What explains variations in journey to work mode shares between and within Australian cities?

Thu 6 December, 2018

Private and public transport journey to work mode shares vary considerably both between Australian cities and within them. Are these differences related to factors such as population density, motor vehicle ownership, employment density, proximity to train stations, proximity to busway stations, jobs within walking distance of homes, and distance from the city centre?

This posts sheds some light on those relationships for Australia’s six largest cities. I’m afraid it isn’t a short post (so get comfortable) but it’s fairly comprehensive (over 30 charts).

I should stress up front that a strong relationship between a certain factor and high or low mode shares does not imply causation. There are complex relationships between many of these factors, for example motor vehicle ownership rates are generally lower in areas of higher residential density (which I will also explore), and more factors beyond what I will explore here.

If you are interested in seeing spatial mode share patterns, see previous posts for Melbourne, Brisbane, and Sydney. You might also be interested in my analysis explaining the mode shifts between 2011 and 2016.

Population density

Higher population densities are commonly associated with higher public transport use. This stands to reason, as high density areas have more potential users per unit of area, but also higher density is likely to mean high land prices, which in turn increases the cost of residential parking. But higher public transport mode share can only happen if government’s invest in higher service levels, and this isn’t guaranteed to happen (although it often does, through pressures of overcrowding).

My preferred measure is population weighted density, which is the weighted average density of land parcels in a city, weighted by their population (this gets around problems of including sparsely populated urban land). I’ve measured it at census district (CD) geography for 2006 and Statistical Area Level 1 (SA1) geography for 2011 and 2016, using 2011 Significant Urban Area boundaries to define cities. The 2006 density figures are not perfectly comparable with 2011 and 2016 because CDs are slightly larger than SA1s, so the density values will be calculated as slightly smaller.

Here is the relationships at city level (the thin end of each worm is 2006 and the thick end 2016, with 2011 in the middle):

The relationship is very strong for Melbourne and Sydney over time. Between 2011 and 2016, Perth and Brisbane saw increased population density but reduced public transport mode share (mostly because of changes in the distribution of jobs between the centre and the suburbs).

Brisbane was a bit of an outlier in 2006 and 2011 with high public transport mode share relative to its lower population density.

Canberra is also perhaps a bit of an outlier, with much lower public transport mode share compared to similarly low density cities. This might be explained by the smaller total population, lower jobs density, and lack of rapid public transport services segregated from traffic.

But Canberra does have higher active transport mode share, so it’s worth doing the same analysis with private transport mode shares:

Brisbane was still an outlier in the relationship in 2006 and 2011, but Canberra is more in line with other data points.

Another interesting note is that Canberra went from being the least dense city in 2006 to the third most dense in 2016.

Drilling down to SA2 geography (SA2s are roughly the size of a suburb), here’s a chart showing all SA2s in all cities across the three census years (filtered for CDs and SA1s with at least 5 persons per hectare). I’ve animated it to highlight one city at a time so you can compare the cities, and I’ve used a log scale on the X-axis to spread out the data points (only the Sydney and Melbourne CBDs go off the chart to the right).

(if these animated GIF charts are not clear on your screen, you can click to enlarge the image, then use “back” to come back to this page).

You can see a fairly strong relationship, although it is very much a “cloud” rather than a tight relationship – there are other factors at play.

What I find interesting is that Sydney has had a lot of SA2s with population weighted densities around 50-100 but private mode shares over 55% (toward the upper-right part of the cloud of data points) – which are rare in all other cities. That’s a lot of traffic generation density, which cannot be great for road congestion. In a future post I might focus in on the outlier SA2s that are in the top right of these charts (can public transport do better in those places?).

In case you are wondering about the Brisbane SA2 with low density and low private transport mode share (middle left of chart) it is the Redland Islands where car-carrying ferries are essential to get off an island, and are counted as public transport in my methodology. The Canberra outlier in the bottom left is Acton (which is dominated by the Australian National University).

