[Last updated September 2012 with revised June 2011 population estimates. First posted April 2010]

How much is Melbourne sprawling? Is urban consolidation happening? Is the Melbourne 2030 target for urban consolidation being realised?

This post sheds some light by looking at ABS population data and DPCD dwelling approval data.

This post has been revised with post-2011 census estimates of June 2011 population. The Melbourne Statistical Division’s population growth rate has been revised down for 2010/11 from 1.7% to 1.5%, and the proportion of population growth in outer growth areas has been revised from 62% to a new high of 65%.

At this stage I have continued to use the older Melbourne Statistical Division, rather than the new Greater Melbourne Statistical Area (see here for discussion and maps showing the changes). ABS now publish annual population estimates at a much finer level (essentially suburb level) which presents opportunities for more detailed population change analysis.

Population growth

The first chart shows net annual population growth by regions of Melbourne. “outer-growth” refers to the designated growth local government areas (LGAs) on the fringe of Melbourne (see below for definitions of regions and note that the areas have different sizes).

As you can see, Melbourne’s population growth accelerated dramatically in the years up to 2008-09 and has since slowed down. There were a net 59,099 new residents in 2010/11, an average of 1137 per week (annual growth of 1.5%).

The following chart shows how the growth was spread across Melbourne:

In 2009-10 there was a significant shift in the balance of growth towards the outer suburban designated growth areas, jumping higher to 65% in 2010-11. As the first chart shows, very significant growth was recorded in 2009-10 and only slightly fewer people moved into the growth areas in 2010-11.

So was this shift to the outer suburbs unexpected? The following chart compares the estimated actual share of population growth in the outer-growth areas with the 2008 and (new) 2012 Victoria In Future projections (which DPCD stresses are not targets or predictions).

The jump in share from around 46% to over 60% occurred in 2010, two years later than the VIF 2008 projection (mostly because VIF 2008 did not anticipate the significant growth in inner city population in 2008 and 2009). The 2008 projection was for the share of population growth in the outer-growth areas to decline slowly over time, while the new 2012 projection is for the share to be steady around 55% for the next 15 years (although it is not clear how it will fall from the 2011 figure of 65%).

Note that not all greenfields sites are in “outer growth” areas – some “outer” areas also include smaller greenfields developments (eg Keysborough in Greater Dandenong).

Growth compared to forecasts

The Victorian government periodically makes projections of population growth in all local government areas (LGAs). The following chart shows the ABS population estimates exceed Victoria In Future (VIF) forecasts made in 2008. The (revised) estimated actual annual growth rates for 2009-10 and 2010-11 were actually below the VIF 2008 forecasts.

Growth in dwellings

Data on dwelling approvals is published by the Department of Planning and Community Development.

The following chart shows a jump in dwelling approvals in 2009-10, after three years of tracking close to VIF 2008 forecasts. Note that dwelling approvals and ‘net new dwellings’ are not quite measuring the same thing, as a number of dwellings are demolished each year.

Impacts on household sizes

The following chart shows the ratio of population growth to dwelling growth. In 2008-09, there was one new dwelling approved for every 2.9 new residents, but this has dropped to around one new dwelling for every 1.6 new residents in 2009-10 and 2010-11, thanks to a surge of dwelling approvals.

The chart also shows the VIF 2008 forecast of average household size (of occupied dwellings), and forecast ratio of population growth to dwelling growth. The forecast was for slowly declining average household size (following a recent trend).

Until 2010, population growth outstripped dwelling growth which would suggest that actual average household sizes have been forced upwards. Given the surge in dwelling approvals in 2009-10, maybe the housing “crisis” has eased?

Curiously, the ratio of new residents to dwelling approvals was only 1.5 in the early parts of the decade, much lower than average household sizes. Does this reflect small dwelling sizes approved in those years, or a housing glut? I’ll leave that to the housing experts.

Note that not all dwelling approvals represent an increase in available housing stock for permanent residents. The RBA has estimated that around 15% of dwelling approvals replace demolished dwellings, and around 8% are second homes or holiday homes.

Measuring progress against the Melbourne 2030 urban consolidation target

Melbourne doesn’t have population targets for different regions, but there was a target for dwellings growth in the (now defunct) Melbourne 2030 strategy. It stated the aim to:

reduce the overall proportion of new dwellings in greenfield sites from the current figure of 38 per cent to 22 per cent by 2030

The greenfield sites in Melbourne 2030 were mostly (but not entirely) located in the designated growth areas. As “greenfields” dwelling approval data isn’t readily available, I have used dwelling approvals in the designated outer growth LGAs as a proxy (from DPCD’s Residential Land Bulletin). The stated figure of 38% appears to match the data for these LGAs.

The dashed red line is a straight line interpolation of the Melbourne 2030 target for greenfields dwelling share. The outer growth LGA’s share of dwelling approvals has been higher than the target, but fluctuates a fair bit, and curiously took a dive around June 2010 but has seemed to level out at around 36% at the end of 2011.

The 2012 Victoria in Future projections show around 48% of net new dwellings in Melbourne occurring in the outer-growth areas between 2011 and 2026, far higher than the original Melbourne 2030 target of 22%.

(Note: The outer-growth LGAs’ share early in the decade was much lower. This may reflect urban growth that was still occurring in areas I have classified as “outer” as opposed to “outer-growth” before the Melbourne 2030 plan was released in 2002.)

However, if you look at absolute volumes of population growth in established areas, the story is very different. The next chart shows the VIF 2004 forecasts for population growth by region (I use VIF 2004 here because it came out soon after Melbourne 2030):

Urban consolidation in Melbourne has vastly exceeded the VIF 2004 forecasts, even with the slowdown in 2010, as the following chart attests:

[note: the above two charts have not been updated with 2011 data because I have misplaced my VIF2004 dataset. Can anyone help?]

I cannot comment on whether the 2004 forecasts were too conservative.

Unfortunately the available data doesn’t tell us whether this urban consolidation has occurred in designated activity centres, or it is spread throughout the urban area. It will be interesting to look at changes in population density between the 2006 and 2011 censuses.

Appendix: Definitions of regions

I have allocated local government areas to regions as follows:

Centre = Melbourne, Yarra, Port Phillip

Inner = Hobsons Bay, Maribyrnong, Moonee Valley, Moreland, Darebin, Banyule, Boroondara, Stonnington, Glen Eira, Bayside

Middle = Brimbank, Manningham, Whitehorse, Monash, Kingston, Greater Dandenong (all but one in the east)

Outer = Nillumbik, Maroondah, Yarra Ranges, Knox, Frankston, Mornington Peninsular (all in the east and south-east)

Outer growth = Wyndham, Melton, Hume, Whittlesea, Casey, Cardinia

Here is a map of Melbourne with the regions shaded (dotted white area indicates within the 2006 urban growth boundary, sorry the colours don’t match exactly).

Here is a reference map for those unfamiliar with Melbourne LGAs. You’ll need to click to enlarge so you can read the text.

[updated in May 2012]

Are greenhouse gas emissions from transport still on the rise in Australia? Are vehicle fuel efficiency improvements making a difference?

This post takes a look at available emissions data.

Australian Transport Emissions

The Department of Climate Change’s National Greenhouse Gas Inventory reports Australia’s emissions in great detail, and 1990 to 2010 data was available at the time of updating this post (there is usually more than a year’s lag before this data is released).

But the Department of Climate Change has recently began publishing quarterly reports that includes more recent transport figures. Here’s what the rolling 12 month trend looks like:

Transport emissions surged by 4.8% in 2011. The quarterly report attributes this growth mostly to aviation and road freight sectors.

Here’s the make up of those emissions to 2010:

In 2011 transport represented 16% of total Australian emissions (excluding land use).

Road transport contributed 86% of transport emissions (down slightly from a peak of 89% in 2004). Cars accounted for 50% of Australia’s transport emissions in 2010, but their share has been declining.

Note that the above chart does not include electric rail emissions (see below), indirect emissions, or emissions from international shipping and aviation. They are included in the following chart lifted from an 2008 ATRF paper by BITRE’s David Cosgrove shows this adds a lot on top (and the future projections are frightfully unsustainable). International transport emissions seem to sneak under the radar in the figures.

Per capita transport emissions

The following chart shows Australian transport emissions per capita have been fairly flat, with a drop in 2009 and 2010 but a resurgence in 2011:

An aside on electric rail emissions

Electric rail emissions are included under stationary energy, rather than “transport” in the main inventory. Melbourne train and tram electricity emissions have been estimated at 505  Gg for 2007 (ref page 8). Apelbaum 2006 estimated that Australia electric rail emissions in 2004/05 were 2,082 Gg (ref page 68), which is very similar to the inventory figures. I’ve struggled to find any other figures on electric rail emissions in the public domain.

Sectoral growth trends

Transport is now Australia’s second largest sector (after stationary energy), and transport has had the third highest rate of emissions growth (very close to second placed industrial processes).

Within the transport sector, civil aviation has had the strongest growth since 1990 (but note that a lot of this relates to the bounce-back from significant disruptions to domestic aviation in 1990). There’s been a lot of growth in light commercial vehicles, trucks and buses, and in more recent times, motorcycles and railways. Car emissions peaked in 2004 and have been trending downwards since.

Transport Emissions by state

The national inventory data allows us to see what is happening at a state level. As my interest is primarily in passenger transport here is a chart for road emissions:

The trends show strongest growth in Queensland and Western Australia, little growth in South Australia, and a recent decline in Victoria. It’s hard to see the trends on these charts for Tasmania and the territories due to the scale (sorry). And these growth rates will of course depend on various factors, such as economic development and population growth.

The following charts attempt to remove population growth by showing emissions per capita figures for each state (unfortunately the climate doesn’t take into account per capita (or per-GDP) emissions). Most states appear to be in decline except for Western Australia and the Northern Territory.

Car emissions reductions – mode shift or fuel efficiency?

The following chart shows car emissions per capita (which essentially removes freight from the road transport figures).

Again, all states (except WA and NT) show a decline in recent years, with stronger reductions in the states with cities showing more mode shift to public transport (refer to earlier post on BITRE data).

Is the drop in road transport emissions related to behaviour change and/or fuel/emissions efficiency?

The following chart shows that the average emissions per km of Australia cars has been trending downwards (I’ve used BITRE Working Paper 124 data on car kms travelled hence a little noise):

We’ve seen more significant declines in car emissions per capita since around 2004. So what if cars had made no improvement in emissions intensity since 2004? The following chart estimates what car emissions per capita would have been in that case:

Car emissions per capita  have dropped from 2.20 to 1.87 tonnes between 2004 and 2010. It would appear that emissions efficiency improvements since 2004 can explain 0.12 tonnes of this difference – around 36% of the overall decline. This would suggest that travel behaviour change has contributed around 64% of the reduction in car emissions since 2004.

What about transport emissions in cities?

As part of the Victorian Transport Plan, the Victorian Department of Transport commissioned the Nous Group to do a wedges exercise on Victorian transport emissions. This report included estimates of Melbourne’s 2007 transport emissions (12,270 Mt). In addition, Apelbaums’s Queensland Transport Facts 2006 was for a brief time on the internet and I was lucky enough to grab a copy. From that report, estimates of Brisbane’s 2003-04 transport emissions can be derived (7,312 Mt).

The breakdowns are remarkably similar:

What does this look like per capita? I’ve also added London and Auckland figures (though I am not aware of the make up of the Auckland data) to create the following chart:

Obviously these cities’ transport systems and energy sources are very different, but it shows what is possible even for a large city like London. Transport emissions will closely follow transport energy use per capita, which has been the focus of a lot of research, particularly by Prof Peter Newman (eg his Garnaut Review submission).

For 1995 measures of passenger transport emissions per capita for other cities, see this wikipedia chart created using UITP Millenium Cities Database for 1995. Note: these figures only include passenger transport and hence are different to the above.

Also, here is some data for US cities from the Brookings Institute, but it excludes industry and non-highway transportation so is not comparable to the above chart.

Where are transport emissions headed?

The most recent data suggests that Australian transport emissions are presently on the rise.

The 2010 Department of Climate Change projections suggest transport emissions will continue to rise, as shown in the following chart lifted from their website:

Most of the forecast growth is expected to come from freight vehicles (trucks and light commercials). Curiously they forecast quite small increases in car emissions.  This is based on forecast significant improvements in emissions intensity but also a return to growth in total car kms travelled (including car kms per capita).

Here is a chart of forecast of car emissions intensity, derived from their forecast data on vehicle kms and emissions:

Perhaps more optimistic is the assumption around future oil prices:

At the time of writing the oil price was $113/barrel (in April 2011 US dollars). Even with an inflation adjustment, this is certainly pushing the high end of their sensitivity testing. This prediction of oil prices doesn’t seem to take into account peak oil, or even much of an oil crunch (where supply cannot keep up with demand).

[Updated June 2012 to include ratios over time, inner city parking, and other updated data. First posted January 2010]

In this post, I test out a number of possible explanations for the trend in Melbourne’s public transport (PT) patronage growth over recent years to try to find out what might be driving growth. Is it population growth, CBD employment, fuel prices, international students, or the widening of CityLink? You’ll have to read on.

The first chart shows estimated financial year public transport patronage in Melbourne. Note: The method of patronage estimation has changed over the years for all modes. I have assumed comparable measurement for trains and trams and applied my own informed adjustments to bus patronage history (although I am less confident about the early 1990s – officially patronage stayed much the same despite significant service cuts).

Patronage was bumpy in the 1990s, followed by modest growth for about 10 years and then a distinct uptick in growth around 2004/05.

I will attempt to find an explanation for this pattern in this analysis (particularly more recent years). Short of a fully comprehensive analysis, I will compare trends in possible drivers with the trend in public transport patronage.

Note due to the nature of available data sources, the years covered in chart will vary – you can spot each year by checking the year range in the chart titles.

Population growth

If this was a dominant factor then you’d expect to see a straight line on this chart. It does show that as population growth has increased, so has public transport patronage growth, but the overall relationship isn’t very linear. Here’s the ratio of patronage to population for all of Melbourne:

We know that public transport use is higher closer to the inner city of Melbourne. So is public transport better correlated with inner city population? The following charts compare PT patronage with “inner” population (LGAs of Melbourne, Port Phillip, Stonnington, Yarra, Hobsons Bay, Maribyrnong, Moonee Valley, Moreland, Darebin, Banyule, Boroondara, and Glen Eira).

The correlation appears to be slightly stronger, but still not very strong.

Employment

People often use PT to get to work. The next chart compares total employed people in Melbourne to public transport patronage (employment figures average monthly total employed people for each financial year, from ABS Labour Force surveys).

Again, the relationship isn’t very linear – despite a small growth in employed persons in 2008-09, public transport patronage still increased significantly. But then in 2009-10, employed persons grew but patronage didn’t. Likewise PT patronage increased more between 2000/01 and 2001/02, despite little growth in total employment, whereas in the previous year employment grew strongly, but PT patronage didn’t.

