riddells Creek and Gisborne have unusually high percentage of commuters travelling to the central city for that distance from the city (made possible by upgraded V/Line train services).  Many of the outliers below the curve are less wealthy areas, where people were less likely to work in the central city.

The previous map showed the proportion of all commuters that went to the central city. The tool can also filter that by mode. Here’s a map showing the percentage of public transport commuters who had a destination of Melbourne, Docklands or Southbank:

Typically around two-thirds of public transport journeys to work from most parts of Greater Melbourne are to Melbourne, Docklands, or Southbank SA2s. The lowest percentages were in the local jobs rich SA2s of Clayton (49%) and Dandenong (40%).

Adding Carlton and East Melbourne to the above three central city SA2s roughly takes the proportion up to around 70%. That’s a lot of public transport commutes to other destinations, but still a vast majority are focussed on the central city.

We can also look at this data from the origin end…

Where do people from a particular area commute to?

As an example, here is a map showing the percentage of commuters from Point Cook – South (a new and relatively wealthy area in Melbourne’s south-west) who worked in each work SA2 (destinations with less than 20 workers excluded):

You can see that 20% worked in the Melbourne CBD, followed by 7% in Docklands, and 6% in each of Point Cook North and Point Cook South (local). The largest nearby employment area is the industrial areas of Laverton, but this industrial area only attracted 4% of commuters from Point Cook South.

Here is a map for “Rowville – Central” SA2:

You can see that journeys to work are very scattered, with only 6% travelling to the Melbourne CBD.

(these maps can also be filtered by mode)

Another way to look at that data is a…

List of top commuter destinations

Here’s a chart showing the top work destinations from Rowville – Central in 2016, split by mode (this is a screenshot so the scroll bar doesn’t work):

You can see local trips are most numerous, and are dominated by private transport (although there were 48 active transport local trips). Dandenong was the second most common destination, with 97% private transport mode share, followed by Melbourne CBD with 40% private transport mode share (137 private transport journeys). The only other destination with high public transport mode share was Docklands at 59% (48 private transport journeys).

Changes between 2011 and 2016

We’ve so far looked at volumes and mode shares, but of course we can also look at the changes in volumes and mode share between 2011 and 2016.

There were around 15,000 more commutes to Dandenong in 2016 compared to 2011. Here are the changes in volumes by main mode for home SA2s with the largest total number of journeys:

You can see almost all of the new journeys to work were by private transport, no doubt putting a lot of pressure on the road network. A lot of the growth was from the suburbs to the east and south-east, none of which had a direct public transport connection to the Dandenong South industrial area at the time of the 2016 census. That’s now changed, with new bus route 890 linking the Cranbourne train line at Lynbrook with the Dandenong South industrial area (it operates every 40 minutes).

Note: a row with no figure or bar drawn (quite common in the Active only column) means that there were no such trips in either 2011 and/or 2016. Unfortunately the tool doesn’t show the change in volume in such circumstances (I’ll try to fix this in the future).

Contrast this with Parkville:

Brunswick is Parkville’s biggest source of workers, and there were many more such workers coming in by public and active transport, and a decline in workers who commuted by private transport. However there was an increase in private transport from places further out like Coburg and Pascoe Vale.

Of course you can do this the other way around too. Here‘s the new trips from Tarneit, a major growth area in Melbourne’s south-west where a train station opened in 2015:

Access to the Melbourne CBD by public transport improved significantly with the new train station, and 527 more people did that trip in 2016 compared to 2011. But the number of people who drove declined by only 35. The train line didn’t reduce the number of people driving out of Tarneit in total, but there probably would have been a lot more had it not opened. In the case of the Melbourne CBD, there were simply a lot more CBD workers living in Tarneit in 2016 (some CBD workers may have moved to Tarneit, and people otherwise in Tarneit were probably more likely to choose the CBD for work).

Here is a map of private transport mode shifts for journeys to the Melbourne CBD (were blue is mode shift to private transport and orange is mode shift away from private transport):

The biggest shifts away from private transport include Narre Warren North (-19%, but small volumes), Tarneit (-17%, with a train station opening in 2015), Wyndham Vale (-15%, also new train station), Don Vale – Park Orchards (-15%, with buses being primary mode for access to the CBD), Melton (-13%), and then -12% in Point Cook (new train station and bus upgrades in 2013), West Footscray – Tottenham, Sunbury (rail electrification 2012), South Morang (new train station), and Warrandyte – Wonga Park (SmartBus to city).

The biggest mode shifts to private transport were in low volume areas, including Monbulk – Silvan (+14%, which is an extra 5 trips), Keilor (+8%, 8 extra trips), Tullamarine (+8%, 16 extra trips), Lysterfield (+7%, 4 extra trips), Panton Hill – St Andrews (+7%, 4 extra trips) and more surprisingly Coburg North (+6%, up from 47 to 97 trips).

Again, you can see the problem with mode share and mode shift figures is that the volumes may be inconsequential. The map doesn’t show regions with less than 30 travellers, or less than 4 travellers by the selected mode. There was an overwhelming mode shift away from private transport for travel to the Melbourne CBD.

Here’s another view of the data: the change in the number of private transport trips to the Melbourne CBD, mapped:

That’s a peculiar mix of increases in decreases, but most of the volume changes are relatively small (note the scale).

The biggest increase was +142 trips from Truganina, a growth area with two nearby train stations built between 2011 and 2016. If that sounds alarming, it should be compared with an increase of 555 public transport trips from Truganina to the Melbourne CBD.

The larger declines were from suburbs like:

  • -85 from Doncaster East (bus upgrades),
  • -67 from Donvale – Park Orchards (bus upgrades),
  • -66 from Templestowe (also bus upgrades), and
  • -61 from Deer Park – Derrimut (also bus and train service upgrades).

Curiously, there was an increase of 71 private transport journeys to work entirely within the Melbourne CBD (to a new total of 236). Why anyone living and working in the CBD would go by private transport is almost beyond me – it’s very walkable and the trams are now free. Digging deeper…in 2016: 137 drove a car, 20 were a car passenger, 17 used motorbike/scooter, 13 a taxi, and 31 were “other” (okay, some of those 31 might have been skateboards or kick scooters, but we don’t know).

We can do the same by home location. Here are the net new trip destinations from Wyndham Vale in Melbourne’s outer south-west:

Wyndham Vale added more trips to the Melbourne CBD than trips to local workplaces.

Find your own stories

As mentioned, I’ve built interactive visualisations for all of this data, in Tableau Public, which you can use for free.

If you have a reasonably large screen, you might want to start with one of these four “dashboards” that show you volumes and mode shares, or volume changes and mode shifts. Choose a state, then an SA2, then you might need to zoom/pan the maps to show the areas of interest (unfortunately I can’t find a way to change the map zoom to be relevant to your selected SA2). The good thing about these dashboards is that you see mode shares and volumes on the same page.

Play around with the various filtering options to get different views of the data, including an option to turn on/off labels (which can overlap a lot when you zoom out), and change the colour scheme for mode share maps.

If you want more detail and/or have a smaller screen, then you might want to use one of the following links to a single map/chart:

Journey volumes by mode on a map to selected work location from selected home location
on a bar chart to selected work location from selected home location
Mode share on a map to selected work location from selected home location
on a bar chart to selected work location from selected home location
Percent of journeys on a map to selected work location(s) from selected home location
on a box chart to selected work location from selected home location
Journey volume change 2011 to 2016 on a map to selected work location from selected home location
on a bar chart to selected work location from selected home location
Mode shift
2011 to 2016
on a map to selected work location from selected home location
on a bar chart to selected work location from selected home location

Once you have the tool open in Tableau Public you can switch between the dashboards and worksheets with the tabs at the top (note: it will reset if you don’t use it for a while). You can mouse over the data to see more details (I’ve tried to list relevant data for each area), and often your filtering selections will apply to related tabs.

Finally remember to be careful in your analysis:

  • A large mode share or mode shift might not be for a significant volume.
  • A large change in volume might not be a significant mode shift.

Have fun!

[This post and the Tableau tool were updated 3 February 2018 with better label positions on maps. For larger SA2s, label positions better reflect the centre of residential or working population, as appropriate to the type of map. The Tableau tool should also be faster to load]


Where are the unoccupied dwellings in Australian cities?

Sat 4 November, 2017

Over one million private dwellings in Australia were unoccupied on census night in 2016 – 11.2% of all private dwellings – up from 10.2% in 2011.