Employment density

I’ve calculated a weighted job density in the same way I’ve calculated population weighted density, but using Destination Zones (which can actually be quite large so it certainly isn’t perfect). Weighted job density is a weighted average of job densities of all destination zones, weighted by the number of jobs in each zone. In a sense it is the density at which the average person works

(technical notes: I’ve actually only counted jobs as people who travelled on census day and reported their mode(s) of travel. Unfortunately I only have 2006 data for Sydney and Melbourne)

This chart suggests a very strong relationship at the city level, with all cities either moving up and left (Adelaide, Perth and Brisbane) or down and right (Sydney, Melbourne, Canberra).

So is the relationship as strong when you break it down to the Destination Zone level? The next chart shows jobs density and private mode share for all destination zones for 2016. Note that there is a log scale on the x-axis, and Adelaide dots are drawn on top of other cities in the top left which explains why that dense cloud of dots appears mostly green.

There’s clearly a strong relationship, although again the data points form a large cloud rather than tightly bunch around a line, so other factors will be at play.

It’s also interesting to see that the blue Sydney dots are generally lower than other cities at all job densities. That is, Sydney generally has lower private transport mode shares than other cities, regardless of employment density.

Which leads us to the next view: the private transport mode shares for jobs in different density ranges in each city for 2011 and 2016.

(click to enlarge if the chart appears blurry)

You can see a fairly consistent relationship between weighted job density and mode shares across all cities in both 2011 and 2016.

At almost all job density ranges, Sydney had the lowest average private transport mode share, while Adelaide and Perth were generally the highest (data points are not shown when there are fewer than 5 destination zones at a density range for a city). This shows that something other than jobs density is impacting private transport mode shares in Sydney. Is it walking catchment, public transport quality & quantity, or something else?

For more on the relationship between job density and mode share, see this previous post.

Proximity to public transport

Trains generally provide the fastest and most punctual public transport services (being largely separated from road traffic and having longer stop spacing), and are the most common form of rapid transit in Australian cities. So you would expect higher public transport mode shares around train stations.

Here is a chart showing average journey to work public transport mode shares by home distance from train stations. It’s animated over the three census years, with a longer pause on 2016.

Technical note: A limitation here is that I’ve measured all census years against train stations that were operational in 2016 – so the 2006 and 2011 mode shares will be under-stated for the operational stations of those years. For example, in Melbourne the following stations opened between 2011 and 2016: Williams Landing, South Morang, Lynbrook, and Cardinia Road.

You can see that public transport shares went up between 2006 and 2011 in most cities at all distances from train stations. In both Perth and Brisbane there were new train lines opened between 2006 and 2011, which will explain some of that growth.

But if you watch carefully you will see public transport mode shares near train stations fell in both Brisbane and Perth between 2011 and 2016. That is, there was a mode shift away from public transport, even for people living close to train stations. As discussed previously, this is most likely related to there being only small jobs growth in the CBDs of those cities between 2011 and 2016, compared to suburban locations.

You can also see that public transport mode shares aren’t that much higher for areas near train stations in Adelaide (I’ll come back to that).

We can do the same for train mode shares (any journey involving train):

Again, Sydney’s train stations seem to have the biggest pulling power, while Adelaide’s have the least.

Busways are the other major form of rapid transit in Australian cities, with major lines in Brisbane, Sydney and Adelaide. Here is a chart of public transport mode share by distance from busway stations, excluding areas also within 1.5 km of a train station:

Note for Adelaide this data only considers suburban stations on the O-bahn, and not bus stops in the CBD. For Sydney all “T-Way” station are included, plus the four busway stations on the M2 motorway for which buses run into the CBD (but not the relatively short busway along Anzac Parade in Moore Park). Sydney’s north west T-Ways opened in 2007

Proximity to a busway station appears to influence public transport mode share strongly in Brisbane and Adelaide, where busways are mostly located in the inner and middle suburbs and cater for trips to the CBD. Sydney’s busway stations are in the “outer” western suburbs, feeding Blacktown, Parramatta, but also relatively long distance services to the Sydney CBD via the M2.

Curiously, public transport mode shares were higher in places between 3 and 5 km from busway stations in Sydney, compared to immediately adjacent areas. I’m not sure that I can explain that easily, but it suggests equally attractive public transport options exist away from busway and train stations.