This chart also shows kinks in the trend around 2005 and in 2008-09 – so employment doesn’t seem to explain the kink. Note also that journeys to work only make up around 40% of public transport trips in Melbourne (according to VISTA data). And public transport has a very low mode share of journeys to work outside the city centre.

Here is the relationship shown as a ratio over time:

ABS publish figures monthly, and here’s the picture for total persons employed in Melbourne. There was virtually no growth between late 2010 and May 2012 (at least). There was also a flat patch between the start of 2008 and the middle of 2009 (2008-09 shows substantial patronage growth on public transport).

City population (including visitors)

Another hypothesis suggests that if PT is heavily focussed on the inner city (where it has the highest destination mode share), then if more people need to travel to the inner city, this would probably increase PT patronage. This sounds very plausible, especially for trains and trams. The City of Melbourne has estimated weekday daytime population for 2004 to 2010 calendar years. So I am mixing calendar year visitor data with financial year PT patronage – which is not ideal. Anyway, here is what that relationship looks like:

The year 2005/06 includes the 2006 Commonwealth Games that were held in March 2006 and boosted city visitors considerably. If you take out this anomaly, the other four data points look like they form a very linear pattern (as drawn), suggesting it is quite probably a strong driver. There was weak growth in both public transport patronage and city population in 2009-10, suggesting a strong relationship.

The next chart shows the same relationship as a ratio over time. The 2006 anomaly is much less noticeable (note not a huge variation in weekday daytime population the chart above). This suggests that City of Melbourne weekday daytime population is not directly proportional to public transport patronage (in other words: the y-intercept is not zero).

A longer time series of CBD data is available for  employment, thanks to the City of Melbourne’s Census of Land Use and Employment. As it hasn’t been an annual survey (red dots are census results), I have made linear interpolations between the years for CBD employment numbers.

Between 1997/98 and 2007/08, the trend was remarkably linear suggesting a strong relationship. When CBD employment grew very weakly between 2002 and 2004, so did PT patronage. Looking at census data for 2001 and 2006, we know that PT mode share to the Melbourne CBD for journeys to work (well, technically the inner Melbourne SLA which is much the same) grew only slightly from 59.1% to 60.8%. So it looks fairly safe to assume that the growth in people using PT to get to jobs in the CBD grew at much the same pace as CBD employment itself.

However between 2007/08 and 2009/10 the trend seems very different. Public transport patronage grew strongly even though the number of employees in the Melbourne CBD did not show much growth at all.

Here’s the same relationship expressed as a ratio over time. The ratio is remarkably flat over time.

Employment has grown around the Melbourne CBD in neighbouring Docklands, Southbank and there are also a number of office buildings in East Melbourne. In fact between 2008 and 2010 there were around 3,300 new jobs in the CBD, and 11,400 new jobs in Docklands.

These areas are also well serviced by public transport. Unfortunately data for these surrounding precincts only goes back to 2002. Here’s a chart comparing PT patronage to total employment in the CBD, Southbank, Docklands and East Melbourne for 2002 to 2010:

Suddenly the trend looks a lot more linear, with a deviation only for the interpolated result in 2008-09 (which might be a product of the GFC in that timeframe). CBD employment alone is no longer a strong driver of public transport patronage. Although bear in mind that public transport mode share in these CBD fringe areas was much lower than the CBD in 2006 (see previous post).

Here’s the same relationship as a ratio over time, which is a little flatter:

While the CLUE data series only runs until 2010 at present, a more timely and regular dataset that might be related to CBD employment is occupied office floor space, calculated from the Property Council of Australia’s Office Market Reports. While I do not have access to the reports themselves, much of the data is available on the internet in various forms, and I have used that data to reconstruct the data series (there is chance of errors creeping in, particularly for earlier years).

Here is the trend in occupied Melbourne CBD office space:

Slow growth until about 2005, then very strong growth. Does that trend sound familiar?

This charts shows very strong correlation (r-squared = 0.99). Although there are still a few small kinks such as 2009-10.

Here it is as a ratio over time, which is not entirely flat:

But the overall strong relationship this confirms the high likelihood of CBD employment being a very significant driver of public transport patronage. Ideally Southbank, Docklands and East Melbourne should be added to the mix, but the data is not readily available.

Inner city parking

A commenter on this blog suggested I look at parking in the inner city. The following chart looks at public transport patronage and total commercial parking spaces the CBD, Southbank, Docklands and East Melbourne.

Between 2004 and 2006, commercial parking spaces grew strongly, while public transport patronage did not. Then public transport patronage grew strongly and there was actually a decline in the number of commercial parking spaces.

I would expect the price of parking to be a stronger driver of public transport use than the capacity available. Unfortunately I do not have a long enough time series of parking prices to test this hypothesis. See also my post on the Melbourne CBD.

Fuel prices

I have taken the monthly average unleaded fuel prices for Melbourne, adjusted for CPI, and then averaged the months for each financial year, to produce the following chart:

Fuel prices are highly volatile, even on an annual basis. Again, even though fuel prices dropped in 2008-09, PT patronage still increased. There seems to be a lot more at work than fuel prices. That said, since 2004-05, real fuel prices jumped from around 115 cents to over 130 cents and have remained higher since. So fuel might be an explanation for the kick up in PT patronage since 2005, perhaps more as the breaking of a psychological price barrier. Or perhaps people’s responses to fuel prices have longer lag times that wash out short-term fluctuations - as people make major decisions – such as the decision to purchase a new car or not. More on that later.

International students

Another hypothesis is that the recent boom in international student numbers drove public transport patronage, as many international students come from countries where public transport is the “default” mode. And while their finances might stretch to studying in Australia, it might not stretch to owning a car (certainly in the car ownership maps we see low car ownership around many universities).

Unfortunately I’ve only found complete data for financial year 2002/03 onwards, and only at the state level (more detailed data is not freely available).

The boom in international students looks like it really took off in 2007, but fell away sharply in 2009-10 and has been lower since. In 2009-10 patronage grew more slowly, perhaps reflecting the drop in international student numbers. But 2010-11 patronage growth was strong again, despite little growth in international student numbers.

The international student numbers are very small in comparison to the total patronage. However if half of those students averaged 10 trips per week for say 40 weeks a year (purely a guess), that’s 38 million trips. I’ve not got data on what their PT use is actually like (I suspect many live close to their school or university and actually walk). And their boom doesn’t coincide with the boom in public transport patronage that started around 2005. So they might be having an impact – hard to conclude much.

Road congestion

Until 2006-07 there was a fairly linear correlation, but then speeds only slowed slight while public transport patronage increased. In 2009-10 speeds increased and public transport patronage grew slowly. Perhaps congestion wasn’t a driver for patronage growth in 2009-10?

Another point to note is the scale on the X axis – the average speed hasn’t changed by very much. Although the variations in AM peak speeds for particular road segments are likely to have changed more significantly, I somewhat doubt whether the average driver would notice the difference between 35.8 km/h and 34.8 km/h (the change between 2005/06 and 2007/08).

The opening of CityLink in 2001 may have led to a slight increase in AM peak speeds, but this seems to have been quickly eroded the following year (so do new freeways ease congestion?). I’m not sure why traffic sped up in 2003/04, but then dropped again significantly the next year.

Road congestion impacts the majority of the tram network, and essentially all of the bus network. So perhaps only trains are attractive as an alternative to driving in congested traffic. Here’s same chart again but plotted only against train patronage:

The chart looks much the same. So congestion might be a driver of PT patronage growth, but it probably doesn’t explain the growth in tram and bus patronage, and the relationship isn’t nearly as linear as other factors.

Perhaps also at play here is congestion being relieved for non-radial commuting, where PT had a low market share beforehand anyway. Further research might look at congestion on CBD-radial roads only, though even then, many will also cater for some cross-city trips.

Two of the radial freeways that feed inner Melbourne are operated as the CityLink toll roads, and quarterly data is available on average daily transactions. If the CityLink toll roads compete with public transport it is probably mostly with trains for longer distance travel to the inner city. Here is a chart showing growth in CityLink transactions and train patronage:

There was very little train growth in the first few years of CityLink (which started in 2001). But then train patronage grew strongly from 2005 while CityLink transaction growth went flat until 2010. A major upgrade project on the eastern leg of CityLink (M1 upgrade) caused delays between 2007 and early 2010, and there was little traffic growth. After the project was largely completed and the fourth lane opened, traffic growth accelerated over 2010. This happened at much the same time that trains recorded weak patronage growth. Then in 2011, train patronage grew again, while traffic seems to have flattened again.

To take a closer look at the two growth rates, here are financial year growth rates on CityLink and trains:

After most of the works were completed, CityLink transaction growth exceeded train patronage growth in 2009-10 and 2010-11 (note that the flattening evident in the previous chart doesn’t show with annual data). The evidence suggests there could well be a relationship between freeway capacity and train patronage, and that the M1 widening project may have reduced patronage growth on the train network. It has certainly enabled a return to strong growth on CityLink.

Car ownership

People who don’t own cars are probably much more likely to use public transport. The following chart uses cars per 100 persons aged 20-74 (as a proxy for people of car driving age).

This chart shows in the early 2000s that car ownership rose quickly, while public transport patronage growth was slow. Then from 2006-07, car ownership levels peaked and public transport patronage grew quickly. Car ownership dropped in 2008-09 just as public transport patronage surged, but recovered in 2009-10, as public transport stalled. This suggests there may be some relationship between PT patronage and car ownership, but the annual change rates aren’t always consistent.

Service kms

Another potential driver of PT patronage is the amount of service provided. Thankfully, this data is available in Victorian State Budget papers (hidden away in budget paper 3) on the number of timetabled service kms for each mode. As the modes are quite different, I’ve plotted modal charts:

Train patronage doesn’t seem to be very strongly related to timetabled kms. Perhaps this is because the service levels at peak times on most lines are already attractive from a frequency point of view at least. Many of the extra train kms are providing capacity without a substantial jump in frequency (although some of the additional kms have been in off-peak periods).  That’s not to suggest there isn’t a relationship, just that it doesn’t look likely to be the dominant driver. In the early 2000s it seems that there wasn’t a strong response to increased timetable kms (including Sydenham electrification in 2002), while in the mid 2000s patronage grew despite kms staying much the same (other factors must be at work).

Again, not a strong relationship between tram kms and patronage, despite strong growth in timetabled kms in the early 2000s (partly from tram extensions into lower density suburbs in 2003 (Box Hill) and 2005 (Vermont South) – see here for more history). It also looks like some cuts in 2000 (when some city routes had to be joined due to the loss of W class trams) were done in a way that didn’t result in a loss of patronage. Perhaps because service frequencies were still fairly good after the cuts.

There does seem to be a stronger relationship between bus kms and patronage. This is perhaps to be expected as bus service levels are on average very low in Melbourne, so improved service levels are likely to result in existing users travelling more, and better attract new users.

What is unexpected is that patronage grew at much the same rate as kms between 2005-06 and 2009-10 – an average elasticity of around 1, which is much higher than you would normally expect. In 2010-11, the annual elasticity fell to 0.42. One possible explanation for the slightly steeper rate in recent years is that more of the new kms have come in the form of SmartBus kms (with higher frequencies). We know that long run implied service elasticities for SmartBus can be around 2 – which is higher than the textbook expectation of service elasticities of up to 1 in the long run. Bus upgrades in the early 2000s were a little more focussed on providing new low-frequency services to the urban fringe, which would be unlikely to lead to as much patronage growth.

Here’s a chart showing the ratio of patronage to service kms for all modes:

This chart shows increasing intensity of use of trains and trams between 2004 and 2009, while buses have remained around 1.0-1.1 boardings per service km for at least 12 years running. The significant difference between trams and buses is best explained by the territory covered: trams mostly the CBD, buses mostly not the CBD.

Comparing annual growth/change rates

The following table shows the annual change in Melbourne public transport patronage and a number of potential explanatory factors. I’ve used conditional formatting such that darker green cells indicate values you might expect to contribute to strong PT patronage growth. Rows that have dark green in the same years as PT patronage are potentially stronger at explaining the trends in public transport patronage. I’ve also included the r-squared value for a correlation for each factor compared to PT patronage (based on annual growth rates, not actual values). You might need to click to enlarge and make it easier to read.

The table confirms a strong correlation with CBD+fringe employment, City of Melbourne visitors (2006 removed due to Commonwealth Games anomaly), international student enrolments, population (particularly inner city), and CityLink volumes.

Fuel prices don’t show a strong relationship, although it is hard to believe that they would have no impact. If you offset the fuel price changes by one year the correlation rises to 0.3 so there might be some lag involved.

Conclusions

Based on these simple charts, I surmise that City of Melbourne (LGA) visitations is likely to be one of the strongest drivers of overall PT patronage in Melbourne (but certainly not the only driver). And it certainly stands to reason, given PT’s dominant mode share of travel to the inner city.

But international students, radial motorway traffic volumes, population are probably also having an impact. The impact of fuel prices appears to be more complex.

Buses probably show less response to growth the inner city travel market (as most do not serve the city centre), so service kms are likely to be the strongest driver of bus patronage.

The PCA’s Office Market Report provides the most timely and frequent data relating to CBD employment growth and reveals much slower growth over calendar 2011 (1.4% in occupied office floor space). We might find this trend reflected in slower patronage growth on the train network as  figures are published.

This post will focus on what personal circumstances are associated with higher and lower public transport use, and possibly why (although of course correlation often doesn’t mean causation). There’s a lot that is as you might expect, but also a few hunches confirmed and possibly some surprises (particularly in part two).

This post (part one) looks at geography, motor vehicle ownership, and driver’s license ownership. The second part will look at other personal circumstances.

About the data

Most of the analysis in this post comes from the Victorian Integrated Survey of Transport and Activity (VISTA), using the 2007-08 and 2009-10 datasets combined. The survey covers Melbourne Statistical Division (MSD), Geelong, Bendigo, Ballarat, Shepparton and the Latrobe Valley (that is, the capital and major regional cities in Victoria, Australia). The combined dataset includes some 85,824 people in 33,526 households who recorded their travel for one calendar day each.

When I measure public transport use, I am measuring whether a person used any public transport on their nominated travel survey day (the survey covered every day of the year). See here for a map showing the geographic breakdown of Melbourne into city, inner, middle and outer.

I must say thanks to the Victorian Department of Transport for making this data available for analysis at no cost.

Public transport use by geography

Firstly as a reference, here is what public transport use looks like spatially across Melbourne and Geelong. Sample sizes with Statistical Local Areas (SLAs) range from around 200 to 1300 people in Melbourne, so the margin of error will be up to around +/-7% in some areas (including a few of the outer suburban areas).