This raises many questions. Where are these unoccupied dwellings and where are they now more prevalent? What type of dwellings are more likely to be unoccupied? How long have these dwellings been unoccupied? Do we know why these dwellings are unoccupied?

This post will focus on dwelling occupancy by geography, dwelling types and trends over time. In a future post I hope look into those last two questions in more detail.

I’ve prepared data for sixteen Australian cities, with various maps in Tableau (you will need to zoom and pan to your city of interest).

Why am I blogging about dwelling occupancy on a transport blog? Well partly because I’m interested in urban issues, but also because land use is very relevant to transport. If dwelling occupancy rates in the inner and middle suburbs were higher, there would be more people living closer to jobs and activities who might be less reliant on private motorised transport for their daily travel.

If you’d like to read more around the associated policy issues, Professor Hal Pawson from UNSW has a good piece in The Conversation highlighting the increasing number of empty properties and spare bedrooms, and advocates  replacing stamp duty with a broad-based land tax to improve housing mobility. Also read Eryk Bagshaw in the Fairfax press, Jonathan Jackson in Finfeed, and a piece in Business Insider where the Commonwealth Bank state that 17% of recently built dwellings are left unoccupied (not sure how that was calculated).

What are the dwelling occupancy rates in Australian cities?

Here’s a chart showing private dwelling occupancy rates for sixteen Australian cities (using 2011 Significant Urban Area boundaries) from the last three censuses:

Note the y-axis only runs from 84% to 94%, so the changes are not massive. However a small change in dwelling occupancy can still have a large impact on housing prices (rental and sales).

The Sunshine and Central Coasts have the lowest occupancy, almost certainly explained by many holiday homes in those regions, although all three have been trending upwards. Curiously, the Gold Coast – Tweed Heads had a significant increase in occupancy between 2011 and 2016 to take it above Perth, Townsville, and Darwin.

Hobart and Cairns also had increased occupancy between 2011 and 2016, but all large cities declined between 2011 and 2016. Perth, Darwin and Townsville had big slides – quite possibly related to the downturn in the mining industry and slowing population growth (all three have seen slowing population growth in recent years after a boom period). Then again, if there are more fly-in-fly-out workers in a city you might expect dwelling occupancy on census night to go down as a portion of them will be away for work on census night.

How does dwelling occupancy in capital cities compare to the rest of the country?

Private dwelling occupancy is significantly lower outside the capital city areas. While the capital city areas contain 63% of all private dwellings, they only contain 51% of unoccupied private dwellings.

How does dwelling occupancy vary by dwelling type?

Here’s a chart of 2016 dwelling occupancy by Greater Capital City Statistical Areas and the most common dwelling types:

In many cities there is a strong correlation between housing type and occupancy, with separate houses having the highest occupancy rates, and multi-storey flats/apartments having the lowest. The pattern is strongest in Perth – perhaps reflecting reduced demand for apartment living following the end of the mining boom(?).

The data suggests higher density apartments are more likely to not be occupied on census night, but it doesn’t tell us why. Of course different dwelling types have different spatial distributions, so is it the dwelling type that drives the occupancy rates? I’ll come back to that shortly.

Where are the unoccupied dwellings?

Quite simply, here is a map showing the density (at SA2 geography) of unoccupied dwellings in Melbourne over time (you might need to click to enlarge to read more clearly):

(I’ve not shaded SA2s with less than 1 unoccupied dwelling per hectare. You can look at other cities in Tableau by zooming out and then in on another city).

You can see a fairly significant increase in the number of unoccupied dwellings in the inner and middle suburbs (at least at densities above 1 per hectare).

From a transport perspective – this isn’t great. If people lived in those dwellings rather than dwellings on the fringe of Melbourne, the transport task would be easier as there would be many more people living closer to jobs and other destinations with non-car modes being more competitive.

But these areas with a relatively high density of unoccupied dwellings are also areas with a high density of dwellings in general. The density of unoccupied dwellings has risen in the same places where total dwelling density has risen:

(see in Tableau – you may need to change the geography type)

Given you would expect a small percentage of dwellings to be unoccupied for good reasons (eg resident temporarily absent, or property on the market), it makes sense that the density of unoccupied dwellings has gone up with total dwelling density.

But a decrease in the dwelling occupancy rate requires the number of unoccupied dwellings to be growing at a faster rate than the total number of dwellings. We already know that is happening at the city level through declining occupancy rates, so how does that look inside cities?

How does dwelling occupancy vary across Melbourne?

Here’s a map of dwelling occupancy in Melbourne and Geelong at CD/SA1 level geography:

(see also in Tableau)

You can see very clearly that occupancy is lowest on Mornington Peninsula beaches to the south – which almost certainly reflects empty holiday homes on census night (a Tuesday night in winter).

In fact, I’ve created a map of dwelling occupancy at SA2 level for all of Australia, and you can see many coastal holiday areas around Melbourne (and other cities) with low occupancy (with Lorne – Anglesea at 32% and Phillip Island at 40%):

The previous Melbourne map at CD/SA1 level is very detailed and so it’s not easy to see the overall trends. Also, apart from the Mornington Peninsula, occupancy rates are almost all above 80%.

So here is a zoomed-in map with a different (narrower) colour scale, with data aggregated at SA2 level (also in Tableau):

Things become much clearer.

The highest dwelling occupancy is generally on the fringe of Melbourne.

Apart from holiday home areas, the lowest occupancy in 2016 was concentrated in wealthier inner suburbs, including Toorak at 83% and South Yarra west at 84%. This was closely followed by the CBD, Docklands, East Melbourne, Southbank, and Albert Park between 84% and 86%. These areas have all had declining occupancy since 2011.

It can be a little difficult to see the changes in occupancy rates, so here is a non-animated map the change in dwelling occupancy rates between 2006 and 2016 (also in Tableau):

There are at least small declines in most parts of Melbourne. The biggest decline was 7% in Bundoora North (with lowest 2016 occupancy of 79% in these new units in University Hill ), followed by 5% in Doncaster (lowest around Doncaster Hill where there are new apartments, perhaps too new to be occupied on census night?), 4% in South Yarra East (lowest in the new apartments around South Yarra Station, again possibly because some are very new) and Prahran – Windsor.

Curiously, Docklands dwelling occupancy increased by 9% from 75% to 85% (rounding means that those numbers don’t perfectly add). Perhaps there were many new yet-to-be-occupied dwellings in 2006? For reference, Dockland’s 2011 occupancy was 84%, only slightly below the 2016 level.

The outer growth areas are a mixed bag of increases and decreases. This possibly depends again on how many brand new but not yet occupied dwellings there were in 2006 and 2016.

What are the dwelling occupancy patterns in other cities?

Sydney

You can see lower occupancy around the CBD, North Sydney, Manly, and the northern beaches, and higher occupancy in the western suburbs.

The largest declines are evident in the city centre and North Ryde – East Ryde:

Brisbane

Brisbane has some big declines to the north-east of the city centre, Rochedale – Burbank, Woodridge, Logan, and Leichhardt – One Mile. The Redland Islands in the east are presumably a popular place for holiday homes.

Perth

Low occupancy is evident around Mandurah in the south (a popular holiday home area). Lower occupancy has spread around the inner city, and beach-side suburbs of Scarborough, Cottesloe, Fremantle, and Rockingham (many of which are areas with higher concentrations of Airbnb properties).

The biggest declines were in Maylands, Victoria Park – Lathlain – Burswood, and South Lake – Cockburn Central. For the first two of these areas the decline was mostly in flats/units/apartments.

Adelaide

The lowest occupancy is on the south coast and in Glenelg. The biggest decline was in Fulham (-5%), followed by Payneham – Felixstowe (-4%):

[Canberra, Hobart and Darwin added 6 November 2017]

Canberra

Dwelling occupancy was lowest around Parliament House (the census was not during a sitting week in 2016), and highest in the outer northern and southern suburbs. The 2006 census was during a sitting week, so it’s little surprise that big dwelling occupancy reductions were seen around Capital Hill between 2006 and 2016.  There was also a 5% decline in Farrer and a 6% growth in Gungahlin between 2006 and 2016 (Gungahlin’s dwellings almost doubled between 2006 and 2011, so the 2006 result might reflect brand new dwellings awaiting occupants).

Hobart

Dwelling occupancy was lowest in central Hobart, with the biggest decline of 4% in Old Beach – Otago, but overall there was little change between 2006 and 2016 (average occupancy did drop slightly in 2011 though).