The station proximity influence appears to extend around 1 km, which possibly reflects the fact that few busway stations have park and ride facilities, and are therefore more dependent on walking as an access mode (although cycling may be another station access mode).

Over time Sydney public transport mode share lifted at all distances from busway stations, while in Brisbane it rose in 2011 and then fell again in 2016, in line with other city mode shares.

So are busway stations similar to train stations in their impact on public transport mode share? To answer this I’ve segmented cities into areas near train stations, near busway stations, near both, and near neither. I’ve used 1.5 km as a proximity threshold that might represent an extended walking catchment.

In Sydney, train stations appear to have a much stronger influence on public transport mode shares than busway stations, but the opposite is true in Brisbane and Adelaide. This possibly reflects the much higher service frequencies on Adelaide and Brisbane busways compared to their trains, and the fact Sydney’s busway stations are so far from the CBD (and thus have fewer workers travelling to the CBD where public transport dominates mode share).

Also of note in this chart is that for areas more than 1.5 km from a train or busway station, Sydney had a much higher public transport mode share compared to the other cities. These areas will be served mostly by on-road buses, but also some ferries and one light rail line. Adelaide has the least difference between mode share for areas near and not-near train or busway stations.

We can do the a similar analysis for workplaces:

The most curious pattern here is Adelaide – where public transport mode share was highest for jobs between 1.5-2.5 kms from train stations. This distance band is dominated by the centre of the Adelaide CBD (the station being on the edge, arguably a “corner”), for which bus was the dominant public transport access mode. Also, there was no destination zone small enough near Adelaide central train station to register as 0 – 0.5 km away, and only one that is 0.5 – 1 km away (I use distances between station data points and destination zone centroids). So the results might look slightly different if smaller destination zones were drawn in the Adelaide CBD.

In all other cities there was a very strong relationship between train station proximity and public transport mode share, as you would expect. And Sydney again stands out with high public transport mode shares for workplaces more distant from train stations.

If you are wondering, the bump in Sydney at 2.5 to 3 km includes the Kensington / Randwick area which has high employment density and a strong bus connection to the central city (partly assisted by the Anzac Parade busway). And the relatively high figure for Melbourne at 1 – 1.5 km includes parts of Docklands, Parkville, Southbank, and St Kilda Road, which all have high tram service levels.

Unfortunately destination zones around busway stations are generally too large to provide meaningful insights so I’m not presenting such data.

Motor vehicle ownership

It will come as little surprise that there is a relationship between household motor vehicle ownership and journey to work mode shares.

Here’s a summary chart for each city for the 2006, 2011 and 2016 censuses:

There appears to be a fairly strong relationship between the two factors at city level.

Sydney and Melbourne have seen the largest mode shift away from private transport, but only Melbourne has also seen declining motor vehicle ownership rates.

Canberra saw only weak growth in motor vehicle ownership between 2011 and 2016, and at the same time there was a shift away from private transport (and a large increase in population weighted density).

Perth and Brisbane saw increasing private transport mode share and increasing motor vehicle ownership between 2011 and 2016.

Here’s a more detailed look at the relationship over time for Melbourne at SA2 geography:

The outliers on the upper left are generally less-wealthy middle-outer suburban areas (lower motor vehicle ownership but high private mode share), while the outliers to the lower-right are wealthy inner suburbs where people can afford to own plenty of motor vehicles, but they didn’t use them all to get to work.

In the bottom left of the chart are inner city SA2s with declining private mode share and declining motor vehicle ownership. For motor vehicle ownership rates around 70-80 (motor vehicles per persons aged 18-84), there are many SA2s with private mode shares that declined 2006 to 2016, but not significantly lowering motor vehicle ownership rates. That suggests that just because people own many motor vehicles, they don’t necessarily use them to drive them to work.

Here is the same data for Sydney:

There are many SA2s with motor vehicle ownership rates around 50 to 70 where the private mode shares are dropping faster than motor vehicle ownership. But there are also many areas with high private mode shares and increasing rates of motor vehicle ownership.