Note: the Melbourne CBD and Southbank/Docklands SLAs unfortunately have very small sample sizes (9 and 31 respectively) so should be ignored (in the map below the 27 belongs to the CBD, the 28 belongs to Southbank/Docklands and the 21 above is “Melbourne (C) – Remainder”).

(click to enlarge)

It is little surprise that public transport use declines in areas further from the city centre as public transport supply decreases.

The following chart shows public transport use also had a lot to do with whether the person travels to/from the City of Melbourne on their survey day:

The green line indicates the proportion of all persons who travelled to/from the City of Melbourne on their survey day, which decreases with distance from the city.

And here is a scatter plot showing public transport use and the proportion of people travelling to/from the City of Melbourne (excluding those who live in the City of Melbourne) at the SLA level:

That’s a strong relationship. And the biggest outlier at {36% travel to/from Melbourne, 16% public transport use} is Port Phillip – West, which is just on the border of the City of Melbourne where walking would be a significant access mode.

So there is little surprise that public transport use had a lot to do with distance from the CBD (most probably as a proxy for public transport supply), and whether a person visited the City of Melbourne (where public transport is a highly competitive transport option).

Has it got anything to do with how close you live to a train station? I’ll just look at Melbourne and exclude the inner city area where trains are probably less important because of the plethora of trams and ease of active transport.

Proximity to train stations has an impact, but perhaps not by as much as you might expect.

Overall people living closer to train stations were slightly more likely to travel to/from the City of Melbourne, and if they did, they were a little more likely to use public transport.

But only those within 1km of a station were more likely to use public transport if they didn’t travel to/from the City of Melbourne (7% v 5%). Because most people living near to a train station didn’t travel to the City of Melbourne, their average rate of public transport use wasn’t much higher than those living further away.

This result is consistent with the 2006 journey to work patterns.

But what else might explain public transport use?

Motor vehicle ownership

If you don’t own a motor vehicle, you’re going to need to some help getting around, particularly for longer distance travel.

I’ve created a three level measure of motor vehicle ownership:

• No MVs: No household motor vehicles at all (you’ll be reliant on lifts, taxis, public transport, or public car share schemes for motorised transport)
• Limited MVs: A household where there are more licensed drivers than motor vehicles (some sharing of vehicles or use of other modes such as public transport will probably required from time to time)
• MV saturated: A household where there are at least as many motor vehicles as licensed drivers (sharing vehicles between drivers is unlikely to be required)

The VISTA data shows that 25% of households had limited or no motor vehicle ownership, and as you might expect it varies by geography, with higher rates of motor vehicle ownership in the outer suburbs.

In fact here is a map showing the percentage of people living in households with saturated car ownership around Melbourne and Geelong according to VISTA (again, margin of error is up to around 7%). Click to enlarge.

You can see very high rates of saturation in the fringe areas of Melbourne, and much lower rates in the inner city.

A more detailed view of car ownership is possible with census data. The following map shows the ratio of household motor vehicles to 100 people aged 20-74 in each census collector district in 2006 (note: I have had to assume “4+” cars averages to 4.2, and no response implies zero cars). The red areas have saturated car ownership as a district (probably closely correlated with households with saturated car ownership).

(click to enlarge)

The census figures show a slightly different distribution but should be more accurate (being a census not a survey). Suburban areas with lower car ownership were generally those that are less well-off (and the large green areas on the western fringe of Melbourne are actually mostly prisoners, who tend not to own cars).

It will come as no surprise that there was a pretty strong relationship between motor vehicle ownership and public transport use:

And here is a scatter plot of saturated car ownership and PT use by SLA (removing SLAs with less than 200 people surveyed):

That’s a fairly strong relationship (the r-squared is higher still (0.87) at the LGA level).

Trends in car ownership are examined in another post.

Driver’s license ownership

It’s not much good having a motor vehicle to yourself if you are not licensed to drive it. In the VISTA sample, the driver’s license ownership rate peaks for people aged 40-54, and drops off more considerably after 85 years of age. Interestingly, 3.5% of people in the sample had their learner permit.

So are there heaps of older people out there without a driver’s license?

Not especially. It almost looks as if many people die in possession of a driver’s license (hard to be sure though).

(note: the chart averages the population in each category over the two VISTA surveys)

In part two we will see the rates of public transport use by age.

Here’s a map showing the percentage of surveyed people aged 20-89 who owned a probationary or full license (click to enlarge).

Similar to motor vehicle ownership, driver’s license ownership was highest in the outer areas of Melbourne, but still quite high in the inner city (please ignore the CBD and Southbank/Docklands figures of 100% and 96% as the sample sizes are too small).

And it will be no surprise that people with a full driver’s license were least likely to use public transport:

Maybe they use public transport less because they have a driver’s license, or maybe they are forced to have a driver’s license because of low public transport supply. I would guess a bit of both.

What you might not have expected is for people with a learner permit to be the most likely to have used public transport, even more than people with no licence at all. They are mostly younger people and you will see their rates of public transport in part two of this series.

Here’s a scatter plot of driver’s license ownership and public transport use by SLA:

The relationship is much weaker than for saturated car ownership.

In fact, 55% of people who used public transport on their survey date had a full or probationary driver’s license. As driver’s license ownership is more saturated than motor vehicle ownership, it appears to be a weaker driver of public transport use.

Click here for some interesting research about why young people are driving less.

How do motor vehicle ownership and driver’s license ownership interplay?

In the following chart I have used “independent license” as shorthand for probationary or full license.

This again suggests that household motor vehicle ownership had more bearing on public transport use than driver’s license ownership (for driving aged adults at least).  In fact, those with a driver’s license but no household vehicles were MORE likely to use public transport than those without a license (I’m not entirely sure why, when I disaggregate the sample sizes get small). But for adults in households with motor vehicles, people without an independent driver’s license were more likely to use public transport than those with licenses.

Home location, City of Melbourne travel and motor vehicle ownership

These three factors seem to be the strongest indicators of public transport use. So what do they look like together?

From this chart we can see:

• For people travelling to/from the City of Melbourne:
  • Public transport use was generally higher for people living further from the city.
  • Public transport use was lower for people from households with saturated motor vehicle ownership (compared to those with limited motor vehicle ownership).
• For people not travelling to/from the City of Melbourne, public transport use seems largely related to distance from the city centre (a rough proxy for PT supply) and the level of motor vehicle ownership, with the exception of those in the inner city where non-motorised transport modes are likely to be more significant.

Is driver’s license ownership still a driver? Unfortunately I can only sensibly disaggregate further for people who didn’t travel to/from the City of Melbourne. The following chart looks at motor vehicle and license ownership groupings with a sample size of 200 or more for different geographies.

This suggests that license ownership was quite a strong driver of public transport use. Those without a license in otherwise saturated households were much more likely to use public transport (purple line) and the red dot indicates people with no license in a limited motor vehicle ownership household were quite strong users of public transport.

Okay, so those findings probably won’t shatter your understanding of the world, but I always find it interesting to test whether your hunches are true.

In part two of this series, I’ll look at patterns across age, gender, income, employment status and household type. There are perhaps a few more surprises in those results.

Part one of this analysis looked at how geography, motor vehicle ownership, driver’s licence ownership related to the use of public transport.

This second post will look at how other personal circumstances relate to public transport, including age, a person’s main activity (occupation), income, employment and household type. Much of this is purely for interest, but I have uncovered a few interesting factors that relate to levels of public transport use.

The analysis is of data from the 2007-08 and 2009-10 Victorian Integrated Survey of Travel and Activity (VISTA).

Make sure you read part one first, so you know how I have gone about this analysis and can decode the terms and acronyms used.

Age and gender

The following chart shows very clearly that public transport use (which includes school bus use) peaked for teenagers and fell away with age:

The chart debunks the myth that older people switch from cars to public transport as they give up driving. For males the trend in public transport use continued to decline with age, while females remained at around 7%.

Also of note is that young children had the lowest rates of public transport use of any age group. As you’ll see in a moment, they travelled a fair bit – just not on public transport.

Women aged 20-29 and over 60 were more likely to use public transport than men, while men aged 35-44 were more likely to use public transport than women of the same age. I’ll come to possible reasons for this soon.

As you might expect there were very similar patterns in driver’s licence ownership (see part one) and public transport use by age; although public transport use continued to be relatively high into the 30-34 age bracket and driver’s licence ownership is over 80% by age 30.

So why are there these discrepancies for people in their 30s and 40s? I’ll get to that soon.

But first, is public transport use related to the amount of travel people make?

People aged 40-44 were the busiest travellers with 3.7 trips per day on average, which then fell with age. Between the ages of 20 and 44 people made many more trips, but became less likely to use public transport with age.

Young children do travel a fair bit, but rarely on public transport.

The average number of public transport trips per day peaked for teenagers, who also had the lowest overall trip making average.

The average number of active transport trips (walking and/or cycling only) did not seem to vary considerably by age.

Main activity

The VISTA survey classifies people by their main activity in life (you might think of this as occupation). Here’s a look at average public transport use on school weekdays.

As we saw with age, public transport use peaked for secondary school children, with full time tertiary students not far behind. Children not yet at school were the least likely to use public transport, with those keeping house the next least likely.

Is that because of their driver’s licence and car ownership status? The following chart tests public transport use by main activity and groupings of licence and motor vehicle ownership (where I could get a cohort of 200 or more – missing values are not 0%).

This chart suggests that full time students, full time workers and part time workers were generally more likely to use public transport even if they had access to private transport. Those unemployed, “keeping house”, or retired were only somewhat likely to use public transport if they had limited access to private transport.

So, motor vehicle ownership does not explain the low rate of public transport use by those “keeping house”. I’ll come back to that.

I expect the general explanation for the above chart is that public transport is more likely to be competitive to places of full time work or study, particularly those in the inner city. We know from a previous post that public transport use to suburban employment destinations is very low.

Here’s the picture for journeys to education in VISTA, by the location of education activity (note: cohort sizes down to 120 – a margin of error of 9%).

Very few primary school children took public transport to school (except in the regional centres), while 25-40% of suburban secondary and tertiary students used public transport. Public transport had a very high mode share in journeys to tertiary education in the inner city of Melbourne (where public transport works well and students probably cannot afford to park, even if they can drive a car).

What about trip making rates by main occupation?

Part-time workers made the largest number of trips on average, while the unemployed and retired travelled the least. Those keeping house did a lot of travel, but very little of it on public transport.

And in case you are interested in the relationship between age and main activity…

No big surprises there when you think about it. Notice that part time work became much more common from the late 30s.

Income

What impact does income have on public transport use?

I have used equivalised weekly household income per person as my measure, as this takes into account household size and the number of adults/children in those households. It essentially brings all households to the equivalent of a solo adult.

The pattern shows those on lower (but not very low) incomes were the least likely to use public transport. Those with no income were just as likely to use public transport as those on $2500 per week equivalised. So that debunks the myth that public transport is only for poor people! In fact people on very high incomes were more likely to use public transport than those on $500-1000 per week (peaking with those on $2250-2500 per week).

What’s driving this pattern? Well, we know that people on higher incomes are more likely to live closer to the city and probably work in the city centre, so what if I take geography out of the equation? The following chart looks at patterns within each home sub region and excludes people who travelled to or from the City of Melbourne (cohorts of less than 300 people not shown).

The trend now looks the reverse – people on higher incomes used public transport less for trips outside the City of Melbourne. But is that because people on higher income were more likely to travel to the City of Melbourne?

Well they certainly were much more likely to travel to/from the City of Melbourne. The shape of this chart is very similar to the chart showing overall public transport use by income, but the variation is much greater.

In order to remove the impact of travel to/from the City of Melbourne, I’ve calculated the use of public transport by those who did and those who did not travel to/from the City of Melbourne (chart shows cohorts with 200 or more):

While the rate of public transport use went down by income for the two divisions (travel to/from City of Melbourne or not), the overall rate increased with income as a result of blending – at higher incomes more people were travelling to/from the City of Melbourne which lifts the overall average use of public transport.

We know from part one that people living closer to the centre of Melbourne are more likely to use public transport for trips not involving the City of Melbourne. So here is a chart showing the rates of public transport use by income for those people not travelling to/from the City of Melbourne:

This suggests there may be a relationship between income and public transport use, though it is much less significant a determinant than whether or not someone travelled to the City of Melbourne.

But what about the other factors – like motor vehicle and licence ownership? In the following chart I’ve again limited myself to groupings where I could get a cohort of 200 or more (margin of error up to 7%).

The pattern now looks like slightly increasing public transport use with income for some groups, when taking out motor vehicle/licence ownership (although the variation is within the margin of error so it might not be a significant pattern).

Might geography be at play here – that wealthier people live in areas with greater PT supply (ie closer to the city)? I cannot prove that because I cannot disaggregate this further.

But thinking about it, wouldn’t licence and motor vehicle ownership increase with income? And we saw in part one that public transport use declines with licence and motor vehicle ownership.

Well, here is licence ownership by income (for adults):

And here is motor vehicle ownership by income:

Licence ownership and motor vehicle ownership certainly increased with income, which you would expect to generally lead to lower public transport use.

Furthermore, people in higher income households travelled more often on average, which might increase their chance of using public transport:

This leads me to conclude that income is very likely a driver of public transport use, and that people on higher incomes are less likely to use public transport, all other things being equal (though I haven’t tested for every other thing!). But the fact that people on higher incomes were more likely to travel to travel to/from the City of Melbourne trumped this income effect.

Employment type

As we saw in a previous post, location of employment has the biggest bearing on public transport use. But here are a few breakdowns anyway (on weekday journey to work):

For comparison, here are the figures from the 2006 census for the whole of Victoria:

The margin of error on the VISTA data is around 4%, so they figures are reasonably similar.

And sure enough the jobs most prevalent in the inner city have the highest public transport mode share:

The two groups with highest public transport use are more likely to work in the inner city, so little surprise that they have the highest public transport use.

Managers are probably widely distributed across the sample area, and many would have packaged cars and/or parking as part of their salary packages.

Unfortunately the dataset is too small for me to disaggregate to people who don’t live or work in the City of Melbourne (in a previous post I found managers had lower rates of public transport use in the journey to work to the Melbourne CBD).

What about employment industry?

I suspect public transport use by employment industry will largely reflect employment location. Melbourne’s recent strong public transport growth could well relate to the changing mix of employment, with a move away from manufacturing and towards professional services. This might also be fuelling growth in CBD employment.

Household type

How does public transport use vary by household type? In some recent work I was looking at young families more closely, as they are a very common household type moving into growth areas on the fringes of our cities. I’ve defined a young family as being one or two parents with all children under 10 years of age.

Consistent with very low rates of public transport use by young children, young families were least likely to use public transport (taken as the average across all household members). Sole person and mixed household structures were most likely to use public transport.

The above chart is a blend of parents and children, so here’s public transport use by age and household type:

You can see between the ages of around 20 to 44 that parents (with children at home) had much lower rates of public transport use than other people. This suggests that becoming a parent is probably a major cause for people to abandon public transport. I suspect this may be because travelling with young children on public transport can be a challenge. But maybe they are also time poor (more on that shortly).