Darwin

Darwin dwelling occupancy was lowest in the city centre at 82% in 2016, while Howard Springs had 100% occupancy (in 2016). Declines are evident between 2006 and 2016 across most parts of Darwin.

Gold Coast

Here’s a map of 2016 occupancy at SA1 level, with the original broader colour scale:

You can see quite clearly that the beach-side areas have low occupancy, while the inland areas have much higher occupancy (some at 100%). Presumably many permanent residents cannot or choose not to compete with tourism for beach-side living.

Sunshine Coast

Similar patterns are evident on the Sunshine Coast, particularly around Noosa and Sunshine Beach in the north:

If you want to see other cities, move around Australia in Tableau for occupancy maps at CD/SA1 and SA2 geography (choose you year of interest), and occupancy change maps (at SA2 geography).

So are there lots of unoccupied inner city apartments in Melbourne?

Some commentators have spoken about many inner city apartments being unoccupied – perhaps through a glut or investors chasing capital gains and not interested rental incomes.

Here is dwelling occupancy in central Melbourne at SA1 geography for 2016, using the broader colour scale (also in Tableau):

There are quite a few pockets of very low occupancy, particularly areas shaded in yellows and greens. The average private dwelling occupancy for the City of Melbourne local government area was 87%, lower than the Greater Melbourne average of 91%.

The lowest occupancy is a block between Adderley, Spencer and Dudley Street in North Melbourne at 56%, which is probably related to the recent completion of an apartment tower not long before the census (from Google Street view we know it was under construction in April 2015 and completed by October 2016).

There are several patches of yellow  (65-70% occupancy) in the CBD, Docklands and Southbank.

But what about apartment towers? For that we need to drill down to mesh blocks – and thankfully 2016 census data is actually provided at this level.

Here’s a map showing dwelling occupancy of mesh blocks in the City of Melbourne (local government area) with at least 100 dwellings per hectare (as an arbitrary threshold for large apartment building – see the appendix for an example of this density):

(explore in Tableau)

Some notable low occupancy apartment towers include:

  • 48% for an apartment tower at 555 Flinders Street (Northbank Place Central Tower) between Spencer and King Street and the railway viaduct. It wasn’t brand new in 2016.
  • 47% in a block that includes the Melbourne ONE apartment tower, possibly because it was only just opened (as I write there are still apartments for sale)
  • 65% for one of the towers at New Quay, Docklands (which seems to include serviced apartments)
  • 66% for a tower at 28 Southgate Ave (corner City Road), and 67% for the Quay West tower next door (almost certainly popular places for Airbnb / serviced apartments).

Several of these towers include advertised serviced apartments, and I expect the towers would contain a mix of serviced apartments, owner-occupied apartments and rentals (regular and Airbnb). However ABS advises me that field officers do speak to building managers, and are therefore likely to not code serviced apartments as private dwellings.

That said, according to the 2016 census data there were only 11 non-private dwellings in Docklands that were classified as “Hotel, motel, bed and breakfast”, and zero non-private dwellings in the New Quay apartment towers.

I snapped this picture at 9pm on a Sunday in September 2017 of the apartments at New Quay (Docklands) that at the 2016 census had 65-70% occupancy:

Of course you wouldn’t expect lights to be on in all rooms in all occupied dwellings at 9pm on a particular Sunday, but I dare say it’s probably a time when fewer people would be out. It looks like a lot less than a quarter of rooms are lit. I know very few of these are on Airbnb (more on that in a future post!), but I don’t know how many are actually serviced apartments.

There’s huge variation in dwelling occupancy across these mesh blocks. So is the lower occupancy more concentrated in higher density areas? Here’s a scatter plot of all mesh blocks in the City of Melbourne by dwelling density and occupancy:

There’s not a strong relationship between density and occupancy. The variation in dwelling occupancy between mesh blocks will probably depend on a lot of local factors.

What about occupancy by dwelling type for the inner city?

(data points removed where dwelling counts were small, the isolated blue dot at the bottom is for Southbank).

There’s no evidence that flats / apartments have lower occupancy than other housing types in the central city. However there is evidence that inner city areas have relatively lower occupancy.

So how does the occupancy of apartment blocks of 4+ storeys vary across Melbourne?

Box Hill had the lowest apartment occupancy of 50% (perhaps some were brand new?), followed by Ringwood, Glen Waverley, and Brighton in the 70-75% range.  Croydon East, Templestowe , Seddon – Kingsville, Clayton, Carnegie, West Footscray, Braybrook and Frankston reported occupancy above 95%. The inner city areas were around 84-85% occupied, and these would make up the majority of such dwellings in Melbourne.

Apartments in blocks of 4+ storeys seem to have lower occupancy on average because most of them are located in the central city, which generally has lower dwelling occupancy.

Here’s a similar map (with a different colour scale) for dwelling occupancy of separate houses across the Melbourne region:

The lowest rates in metropolitan Melbourne are 82-83% in some inner city areas, while the urban growth shows up in pink and purple, mostly 94-96%.

Explore the 2016 occupancy rates at SA2 geography for different dwelling types for any part of Australia in Tableau. You can also view changes in occupancy rates since 2006 for separate houses, flats/units/apartments, and semi-detached/townhouses.

Why are there lower dwelling occupancy rates in the central city?

The census doesn’t answer this, and I’m not a housing expert, but I dare say there are plenty of plausible explanations:

  • Many dwellings are rented out on Airbnb (and/or other platforms) – but are not in high demand on a weeknight in mid-winter (more on that in a future post).
  • Many dwellings are serviced apartments that are indistinguishable from regular private dwellings (in buildings with a mixture of dwelling use). ABS say they don’t count these as private dwellings, however they are not showing up as non-private dwellings.
  • Dwellings are more likely to occupied by executives who travel more frequently.
  • Dwellings might be second homes for people living outside the city.
  • Dwellings might be owned by employers for interstate staff visiting Melbourne.
  • Dwellings might be poorly constructed and uninhabitable (eg mould issues).
  • Investors who are not interested in rental income might deliberately leave properties vacant (something that is disputed).

But I’m just speculating.

What about dwelling occupancy in the centre of other cities?

Here’s a map of the Sydney CBD area at SA1 geography:

There are some very low occupancy rates in the north end of the CBD, but very high occupancy rates around Darling Harbour and Pyrmont.

Here’s central Brisbane:

Here are occupancy rates for different dwelling types for selected inner city SA2s in Sydney, Brisbane, Adelaide and Perth:

In all SA2s except Surrey Hills (Sydney) and South Brisbane, flats or apartments in 4+ storey blocks had the lowest dwelling occupancy in 2016. Only in Perth City SA2 (which is quite a bit larger than the CBD) is there a reasonably clear relationship between housing type and occupancy.

Summary of findings

Couldn’t be bothered reading all of the above, or forgot what you learnt? Here’s a summary of findings:

  • Dwelling occupancy, as measured by the census, has declined in most Australian cities between 2006 and 2016 (particularly larger cities).
  • Dwelling occupancy is generally very low in popular holiday home areas, but also relatively low in central city locations.
  • Dwelling occupancy is generally highest in outer suburban areas.
  • Higher density housing types generally have lower occupancy, but that is probably because they are more often found in inner city areas.
  • There are examples of low occupancy apartment towers in Melbourne, but there’s not a clear relationship between dwelling density and dwelling occupancy in central Melbourne.

In a future post I plan to look more at why properties might be unoccupied, and for how long they are unoccupied, drawing on Airbnb and water usage datasets.  I might also look at bedrooms and bedroom occupancy which is a whole other topic.

 

Appendix – About the census dwelling data

I’ve loaded census data about occupied and unoccupied private dwellings data into Tableau for 2006, 2011, and 2016 censuses for sixteen Australian cities at the CD (2006) / SA1 (2011,2016) level, which the smallest geography available for all censuses. I’ve mapped all these CDs and SA1s to boundaries of 2016 SA2s and 2011 Significant Urban Areas (as per my last post). Those mappings are unfortunately not perfect, particularly for 2006 CDs.

The ABS determine a private dwelling to be occupied if they have information to suggest someone was living in that dwelling on census night (eg a form was returned, or there was some evidence of occupation). Under this definition, unoccupied dwellings include those with usual residents temporarily absent, and those with no usual residents (vacant).

For my detailed maps I’ve only included CDs / SA1s with a density of 2 dwellings per hectare or more.

