## Comparing the residential densities of Australian cities (2011)

Fri 19 October, 2012

I’ve looked at Melbourne residential density in detail, so what about other Australian cities?  Is population weighted density a useful measure? Does population weighted density help explain differences in public transport mode shares?

For this exercise, I’ve looked at 2011 census data at the Statistical Area Level 1 (SA1) geography (currently the smallest geography for which population data is available) for Greater Capital City Statistical Areas (which include large tracts of rural hinterland). I’ve sometimes applied an arbitrary threshold of 3 persons per hectare to define urban residential areas.

### Measures of overall density

Population weighted density is a weighted average of the density of all the parcels of land in the city, with the population of each parcel of land providing the weighting. This provides a figure indicative of the residential density of the “average person”, although that’s still a little abstract. A city where a large proportion of people live in dense areas will have a much higher weighted population density than average population density.

Average density is simply the total population divided by the area of the city (or if you like, the average density weighted by the areas of each parcel of land). In calculating average residential density (which I’m doing in this post), the area would only include residential areas (I’ve arbitrarily used a threshold of SA1s with at least 3 persons per hectare).

Another measure is urban density, which considers all the land that makes up the urban city, including non-residential areas, but excluding the rural land that makes up large parts of most metropolitan areas when defined by administrative boundaries. I have not attempted to measure ‘urban’ density in this post.

Firstly here’s a table of data for the six largest Australian cities with three different measures of 2011 residential density:

 Greater Capital City Statistical Area Pop Pop (>3/ha) Area, square km (>3/ha) Pop-weighted density, persons/ ha (all SA1s) Pop-weighted density, persons/ ha (SA1s >3/ha) Average residential density, persons/ ha (SA1s >3/ha) Greater Sydney 4391578 4225278 1530 50.2 52.1 27.6 Greater Melbourne 3999924 3832366 1812 31.8 33.1 21.1 Greater Brisbane 2066134 1866794 1127 22.6 24.8 16.6 Greater Perth 1728567 1639849 963 21.6 22.7 17.0 Greater Adelaide 1225136 1161668 644 21.2 22.3 18.0 Australian Capital Territory 356563 350917 221 20.5 20.8 15.9

You’ll notice that Melbourne has a lower population than Sydney, but the total land area above 3 persons/ha is much larger.

Here are those densities in chart form:

You can see Sydney has around double the population weighted density of most other cities, but its average density is only about 60% higher. These figures show Sydney has a very different density pattern compared other Australian cities.

You can also see very little difference in weighted density whether you exclude low density land parcels or not (the blue and red bars). The density is brought down only slightly by the relatively small number of people living in very low density areas (below 3 persons/ha) within the statistical geography. Thus weighted average density is a good way to get around arguments about the boundary of the “urban” area. But then we are only measuring residential density here – and the large unoccupied spaces between residents of a city are very important when it comes to transport issues.

Can you compare population weighted density of Australian cities with international cities? Yes, but only if the parcels of land used are of a similar size and created in a similar fashion. The more fine-grained the geography (ie smaller the parcels of land), the more non-residential pockets of land will be excluded from the calculation. Anyone doing an international comparison should compare how the ABS create their geography at SA1 level with approaches of other statistical agencies. And please comment below if you get a set of comparable figures.

### Density by distance from the CBD

The differences in density can be seen a little more clearly when you look at weighted average density by distance from the city centre:

(note: I’ve chopped the vertical scale at 100 persons/ha so parts of central Sydney, Melbourne and Brisbane are off the scale).

For Perth, Adelaide, Brisbane and Canberra (ACT) you can see a weighted average density in the mid to low 20s for large areas of the city, indicating large tracts of what you might describe as traditional Australian suburbia. In Canberra this kicks in at just 2 km from the CBD, and in Adelaide it kicks in 3 km from the city.

In Melbourne the weighted average density doesn’t get below 30 until 9 kms from the CBD indicating a larger denser inner area, and in Sydney it doesn’t drop below 30 until you are 39 km from the CBD!

### Distribution of population at different densities

Here’s a frequency distribution of densities in the cities:

I’m using an interval of 1 person/ha, and the figures are rounded down to form the values on the X axis (ie: the value you see at 20 persons/ha is the proportion of the population living between 20 and 21 persons/ha).

You can see Sydney has the flattest distribution of all – indicating it has the widest range of densities of any city. Melbourne is not far behind, whereas Canberra has a lot of people living in areas between 12 and 24 persons/ha.

Note that many cities have a significant proportion of the population living at rural densities (0 to 1 person per hectare), particularly Greater Brisbane.

Another way to look at this data is a cumulative frequency distribution:

You can read off the median densities for the cities: Sydney 33, Melbourne 28, Brisbane 22, Perth 22, Adelaide 22, Canberra 19.