I note also that sole person households had higher rates of public transport use, particularly after 35 years of age. Perhaps the slow demographic shift towards smaller households might lead to increased public transport use? A topic for further research perhaps.

Anyway, investigating family households further, I have defined each person by their household family position: mum, dad, child, or other (everyone not in a simple family household structure).

You can see here that children’s public transport use peaked at ages 15-19 and then fell with age. My cut-off for this chart was 400 persons in the cohort, and yes there were over 400 children aged 35-39 living with their parents in the sample.

Mums used public transport a lot less than dads, particularly younger mums. Perhaps this is because they made a lot more trips per day?

This result is consistent with the data showing that mums were much less likely to be working full time than dad. In fact over half were “keeping house” or working part time. Be careful of the subtle colour differences in the following chart:

So does making more trips in a day reduce your chance of using public transport?

This chart excludes people who travel to/from the City of Melbourne (sorry about the mouthful of a chart title!). Having three or more trips in your day significantly reduced your chances of using public transport, but only really if you had limited household motor vehicle ownership. I’m guessing that the motor vehicles were more used by the people in the household who had to make more trips.

Curiously, a lot of single parents are retired. The data shows them to indeed be of retirement age – probably with adult children caring for them. They are probably not what you generally think of as single parent households, but technically that’s how they get classified.

So what are the strongest determinates of public transport use?

In my first post on this topic, the likely determinants of public transport use were:

• Much higher for people travelling to/from the City of Melbourne (possibly increasing with home distance from the central Melbourne)
• Decreases with distance from central Melbourne (probably a proxy for PT supply)
• Higher for people with no or limited household motor vehicle ownership.
• Higher for people without a probationary/full driver’s licence.

From this post we can probably add:

• Very low usage by young children (primary school and below);
• Very low for those for keeping house or working part time (often mums);
• Lower for parents (in family households with non-adult children);
• Lower for people on higher incomes (all other things being equal, which they usually are not!); and,
• Lower for people making more trips per day.

Ideally I should run a logistic regression model to the data to analyse the drivers more systematically. I might see if I can do that in a part three.

With the gradual release of 2011 census data, I thought it would be worth looking at some transport related themes. I’ll start with residential density (for my look at 2006 density, see an earlier post). This post looks at 2011 density, and how density has changed over the years.

The big issue with residential density is how you measure it. In showing it graphically, I prefer to use the smallest available geographic areas, as that can remove tracts of land that are not used for residential purposes (such as parks, creeks, wide road reservations etc).

At the time of posting, 2011 census population data was only available at “Statistical Area Level 1″ (SA1). In 2013, population figures for the smallest ABS geographic unit – mesh blocks – will be available for a fine grain look at density.

However, land use descriptions for mesh blocks were available at the time of posting. I have used the indicated land use of each block to mask out land where you would not expect people to live – including land that is classed as parkland, industrial, water, or transport.

So the map below shows the residential density of Melbourne for SA1s, after stripping out non-residential land. The densities will be higher than if you simply looked at straight SA1 density, but I think they will be a better representation (although not as good as what can be drawn when 2011 mesh block population figures are available). You’ll want to click on the map to zoom in.

The map doesn’t show areas with less than 5 persons per hectare (otherwise there would be a sea of red in rural areas). Many of the red areas on the urban fringe are larger SA1s which will be fully residential in future but were only partially populated at the time of the census. However some are just low density semi-rural areas.

Note that the older middle and outer eastern suburbs are much less dense than the newer growth areas to Melbourne’s north and north-west.

How has density changed between 2006 and 2011?

I think the most interesting comparison will be between 2006 and 2011 mesh block density maps. We will be able to see in detail where densification has occurred, and it will be particularly interesting to look at activity centres.

Until that data is available, the smallest geography level with time series data available is at Statistical Area Level 2 (SA2) – which generally contain one large suburb or a couple of smaller suburbs. Data is available for all years 1991 to 2011 (estimates for June 30, based on census results).

The following map shows the change in estimated density from 2006 to 2011 (using full SA2 land parcels, including any non-residential land). This could equally be considered density of population growth. Unfortunately urban growth in pockets of larger SA2s are less likely to show up as the impacts are washed across the entire SA2, but it gives some idea.

The map shows several SA2s with reduced population density, mostly outer established suburbs:

• Mill Park – South -1.4 persons/ha
• Mill Park – North -0.6 persons/ha
• Bundoora West -0.5 persons/ha
• Kings Park -1.5 persons/ha
• Keilor Downs -0.8 persons/ha
• Wheelers Hill -0.7 persons/ha
• Toorak -0.4 persons/ha
• Hoppers Crossing South -0.9 persons/ha
• Rowville Central -0.5 persons/ha
• Clarinda – Oakleigh South -0.5 persons/ha

There are increases in many areas, particularly:

• the Melbourne CBD and immediate north
• many of the inner suburbs
• the outer growth areas, particularly to the west, north and south-east.
• Ormond – Glen Huntly, up 4.4 persons per hectare (not sure what the story is there!)

How has density changed between 1991 and 2011?

Here is an animation showing how Melbourne’s density has changed between 1991 and 2011. You’ll need to click on this to see the animation and more detail.

Note in particular:

• The CBD and Southbank area going from very sparse to very dense population.
• The significant densification of Port Melbourne.
• The significant densification of the inner northern suburbs, particularly in the late 2000s.
• Some large SA2s in the growth areas don’t show up as becoming more dense as they are very large parcels of land with urbanisation only occurring in a small section. This is especially the case for Wyndham and Whittlesea.

So what was Melbourne’s “urban” density in 2011?

That all depends how you define “urban” Melbourne! The table below shows some calculations based on different criteria for including land. The more restrictive criteria will give an answer that is more of a “residential” than “urban” density.

The different geographies are confusing, so I have produced a map below to try to help.

When more census data is available I will aim to update this list (eg to include density of the Melbourne urban locality).

*This area is actually larger than the row above, because more SA1s meet the criteria. Confused? It’s because I’ve cut out the non-residential land from each SA1, which increases the average density of what remains meaning more SA1s meet the criteria. The residential land area of the extra SA1s was slightly more than the non-residential land that was cut out. On the map below there are some yellow and green areas that do not have red “underneath”. The red areas you see on the map below are non-residential land in SA1s.

I’ve calculated the average density of “Greater Melbourne” in the first row for completeness, but this is a bit meaningless as the vast majority of land in “Greater Melbourne” is non-urban land (the white area in the map below).

Here is a map showing the various land areas used in the calculations above (note green and yellow areas overlay most red areas):

I’ll aim to post more about 2011 density when ABS release more census data (including population figures for mesh blocks and ‘urban centres and localities’)

My second look at 2011 census data focusses on motor vehicle ownership rates. Is the rate of car ownership still increasing? Has the rate of car ownership dropped in any areas?

Measuring motor vehicle ownership rates

The raw census data provides the number of dwellings with 0, 1, 2, 3 or 4+ motor vehicles in each geographic area. Often people draw maps showing the proportion of dwellings with 2+ motor vehicles. That is easy to do, but it ignores the number of driving aged adults likely to be in those households.

Here’s a map showing the median household size in persons for 2011 (click to enlarge):

There’s a very distinct trend that household sizes are larger on the fringe. Looking at VISTA data, households in the outer suburbs are more likely to have more licensed drivers (I define “independent licensed drivers” as people with a full or probationary license, and MSD refers to Melbourne Statistical Division):

16% of households in the outer suburbs of Melbourne have 3 or more independent licensed drivers whereas the figure is only 10% in the inner suburbs.

My preferred measure is to estimated the ratio of home-based motor vehicles to the driving age population (unfortunately the census doesn’t provide data on driver’s license ownership). To make such a calculation I have to make a few assumptions:

• Dwellings that did not state number of motor vehicles had no motor vehicles.
• Dwellings that stated 4 or more motor vehicles had an average 4.3 vehicles (average figure obtained from VISTA 2007/08 and 2009/10 combined). This average could of course change over time, so there’s a slight imperfection in the calculation for around 5-6% of dwellings. I have assumed a constant 4.3 across 2006 and 2011.
• Driving aged population is approximated by people aged 20-74 (I used 20-74 as I only have population counts in 5 year groupings for small areas). Of course there are some people aged 20-74 who do not have a driver’s license, and there are people aged under 20 and over 74 who do have a driver’s license. See my previous post about who uses public transport for charts showing the rate of driver’s license ownership by age group.

Melbourne motor vehicle ownership maps

I have calculated an estimated ratio of home-based motor vehicles to the notional driving aged population for Melbourne, at smallest available geographies for 2006 and 2011 (Census Collection Districts and Statistical Area Level 1 respectively).

Here is a map showing the estimated rate of motor vehicle ownership in 2006 at the Census Collection District level:

Here is a map showing the estimated rate of motor vehicle ownership in 2011 at Statistical Area Level 1:

You can see lower motor vehicle ownership rates around:

• the inner city areas where there is a high quality public transport;
• some lower socio-economic suburban areas such as St Albans, Broadmeadows, Preston, Springvale, Dandenong, Frankston; and,
• tertiary education campuses including Clayton, Bundoora, Burwood, Glenferrie, Box Hill, Holmesglen.

The highest rates of motor vehicle ownership are seen in:

• relatively wealthy suburbs on the urban fringe (often with low density rural residential style developments), including Greenvale, Eltham north, Donvale, Mt Eliza, Narre Warren north, Lysterfield; and,
• relatively wealthy middle suburbs, such as Ivanhoe, Toorak, Beaumaris, Essendon, Kew, Brighton.

Changes in motor vehicle ownership 2006 to 2011

So how have motor vehicle ownership rates changed? You could flip back and forwards between the above two maps, but with different geographies it isn’t easy to spot all the changes.

Some areas that appear to have had reductions in motor vehicle ownership rates include pockets of Werribee/Hoppers Crossing, Burwood (around the Deakin University campus), and central Frankston. Some areas that appear to have had significant increases in motor vehicle ownership rates include Mt Eliza, Doncaster, Templestowe, Williamstown, and North Ringwood.

A more systemic comparison requires use of the smallest common geographical unit common to both the 2006 and 2011 censuses, which is the Statistical Local Area (SLA). The following map shows the change in estimated motor vehicle ownership rates between 2006 and 2011 at the SLA level:

There are a few notable reductions in the rate of car ownership:

• The City of Melbourne, particularly the CBD and Southbank/Docklands
• Box Hill (perhaps due to an influx of students at Deakin University Burwood campus)
• Monash – south west (which includes Monash University)
• The outer western and northern suburbs
• Yarra Ranges – Part B (non-metropolitan, and I’m not sure what might be happening there)

The biggest rises can be seen in:

• Manningham (west and east)
• Moonee Valley
• Rowville
• Sunbury
• Nillumbik
• Yarra Ranges
• Cardinia – north (non-urban)
• Kingston – south
• Casey – south
• Mornington Peninsula – West

So what might explain these patterns?

• There has been a long term trend of increasing car ownership (refer previous post). Certainly the real cost of car ownership has been going down for some time now.
• Areas with large numbers of tertiary students appear to have had a decline in car ownership, perhaps reflecting successful mode shift campaigns with staff and students, and/or an influx of international students who might be less inclined to buy a car and/or drive.
• A growth in apartment living in the inner city, where there is less need to own a car due to high quality public transport and many destinations within walking distance. Although I note that motor vehicle ownership rates still rose in the neighbouring City of Yarra, suggesting densification a couple of suburbs out from the CBD seems to still be introducing more cars (and/or other motor vehicles).
• I’m really not sure why the rates of car ownership appeared to decline slightly in the outer growth areas to the west and north, but not the  south-east (although the Cranbourne and Pakenham SLAs only showed relatively small increases of 1.7 and 1.9 respectively). I should point out that the decreases are very small (all less than 0.8) and probably not significant when considering the assumptions I have had to make in calculating the estimates.

I’d also make the comment that increased car ownership doesn’t mean increased car use. As I’ve pointed out elsewhere on this blog, average km travelled per car has peaked in Australia, as has car passenger km per capita.

Other motor vehicle ownership analysis

For more on car/motor vehicle ownership see:

• a previous post about trends in car ownership over the years at a state (and whole of Melbourne) level using data from the annual ABS Census of Motor Vehicles
• analysis of motor vehicle ownership saturation in households, in my first post on who uses public transport.

[updated December 2012 with more Canberra and Hobart data, and removing 'method of travel not stated' from all mode share calculations]

The ABS has just released census data for the 2011 journey to work (amongst other things). This post takes a city-level view of mode share trends.

Public transport

The following chart shows the public transport share for journeys to work for people within Statistical Divisions (up to 2006) and Greater Capital City Statistical Areas (for 2011) for each of the Australian major capital cities.

You can see 2011 increases in public transport more share in all cities except Adelaide, Hobart and Canberra. Melbourne grew by 2.2%, Perth by 2.1%, Sydney by 2.0%, Brisbane by 1.1% while Adelaide, Canberra and Hobart dropped by 0.1%.

But there are limitations of this data:

• Census data is usually available by place of enumeration (where you actually were on census night) and/or place of usual residence. In the above chart the following years are by place of enumeration: 1991,  2001, 2006, 2011. I am just not sure whether the other years are place of enumeration or place of usual residence (ABS were unfortunately not as rigorous with their labelling of data tables in the past). There may be small differences in the results for place of usual residence.
• The data available to me has been summarised in a “lossy” fashion when it comes to public transport mode share. It means that a journey involving tram or ferry and one or more non-PT modes is not counted as public transport in any of the results (it falls under “other two modes” or “other three modes” which includes PT and non PT journeys). For example, car + ferry or bicycle + tram. That means the true share of trips involving public transport will be slightly higher than the charts above, particularly for Melbourne and Sydney.
• The 2011 figures relate to Greater Capital City Statistical Areas. For Perth, Melbourne, Adelaide, Brisbane and Hobart these are larger than the statistical divisions used for 2006 and early data. This means people on the fringe are now included, and they are likely to have lower rates of public transport use. So the underlying trends are likely to be higher growth in public transport mode share.

The limitations in counting of tram and ferry trips can be overcome by measuring mode share by workplace location, although I can only get such data for 2001, 2006 and 2011:

These figures are all higher because they include people travelling to work in the metropolitan areas from outside (where PT might have a higher mode share via rail networks for example) and they count all journeys involving ferry and tram. Between 2006 and 2011, Melbourne grew the fastest – by 2.4%, Sydney and Perth were up 2.0%, Brisbane up 1.2% and very little change in Adelaide, Canberra and Hobart.

Cycling

The following chart shows cycling only journey to work mode share:

(Adelaide and Perth are both on 1.3% in 2011)

Canberra is the stand-out city, owing to a good network of off-road bicycle paths through the city. But Melbourne has shown the fastest increase, going from 1.o% in 2001 to 1.6% in 2011.