For reference, here is a Melbourne mesh block with 100 dwellings per hectare:

And here is a mesh block with 206 dwellings per hectare (note only a small part of mesh block footprint contains towers):


Trends in journey to work mode shares in Australian cities to 2016 (second edition)

Tue 24 October, 2017

[Updated 1 December 2017 with reissued Place of Work data]

The ABS has now released all census data for the 2016 journey to work. This post takes a city-level view of mode share trends. It has been expanded and updated from a first edition that only looked at place of work data.

My preferred measure of mode share is by place of enumeration – ie how did you travel to work based on where you were on census night (see appendix for discussion on other measures).

I’m using Greater Capital City Statistical Areas (GCCSA) geography for 2011 and 2016 and Statistical Divisions for earlier years. For Perth, Melbourne, Adelaide, Brisbane and Hobart the GCCSAs are larger than the Statistical Divisions used for earlier years, but then those cities have also grown over time. See appendix 1 for more discussion.

Some of my data goes back to 1976 – I’ll show as much history as I have for each mode/modal combination.

Public transport mode share

Sydney continues to have the largest public transport mode share, and the largest shift of the big cities. Melbourne also saw significant positive mode shift, but Perth and particularly Brisbane had mode shift away from public transport.

There’s so much to unpack behind these trends, particularly around the changing distribution of jobs in cities that I’m going to save that lengthy discussion for another blog post.

But what about the…

Massive mode shift to “public transport” in Darwin?!?

[this section updated 26 Oct 2017]

Yes, I have triple-checked I downloaded the right data. “Public transport” mode share increased from 4.3% to 10.9%. The number of people reporting bus-only journeys went from 1648 in 2011 to 5661 in 2016, which is growth of 244%. There has also been a spike in the total number of journeys to work in 2011, 30% higher than in 2011, while population growth was 13%.

Initially I thought this might have been a data error, but I’ve since learnt that there is a large LNG gas project just outside Darwin, and up to 180 privately operated buses are being used to transport up to 4700 workers to the site. This massive commuter task is swamping the usage of public buses.

Here’s the percentage growth in selected journey types between 2011 and 2016:

Bus + car as driver grew from 74 to 866 journeys, which reflects the establishment of park and ride sites around Darwin for the special commuter buses. Bus only journeys increased from 1953 to 5744. So it looks like most workers are getting the bus from home and/or forgot to mention the car part of their journey (in previous censuses I’ve seen many people living kilometres from a train station saying they got to work by train and walking only).

So this new project has swamped organic trends, although it is quite plausible that some people have shifted from cycling/walking to local jobs to using buses to commute to the LNG project (which is outside urban Darwin). When I look at workplaces within the Darwin Significant Urban Area (2011 boundary), public transport mode share is 6.0%, in 2016, still an increase from 4.4% in 2011. More on that in a future post.

Train

Sydney saw the fastest train mode share growth, followed by Melbourne, while Brisbane and Perth went backwards.

Bus

Darwin just overtook Sydney for top spot thanks to the LNG project. Otherwise only Sydney, Canberra and Melbourne saw growth in bus mode share. Melbourne’s figure remains very low, however it is important to keep in mind that trams provide most of the on-street inner suburban radial public transport function in Melbourne.

Train and bus

Sydney comes out on top, with a large increase in 2016 (although much of this is still concentrated around Bondi where there are high bus frequencies and no fare penalties for transfers – more on that in an upcoming post). Melbourne is seeing substantial growth (perhaps due to improvements in modal coordination), while Perth, Adelaide and Brisbane had declines in terms of mode share (Brisbane and Adelaide were also declines on raw counts, not just mode share). I’m sure some people will want to comment about degrees of modal integration in different cities.

Train and bicycle

Some cities are also trying to promote the bicycle and train combination as an efficient way to get around (they are the fastest motorised and (mostly)non-motorised surface modes because they can generally sail past congested traffic). The mode shares are still tiny however:

Sydney and Melbourne are growing but the other cities are in decline in terms of mode share.

As this modal combination is coming off an almost zero base, it’s also probably worth looking at the raw counts:

The downturns in Brisbane and Perth are not huge in raw numbers, and probably reflect the general mode shift away from public transport (which is probably more to do with changing job distributions than bicycle facilities at train stations).

Cycling

I have a longer time-series of bicycle-only mode share, compared to “involving bicycle”, so two charts here:

Observations:

  • Darwin lost top placing for cycling to work with a large decline in mode share (refer discussion above about the massive shift to bus).
  • Canberra took the lead with more strong growth.
  • Melbourne increased slightly between 2011 and 2016 (note: rain was forecast on census day which may have suppressed growth, more on that in a moment).
  • Hobart had a big increase in 2016, following rain in 2011.
  • Sydney remains at the bottom of the pack and declined in 2016.

Walking and cycling mode share is likely to be impacted by weather. Here’s a summary of recent census weather conditions for most cities (note: Canberra minimums were -3 in 2001, -7 in 2006, 0 in 2011 and -1 in 2016):

Perth had rain on all of the last four census days, while Adelaide had significant rain only in 2001 and 2011 (and indeed 2006 shows up with higher active transport mode share). Hobart had significant rain in 2011, which appears to have suppressed active transport mode share that year.

But perhaps equally important is the forecast weather as that could set people’s plans the night before. Here was the forecast for the 2016 census day,  from the BOM website the night before:

Note that it didn’t end up raining in Melbourne, Adelaide, or Hobart.

The census is conducted in winter – which is the best time to cycle in Darwin (dry season) and not a great time to cycle in other cities. However the icy weather in Canberra clearly hasn’t stopped it getting the highest and fastest growing cycling mode share of all cities!

Indeed here is a chart from VicRoads showing the seasonality of cycling in Melbourne at their bicycle counters:

And in case you are interested, here are the (small) mode shares of journeys involving bicycle and some other modes (other than walking):

Walking only

Canberra was the only city to have a big increase, while there were declines in Darwin, Perth, Adelaide, Brisbane, and Sydney.

The smaller cities had the highest walking share, perhaps as people are – on average – closer to their workplace, followed by Sydney – the densest city. But city size doesn’t seem to explain cycling mode shares.

Car

The following chart shows the proportion of journeys to work made by car only (either as driver or passenger):

Sydney has the lowest car only mode share and it declined again in 2016. It was followed by Melbourne in 2016. Brisbane and Perth had large increases in car mode share in 2016 (in line with the PT decline mentioned above). Darwin also shows a big shift away from the car to public transport (although the total number of car trips still increased by 24%). Adelaide hit top spot, followed by Hobart and Perth.

Here is car as driver only:

And here is car as passenger only:

Car as passenger declined in all cities again in 2016, but was more common in the smaller cities, and least common in the bigger cities. I’m not sure why car as passenger declines paused for Perth and Sydney in 2006.

We can calculate an implied notional journey to work car occupancy by comparing car driver only and car passenger only journeys. This is not actual car occupancy, because it excludes people not travelling to work and excludes journeys that involved cars and other modes. However it does provide an indication of trends in car pooling for journeys to work.

There were further significant decreases in car commuter occupancy, in line with increasing car ownership and affordability.

Private transport

Here is a chart summing all modal combinations involving cars (driver or passenger), motorcycle/scooter, taxis, and trucks, but excluding any journeys that also include public transport.

The trends mirror what we have seen above, and are very similar to car-only travel.

 

Overall mode split

Here’s an overall split of journeys to work by “main mode” (click to enlarge):

Note: the 2001 data includes estimated splits of aggregated modes based on 2006 data.

I assigned a ‘main mode’ based on a hierarchy as follows:

  • Any journey involving train is counted with the main mode as train
  • Any other journey involving bus is counted with the main mode as bus
  • Any other journey involving tram and/or ferry is counted as “tram/ferry”
  • Any other journey involving car as driver, truck or motorbike/scooter is counted as “vehicle driver”
  • Any other journey involving car as passenger or taxi is counted as “vehicle passenger”
  • Any other journey involving walking or cycling only as “active”

How different are “place of work” and “place of enumeration” mode shares?

[this section updated 1 December 2017 with re-issued Place of Work data. See new Appendix 3 below for analysis of the changes]

The first edition of this post reported only “place of work” data, as place of enumeration data wasn’t released until 11 November 2017. This second edition now focuses on place of enumeration – where people were on census night.

The differences are not huge, as most people who live in a city also work in that city, but there are still a number of people who leave or enter cities’ statistical boundaries to go to work. Here’s an animation showing the main mode split by place of work and enumeration so you can compare the differences (you’ll need to click to enlarge). The animation dwells longer on place of work data.