You can also see that 30% of people in Sydney live in densities of 44 persons/ha or more – compared to only 12% of Melburnians, 5% of Brisbanites, and less than 2% of people in the other cities.

If 15-30 persons per hectare is what you define as suburbia, then that’s 26% of Sydney, 37% of Melbourne, 44% of Brisbane, 55% of Perth, 57% of Canberra and 62% of Adelaide.

### Spatial distribution of density

For the purest view of density you cannot get past a map. The following maps show a simple density calculation at the SA1 geography.

#### Sydney

You can see vast areas of darker green (40+/ha), particularly between Sydney Harbour and Botany Bay. There are also quite a few green areas in the western suburbs, while the northern north shore has the lowest density. There are many concentrations of density around the passenger rail lines.

#### Melbourne (and Geelong)

You can see areas of dark green around the inner city, with large tracts of yellow and green in the suburbs (25-35 persons/ha). There are however areas of moderate green (30-40) in some of the newer outer growth areas to the west and north, reflecting recent planning. There’s a not a strong relationship to train lines, but this might reflect higher densities equally attracted to tram lines (not shown on the map).

Note this map is slightly different to that in a recent post where I masked out non-residential mesh blocks.

#### Brisbane

You can see dark green patches around the river/CBD, but then mostly medium to low densities in the suburbs. There’s very little evidence of higher densities in fringe growth areas. There are some denser areas around railway lines (note the map does not show Brisbane’s busway network).

#### Perth

You can see green patches around the city, but also in some fringe growth areas where new planning controls are presumably forcing up densities. There are however vast tracts of orange (15-25 persons/ha), and little evidence of higher density around the rail lines (note: a lot of the lines are freight only and the north-south passenger line has very broad station spacing and limited walking catchment as most of it is within a freeway median).

Adelaide some inner city blocks of high density, but once you get outside the green belt surrounding the city blocks, you fairly quickly head into suburban densities. There are only a few pockets of high density in the middle and outer suburbs, and very little relationship evident between density and the rail lines.

#### Canberra (and Queanbeyan)

Canberra has vast areas at low density, and only a few pockets with dark green. There are however green patches on the fringes (particularly in the far north and far south), perhaps again reflecting planning policies forcing up densities.

Sydney is really quite a different city compared to the rest of Australia, with a much larger share of the population living in high density residential areas (more than I had expected). Melbourne has a much lower population weighted density (still quite a few people living in high density areas, but much less so than Sydney), followed by four cities that aren’t that different when it comes to density: Brisbane, Perth, Adelaide and Canberra.

### What about density and public transport use?

Here’s a comparison of density (measured as both average and population weighted) and the most recent estimate of public transport mode share of motorised passenger kms for Australian cities:

Population weighted density certainly shows a stronger relationship with public transport use than average density (r-squared of 0.89 versus 0.82 on a linear regression).

If you believe that higher population density will lead to higher public transport use (for a given level of public transport service), then you would expect Sydney to be well placed to have a higher public transport mode share. Which indeed it does, but does it have the same level of public transport supply as other cities, and are all other factors equal? That’s a very difficult question to answer.

You could measure public transport service kilometres per capita, but different modes have different speeds, stopping frequencies and capacities, public transport supply will vary greatly across the city, and some cities might have more effective service network designs that others.

If all cities had the same levels of public transport supply and all other things were equal, you might expect a straight line relationship (or perhaps an exponential relationship). But Brisbane and Melbourne (and to a small extent Perth) seem to be bucking what otherwise might be a linear pattern. Are these cities doing much better with quality and quantity of public transport supply? Or is it something else about those cities?

Car ownership rates do vary between Australian cities, but this might be more a product of public transport viability for particular residents:

Also, we know that car ownership doesn’t have a strong relationship with car use.

When working population census data comes out I would like to look at the distribution of employment within cities. We know that public transport use is highest for journeys to work in the CBD (as it usually competes strongly against the car), so the proportion of a city’s jobs that are in the CBD is likely to impact the public transport mode share (at least for journeys to work). Moreover, a higher average employment density in general might be easier to serve with competitive public transport, and thus lead to a higher public transport mode share. It will hopefully also be possible to calculate weighted density of employment (at least at the SA2 level).

Finally, I’d like thank Alan Davies (The Urbanist) for inspiring this post.

## Changes in Melbourne motor vehicle ownership 2006 to 2011

Fri 12 October, 2012

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.

## What sorts of people use public transport? (part two)

Sun 24 June, 2012

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).

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.

## What sorts of people use public transport? (part one)

Fri 15 June, 2012

On this blog I’ve previously had a good look at public transport mode share by where people live and where they work, and I did some profiling for Melbourne CBD commuters by age, gender, income, profession.

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.

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.

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.

### 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.