Adelaide, Perth, Brisbane and Melbourne had a significant drop between 1991 and 1996, but this did not occur in Hobart, Canberra or Sydney.

Canberra, Melbourne and Sydney have shown the most growth in recent times. Adelaide and Hobart unfortunately went backwards in 2011. I’m not sure why Adelaide dropped so much, maybe it was a product of weather on the two census days?

Here’s another view that includes journeys with bicycle and other modes (by work location, not home location):

Perth and Canberra had the largest growth in journeys involving cycling and other modes.

Walking only

Walking only rose in all cities 2001 to 2006, but then fell in most cities between 2006 and 2011 (Perth and Brisbane the exceptions). Perhaps surprisingly, Hobart had a higher rates of walking to work than all other cities.

Car

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

(both Adelaide and Hobart were on 82.7% in 2011)

You can see car mode share peaked in 1996 in all cities except Canberra where it peaked in 2001, and Hobart where the 2011 result was just under the 1996 result.

Hobart, Adelaide and Canberra had small rises in 2011 (1.0%, 0.4% and 0.1% respectively) while Perth had the biggest drop in car mode share (down 2.6%), followed by Melbourne (down 2.0%), Sydney (down 1.8%) and Brisbane (down 0.9%).

Vehicle passenger

Travel as a vehicle passenger has declined in all cities, suggesting we are doing a lot less car pooling and commuter vehicle occupancy is continuing to decline in line with increasing car ownership. Curiously Hobart and Canberra topped the cities for vehicle passenger mode share.

Overall mode split

Because of the issue of under-counting of tram and ferry data for place of enumeration, I’ve constructed the following chart using place of work and a “main mode” summary:

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 “PT Other”
• 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

In future posts I plan to look at the change in spatial distribution of journey to work mode share (by home and work location).

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

How have the mode shares of journeys to work changed by different home locations in Melbourne?

The following animations show various mode shares for journeys to work from census collection districts for 2006 and Statistical Area Level 1 (SA1) for 2011. These are the smallest geographies available for each census. All the data is by place of usual residence.

I’ve animated each image to alternate between 2006 and 2011, so you can gaze at them and spot the changes. But you’ll need to click on them to enlarge and see the animation.

Public transport

Public transport mode share is mostly up across the board. Some exceptions include:

• Langwarrin (east of Frankston)
• Dingley
• Greenvale
• Hillside
• Eastern parts of Rowville

Sustainable transport (only)

This map excludes those who used private transport to reach public transport. It shows that on the suburban fringe, the vast majority of people are still using private motorised transport to get to work. Areas without significant growth include Sunbury, South Morang, Greenvale, Rowville, Berwick north, Skye/Carrum Downs, Mt Eliza, Dingley, areas around the Ringwood-Lilydale rail line, and Westmeadows.

[minor corrections to map made 5 Nov 2012]

Train

There is growth across mode areas of Melbourne. You can see a massive difference in Roxburgh Park Craigieburn area following the extension of suburban electric services to Craigieburn.

Bus

You can see a substantial increases:

• in Doncaster area following the introduction of 7 SmartBus routes (including 4 to the CBD).
• in pockets between the Ringwood and Dandenong rail lines in the middle eastern suburbs. These areas had SmartBus routes introduced in 2002/2005, and perhaps it is taking a while to translate to bus in journey to work.
• Around Abbotsford/Collingwood, perhaps reflecting increased train crowding and introduction of four SmartBus routes along Hoddle Street creating an extremely frequent service to the city.

Tram

You can see increased mode share across the network, particularly around the outer end of the tram route to Bundoora (zone 2 only in 2006, included in zone 1 in 2011) (but less so in Vermont South).

Active transport (only)

You can see gains in the Brunswick, Northcote, Kew and Foostcray areas.

Walking only

I can see little change between 2006 and 2011, which is in line with little change in the overall share for Melbourne.

Cycling

Cycling continues to grow rapidly in the inner northern suburbs, but also a little to the inner east and inner south.

Train and Bicycle

With the introduction of Parkiteer cages at train stations, was there any increase in the number of people riding to train stations?

The numbers are so small, it is difficult to see spatially, but there was a substantial increase in overall numbers from around 1200 to 1800.

Train and bus

You can see increases around the Dandenong rail line, between the Glen Waverley and Ringwood rail lines, around Werribee/Tarneit, and around Sydenham.

Public transport mode shift by SLA

Here’s a map showing the mode shift towards public transport by Statistical Local Area (SLA), the smallest geography for which results are available for both the 2006 and 2011 censuses.

The biggest mode shifts were in the City of Melbourne, followed by Wyndham – south (Point Cook), South Yarra/Prahran, and Moreland – north. Nowhere in Melbourne did public transport mode share reduce.

I’m sure other people will find more patterns in the maps than I have been able to today. Please comment on any interesting finds. I might come back later and update this post when I have more time.

I will aim to do a similar exercise for other cities soon.

My earlier post about Melbourne journey to work 2011 focussed on where people live. This post focuses on where people work and what modes of transport they used to get there in 2006 and 2011. It also covers employment density and the home locations and associated mode shares for people travelling to the central city.

As per other posts, you will need to click on maps to see the detail/animation.

Note: I have mode share data at work place locations at destination zone level for 2006 (smallest resolution available) but only at SA2 level for 2011. For the purposes of direct comparison, I have mapped 2006 destination zones to SA2s based on the centroid of each 2006 destination zone (so not a perfect mapping – see here for a comparison map).

See also an earlier similar post covering 2006 journey to work data for Melbourne, and a similar post covering journeys to workplaces in Brisbane.

Employment density

Firstly, what does the employment density of Melbourne look like? If I had travel zone data for both years I’d be able to draw a much higher resolution picture, but for now I will have to suffice with SLA/SA2 employment density. Note that 2011 SA2s are generally smaller than 2006 SLAs so this isn’t a direct comparison.

A lot of the differences you can see between 2006 and 2011 are to do with the change in boundaries, not necessarily changes on the ground. For example, there are many more SA2s than SLAs in the Doncaster area, which has meant the 2011 data shows a slightly dense area around Doncaster Hill that washed out in the 2006 data.

I do note the absence of many relatively dense employment areas on the western side of Melbourne.

Mode share by workplace location

Public transport

Public transport mode share was highest in the CBD, then for areas around the CBD and stretching a little more to the inner east. Box Hill stands out as a suburban location with a relatively high mode share (13% in 2011).

Here is a map that shows the mode shift for each SA2 (bearing in mind that there isn’t a perfect mapping from 2006 destination zones to 2011 SA2s):

The biggest mode shifts to public transport were:

Most of the above are in the inner city, but there are exceptions of Watsonia, Mount Evelyn and Springvale South (all off a very small base in 2006).

Some interesting rises in the suburbs include:

• Doncaster 5.5% to 8.3%, probably related to the introduction of several SmartBus services
• Frankston North 2.6% to 5.0%, again probably influenced by the introduction of SmartBus services
• Forest Hill 5.2% to 7.8% (not sure why)
• Mill Park North 1.7% to 4.2% (note the South Morang rail extension was not open in 2011, but SmartBus services had been introduced by the 2011 census)
• Box Hill 10.2% to 12.7%, possibly related to upgraded SmartBus services
• Noble Park 3.0% to 5.4% (not sure why)

Some interesting declines include:

• Montrose – there are boundary differences between 2006 and 2011 with many more jobs counted in 2011.
• Cairnlea 6.6% to 2.4% (probably because Victoria University St Albans Campus is mapped to this SA2 in 2006 but not 2011)
• Carlton North – Princes Hill 13.1% to 10.4% (which also had an increase in walking and cycling)
• Port Melbourne 14.7% to 12.6% (not sure why, perhaps more people walked to work from the increasingly dense local residential area)

As an aside, here are 2011 public transport mode share for journeys to work at major Australian airports (where there is an “Airport” named SA2):

• Sydney 13.9%
• Melbourne 3.8% (up from 2.5% in 2006)
• Brisbane 3.1%
• Adelaide 2.6%
• Perth 1.7%
• Darwin 1.7%

Train

Train mode share was highest in the CBD and surrounding inner city areas. Notably, mode shares were relatively higher in the inner east and south-east (particularly Caulfield, Camberwell and Hawthorn) compared to other axes.

Here is the mode shift to trains between 2006 and 2011:

The biggest rises were in Docklands (up 9.2%), South Yarra (up 5.6%) and then a few other inner suburban destinations.

In 2011, 47% of journeys to work in Greater Melbourne involving train were to the Melbourne CBD. This rises to 59% when adding Southbank and Docklands.

Tram

Unfortunately I do not have 2006 data for “any journey involving tram” below the SLA level, so here is the 2011 picture at SA2 level, with the tram network shown as green lines:

I must say I was surprised by the CBD figure of only 14.9% (and I did double-check the data).

Tram mode share was highest in the SA2s of Albert Park and South Yarra West (which straddle the St Kilda Road office precinct which has very high tram frequencies). Other work destinations with higher tram mode shares included Parkville, Carlton, Fitzroy and South Melbourne.

Perhaps there was some under-reporting of tram journeys as a “secondary” mode in people’s journey to work? In Parkville (which includes the main University of Melbourne campus, the hospitals precinct and Royal Park), there were more people reporting only train (934) than train+tram (772) and train+bus (275). I would expect most of those jobs to be remote from Royal Park station, and the southern section of the SA2 is at least a 1 km walk from Melbourne Central train station. Another example is South Melbourne – all of which is more than 1.2 km from a train station, yet 1240 people reported only train in their journey to work, while 894 reported train+tram. While of course some people will walk longer distances from train stations to work, the numbers seem a little high to me.

37% of journeys to work in Greater Melbourne involving tram were to a destination in the Melbourne CBD. If you add in Southbank, Docklands, Parkville and South Melbourne the share goes to 56%.

Bus

Again, I do not have comparable data for 2006, so here is a 2011 map:

Bus mode share was highest in Malvern East (which includes Chadstone Shopping Centre), followed by Doncaster, Maribyrnong (which includes Highpoint Shopping Centre), Carlton and the Melbourne CBD. Mount Evelyn is curiously high at 5.8%, with 45 people travelling by bus to workplaces there.

Only 21% (9905) of journeys to Greater Melbourne workplaces involving bus were to the CBD, with the next highest SA2 counts in Docklands (1175), Clayton (1160), Dandenong (1157), Southbank (1071) and Parkville (1046). This would suggest that growth in CBD employment is unlikely to be one of the major factors in bus patronage growth in Melbourne (unlike train and tram).

Cycling

Due to the nature of the data I have for 2006, this analysis excludes journeys also involving public transport or trucks (yes, there were 39 people who said they travelled to work by truck and bicycle in Australia in 2011!):

Cycling to work boomed in inner Melbourne between 2006 and 2011, particularly to workplaces in the inner north, with the Parkville SA2 recording the highest bicycle share.

Here’s a view of the mode shift to bicycle:

The biggest mode shifts towards bicycle were for workplaces in the inner northern suburbs, while relatively small mode shifts away from bicycle were observed in the outer eastern suburbs and around Aspendale to Carrum.

I should point out that the census is conducted in winter (August), and warmer weather bicycle mode shares of journeys to work are likely to be higher.

Walking (only)

Walking mode share is a mixed bag across the city. High walking mode shares are evident in Parkville, Carlton North/Princes Hill, around St Kilda, the Simpson Army Barracks (in Yallambie), but also some rural areas. In the Koo Wee Rup SA2, 8.7% of employees walked to work, 41% of whom were in the “Agriculture, Forestry and Fishing” industry.

The lowest walking only mode shares were at the major airports (Melbourne, Essendon and Moorabbin), some industrial areas and generally in the outer suburbs of Melbourne.

Here is mode shift to walking:

Mode shift to walking was more common in the northern suburbs and some outer eastern suburbs, but not so much in the inner city. Mode shift away from walking only to work was observed in many outer eastern and north-eastern suburbs.

Note: the neighbouring SA2s of Wheelers Hill and Glen Waverly East each showed mode shifts in opposite directions. This is almost certainly to do with the Police Academy being mapped into a different SA2 in 2006 due to the imperfect mapping between 2006 destination zones and 2011 SA2s.

Sustainable transport

I’ve defined sustainable transport here as any journey involving public transport, plus any journey that only involved walking and/or cycling.

Sustainable transport mode share was highest in the CBD and immediate surrounding areas. Sustainable transport was relatively higher for workplaces in the inner north, east and south-east compared to the inner west.

Mode shift to sustainable transport was most prevalent in the inner north and inner south.

Some interesting suburban mode shifts to sustainable transport include:

• Upwey - Tecoma (mainly walking)
• Dandenong North (mostly a mix of walking and public transport)
• Gladstone Park - Westmeadows 3.1% (most of which was public transport mode shift, possibly relating to the introduction of SmartBus services),
• Altona Meadows (mostly public transport, perhaps relating to the City West waste purification plant being mapped into this SA2 only in 2006 but this is not clear)
• Watsonia (possibly a result of destination zone to SA2 mapping issues )

Commuting to the central city, 2011

The central city is an important destination as it has the highest employment density and public transport is best-placed to compete against the car. For analysis in this section I am using the combination of the Melbourne CBD, Southbank, Docklands, Carlton, North Melbourne and East Melbourne SA2s as my definition of the “central city” (which is different to other posts on this blog – I am deliberately choosing a larger area to get a better sense of origins and mode shares).

Here’s a map showing the proportion (%) of commuters who had a destination of central Melbourne in 2011 (by place of usual residence at SA1 geography):

The prevalence of the CBD as a work destination is almost directly proportional to the distance people live from the CBD, although rates are relatively higher around train lines.

Notable outliers include:

• Point Cook, Tarneit, Caroline Springs in the western suburbs with a higher central city share, possibly reflecting a workers-to-jobs imbalance in the outer western suburbs, particularly for white-collar workers (I might explore that more in a future post)
• East Doncaster, which has a relatively high central city share, possibly as a result of frequent express bus services to the city
• A pocket of St Kilda East and Caulfield North between the Sandringham and Caulfield rail lines that has a low share despite being relatively close to the city (not sure why that might be)

The next map shows the share of central city commuters who used public transport in their journey to work (by home location). I’ve only shaded SA1s with 20 or more central city commuters (which I admit is quite small for calculating mode shares).

Note: I have not filtered SA1s by density on the following maps (unlike others), so some low density SA1s are included.

Public transport mode share was particularly high for those in middle to outer suburbs (where such a long drive would probably not be fun or cheap).