Public + active transport main mode shares are generally higher for larger cities with place of work data, and smaller for smaller cities.

Here’s a closer look at the 2016 public transport mode shares by the two measures:

See also a detailed comparison in Appendix 1 below for 2011 Melbourne data.

I’d like to acknowledge Dr John Stone for assistance with historical journey to work data.

Appendix 1 – How to measure journey to work mode share

Firstly, I exclude people who did not work, worked at home, or did not state how they worked. The first two categories generate no transport activity, and if the actual results for “not stated” were biased in any way we would have no way of knowing how.

I prefer to use “place of enumeration” data (ie where people were on census night). “Place of usual residence” data is also available, but is unfortunately contaminated by people who were away from home on census day. The other data source is “Place of work”.

Some people might prefer to measure mode shares on Urban Centres which excludes rural areas within the larger blobs that are Greater Capital City Statistical Areas and Statistical Divisions (use this ABS map page to compare boundaries). However, “place of work” data is not readily available for that geography, and this method also excludes satellite urban centres that might be detached from the main urban centre, but are very much part of the economic unit of the city.

Another option is “Significant Urban Area”, which includes more fringe areas, and some more satellite towns, and in Canberra’s case crosses the NSW border to capture Queanbeyan.

What difference does it make?

Here’s a comparison of public transport mode shares for the different methods for 2011.

If you look closely, you’ll notice:

  • The more than you remove non-urban areas, the higher your public transport mode share, which makes sense, as those non-urban areas are mostly not served by public transport.
  • Place of usual residence tends to increase public transport mode shares for smaller cities (people probably visiting larger cities) and depresses public transport mode share in larger cities (people visiting smaller cities and towns).
  • Place of work is only readily available for Greater Capital City Statistical Areas. For the bigger cities it tends to inflate PT mode share where people might be using good inter-urban public transport options, or driving to good public transport options on the edges of cities (eg trains). However it has the opposite impact in Darwin and Canberra, where driving into the city is probably easier.

But I think the main point is that for any time series trend analysis you should use the same measure if possible.

If you want to compare the two, I’ve created a Tableau Public visualisation that has a large number of mode shares by both place of work and place of enumeration.

Appendix 2 – Estimating pre-2006 mode shares from aggregated data

For 2006 onwards, ABS TableBuilder provides counts for every possible combination of up to three modes (other than walking, which is assumed to be part of every journey). For example, in Melbourne in 2006, 36 people went to work by taxi, car as driver, and car as passenger (or so they said!). Unfortunately for years before 2006 data is not readily available with a full breakdown.

The 2001 data includes only aggregated counts for the following categories:

  • train and other (excluding bus)
  • bus and other (excluding train)
  • other two modes (no train or bus)
  • train and two other modes
  • bus and two other modes (excluding train)
  • three other modes (no train or bus)

Together these accounted for 3.7% of journeys in Melbourne and 4.5% of journeys in Sydney.

However all but two of those aggregate categories definitely involve train and/or bus, so can be included in public transport mode share calculations.

Journeys in the aggregate categories “Other two modes” and “Other three modes” might involve tram and/or ferry trips (if such modes exist in a city), but we don’t know for sure.

I’ve used the complete modal data for 2006 to calculate the percentage of 2006 journeys that fit into these two categories that are by public transport. I’ve then assumed these same percentage apply in 2001 to estimate total public transport mode shares for 2001 (for want of a better method).

Here are the 2001 relevant stats for each city:

(note: totals do not add perfectly due to rounding)

The estimates add up to 0.2% to the total public transport mode shares in cities with significant modes beyond train and bus (namely ferry and tram in Sydney, tram in Melbourne, ferry in Brisbane, tram and Adelaide). This almost entirely comes from “other two modes” category while “other three modes” is tiny. For these categories, almost no journeys in Perth, Canberra and Hobart actually involved a public transport mode.

In the past I have knowingly ignored public transport journeys that might be part of these categories, which almost certainly means public transport mode share is underestimated (I suspect most other analysts have too). By including some assumed public transport journeys my estimate should be closer to the true value, which I think is better than an underestimate.

But are these reasonable estimates? Are the 2001 modal breakdowns for these categories likely to be the same as 2006? Maybe not exactly, but because we are multiplying small numbers by small numbers, the impact of slightly inaccurate estimates is unlikely to shift the total by more than 0.1%. I tested the methodology between 2006 and 2011 results (eg using 2011 full breakdown against created 2006 aggregate categories and vice versa) and the estimated total mode shares were almost always exactly the same as the perfectly calculated shares (at worst there was a difference of 0.1% when rounding to one decimal place).

In the first edition of this post I had to estimate 2016 place of work mode shares in a similar way for public and private transport, but I wasn’t confident enough to estimate mode share of journeys involving cycling.

I now have the final data and I promised to see how I went, so here’s a comparison:

If you round to one decimal place, the estimates were no different for public and private transport and out by up to 0.1% for cycling (which is relatively significant for the small cycling mode shares).

I’ve applied a similar approach to estimate several other mode share types, and these are marked on charts.

Appendix 3 – How different is the re-issued place of work data?

In December 2017, ABS re-issued Place of Work data due to data quality issues. This is how they described it:

**The place of work data for the 2016 Census has been temporarily removed from the ABS website so an issue can be corrected. There was a discrepancy in the process used to transform detailed workplace location information into data suitable for output. The ABS will release the updated information in TableBuilder on December 2. The Working Population Profiles will be updated on December 13.**

I have loaded the new data, and here are differences in public transport and private transport mode shares for capital cities:

You can see differences of up to 0.3% (Melbourne PT mode share), but mostly quite small.


What does the census tell us about motor vehicle ownership in Australian cities? (2006-2016)

Sun 30 July, 2017

With the latest release of census data it’s possible to take a detailed look at motor vehicle ownership in Australian cities.  This post will look at ownership rates across time and space, and compare trends between car ownership, population growth, and population density. And this time I will cover 16 large Australian cities (but with a more detailed look at Melbourne).

I’ve measured motor vehicle ownership as motor vehicles per 100 persons in private occupied dwellings. If you want the boring but important details about how I’ve analysed the data, see the appendix at the end of this post.

I’ve used Tableau Public for this post, so all the charts and maps can be explored, and they cover all sixteen cities.

Is motor vehicle ownership increasing in all cities?

Elsewhere on this blog I’ve shown that motor vehicle ownership is increasing in all Australian states, but what about the cities? Here are the overall results for Australia’s larger cities, on motor vehicles per 100 persons basis. Note that the Y-axis only goes from 54 to 70, so the rate of change looks steeper than it really is.

(you can explore this data in Tableau)

Sydney unsurprisingly has the lowest average motor vehicle ownership, followed by Melbourne, Brisbane (Australia’s third biggest city), and then Cairns and Darwin. Perth was well on top, with Sunshine Coach rapidly increasing to claim second place. Most of the rest were around 66-68 motor vehicles per 100 persons in 2016.

But Melbourne is showing a very different trend to most other cities, with hardly any increase in ownership rate across the ten years (also, Canberra-Queanbeyan saw very little growth between 2011 and 2016).

At first I wondered whether Melbourne was a data error. However, I did the one data extract for all cities for both population and motor vehicle responses, and I’ve also checked for any potential duplicate SA1s. So I’m confident something very different is happening in Melbourne.

So let’s have a look at Melbourne in more spatial detail, starting with maximum detail over time:

(you can zoom in and explore this data in Tableau).

You can see lower ownership in the inner city, inner north, inner west, and the more socio-economically disadvantaged suburbs in the north and south-east. You can also see lower motor vehicle ownership around train lines in many middle suburbs. Other pockets of low motor vehicle ownership are in Clayton (presumably associated with university students) and Box Hill, and curiously some of the growth areas in the west and north. Very high motor vehicle ownership can be seen in wealthier areas and the outer east.

It’s a bit hard to see the trends with such a detailed map, so here’s a view aggregated at SA2 level (SA2s are roughly suburb-sized).

No doubt you are probably distracted by the changes in the legend. That’s because in 2006 there were no SA2s in the <20 and 30-40 ranges at all, and the 30-40 range is only present in 2016. That is, the legend has to expand over time to take into account SA2s with lower motor vehicle ownership rates.

You’ll notice a lot more light blue and green SA2s around the city centre, plus Clayton in the middle south-east switches to green in 2016.

Looking at it spatially, more areas appear to have increasing rather than decreasing motor vehicle ownership. But not all SA2s have the same population – or more particularly – the same population growth. So we need to look at the data in a non-spatial way.