How do the other cities compare? Here are all the SA2s for all cities on the same chart, with alternating highlighted cities:

You can see big differences between the cities, but also that Brisbane and Perth have many SA2s with very high private mode share and rapidly increasing motor vehicle ownership (ie moving up and right, although it’s a little difficult to see with so many lines overlapping). Melbourne and Sydney have plenty of SA2s moving down and left – reducing motor vehicle ownership and declining private transport mode share (which may make some planners proud).

Of course there will be a relationship between motor vehicle ownership and where people choose to live and work. People working in the central city may prefer to live near train stations so they can avoid driving in congested traffic to expensive car parks. People who prefer not to drive might choose to live close to work and/or a frequent public transport line. People who are happy to drive to work in the suburbs might avoid higher priced real estate near train stations or the inner city.

As an aside, we can compare total household motor vehicles to the number of people driving to work, to estimate the proportion of household motor vehicles actually used in the journey to work. Here is Melbourne:

SA2s with a lower estimate are generally nearer the CBD, are wealthier areas, have reasonable public transport accessibility, and/or might be areas with a higher proportion of people not in the workforce (for whatever reason). The areas where the highest proportion of motor vehicles are required for the journey to work are relatively new outer suburbs on the fringe (perhaps suggesting forced car ownership), where adult workforce participation is probably high and public transport accessibility is lower.

The proportion of cars used in the journey to work declined on average in many parts of Melbourne. Given that motor vehicle ownership rates in Melbourne barely changed between 2011 and 2016, this probably represents people mode shifting, rather than people acquiring more motor vehicles even though they don’t need them to drive to work.

Jobs within walking distance of home

It stands to reason that people would be more likely to walk to work if there were more work opportunities within walking distance of their home.

For every SA1 I’ve measured how many jobs are approximately within 1 km as a notional walking catchment (measured as the sum of jobs in destination zones whose centroid are within 1 km of the centroid of each home SA1, so it is not perfect). Here’s the relationship with walking mode share:

(there are a lot of dots overlapping in the bottom left-corner and Adelaide dots have been drawn on top so try not to get thrown by that).

You don’t have to have a lot of nearby jobs to get a higher walking mode share, but if you do, you are very likely to get a high walking more share. The exceptions (many jobs, but low walking share) include many parts of Parramatta (Sydney), and areas separated from nearby jobs by water bodies or other topographical barriers (eg Kangaroo Point in Brisbane).

Workplace distance from the city centre

As was seen in a previous post, workplaces closer to city centres had much lower private transport mode shares, which is unsurprising as these are locations with generally the best public transport accessibility, high land values that can lead to higher car parking prices (which impact commuters who pay them), and often higher traffic congestion.

Here is a chart showing private transport mode share by workplace distance from the city centre. I’ve used faded lines to show 2011 and 2006 results (2006 only available for Sydney and Melbourne).

Here’s a chart that shows the mode shifts between 2011 and 2016:

Inner Melbourne had the biggest mode shifts away from private transport (particularly in Docklands that falls into the 1-2 km range, which saw significant employment and tram service growth), but Sydney had more consistent mode shifts across most distances from the city centre. Adelaide and Canberra saw mode shifts away from private transport in the inner city but towards private transport further out.

Brisbane and Perth saw – on average – a mode shift to private transport across almost all distances from the city centre, with the highest mode shift to private transport in Brisbane actually for the CBD itself(!).

Home distance from the city centre

There’s unquestionably a relationship here too, and it’s probably mostly driven by public transport service levels being roughly proportional to distance from the CBD, but also the proportion of the population who work in the CBD being much higher for homes nearer the CBD.

Sydney had the lowest average private transport mode share at all distances up to 20 km from the CBD, followed by Melbourne and Brisbane, in line with overall mode shares.

The trends over time are also interesting. Brisbane saw mode shifts towards private transport at all distances more than 2 km from the city centre between 2011 and 2016. However there were not significant shifts for Perth outside the city centre – that is: modes shares by geography didn’t change very much. The mode shift away from public transport in Perth is best explained by the shift in jobs balance away from the city centre.