It was lowest around:

• the city centre itself (more on that in a moment)
• Western Kew in the inner east (a relatively wealthy area)
• Sanctuary Lakes in the south-western suburbs (largely remote from public transport in 2011)
• Pockets of Caroline Springs
• Areas of Templestowe, Donvale, Research and North Warrandyte in the east-north-eastern suburbs
• Areas north of Sunbury
• Areas around Keilor East and Avondale Heights (like Kew, close to the CBD but remote from train lines)
• Greenvale (a relatively wealthy area)
• Brighton and Toorak (very wealthy areas)

Here’s the share of people who only used private motorised transport to commute to the CBD:

This map is largely the inverse of the previous map, except for areas near the inner city, suggesting active transport is being used by residents of the central city to get to work in the central city, as you might expect.

Finally, here is a map showing the density of people who work in the central city:

This map effectively combines population density with the proportion of workers travelling to the central city. The density falls away with distance from the city (quite markedly south of Elwood), but there are outliers in pockets of Carnegie, Point Cook, East Doncaster, Deer Park, Mitcham, Bundoora, and Heatherton (not all of which are connected to the city by high quality public transport).

A similar analysis could be conducted to other employment centres, although numbers per SA1 will be much smaller, and it would be time-consuming.

If you spot any other interesting changes and/or have explanations for them, I would welcome comments.

Following on from my last post about public transport multi-modality in the journey to work, this post takes a more detailed look at what modes were used in conjunction with trains in journeys to work.

Trains provide a backbone for public transport systems in Australia’s five largest cities, but only a minority of the population within each city is within walking distance of a train station. So what other modes were used in combination with trains for journeys to work in 2011? (according to the ABS census)

This chart shows that ‘walking only’ (ie no modes other than ‘train’ specified) was the most common response for people who used trains in four of the five cities, with Perth the notable exception. Perth’s rail network includes two heavily patronised lines that are largely within freeway corridors, with longer than traditional station spacing and much smaller walking catchments for each station. Perth train commuters were therefore much more likely to involve other modes of transport in their journey to work.

Private (motorised) vehicle transport was more common than other modes of public transport in Brisbane, but the other cities were fairly evenly balanced between private vehicle transport and other public transport modes.

Perth had the highest share of train commuters reporting also using buses (almost a third), suggesting the train feeder bus networks are working quite well.

Sydney had a similar rate of other public transport mode use to other cities, despite limited multi-modal fare integration, although Sydney did have the highest reported rate of ‘walking only’ for train commuters.

Melbourne had the second highest rate of other public transport modes being involved, with roughly equal amounts of bus and tram.

What modes are used to access train stations?

The census doesn’t tell us the order of modes used in the journey to work, but I can get a picture of this from Melbourne’s household travel survey, VISTA:

(note that train does not appear as this analysis looks at the mode preceding the first use of train).

Some recently published PTV data on use of train stations also allows analysis of estimated access mode splits for 7am-7pm weekday train station entries based on origin-destination surveys of journeys of any purpose.

The following chart shows access modes to non-CBD stations (i.e. excluding Flinders Street, Southern Cross, Flagstaff, Melbourne Central, and Parliament):

The data sets aren’t in strong agreement about ‘walking only’ and private vehicle use, although they all have different measurement frames.

The disparity may support the suggestion that there is under-reporting of rail-feeder modes other than walking in the census – particularly vehicle driver/passenger (see also an earlier post that found people living beyond reasonable walking distance of train stations reporting train and walking only to get to work). On the other hand, it may also be that train-based journeys to work have lower rates of private vehicle use than for other journey purposes.

All the figures also suggest that trams are much more likely to be used after trains in the journey to work in Melbourne, which makes sense, as there are only a few tram lines in suburban Melbourne that feed the rail network, and trams provide comprehensive street-based transport within the inner city area helping to distribute people who arrive by train.

In fact, here is a chart showing the reported access modes for Melbourne’s CBD train stations, showing a much higher tram share of access modes:

The data shows walking as the dominant access mode, but also a quite large number of train-train transfers at CBD stations.

Changes over time

So how have these trends changed over time? (at least, as far as people fill out their census forms)

Unfortunately sufficiently detailed data isn’t available for 2001, but here is a comparison of 2006 and 2011 census journey to work data for the five cities:

You can see for Perth that the ‘walking only’ share dropped in favour of most other modes (following opening of the Mandurah rail line).

Brisbane also had a notable shift away from ‘walking only’, particularly to the use of other public transport modes, which might reflect continued changes in travel habits following full multi-modal fare integration in 2004-05. However Brisbane retained the rate of use of other public transport modes in journeys involving train of all cities.

Adelaide had a decline in buses being part of train-based journeys to work, but an increase in trams and private vehicle drivers.

Melbourne saw an increase in bus use with train journeys, with a decline in all other modes and ‘walking only’.

Sydney saw small increases in ‘walking only’ and bus use for people making journeys to work involving trains.

In terms of bicycles being part of train-based journeys, Melbourne had the biggest increase (from 1.0% to 1.2% of journeys involving trains), while Adelaide went backwards (1.6% to 1.0%, although I have no idea if this might have been weather related).

You might be wondering about trucks, taxis and motorbikes. Okay, well even if you aren’t, I should point out that I have made some assumptions in aggregating the census data:

• Anyone reporting truck or motorcycle/scooter has been counted as private vehicle driver (although they may have been passengers on such vehicles, although I’m guessing this is less likely than them being drivers)
• Anyone reporting taxi I have counted as private vehicle passenger.

For more information on other modes used with trains in Melbourne see pages 26-27 of the PTV Network Development Plan for Metropolitan Rail, and recently published PTV data for use of train stations, including access modes.

Since my first post looking at 2011 Melbourne residential density, there’s been a heap of new 2011 census data released. This post includes new maps showing Melbourne’s population density in maximum detail, as well as some more calculations of Melbourne’s urban/residential density for the density nerds.

Melbourne’s residential density in extremely high resolution

2011 population figures are now available for mesh blocks – the smallest ABS geographic unit. This allows a fine-grained look at 2011 residential density, and comparisons with 2006 as we now have a time series.

Here’s a very large animated map (4.7MB, 6825 x 4799 pixels) showing residential density at mesh block level for 2006 and 2011. You’ll need to click on it to download and see the animation (I’d suggest a new tab or window). Use your browser to zoom in and scroll around to areas of interest.

[update 10 July: It has been brought to my attention that some people are unable to view this map because they are restricted to using certain versions of Internet Explorer. If you cannot see the large map above, I have also created a smaller animated map showing only the inner areas of Melbourne]

You can see that new growth areas on the fringe actually have relatively high densities, contrary to conventional wisdom. I also note a relatively high and increasing density in the Springvale/Keysborough/Noble Park area, quite some distance from the CBD. If you look carefully you will also spot infill developments like Waverley Park, Parkville (ex-Commonwealth Games village), Gresswell Hill in Macleod, Docklands, Maidstone, Edgewater estate in Maribyrnong, along St Kilda Road, Waterways, and no doubt many more.

More values for the urban/residential density of Melbourne

Okay, you might want to stop reading here unless you have a deep interest in density calculation methodology.

Along with mesh blocks, the recently released census data provides boundaries for urban centres and localities, which each representing a relatively continuous urban area (including residential and non-residential land). There is an urban centre of “Melbourne” defined, which excludes the satellite urban centres of Pakenham, Melton, Sunbury, Healesville and towns along the Warburton Highway, but includes the major urban regions along the Mornington Peninsula to Portsea and Hastings.

All this new data enables calculation of yet more values of the urban/residential density of Melbourne, adding to my previous list (some of which I have repeated for comparison purposes). The areas covered by each calculation are shown on the map below.

I note that the Melbourne urban centre is approximately a quarter of the area of “Greater Melbourne”.

Here’s a reference map of Melbourne showing the Greater Capital City Statistical Area, Statistical Division and Urban Centre boundaries of “Melbourne”, together with mesh blocks of above 1 and 5 persons/ha.

Finally, for the density nerds who are still reading this post, I have calculated the 2011 population-weighted density of Greater Melbourne using mesh blocks to be 42.8 persons/ha, which is much higher than the population-weighted density using SA1 geography of 31.8 persons/ha. It’s higher because more non-residential land parcels have been excluded from the overall calculation. If I restrict myself to mesh blocks within the Melbourne urban centre, the population-weighted density is only slightly higher at 45.1 persons/ha.

So if you want to compare population-weighted densities of different cities, you’ll need to make sure you are using equivalent geographic units, which I suspect would be very difficult for international comparisons. An attempt at Australian and Canadian city comparisons was made in the comments section of a previous post.

There you go. Next time someone claims to know the urban density of Melbourne, you can now argue with them for hours about whether you agree with their number and how it should be measured.

This post is drifting a little away from transport, but I hope you will find this interesting…

How has the spatial distribution of socio-economic advantage and disadvantage changed over time in Melbourne? (oh, and Geelong too)

The animated maps below are fascinating, but of course there’s lots of important caveats regarding the data.

About the data

Since 1986, the Australian Bureau of Statistics (ABS) has calculated Socio-Economic Indexes For Areas (SEIFA) based on five-yearly census data. These include indexes of relative socio-economic disadvantage (IRSD), and – since 2001 – an index of relative socio-economic advantage and disadvantage (IRSAD). For 2006 and 2011, SEIFA was explicitly designed to measure “people’s access to material and social resources, and their ability to participate in society” (with similar intent for prior years).

This post looks at the spatial changes over time in these index values. I must be upfront: ABS explicitly cautions this type of analysis. This is mostly because the component census variables that make up SEIFA scores and their respective weightings vary between each census, but also because statistical area boundaries change, the number of areas has increased, and indexes were calculated on usual residents from 2006 onwards (as opposed to people present on census night for 2001 and earlier). ABS also notes that middle range scores are very similar, so time-series analysis should focus more on the top and bottom ends of the spectrum. More discussion on this issue is available from ABS and .id consulting.

However, I’m going ahead noting the above (as readers also should!), on the following basis:

• The intent of the indexes has not changed over time, although the quality has (perhaps one day ABS will recalculate SEIFA values for previous census using better measures where possible)
• I’ve used percentile ranks within Victoria to get around the issue of the changing meaning of particular index values (although this might cause some issues if there has been a relative difference in changes between Melbourne and regional Victoria)
• I’ve included a summary of the component variables that have changed between censuses (documentation is available from 1996 onwards)
• I’m mapping this at a metropolitan scale with a view to looking at regional variations, rather than very local changes. In the following maps you’ll see fairly strong regional patterns
• My analysis will focus only on substantial shifts (which have indeed occurred)
• Excessive caution may mean that we never do any interesting analysis!

Changes in Index of Relative Socio-economic Disadvantage (IRSD)

This index has been available from 1986 onwards.

More significant changes in the make up of this index in recent years include:

• 2011 added: families with jobless parents
• 2011 dropped: indigenous persons, renting housing from government authority
• 2006 added: household overcrowding (replacing multiple-family households), low rent payments, lack of an internet connection, low skill community and personal services workers, people who need assistance with core activities
• 2006 dropped Elementary Clerical, Sales and Service workers and tradepersons
• 2006 changed the evaluation of household income to consider ‘equivalised household income’ replacing a number of measures that try to capture income levels relating different household make-up scenarios. It also stopped using gender specific measures of people with certain occupations or unemployed
• 2001 saw no changes to the included variables from 1996
• Variables for persons who did or didn’t finish year 12 at school have changed slightly in both 2006 and 2011

check the SEIFA documentation for full details.

Click on this map to enlarge and see an animation of IRSD percentile values for the years 1986 to 2011.

You can see some quite dramatic changes over time. Two big trends of note are:

• Most inner city suburbs have gone from being some of the most disadvantaged to much less disadvantaged. It’s hard to imagine suburbs such as South Yarra and East Melbourne as being highly disadvantaged, but the data suggests that was the case in the 1980s. During this transformation, pockets of high disadvantage have remained, probably reflecting older government housing estates. There appears to have a been a fairly large change between 1986 and 1991. This could represent dramatic demographic change and/or reflect changes in the calculations of SEIFA index values.
• Areas with the highest disadvantage have generally shifted away from the city centre (including some middle suburbs such as Carnegie), perhaps reflecting the growth in high-end CBD jobs driving the attractiveness of near city living.
• New urban fringe growth areas often begin with low levels of disadvantage, but have become more disadvantaged over time. This is particularly evident in areas such as Hoppers Crossing, Werribee, Melton, Deer Park, Craigieburn, Keysborough, Karingal, Epping, Hampton Park, Cranbourne, Altona Meadows and Keilor Downs. Perhaps this is because when these areas were initially settled there were many double-income-no-kids households that now have more kids and less income? It could also be a reflection of a turnover in the resident population.
• The maps only show geographic units with a population density of 5 per hectare or more, so you can also see the urban growth of Melbourne (more on that in a upcoming post).

Changes in Index of Relative Socio-economic Advantage and Disadvantage (IRSAD)

This index was first calculated in 2001 and aims to also measure advantage, not just factors that suggest disadvantage. In 2011 it included all but one of the IRSD variables, plus a number that describe levels of advantage (eg high income, higher education, occupations such as managers and professionals, high rent or mortgage payments, spare bedrooms).

The component variables of IRSAD have changed in line with the changes to IRSD, plus some other variables:

• 2011 added people with occupation classed as managers, houses with spare bedrooms, households with 3 or more cars
• 2006 added people paying low/high rent, high mortgage payments, renting from government authority, households with no car, households with broadband internet connection (replacing persons using the internet at home)

Again, check the SEIFA documentation for full details.

An aside: SEIFA associates higher car ownership with advantage, but I suspect some inner-city types might consider not needing to own a car an advantage.

Here is an animation of the Index of Relative Socio-economic Advantage and Disadvantage for years 2001 to 2011.  Again, click to enlarge and see the animation.

The changes between 2001 and 2011 are much less dramatic, probably because of the shorter time span. Some observations:

• The Melbourne CBD drops in 2011 – possibly because of a change of demographics (more students?) and/or a change in the component variables.
• Many parts of the middle eastern suburbs (particularly the Whitehorse area) appear to drop from the upper to the middle percentiles in 2011.

What’s also interesting to see is some socio-economic fault lines in Melbourne, such as:

• Altona North versus South Kingsville/Newport (north-south divide along Blenheim Road/Hansen Street/New Street)
• Skeleton Creek between Point Cook (including Sanctuary Lakes) and Altona Meadows
• A north-south line being the boundary between the Shire of Melton and the City of Brimbank in the north-western suburbs
• Along Hume Drive / Lady Nelson Way (an east/west line in northern Brimbank)
• Greenvale versus Meadow Heights (split by the proposed north-south Aitken Boulevard)
• A north-south divide through Heidelberg Heights, roughly parallel to the Hurstbridge rail line
• Along the Dingley Arterial between Dingley Village and Springvale

How different are IRSAD and IRSD values?

IRSAD contains a lot more variables and uses different weightings. See the ABS website for full details.