Here’s a plot of population and motor vehicle ownership for all Melbourne SA2s, with the thin end of each “worm” being 2006 and the thick end being 2016.

Okay yes that does looks like a lot of scribbles (and you can explore the data in Tableau to find out what is what), but take a look at the patterns. There are lots of short worms heading to the right – these have very little population growth but some growth in motor vehicle ownership. Then there are lots of long worms that are heading up and to the left – which means large population growth and mostly declining motor vehicle ownership.

Here’s a similar view, but with a Y-axis of change in population since 2006:

(explore in Tableau)

The worms heading up and to the left include both inner city areas and outer growth areas. These areas seem to balance out the rest of Melbourne resulting in a stable ownership rate overall.

Some SA2s that are moving up and to the right more than others include Sunbury – South, Langwarrin, and Mount Martha. And there are a few in population decline like Endeavour Hills – South, Mill Park – South, and Keilor Downs.

The inner city results are not surprising, but declining ownership in outer growth areas is a little more surprising.

Is this to do with growth areas being popular with young families, and therefore containing proportionately more children?

Here’s a map of the percent of the population in each CD/SA1 that is aged 18-84 (ie approximately of “driving age”):

(view in Tableau)

The rates are highest in the central city and lowest in urban growth areas. And if you watch the animation closely, you’ll see areas that were “fringe growth” in 2006 have since had increasing portions of population aged 18-84, presumably as the children of the first residents have reached driving age (and/or moved out).

So what is happening with motor vehicles per 100 persons aged 18-84? Is there high motor vehicle ownership amongst driving aged people in growth areas?

Yes, a lot of growth areas are in the 80-85 range, similar to many middle suburban areas (view in Tableau)

Here’s the same thing but aggregated to SA2 level (explore in Tableau):

Motor vehicle ownership rates in most growth areas are similar to many established middle suburbs, but lower than non-growth fringe areas which show “saturated” levels of ownership (where there is roughly a one motor vehicle per person aged 18-84), particularly the outer east.

However in the outer growth areas of Sunbury (north-west) and Doreen (north-north-east), ownership rates are close to saturation in 2016.

But is the rate of motor vehicle ownership still declining amongst persons aged 18-84 in the outer growth areas? Here’s a similar chart to the previous one, but with ownership by persons aged 18-84 (explore in Tableau):

You can see most of the outer growth areas still have declining ownership rates. You can also see some established suburbs with strong population growth and increased ownership, including Dandenong and Braybrook (which includes the rapidly densifying suburbs of Maidstone and Maribyrnong).

Here’s a spatial view of the changes in ownership rates (area shading), as well as total changes in the household motor vehicle fleet (dots ). (I’ve assumed non-reporting private dwellings have the same average motor vehicle ownership as reporting dwellings in each area).

(explore in Tableau)

You can see outer growth areas shaded green (declining ownership), but also with large dots (large fleet growth).

But also you can see some declines in ownership in the middle eastern and north-eastern suburbs, and some non-growth outer suburbs, which is quite surprising. I’m not quite sure what might explain that.

You’ll also notice the scale for the dots starts at -830, which accommodates Wheelers Hill (in the middle south-east) where there has been a 2% decline in population, and 6% decline in motor vehicle fleet.

Okay, so that’s Melbourne, what about ownership rates amongst “driving aged” people in other cities?

Trends in motor vehicles per persons aged 18-84

(explore in Tableau)

The trends are similar, but Melbourne is even more interesting on this measure. It has declined from 81.3 to 80.7, bucking the trend of all other cities (although Canberra only grew from 88.4 in 2011 to 88.5 in 2016).

How does motor vehicle ownership relate to density?

Here’s a chart showing population weighted density and motor vehicle ownership for persons aged 18-84 for SA2s across all the big cities in 2016 (explore in Tableau):

Some dots (central Melbourne and Sydney) are off the chart so you can see patterns in the rest. I’ve labelled some of the outliers. The general pattern shows higher density areas generally having lower motor vehicle ownership.

Is densification related to lower motor vehicle ownership?

Here’s a chart showing how each city has moved in terms of population-weighted density (measured at CD or SA1 level) and ownership for persons aged 18-84, with the thick end of each worm 2016, and the thin end 2006.

(Note that the 2006 population weighted density figures are not perfectly comparable with 2011 and 2016 because they are measured at CD level rather than SA1 level, and CDs are slightly larger on average than SA1s)

(explore in Tableau)

You can see Sydney is a completely different city on these measures, and also that Melbourne is the only city heading to the left of the chart. Canberra is also bucking the trend between 2011 and 2016.

We can look at this within cities too. Here’s all the Local Government Areas (LGAs) for all the cities (note: City of Sydney and City of Melbourne are off the top-left of the chart)

(explore in Tableau)

Many Melbourne and Sydney LGAs are rising sharply with mostly declining motor vehicle ownership. But then there are Sydney LGAs like Woollahra, Mosman and Northern Beaches in Sydney that are showing increasing motor vehicle ownership while they densify (probably not great for traffic congestion!).

And we can then look inside cities. Here is Melbourne (again, several inner city SA2s are off the chart):

Some interesting outliers include:

  • The relatively dense Port Melbourne, Albert Park, Elwood with relatively high motor vehicle ownership.
  • The land-locked suburb of Gowanbrae with medium density but rapidly increasing car ownership (which has a limited Monday to Saturday bus service).
  • The growth area of Cranbourne South with reasonable density but more than saturated car ownership.
  • Relatively medium dense but low motor vehicle ownership of Clayton and Footscray.

Explore your own city in Tableau. You know you want to.

What are the spatial patterns of motor vehicle ownership in other cities?

The detail above has focussed on Melbourne, so here are some maps for others cities. You can explore any of the cities by zooming in from this Tableau map (be warned: it may take some time to load as I’ve ignored Tableau’s recommendations about how many showing more than 10,000 data points!). In fact for any of the maps you’ve seen on this blog, you can pan and zoom to see other cities.

To help see the changes in motor vehicle ownership between censuses more easily, I’ve prepared the following detailed animations.

Sydney

 

Brisbane

 

Adelaide

Perth

(Find Mandurah in Tableau)

Canberra

Hobart

Darwin

Cairns

Townsville

Sunshine Coast

Geelong

Central Coast (NSW)

Newcastle – Maitland

This post has only looked at spatial trends and the relationship with population density. There’s plenty more to explore about car ownership with census data, which I aim to cover in future posts.

I hope you’ve enjoyed this post, and found the interactive data at least half as fascinating as I have.

Oh, and sorry about some of the maps showing defunct train lines. I’m using what I can get from the WMS feed from Geoscience Australia.

Appendix – About the data

The Australian census includes the following question about how many registered motor vehicles were present at each occupied private dwelling on census night. This excludes motorcycles but includes some vehicles other than cars (probably mostly light vehicles).

96% of people counted in the 2016 census were in a private dwelling on census night, and 93.6% of occupied dwellings filled in the census and gave an answer to the motor vehicle question. So the data can give a very detailed – and hopefully quite accurate – picture.

I’ve used two measures of motor vehicle ownership:

  • Motor vehicles per 100 population (often referred to as “motorisation” in Europe), and
  • Motor vehicles per 100 persons aged 18-84

The first is easy to measure and easily comparable with other jurisdictions, but the second gives a better feel for what proportion of the “driving aged” population own a car. In an area with good alternatives to private transport, you might expect lower ownership rates.

Setting the lower age threshold at 18 works well for Victoria (imperfectly for other states with a lower licensing age), and 84 is an arbitrary threshold during the general decline in drivers license ownership by older people. So it’s not perfect, but is indicative, and certainly takes most children out of the equation.

As the motor vehicle question is based on what was parked at the dwelling on census night, I’ve used population present on census night (place of enumeration). That works well if someone was absent on census night and took their car with them, but not so well if they were absent and left their car behind (e.g. they took a taxi to the airport). You cannot win with that, but the census is timed in August during school and university term to try to minimise absences.

When calculating ownership rates, I’ve excluded people in dwellings that did not answer the motor vehicle question, and people in non-private dwellings. This is more robust than assumptions I made in previous posts on this topic so results will vary a little.

For 2011 and 2016, the census data provides counts of the number of dwellings with 0, 1, 2, 3, .. , 29 motor vehicles, and then bundles the rest as “30 of more”. For want of a better assumption, I’ve assumed dwellings with 30 or more motor vehicles have an average of 31 motor vehicles, which is probably conservative. But these are so rare they shouldn’t make any noticeable difference on the overall results.