Here are public transport mode shares by home distance from city centres:

In most cities, public transport mode share peaked at a few kilometres from the city (as active transport has a higher mode in the central city).

Here are public transport mode shifts by distance from the city centre between 2011 and 2016:

The significant shift in central Melbourne is likely to be largely explained by the Free Tram Zone introduced in 2015. Outside of the city centre the mode shifts are surprisingly uniform across each city.

Here’s the same chart for 2006-2011, and you can clearly see the impact of the opening of the Mandurah railway line in Perth with significant mode shift beyond 30 km:

Curiously there was a massive shift to public transport for CBD residents in Melbourne (and this is before the free tram zone was introduced).

So which factors best explain the patterns in mode shares across cities?

What we’ve clearly seen is that higher public transport mode shares are seen for journeys to work…

  • to higher density workplaces
  • from areas of lower motor vehicle ownership
  • to workplaces closer to train stations
  • from higher density residential areas
  • from areas around train and busway stations
  • to and from areas closer to city centres (except from the central city where walking takes over)
  • from less wealthy areas (while I haven’t tested this directly, wealth seems to explain a lot of the outliers in the scatter plots)

I’ve listed these roughly in order of the strength of the relationships seen in the data, but I haven’t put them all in a regression model (yet, sorry).

Of course most of these factors are inter-related, so we cannot isolate causation factors. I’m going to run through many of them, because they are often interesting: (note I have sometimes used log scales)

Population density is roughly related to distance from the city centre:

Motor vehicle ownership has a strong relationship with population density (see this post for more analysis):

Motor vehicle ownership has a weaker relationships with distance from the city centre:

Motor vehicle ownership is related to home distance from train stations, except in Adelaide:

Technical note: For this chart (and some below) I’ve calculated average quantities for the variable on the Y axis, as there would otherwise there are too many data points on the chart and it becomes very hard to see the relationship (I would need to show all SA1s because SA2s are too large in terms of distance from stations). The downside is that these style of charts don’t indicate the strength of relationships.

Population weighted density is related to distance from train stations, especially in Melbourne and Sydney, but not at all in Adelaide:

There is a relationship – although not strong – between weighted job density and distance from city centres:

There’s some relationship between average weighted jobs density and distance from train stations, except in Adelaide:

Here’s the same data, but as a scatter plot with a point for each destination zone, scaled by the number of journeys to each destination zone, and a linear Y-axis:

Technical note: the X-axis appears green mostly because Adelaide data points are drawn on top of other cities, but those data points aren’t of much interest.

In most cities, destination zones with high jobs density (over 700 jobs/ha) were only found within 1 km of a train station – with the notable major exception of Adelaide (again!).

(If you are curious, the large Melbourne zone at 1.4 km from a train station and 659 jobs/ha is the Parkville hospital precinct – where incidentally a train station is currently under construction).

There is a relationship between motor vehicle ownership and proximity to busway stations, but it varies between cities:

But there’s not much relationship between population density and proximity to busway stations (except in the immediate vicinity of busway stations in Brisbane):

Final remarks: there’s something about Adelaide’s train network

A few key observations come through clearly about the catchments around Adelaide’s train stations:

  • In aggregate they do not have higher population density, unlike other cities.
  • In aggregate they do not have particularly high public transport mode shares, unlike other cities.
  • In aggregate they do not have lower rates of motor vehicle ownership, unlike other cities.
  • They do not include the area of highest job density in the CBD (a longer walk or transfer to tram or bus is required), unlike other cities.

Few cities have spare land corridors available for new at-grade rapid public transport lines, and so transport planners generally want to make maximum use of the ones they’ve got, before opting for expensive and/or disruptive tunnelling or viaducts solutions. It looks like Adelaide’s rail corridors are not reaching their people-moving potential.

By contrast, Adelaide’s “O-Bahn” busway does go into the job dense heart of the CBD and the busway station catchments do have higher public transport mode share and lower motor vehicle ownership. However they do not have higher population density, possibly because the stations are surrounded by car parks, green space, and one large shopping centre (Tea Tree Plaza).