For those who are interested in the correlation between the two, here’s a scatter plot for both 2006 and 2011 data comparing the two index values (as percentile ranks) for all CDs and SA1s (respectively) in Victoria:

You can see the relationships between the two indexes is stronger in 2011 (R-squared = 0.96) versus 2006 (R-squared = 0.89). This might reflect the make up of the variables in each year and/or the smaller geographic units in 2011 (SA1s) which may reduce diversity within each geographic unit.

I’m sure others could spot other interesting patterns, and/or offer explanations for the changes over time (comments welcome).

Following on from my recent post about the changing socio-economic landscape of Melbourne, this post simply looks at the changing shape and density of urban Melbourne using 5-yearly census data at collector district (1986-2006) and SA1 level (2011).

Straight to it: here is map of Melbourne residential density, click to enlarge and animate:

You can see the sprawl of Melbourne over the years, including changes that suggest shifts in the urban growth boundary after development previously seemed to have stopped against a line (particularly evident on the western edge of the City of Brimbank).

Here is another animated map showing the inner city area, with a density scale ranging from 10 to 100 persons/ha, so you can distinguish higher densities than the map above. Click to enlarge and animate.

You can see a lot more going on in established areas on this map, including densification in the CBD, St Kilda, St Kilda Road (conversion from office space), Parkville, Port Melbourne around Bay Street, Kensington Banks, Brunswick, Fitzroy, Southbank, South Melbourne, Elwood, Maribyrnong, Carlton, and many more.

A few things to note:

• The size of the districts changes each year, particularly around the fringe. You’ll often see a large red patch where a larger block is only partly inhabited in one year, only to be replaced by smaller denser patches in future years. Patches of green that disappear might be the enlargement of a district causing a blending out of a small pocket of high density, rather than an actual drop in density.
• Shades of pink indicate densities between 5 and 10 per hectare on the large map, and between 10 and 20 per hectare on the inner map. Lower densities are shown as white.
• In 2011 the ABS changed their statistical geography. I have used SA1s from 2011 as the most comparable area unit to a census collector district, however they are generally smaller and so densities may appear to jump slightly in 2011 in some areas.

See also earlier posts for:

• animations of density at the (larger) SA2 level for larger Australian cities,
• a comparison of Melbourne residential density between 2006 and 2011 at the (small) meshblock level, and
• a look at the change in density at SA2 level, 2006 to 2011

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 some of the issues in calculating population-weighted density.

Measuring density

Under the traditional measure of density, you’d simply divide the population of a city by the total metropolitan area’s area (in hectares). As the boundary of the metropolitan area seldom changes, 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, and so 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 pretty much overcomes 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.

Population-weighted density of Australian cities over time

Firstly, here is a look at population-weighted density of the six largest Australian cities, measured at SA2 level (the smallest geography for which there exists a good consistent set of time-series estimates). I’ve made this chart tall so you can see the trends in the less dense cities.

According to this data, most cities bottomed out in density in the mid 1990s, but SA2 population data is only available back to 1991.

We can 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).

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.

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. I would also guess that using smaller geographic units would produce “cleaner” results as larger tracts of non-urban land are more likely to be split from residential land.

I’ve not included the Australian Capital Territory (Canberra) on this chart because it only has nine SA3s, and the shape of its curve does not resemble the SA2 trend at all.

Melbourne’s population-weighted density over time

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

The 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)).

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) from residential land. 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.

Implications for international comparisons of population-weighted density

To be able to meaningfully compare population-weighted densities internationally, two statistical agencies would have to have a unit geography level created with the same guidelines in terms of area and average population, which I’m guessing is quite unlikely (Australia’s new statistical geography is described here).

For reference:

• 90% of Australian mesh blocks have a population of less than 130 people.
• 90% of Australian SA1s have a population of less than 568 (but only 10% have less than 223). The guideline is 200-800 residents, with an average of 400 (the actual average was 392 in 2011).
• 90% of Australian SA2s have a population of less than 20,000 and 10% have less than 3,000, making them highly variable and difficult to compare with other cities.
• US census tracts have a target population of 2,500 to 8,000 residents (averaging 4,000).
• US census blocks have a target population of around 400 housing units (ranging 250 to 550), which is probably a population of roughly 500 to 1500 persons, quite a bit larger than an Australian SA1.
• Canadian dissemination areas have a target population of 400 to 700 persons, slightly larger than an Australian SA1.
• Census output areas in England and Wales have a target population of between 100 and 625 persons, a little smaller than Australian SA1s.

I’ll stop there, but it doesn’t look good for finding compatible geography levels.

Maybe there is some statistically sound way of adjusting values for different sizes of geographic units(?). Or maybe we’ll just never really know which cities are truly denser than others.

I would therefore suggest great caution is applied in comparing the densities of cities internationally (and therefore inferring trends on the relationship between density and transport patterns using a mix of cities from different countries).

You may also like

• Comparing the residential densities of Australian cities (2011)
• Visualising the changing density of Australian cities
• The growth of Melbourne 1986-2011, animated

Who is cycling to work? Where do they live? Where do they work? How old are they? What work do they do? Do men commute by bicycle more than women? How far are cyclists commuting? What other modes are cyclists using?

The census provides some answer to these questions for the entire Australian working population, albeit for one winter’s day every five years.

This post builds on material I presented at the Bike Futures 2013 conference, using census data from across Australian with a little more detail on capital cities and my home city Melbourne.

It’s not a short post, so settle in for 13 charts and 17 maps of data analysis.

How has cycling mode share changed over time?

The first chart shows the proportion of journeys to work by bicycle (only) in Australia’s capital cities.

Darwin appears to the capital of cycling to work, although it is quickly losing ground to Canberra (unfortunately I don’t have figures for Darwin pre-1996).  The census is conducted in Darwin’s dry season, but other data suggests there is little difference in bicycle activity between the wet and dry seasons.

Melbourne has shown very strong growth since 2001 and Sydney showed strong growth between 2006 and 2011. Cycling mode share has grown in all cities since 1996.

Mode shares collapsed in Adelaide, Sydney, Brisbane, and Melbourne between 1991 and 1996, which many people have attributed to the introduction of mandatory helmet laws (Alan Davies has a good discussion about this issue on his blog).

But as I pointed out at the start, census data is only good for one winter’s day every five years. Does the weather on these days impact the results?

Here is a chart roughly summarising the weather in (most of) the capital cities for 2001, 2006 and 2011 in terms of minimum temperature, maximum temperature and rainfall. It doesn’t cover wind, nor what time of day it rained (although perhaps some fair-weather cyclists might avoid riding on any forecast rain). It also fails to show the sub-zero minimums in Canberra (involves asking too much from Excel).

You can see that 2011 was wetter in Adelaide and Hobart than previous years, and this coincides with lower cycling mode shares in these cities in 2011. So census data is quite problematic from a weather point of view. That said, most cities had very little or no rain on the last three census days.

Where were the commuter cyclists living and working?

Other posts on this blog have also covered some of these maps, but not for all cities.

Some of the following maps are animated to show both 2006 and 2011 results, and note that the colour scales are not the same for all maps. I’ve sometimes zoomed into inner city areas when these are the only places with significant cycling mode share. White sections on maps represent areas with low density, or where the number of overall commuters was very small (sorry I haven’t gone to the effort of making every map 100% consistent, but rest assured the areas in white are less interesting). Click on the maps to see more detail.

Canberra

Firstly home locations:

The cycling commuters mostly appear to be coming from the inner northern suburbs. I don’t know Canberra intimately, but Google maps doesn’t show a higher concentration of cycling infrastructure in this area compared to the rest of Canberra.

Secondly, bicycle mode share by work destination (at the larger SA2 geography):

The highest mode share was 12% in the SA2 of Acton, which is dominated by the Australian National University. Perhaps a lot of the university staff live in the inner northern suburbs of Canberra?

Melbourne

By home location:

Cycling mode share is highest for origins in the inner northern suburbs and has grown strongly since 2006. There’s also been some growth in the Maribyrnong  and Port Phillip council areas off a lower base.

By work location (note: this data is at the smaller destination zone geography):

Cycling to work boomed in inner Melbourne between 2006 and 2011, particularly to workplaces in the inner north. Princess Hill had the highest bike share of 14% in 2011 (possibly dominated by Princess Hill Secondary College employees), followed by a pocket of south-west Carlton that jumped from around 5% to 13%. Apart from the inner north, there were notable increases in Richmond, Balaclava, Yarraville and Southbank. Cycling rates within the CBD are relatively low, perhaps reflecting limited cycling infrastructure on CBD most streets in 2006 and 2011.

Adelaide

Firstly, by home:

Adelaide appears to lack any major concentrations of cycling, although slightly higher levels are found just outside the parkland surrounding the CBD.

Secondly, bicycle mode share by work destination at the (larger) SA2 geography:

The numbers are all small, with 3% in the (large) Adelaide CBD. I imagine a map based on destination zones might show some pockets with higher mode share, but that data isn’t freely available unfortunately.

Perth

By home location:

The inner northern and western suburbs, and south of Fremantle seem to be the main areas of cycling growth.

For workplaces at the larger SA2 geography:

The highest mode share was in ‘Swanbourne – Mount Claremont’, only slightly ahead of ‘Nedlands – Dalkeith – Crawley’ – which contains the University of Western Australia. The Fremantle SA2 (with 3% bicycle mode share by destination) includes of Rottnest Island where around 20% of the 73 resident commuters cycled to work, but the result will be easily dominated by the mainland Fremantle section.

Again, I suspect some smaller pockets would have had higher mode shares if I had access to destination zone data.

Brisbane

By home location:

There was significant growth in cycling from the West End, and around the University of Queensland/St Lucia – which may be related to the opening of the Eleanor Schonell Bridge (after the 2006 census) which only carries pedestrians, cyclists and buses.

By work location (at larger SA2 geography):

The highest share was in St Lucia – which is probably dominated by the University of Queensland. Neighbouring Fairfield – Dutton Park came in second. These two areas are directly joined by the Eleanor Schonell Bridge which provides cycling a major advantage over private transport. It seems to have been quite successful at promoting cycling in these areas.

Sydney

First by home location:

There were quite noticeable shifts to cycling in the inner south and around Manly.

By work location (by smaller destination zone geography):

There was strong growth, again in the inner southern suburbs. In 2011 bicycle mode share was highest in Everleigh (11.5%) following by the University of NSW (Paddington) at 7.9% (excluding travel zones with less than 200 employees who travelled).

Rural Australia

Here’s a map showing bicycle share by SA2 workplace location for all of Australia, which gives a sense of bicycle mode shares in rural areas.

Higher regional/rural bicycle mode shares are evident in southern Northern Territory (Petermann – Simpson), Katherine (NT), the Exmouth region, the Otway SA2 on the Great Ocean Road in western Victoria, and Longford – Loch Sport in eastern Victoria. I’ll let other people explain those.

The SA2s in Australia with the highest cycling mode shares in 2011 (by home location) were:

• Lord Howe Island, NSW: 21%
• Acton, ACT (covering Australian National University): 12%
• Port Douglas, Queensland: 10%
• Parkville, Victoria (covering the University of Melbourne main campus): 8%
• East Side, Northern Territory (Alice Springs): 8%
• St Lucia, Queensland (covering the University of Queensland): 8%

How far did people cycle to work? (in Melbourne)

It is difficult to get precise distances for journeys to work, but approximations are possible. I’ve calculated the approximate distance for each journey to work by measuring the straight line distance between the centroid of the home and work SA2s and then rounded to the nearest whole km. To give a feel for how this looks, here is a map showing inner Melbourne SA2s and the approximate distances between selected SA2s:

This distance measure generally works well in inner city areas. However in the outer suburbs SA2s are often much larger in size, and sometimes only partially urbanised. However as we’ve seen above the volumes of cycling journeys to work are very low in these places, so that hopefully won’t skew the results signficantly.

Two-thirds of cycling journeys to work in Melbourne were approximately 5km or less, with 80% less than 7 km, and 30% were 2 km or less.

The longest commute recorded within Greater Melbourne was approximately 44km.

Was cycling combined with other modes?

The following chart shows that bicycles were seldom combined with other modes:

Around 16-17% of cycling commuters in the four largest cities in 2011 involved another mode. Use of other modes with cycling grew in all cities between 2006 and 2011

The next chart shows what these other modes were:

Sydney, Melbourne, Brisbane and Perth had high rates of bicycle use with trains, while combining car and bicycle was more common in the smaller cities.

The next chart shows the number of trips involving bicycle and trains in 2006 and 2011:

The chart shows the relative success of Melbourne Parkiteer program of introducing high quality bicycle cages at train stations, which has helped boost the number of people access the train network by bicycle by around 600 between 2006 and 2011. I understand a similar project has been undertaken in Perth which saw growth of around 250.

In Melbourne, the home locations for people using bicycle and train are extremely scattered – the following map shows a seemingly random smattering:

How does commuter cycling vary by age and sex?

This chart shows remarkably clear patterns. Males were much more likely to cycle to work. Teenage boys (particularly those under driving age) had the highest cycling mode shares (with teenage girls much less likely to cycle). The next peak for men was around the mid thirties, and women’s mode share peaked around ages 28-32.

Where are women more likely to cycle to work?

Women are sometimes talked about as the “indicator species” for cycling – ie if you have large numbers of women cycling compared to men then maybe you have good cycling infrastructure that attracts a broader range of people.

The census data can shed some light on this. For each SA2 in Melbourne, I have calculated the male and female cycling mode shares both as a home origin, and as a work destination (this analysis looks at people who only used bicycle (and walking) in their journey to work). I’ve then calculated the ratio of male mode share to female mode for each area (SA2).

I’ve used the ratio of mode shares in preference to the straight gender split of cycling commuters – as female workforce participation is generally lower and there can be spatial variations in the gender split of the workforce. 46% of all journeys within Greater Melbourne in the 2011 census were by females, but only 28% of cycling journeys to work were by females.

The following map shows the ratio of male to female cycling mode shares by home location for SA2s (with more than 50 commuter cyclists, and where the bicycle mode share is above 1%):

Areas attracting comparable female and male bicycle shares include the inner northern suburbs and – curiously – Toorak (probably many using the off-road Gardiners Creek and Yarra Trails to access the city centre).

Here’s a similar map, but by workplace areas:

The patterns are much more pronounced. Six SA2s had higher female mode shares than male: Yarraville, Fitzroy North, Brunswick East, Ascot Vale, Carlton North – Princes Hill, and Elsternwick.

The areas with near-1 ratios of male to female mode shares were similar to the areas with higher total cycling mode shares. The following chart confirms this relationship (note areas with cycling mode shares below 1% not shown):

What this also shows is that home-area mode shares reach much higher values than workplace-area mode shares. Perhaps the secret is in the home-area cycling infrastructure? Or perhaps it’s more to do with the residential demographics?

See the Bicycle Network Victoria website for more data about female cycling rates in Melbourne.

Do women cycle the same distances as men?