As shorthand, I’ve referred to “motor vehicle ownership” rates, but you’ll note the census question includes company vehicles kept at home, so it’s not a perfect term to use, but then company vehicles are often available for general use.

I’ve used the 2011 boundaries of Significant Urban Areas (SUA) for each city, which are made up of SA2s and leave a good amount of room for urban fringe growth in 2016. However they do exclude some satellite towns (such as Melton, west of Melbourne).

I’ve extracted data at SA1 level geography for 2011 and 2016, and Collector District (CD) geography for 2006. In urban areas, SA1s average around 400 people while the older Collector Districts of 2006 averaged around 550 people. These are the smallest geographies for which motor vehicle and age data is available in each census. ABS do introduce some small data randomisation to protect privacy so there will be a little error well summing up lots of parcels.

I’ve generally excluded parcels with less than 5 people per hectare as an (arbitrary) threshold for “urban” residential areas. I’ve mapped all parcels to the 2016 boundaries of Local Government Areas and SA2s, and the 2011 boundaries of SUAs (2016 boundaries have not yet been released). Where boundaries do not line up perfectly, I’ve included a parcel in an SAU, LGA, or SA2 if more than 51% of the parcel’s area is within that boundary. The mapping isn’t perfect in all cases, particularly for growth area SA2s and 2006 CDs. See the alignments for SA2s, LGAs in Tableau.


Comparing the densities of Australian, European, Canadian, and New Zealand cities

Thu 26 November, 2015

[updated March 2016 to add Canadian and New Zealand cities]

Just how much denser are European cities compared to Australian cities? What about Canadian and New Zealand cities? And does Australian style suburbia exist in European cities?

This post calculates the population-weighted density of 53 Australian, European, and Canadian cities with a population over 1 million, plus the three largest New Zealand cities (only Auckland is over 1 million population). It also shows a breakdown of the densities at which these cities’ residents live, and includes a set of density maps with identical scale and density shading.

Why Population Weighted Density?

As discussed in previous posts, population-weighted density attempts to measure the density at which the average city resident lives. Rather than divide the total population of a city by the entire city area (which usually includes large amounts of sparsely populated land), population weighted density is a weighted average of population density of all the parcels that make up the city. As I’ve shown previously, the size of the parcels used makes a big difference in the calculation of population-weighted density, which makes comparing cities difficult internationally.

To overcome the issue of different parcel sizes, I’ve used kilometre grid population data that is now available for both Europe and Australia. I’ve also generated my own kilometre population grids for Canadian and New Zealand cities by proportionally summing populations of the smallest census parcels available.

Some measures of density exclude all non-residential land, but the square kilometre grid approach means that partially populated grid parcels are counted, and many of these parcels will include non-residential land, and possibly even large amounts of water. It’s not perfect, particularly for cities with small footprints. For example, here is a density map around Sydney harbour (where light green is lower density, dark green is medium density and red is higher density):

Sydney harbour

You can see that many of the grid cells that include significant amounts of water show a lower density, when it fact the population of those cells are contained within the non-water parts of the grid cell. The more watery cells, the lower the calculated density. This is could count against a city like Sydney with a large harbour.

Defining cities

The second challenge with these calculations is a definition of the city limits. For Australia I’ve used Urban Centre boundaries, which attempt to include contiguous urbanised areas (read the full definition). For Europe I’ve used 2011 Morphological Urban Areas, which have fairly similar rules for boundaries. For Canada I’ve used Population Centre, and for New Zealand I’ve used Urban Areas.

These methodologies tend to exclude satellite towns of cities (less so in New Zealand and Canada). While these boundaries are not determined in the exactly the same way, one good thing about population-weighted density is that parcels of land that have very little population don’t have much impact on the overall result (because their low population has little weighting).

For each city, I’ve included every grid cell where the centroid of that cell is within the defined boundaries of the city. Yes that’s slightly arbitrary and not ideal for cities with dense cores on coastlines, but at least I’ve been consistent. It also means some of the cells around the boundary are excluded from the calculation, which to some extent offsets the coastline issues. It also means the values for Australian cities are slightly different to a previous post.

All source data is dated 2011, except for France which is 2010, and New Zealand which is 2013.

Comparing population-weighted density of Australian, European, Canadian and New Zealand cities

AU EU CA NZ Population Weighted Density

You can see the five Australian cities are all at the bottom, most UK cities are in the bottom third, and the four large Spanish cities are within the top seven.

Sydney is not far below Glasgow and Helsinki. Adelaide, Perth and Brisbane are nothing like the European cities when it comes to (average) population-weighted density.

Three Canadian cities (Vancouver, Toronto and Montreal) are mid-range, while the other three are more comparable with Australia. Of the New Zealand cities, Auckland is surprisingly more dense than Melbourne. Wellington is more dense that Vancouver (both topographically constrained cities).

But these figures are only averages, which makes we wonder…

How much diversity is there in urban density?

The following chart shows the proportion of each city’s population that lives at various urban density ranges:

AU EU CA NZ urban density distribution

Because of the massive variations in density, I had to break the scale interval sizes at 100 persons per hectare, and even then, the low density Australian cities are almost entirely composed of the bottom two intervals. You can see a lot of density diversity across European cities, and very little in Australian cities, except perhaps for Sydney.

You can also see that only 10% of Barcelona has an urban density similar to Perth or Adelaide. Which makes me wonder…

Do many people in European cities live at typical Australian suburban densities?

Do many Europeans living in cities live in detached dwellings with backyards, as is so common in Australian cities?

To try to answer this question, I’ve calculated the percentage of the population of each city that lives at between 10 and 30 people per hectare, which is a generous interpretation of typical Australian “suburbia”.

AU EU CA NZ cities percent at 10 to 30 per hectare

It’s a minority of the population in all European cities (and even for Sydney). But it does exist. Here are examples of Australian-style suburbia in outer Hamburg, Berlin, LondonMilan, and even Barcelona (though I hate to think what some of the property prices might be!)

How different is population-weighted density from regular density?

Now that I’ve got a large sample of cities, I can compare regular density with population weighted densities (PWD):

PWD v regular density 2

The correlation is relatively high, but there are plenty of outliers, and rankings are very different. Rome has a regular density of 18, but a PWD of 89, while London has a regular density of 41 and PWD of 80. Dublin’s regular density of 31 is relatively close to its PWD of 47.

Wellington’s regular density is 17, but its PWD is 49 (though the New Zealand cities regular density values are impacted by larger inclusions of non-urbanised land within definitions of Urban Areas).

So what does the density of these cities look like on a map?

The following maps are all at the same scale both geographically and for density shading. The blue outlines are urban area boundaries, and the black lines represent rail lines (passenger or otherwise, and including some tramways). The density values are in persons per square kilometre (1000 persons per square kilometre = 10 persons per hectare). (Apologies for not having coastlines and for some of the blue labels being difficult to read).

Here’s Barcelona (and several neighbouring towns), Europe’s densest large city, hemmed in by hills and a coastline:

Barcelona

At the other extreme, here is Perth, a sea of low density and the only city that doesn’t fit on one tile at the same scale as the other cities (Mandurah is cut off in the south):

 

Perth

Here is Paris, where you can see the small high density inner core matches the high density Metro railway area:

Paris

Similarly the dense inner core of London correlates with the inner area covered by a mesh of radial and orbital railways, with relatively lower density outer London more dominated by radial railways:

London

There are many more interesting patterns in other cities.

What does this mean for transport?

Few people would disagree that higher population densities increase the viability of high frequency public transport services, and enable higher non-car mode shares – all other things being equal. But many (notably including the late Paul Mees) would argue that “density is not destiny” – and that careful design of public and active transport systems is critical to transport outcomes.

Zurich is a city often lauded for the high quality of its public transport system, and its population weighted density is 51 persons/ha (calculated on the kilometre grid data for a population of 768,000 people) – which is quite low relative to larger European cities.

In a future post I’ll look at the relationship between population-weighted density and transport mode shares in European cities.

All the density maps

Finally, here is a gallery of grid density maps of all the cities for your perusing pleasure (plus Zurich, plus many smaller neighbouring cities that fit onto the maps). All maps have the same scale and density shading colours.

Please note that the New Zealand and Canada maps do not include all nearby urbanised areas. Apologies that the formats are not all identical.


Are Australian cities becoming denser?