Mode shares, population densities, and motor vehicle ownership rates would quite probably change significantly if Adelaide could address the fourth issue by building a train station near the centre of the CBD.

In fact, Auckland had a very similar problem with its previous main city station being located away from the centre of the CBD. They fixed that with Britomart station opening in 2003 and train patronage soon rose quite dramatically (off a very low base, and also helped by service upgrades, subsequent electrification, and many other investments).

Should Adelaide do the same? It would certainly not be cheap and you would have to weigh up the costs and benefits.

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

Sun 30 July, 2017

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

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

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

Is motor vehicle ownership increasing in all cities?

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

(you can explore this data in Tableau)

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

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

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

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

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

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

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

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

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

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

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

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

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

(explore in Tableau)

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

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

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

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

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

(view in Tableau)

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

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

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

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

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

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

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

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

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

(explore in Tableau)

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

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

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

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

Trends in motor vehicles per persons aged 18-84

(explore in Tableau)

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

How does motor vehicle ownership relate to density?

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

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

Is densification related to lower motor vehicle ownership?

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

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

(explore in Tableau)

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

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

(explore in Tableau)

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

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

Some interesting outliers include:

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

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

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

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

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

Sydney

 

Brisbane

 

Adelaide

Perth

(Find Mandurah in Tableau)

Canberra

Hobart

Darwin

Cairns

Townsville

Sunshine Coast

Geelong

Central Coast (NSW)

Newcastle – Maitland

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

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

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

Appendix – About the data

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

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

I’ve used two measures of motor vehicle ownership:

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

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

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

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

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

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

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

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

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

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


Update on trends in Australian transport

Sat 28 January, 2017

This post charts some key Australian transport trends based on the latest available official data estimates as at January 2017 (including the Bureau of Infrastructure, Transport, and Regional Economics 2016 Yearbook).

Car use per capita has continued to decline in most Australian cities (the exceptions being Adelaide and Brisbane, but still well down on the peak of 2004):

car-pass-kms-per-capita-5

Mass transit’s share of motorised passenger kms was very slightly in decline in most cities in 2014-15 (the exceptions being Sydney and Adelaide)

mass-transit-share-of-pass-kms-6

(note: “mass transit” includes trains, trams, ferries, and both public and private buses)

At the same time, estimated total vehicle kilometres in Australian cities has been increasing:

city-vkm-growth

However, mass transit use has outpaced growth in car usage since 2003-04 across the five big cities:

car-v-pt-growth-aus-large-cities-3

In terms of percentage annual growth, car use growth only exceeded mass transit in 2009-10, and 2012-13.

Car ownership has still been slowly increasing (note the Y axis scale):

car-ownership-2000-onwards-by-state-3

Australia’s domestic transport greenhouse gas emissions actually ever-so-slightly declined in 2015-16:

australian-domestic-transport-emissions

Here is driver licence ownership by age group for Australia:

au-licence-ownership-by-age

(note: the rate is calculated as the sum of car, motorbike and truck licenses – including learner and probationary licences, divided by population. Some people have more than one driver’s licence so it’s not a perfect measure)

From June 2014 to June 2015, license ownership rates increased in all age groups except 30-39, 60-69 and 80+.

2015 saw a change in the trend on licence ownership rates for teenagers, with a slight increase after four years of decline. However the trends are quite different in each state:

au-licence-ownership-by-aged-16-19-trend

(note: in most states 16 is the age where people are able to obtain a learner’s permit)

I’m really not sure why Western Australia has such a low licence ownership rate compared to the other states (maybe the data doesn’t actually include learner permits).

And finally, here are licence ownership rates for people aged 20-24, showing quite different trends in different states:

au-licence-ownership-by-aged-20-24-trend

I’ll aim to elaborate more on these trends in updates to subject-specific posts when I get time.


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.

Update 22 Oct 2012: maps now include railway lines using OpenStreetMap data provided by Maps Without Borders. The data is licensed under Creative Commons Attribution-ShareAlike 2.0, copyright OpenStreetMap and contributors.

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

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.

Other posts about density:


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).

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.


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.

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.