Again using the approximate straight line commuting distances (as explained above) the following chart shows that women’s cycling commutes are a little shorter than men’s, but not by much:

The median female cycling commute was approximately 1.8 km shorter than for males.

What types of workers are more likely to cycle to work?

Firstly, I’ve looked at the differences between public and private sector employees.

Before I dive into the data, it’s important to recognise that different types of workers are not evenly spread across Australia. Some types of jobs concentrate in city centres while others might be more likely to be found in the suburbs or the country. Therefore many of the following charts show results for Australia as a whole, but also for people working in central Melbourne (the SA2s of Melbourne, Carlton, Docklands, East Melbourne, North Melbourne and Southbank), which has a relatively high rate of cycling to work.

The data suggests public servants were much more likely to cycle to work:

The local government result has prompted me to calculate the cycling mode shares for local government workers across Australia (assuming workers work within the council for which they work). Here are bicycle mode shares for the top 20 councils for employee cycling mode share in the census:

Most of the top 20 are non-metropolitan councils. Melbourne’s City of Yarra is the top metropolitan city council (within Greater Melbourne the next highest councils are Moreland 6.1%, Port Phillip 5.6%, Melbourne 5.6% and then Stonnington 4.9%).

National government employees had the highest bicycle mode share of all of Australia. I suspect this relates to university staff, as many of the earlier maps showed university campuses often had relatively high rates of employees cycling (85% of “higher education” employees count as “national government” employees).

The census data can also be disaggregated by income:

Cycling mode shares were highest for people on high incomes. Initially I thought this might reflect the fact that high income jobs are often in city centres were cycling is relatively competitive with private and public transport. However, even within central Melbourne workers, cycling rates are higher for those on high incomes (curiously with a second peak for those on incomes between $300 and $399 per week).

Does cycling to work make you healthier and therefore more likely to get promoted and earn a higher income? Or are employers offering workplace cycling facilities to attract highly paid staff? I haven’t got data that answer those questions.

Consistent with higher rates of cycling for higher income earners, those in more highly skilled occupations were more likely to cycle to work:

I suspect this might reflect the presence/absence of workplace cycling facilities (perhaps office workplaces are more likely to provide cycling facilities than retailers for example) and/or the ability to afford to live close to work (which makes cycling easier).

Are recent immigrants more likely to ride to work?

This one really surprised me and I only investigated it because it was possible to do. The census asks what year people migrated to Australia (if not born here), and it turns out that recent immigrants were much more likely to cycle to work:

This might be explained by the demographics of recent immigrants (eg car ownership, home location, income levels, occupation and age).

I’d welcome comments on any other trends people might spot in the data.

[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):

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

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:

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”.

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, London, Milan, 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):

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 it’s 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:

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):

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

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:

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 it’s public transport system, and it’s 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.

A lot has been said about the great Australian dream of moving to the suburbs and living on a quarter acre block. But is Melbourne suburbia actually full of quarter acre blocks? Where are they to be found? Are they disappearing? This post delves into block sizes in Melbourne.

Where are the quarter acre blocks?

A quarter-acre translates to 1011.7 square metres in modern units, but for the purposes of this post I’ll allow some leeway and count any block between 900 and 1100 square metres. For this post I’ve also filtered out blocks in planning zones that cannot include dwellings (eg industrial areas), but that does mean I’ve included blocks in mixed use zones, commercial zones, etc. So not every block counted is residential. Also some larger blocks might contain multiple small dwellings but not actually be subdivided (eg a block of flats).

First up, here is a map of Melbourne showing the prevalence of quarter acre blocks. It looks like there are lots of them, but because the blocks are so small, the total area occupied by quarter-acre blocks is significantly over-represented on this large scale map.

There are larger concentrations in the outer north-east and outer-east, but very few blocks in the current growth areas to the west, north and south-east.

Here are the top 20 suburbs for numbers of quarter-acre blocks:

There are almost 78,000 quarter-acre blocks within Melbourne’s Urban Growth Boundary, which sounds like a lot, but is only 3.75% of the 1.8 million blocks in my dataset.

So what are typical block sizes in Melbourne?

For this analysis I’m considering blocks within land use zones that can include dwellings, that are also within the urban growth boundary. But I’ve excluded blocks of less than 40 square metres on the assumption these are unlikely to contain dwellings.

Here’s the frequency distribution of block sizes in Melbourne:

The most common block size is 640-660 square metres, and 34.5% of blocks are between 520 and 740 square metres. The median is 540-560 square metres. 180-200 is the most common smaller block size, and there is a small spike in block sizes of 1000-1020 square metres, which includes the quarter-acre block. But quarter-acre blocks are certainly very uncommon.

I’ve calculated the median block sizes for all suburbs within Melbourne’s Urban Growth Boundary.

The inner city has median block sizes under 300 square metres, and 300-500 is typical in the inner northern and western suburbs. Block sizes are larger in the middle and outer eastern suburbs, older suburbs in the south-east, and blocks along the Mornington Peninsula. But the more recent growth areas to the west, north and south-east see median block sizes of between 400 and 500 square metres (purple), reflecting higher dwelling densities encouraged by current planning policy for growth areas. Quarter-acre blocks are the median only in places like Upwey, Belgrave and Portsea.

Inner city Carlton has the lowest median of 100-120 square metres, followed by Cremorne, North Melbourne, South Melbourne at 120-140 square metres, and then Abbotsford, Fitzroy North, Port Melbourne, Richmond, West Melbourne at 140-160 square metres. Urbanised suburbs at the other end of the scale include Park Orchards at 3020, Selby at 1440, and Warrandyte at 1260.

There are two interesting outliers in the central city: Southbank (in yellow) has a median block size of 980 square metres, and Docklands (in blue) has a median of 660 square metres. Both have been redeveloped in recent decades with many medium to high-rise apartment towers on those larger blocks.

Beyond these medians, there is a lot of variation within suburbs. Let’s go for a wander around the city.

Mooroolbark has the highest count of quarter-acre blocks and a median size of 840 square metres. As well as larger blocks, you can see a lot of further subdivision, particularly close to the train line (thin black line).

You may have noticed in the suburb map above a black coloured suburb in the middle south-eastern suburbs. That suburb is Clayton, and here is how it looks:

While blocks of 700-800 square metres were probably typical in the original subdivision, further subdivided blocks now outnumber the larger blocks, with a median of 260 square metres. Clayton of course is home to a major Monash University campus, and I suspect a lot of the smaller blocks house students.

A bit further down the line in Noble Park you can see extensive further subdivision near the rail line, surrounded by almost uniform blocks of 500-600 square metres:

Heading further south, Cranbourne is an interesting mix. The inner core (old town) has larger blocks but lots of further subdivision. This is surrounded by many blocks of around 700-800 square metres, but the most recent development has much smaller bocks, most less than 500. It’s a bit like tree rings, with each ring of incremental urban growth reflecting the preferred new block size of the time.

The area around Berwick also has a wide variety of block sizes, depending on the timing of development:

Here is the Frankston area:

Again significant further subdivision in central Frankston, a variety of block sizes in different parts of Langwarrin, and lots of large blocks in Frankston South and Mount Eliza (in some of the pink areas most blocks are over 2500 square metres).

In the middle northern suburbs you can see suburbs from an era when new block sizes were relatively large, and they’ve since had extensive subdivision. Here is Pascoe Vale:

Here is Reservoir. You can see smaller blocks in the surrounding suburbs:

The large block area to the west of the train line was apparently developed around the 1960s.

And to the west St Albans is another suburb with larger blocks being subdivided:

And further east there is a lot of further subdivision in Boronia and Bayswater, particularly near the rail stations:

The north-west corner of Templestowe has not too many larger blocks yet to be subdivided. But to the south-east you can see areas with blocks larger than 1200 square metres (light pink).

The area around Eltham has many large blocks, including many larger than quarter-acres. There has been quite a bit of subdivision around the rail stations however.

Another area with many large blocks is around Upwey/Belgrave:

A significant proportion of blocks are larger than a quarter-acre, with a median of 1060 in Belgrave, 1120 in Upwey, 1000 in Tecoma, and 980 in Upper Ferntree Gully.

If you want a quarter-acre block relatively close to the city, then Balwyn North has quite a few (many with swimming pools). Good luck saving a deposit for those.

But if a quarter-acre block isn’t big enough and you can afford the real estate, then you might want to try Canterbury or Deepdene, also relatively close to the city:

Or of course Toorak with plenty of very large blocks even closer to the city (although many will contain apartment buildings).

Essendon also has some larger blocks, including some quarter-acres:

There has been plenty of further subdivision, but there is also a stripe of green that is mostly in tact (a restrictive covenant applied perhaps?). You can also see the recent Valley Lake development in purple in Niddrie.

Most of the growth areas have small blocks, but here are some exceptions in eastern Doreen:

So there is plenty of variation in block sizes across Melbourne, but not that many quarter-acre blocks. Perhaps we should talk more about the one-seventh-acre block.

Data acknowledgement

This analysis was made possible with data available from data.vic.gov.au under a creative commons license. The data is Copyright © The State of Victoria, Department of Environment, Land, Water & Planning 2016.

I have used November 2015 property boundary data and May 2016 planning zones (sorry, not quite aligned, but this post has been a while in the making and the differences are unlikely to be significant).

With the first major release of 2016 census data, it’s possible to take a detailed look at the latest population density numbers in Melbourne. This post will explore how and where Melbourne’s density is increasing by comparing data from the 2006, 2011, and 2016 censuses.

About the data

This post looks at data mostly at the mesh block level. Mesh blocks are the smallest geographic unit at which the ABS publishes population and dwelling counts. They aim for each mesh block to have the same land use, and between 30 and 60 dwellings (where residential).

I’ve used Tableau Public to create this post, so you will be able to explore the maps in more detail yourself, using the links in this post. Be warned though: Tableau tried to dissuade me several times from adding so many mesh blocks to the maps and charts, so they may take a little time to load and update.

Background map data has been used that is copyright © The State of Victoria, Department of Environment, Land, Water & Planning 2017

What does Melbourne’s population density look like?

Firstly, here’s the population density picture for most of Melbourne (you will probably need to open this is in a new window to see it more clearly).

(explore in Tableau)

Here is a closer look at Melbourne’s growing west, stretching as far as Bacchus Marsh:

You can see the expanding urban area, and you might also notice some of the new areas are coming up in red (densities in the 60-70s). This demonstrates that recent urban growth areas are much more dense than growth areas of 5-10 years ago. However that’s not happening in growth areas of Bacchus Marsh (which is outside Melbourne’s Urban Growth Boundary.

Here is a closer look at the northern growth areas:

You can see large areas of orange and red in north-western Craigieburn (top left of map) and Roxburgh Park in 2016 – that’s around 50-60 people per hectare, around double that of old-school suburbia.

Here’s the south-east growth corridors, where new high density areas are less widespread:

There’s also been plenty of change in population density in the inner city:

If you look carefully you can see a lot more purple around the city centre, but also plenty of population density increase around Brunswick in the north and Footscray in the west.

So this is this increase in population density due to rising dwelling densities or more people per dwelling?

Here is the dwelling density around Craigieburn:

(explore in Tableau)

There are dwelling densities of over 20 per hectare in the new north-western areas, which is likely to be contributing to higher population density.

Here’s a map showing the average dwelling occupancy – the ratio of population to dwelling counts. Note: this includes unoccupied dwellings at census time, so it’s not the average occupancy of occupied dwellings.

(explore in Tableau)

One clear trend is that the growth areas have higher average dwelling occupancy, quite probably related to young families moving into those areas. This, together with smaller block sizes, is likely leading to higher population density in growth areas.

If you look carefully you’ll also see some older outer areas with reducing average dwelling occupancy – quite possibly family homes where children have moved out.

What are the broader trends in density?

The above maps are incredibly detailed, and you are probably struggling a little with so many blocks of different colours. Time to take a step back.

Calculating the straight population density of Greater Melbourne makes no sense because most of the land within the statistical boundary is non-urban.

In other posts I’ve looked at population-weighted density, which is the average population density of all areas, weighted by the population of each area. It aims to summarise the population density at which the “average” person lives, which takes out the impact of large areas that are sparsely populated. But it is important to keep in mind that “average” does not mean typical (I’ll come back to that).

Here’s a chart showing Melbourne’s populated weighted density, as well as average density for mesh blocks with a population density of at least 5 persons/ha (an arbitrary threshold for urban residential areas).

Yes, that’s a massive increase in population-weighted density. So what’s going on here?

Well, here’s a chart showing the densities at which people in Melbourne lived at each census:

If you look at the green levels and below, you’ll notice in all years less than 2.5 million people lived at densities of below 35 persons/ha. There’s been little population growth at such lower densities – it’s mostly been at 35 persons/ha and above, pushing up the population-weighted density.

Greater Melbourne’s population-weighted density of 59 is quite high relative to the density distribution within Melbourne. Only around 600,000 people live at this or higher densities, with around 4 million living at lower densities. That’s a classic problem with summary statistics.

Where is the population-weighted density increasing the most? Here’s a map showing population-weighted densities by SA2 (2016 boundaries):

(explore in Tableau)

There are big increases in population weighted densities across inner Melbourne, but also in places like Clayton, Box Hill, Preston East, Doncaster.

What’s going on there? Box Hill’s population-weighted density went from 45 in 2011 to 72 in 2016. Here’s a look at the mesh block density for the area:

You can see a little densification outside the main centre on the rail line, but if you look really carefully, you’ll see some tiny purple mesh blocks right in the centre – apartment towers with large populations are bringing up the populated weighted density of the whole SA2.

What about median densities?

While no one statistic will tell you about “typical” density, we can calculate median density, which tells you the density in which the middle person lives.

Greater Melbourne’s median population density hasn’t increased a great deal:

Here’s a look at median density by SA2 (open in a new window to see more detail, including the numbers):

(explore in Tableau)

(note that a different set of colour ranges used to the previous maps because the medians are so close)

You can see a lot more red on the map – i.e. more and more areas of Melbourne have a median population density of in the 40s.

How has population and density changed by distance from the CBD?

Firstly, here’s a reference map of distances from the CBD:

(explore in Tableau)

Here’s the population of Melbourne by density and distance from the CBD:

(explore in Tableau)

You can see a lot of growth close to the CBD, but also around 20-23 km from the CBD, which includes several outer suburban growth areas.

Here’s a look at five year population growth by distance from the CBD:

In the five years to 2016 there was a lot more growth within 30 kms of the CBD, particularly within 5 km.

Finally, which mesh block densities are becoming more common. Here is the five year change in population by (mesh block) population density:

In the five years to 2011, the biggest population increase was at densities of 30 to 45 persons/ha. In the five years to 2016, the biggest population growth was at densities of 35 to 55 persons/ha. There was also considerable growth at densities of more than 400 persons/ha, which is likely to reflect new apartment towers.

You’ll find a few other charts in Tableau. Hope you enjoyed this post.

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.