Tue 5 November, 2013

[Updated April 2017 with 2015-16 population estimates. First published November 2013]

While Australian cities have been growing outwards with new suburbia, they have also been getting denser in established areas, and the new areas on the fringe are often more dense than growth areas used to be (see last post). So what’s the net effect – are Australian cities getting more or less dense?

This post also explores measures of population-weighted density for Australian cities large and small over time. It also tries to resolve some of the issues in the calculation methodology by using square kilometre geometry, looks at longer term trends for Australian cities, and then compares multiple density measures for Melbourne over time.

Measuring density

Under the traditional measure of density, you’d simply divide the population of a city by the metropolitan area’s area (in hectares). As the boundary of the metropolitan areas seldom change, the average density would simply increase in line with population with this measure. But that density value would also be way below the density at which the average resident lives because of the inclusion of vast swaths of unpopulated land within “metropolitan areas”, and so be not very meaningful.

Enter population-weighted density (which I’ve looked at previously here and here). Population-weighted density takes a weighted average of the density of all parcels of land that make up a city, with each parcel weighted by its population. One way to think about it is the residential density in which the “average resident” lives.

So the large low-density parcels of rural land outside the urbanised area but inside the “metropolitan area” count very little in the weighted average because of their small population relative to the urbanised areas. This means population-weighted density goes a long way to overcoming having to worry about the boundaries of the “urban area” of a city. Indeed, in a previous post I found that removing low density parcels of land had very little impact on calculations of population-weighted density for Australian cities. However, the size of the parcels of land used in a population-weighted density calculation will have an impact, as we will see shortly.

Calculations of population-weighted density can answer the question about whether the “average density” of a city has been increasing or decreasing. But as we will see below, using geographic regions put together by statisticians based on historical boundaries is not always a fair way to compare different cities.

Population-weighted density of Australian cities over time

Firstly, here is a look at population-weighted density of the five largest Australian cities (as defined by ABS Significant Urban Areas), measured at SA2 level (the smallest geography for which there exists a good consistent set of time-series estimates). SA2s roughly equate to suburbs.

According to this data, most cities bottomed out in density in the mid 1990s. Sydney, Melbourne and Brisbane have shown the fastest rates of densification in the last three years.

What about smaller Australian cities? (120,000+ residents in 2014):

Darwin comes out as the third most dense city in Australia on this measure, with Brisbane rising quickly in recent years into fourth place. Most cities have shown densification in recent times, with the main exception being Townsville. On an SA2 level, population weighted density in Perth hardly rose at all in 2015-16 (a year when 92% of population growth was in the outer suburbs)

However, we need to sanity test these values. Old-school suburban areas of Australian cities typically have a density of around 15 persons per hectare, so the values for Geelong, Newcastle, Darwin, Townsville, and Hobart all seem a bit too low for anyone who has visited them. I’d suggest the results may well be an artefact of the arbitrary geographic boundaries used – and this effect would be greater for smaller cities because they would have more SA2s on the interface between urban and rural areas (indeed all of those cities are less than 210,000 in population).

For reference, here are the June 2014 populations of all the above cities:

Australian cities population 2014

The following map shows Hobart, with meshblock boundaries in black (very small blocks indicate urban areas), SA2s in pink, and the Significant Urban Area (SUA) boundary in green.  You can see that many of the SA2s within the Hobart SUA have pockets of dense urban settlement, together with large areas that are non-urban – ie SA2s on the urban/rural interface. The density of these pockets will be washed out because of the size of the SA2s.

Hobart SUA image

 

 

Reducing the impact of arbitrary geographic boundaries

As we saw above, the population-weighted density results for smaller cities were very low, and probably not reflective of the actual typical densities, which might be caused by arbitrary geographic boundaries.

Thankfully ABS have followed Europe and released of a square kilometre grid density for Australia which ensures that geographic zones are all the same size. While it is still somewhat arbitrary where exactly this grid falls on any given city, it is arguably less arbitrary than geographic zones that follow traditional notions of area boundaries.

Using that data, I’ve been able to calculate population weighted density for the larger cities of Australia. The following chart shows those values compared to values calculated on SA2 geography:

pop weighted density 2011 grid and SA2 australian cities

You’ll see that the five smaller cities (Newcastle, Hobart, Geelong, Townsville and Cairns) that had very low results at SA2 level get more realistic values on the kilometre grid.

You’ll notice that most cities (except big Melbourne and Sydney) are in the 15 to 18 persons per hectare range, which is around typical Australian suburban density.

While the Hobart figure is higher using the grid geography, it’s still quite low (indeed the lowest of all the cities). You’ll notice on the map above that urban Hobart hugs the quite wide and windy Derwent River, and as such a larger portion of Hobart’s grid squares are likely to contain both urban and water portions – with the water portions washing out the density (pardon the pun!). While most other cities also have some coastline, much more of Hobart’s urban settlement is near to a coastline.

But stepping back, every city has urban/rural and/or urban/water boundaries and the boundary has to be drawn somewhere. So smaller cities are always going to have a higher proportion of their land parcels being on the interface – and this is even more the case if you are using larger parcel sizes. There is also the issue of what “satellite” urban settlements to include within a city which ultimately becomes arbitrary at some point. Perhaps there is some way of adjusting for this interface effect depending on the size of the city, but I’m not going to attempt to resolve it in this post.

International comparisons of population-weighted density

See another post for some international comparisons using square km grids.

Changes in density of larger Australian cities since 1981

We can also calculate population-weighted density back to 1981 using the larger SA3 geography. An SA3 is roughly similar to a local government area (in Melbourne at least), so getting quite large and including more non-urban land. Also, as Significant Urban Areas are defined only at the SA2 level, I need to resort to Greater Capital City Statistical Areas for the next chart:

This shows that most cities were getting less dense in the 1980s (Melbourne quite dramatically), with the notable exception of Perth. I expect these trends could be related to changes in housing/planning policy over time. This calculation has Adelaide ahead of the other smaller cities – which is different ordering to the SA2 calculations above.

On the SA3 level, Perth declined in population-weighted density in 2015-16.

When measured at SA2 level, the four smaller cities had almost the same density in 2011, but at SA3 level, there is more separating them. My guess is that the arbitrary nature of geographic boundaries is having an impact here. Also, the share of SA3s in a city that are on the urban/rural interface is likely to be higher, which again will have more impact for smaller cities. Indeed the trend for the ACT at SA3 level is very different to Canberra at SA2 level.

Melbourne’s population-weighted density over time

I’ve taken a more detailed look at my home city Melbourne, using all available ABS population figures for the geographic units ranging from mesh blocks to SA3s inside “Greater Melbourne” (as defined in 2011) or inside the Melbourne Significant Urban Area (SUA, where marked), to produce the following chart:

Note: I’ve calculated population-weighted density at the SA2 level for both the Greater Capital City Statistical Area (ie “Greater Melbourne”, which includes Bacchus Marsh, Gisborne and Wallan) and the Melbourne Significant Urban Area (slightly smaller), which yield slightly different values.

All of the time series data suggests 1994 was the turning point in Melbourne where the population-weighted density started increasing (not that 1994 was a particularly momentous year – the population-weighted density increased by a whopping 0.0559 persons per hectare in the year to June 1995 (measured at SA2 level for Greater Melbourne)).

You’ll also note that the density values are very different when measured on different geographic units. That’s because larger units include more of a mix of residential and non-residential land. The highest density values are calculated using mesh blocks (MB), which often separate out even small pockets of non-residential land (eg local parks). Indeed 25% of mesh blocks in Australia had zero population, while only 2% of SA1s had zero population (at the 2011 census). At the other end of the scale, SA3s are roughly the size of local councils and include parklands, employment land, rural land, airports, freeways, etc which dilutes their average density.

In the case of SA2 and SA3 units, the same geographic areas have been used in the data for all years. On the other hand, Census Collector Districts (CD) often changed between each five-yearly census, but I am assuming the guidelines for their creation would not have changed significantly.

Now why is a transport blog so interested in density again? There is a suggested relationship between (potential) public transport efficiency and urban density – ie there will be more potential customers per route kilometre in a denser area. In reality longer distance public transport services are going to be mostly serving the larger urban blob that is a city – and these vehicles need to pass large parklands, industrial areas, water bodies, etc to connect urban origins and destinations. The relevant density measure to consider for such services might best be based on larger geographic areas – eg SA3. Buses are more likely to be serving only urbanised areas, and so are perhaps more dependent on residential density – best calculated on a smaller geographic scale, probably km grid (somewhere between SA1 and SA2).

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