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

Wed 17 October, 2018

Over a quarter of Sydney commuters (26.3%) went to work by public transport in 2016, the highest rate of Australian cities, and an increase of 3.0% on 2011. This post provides an overview of mode shares and mode shifts across Sydney from 2006 to 2016 (following on from my previous analysis of Melbourne and Brisbane).

I’m going to mostly look at trends in private motorised transport mode shares, as it is generally the least space-efficient and most polluting method of travel on a per person basis, and many cities aim to shift people away from private transport to active or public transport.

Firstly, here are private transport mode shares by home location (click to enlarge or explore in Tableau Public but be patient):

You can see lower private mode shares in the inner city and around train lines, as you might expect. In many places private transport accounts for a minority of commuters.

Here are the private transport mode shifts by home location (also in Tableau):

There were significant mode shifts away from private transport almost all over Sydney, but particularly strong in the inner south, inner west, north shore and hills area, including many areas served only by buses for public transport.

You can see the mode split of net new commuter origins on the next chart, with public transport dominating new trips from many areas on the north shore, eastern suburbs, and inner west and south-west (also in Tableau):

Private transport dominated new commuters in the outer western suburbs. Compared to other cities, a smaller proportion of new commutes came from the outer fringe, which may partly explain why Sydney had the strongest mode shift to public transport.

Here’s another look at that data, with the private transport mode share of net new journeys to work:

In many parts of Sydney there was an absolute reduction in the number of private transport journeys to  work (pink areas), and many where it represented a small minority. Private transport did however dominate new commutes from most outer western suburbs and the northern beaches.

Summarising the above, Sydney saw public transport journeys grow faster than private transport journeys across all but the outer suburbs:

Here are the private transport mode shares by work location (also in Tableau):

Sydney is distinctly different to the other cities in that there are many major employment centres outside the CBD with quite low private mode shares. The lowest 2016 private transport mode share destination zone in Macquarie Park was 59%, in Strathfield was 53%, in Manly was 55%, in Parramatta was 40%, in Chatswood was 40%, in St Leonards was 43%, in Bondi Beach was 43%, in Burwood was 46%, in Kensington was 45%, in Bondi Junction was 35%, and in North Sydney was 22%. Refer to my recent post about suburban employment clusters for more on this.

The Sydney CBD itself has a destination zone with only 6% private mode share in 2016. Sub-50% private mode shares stretch out from the CBD as far as Newtown south-west of the CBD.

Here are private transport mode shifts by work location:

There were significant mode shifts away from private transport across much of Sydney, with the largest in Mascot (-9%, noting that train fares were reduced at stations in Mascot in March 2011), and 7% declines in Sydney Airport, Kogarah, Waterloo – Beaconsfield, Newtown – Camperdown – Darlington, Redfern – Chippendale, Chatswood (East) – Artarmon. There was a 6% mode shift away from private transport in both North Ryde and Macquarie Park, where new train stations opened in 2009.

Here is a map showing the volume and mode split of new commuter destinations in Sydney:

The Sydney CBD is such a big pie chart it swamps all others with 63,732 new commuters, 86% of which were accounted for by public transport. Public transport also dominated in North Sydney – Lavender Bay (which actually had a net reduction in private transport trips), Surrey Hills (88% by public transport) and Pyrmont – Ultimo (84% by public transport).

It’s also notable that Sydney’s major regional centres had a significant share of their jobs growth accounted for by public transport trips, as explored in my earlier post on employment clusters.

Here’s a map of private transport mode share of net new trips by workplace:

There was a net reduction in private transport journeys to many SA2s, including North Sydney, Homebush, Epping – North Epping, and Mascot – Eastlakes (note: some others might be artifacts of boundary changes between 2011 and 2016). Private transport again dominated new journeys to the outer west and northern beaches.

You can see on the following chart that the central city accounted for a significant portion of the jobs growth and public transport accounted for almost all of those new trips, which helps explain the overall shift to public transport. Private transport only significantly dominated new jobs more than 10 km from the city centre.

For more on the journey to work, you might like another post about likely factors explaining city-wide mode shifts across Australia’s larger cities.

About the data

The mode share maps are filtered for residential areas (CD or SA1) with at least 5 persons/hectare or destination zones (DZs) with at least 4 jobs/hectare (as appropriate). Mode shifts, mode splits, and mode shares of net new commutes are calculated and shown on 2016 SA2 boundaries, with 2006 and 2011 CDs, SA1s and DZs mapped to 2016 SA2 boundaries on a majority overlap basis (mostly a perfect alignment, but sometimes not). I’ve only counted people who travelled on census day and stated what mode(s) they used, and – for work destinations – where the work SA2 is known. See my Brisbane post for a longer explanation.

Advertisements

Suburban employment clusters and the journey to work in Australian cities

Sun 8 July, 2018

Relatively dense suburban employment clusters can deliver more knowledge-based jobs closer to people living in the outer suburbs. Sydney has many such clusters, and Melbourne is now aiming to develop “National Employment and Innovation Clusters” as part of the city’s land use strategy, Plan Melbourne.

So what can we learn about existing employment clusters in Australian cities, particularly in regards to journeys to work? Can relatively dense suburban employment clusters contribute to more sustainable transport outcomes? Do such clusters have lower private transport mode shares than other parts of cities? How are mode shares changing for these clusters? How far do people travel to work in these clusters? Is there a relationship between job density, parking prices, and mode shares? How well served are these clusters by public transport? How do these clusters compare between cities?

This post investigates 46 existing clusters in Australia’s six largest cities. This is a longer post (there is a summary at the end), but I hope you find at least half as interesting as I do.

What’s a dense suburban employment cluster?

That’s always going to be an arbitrary matter. For my analysis, I’ve created clusters based on destination zones that had at least 40 employees per hectare in 2011 or 2016, were more than 4km from the city’s main CBD, and where collectively at least around 6,000 employees travelled on census day in 2016.

Unfortunately I can only work with the destination zone boundaries which may or may not tightly wrap around dense employment areas. Also, in order to ensure reasonable comparisons between census years, I’ve had to add in some otherwise non-qualifying zones to keep the footprints fairly similar. To mitigate potential issues with low density zones being included, I’ve used weighted employment density for each cluster in my analysis. But still, please don’t get too excited by differences in weighted job density as it’s far from a perfect representation of reality.

In particular, the following clusters include destination zones comprising both dense employment and non-employment land and so will potentially have understated weighted job density:

  • Nedlands
  • Fremantle
  • Bedford Park
  • Tooronga
  • Camberwell Junction
  • Hawthorn
  • Belconnen
  • Campbelltown
  • Hurstville
  • Kogarah
  • Randwick
  • North Ryde (quite significant – actual density is probably double)
  • Macquarie Park (a destination zone for the university includes large green areas)
  • Rhodes (significant residential area)
  • Parramatta (includes parkland)
  • Penrith (residential areas)
  • Bella Vista – Norwest – Castle Hill (includes a golf course)

Some of these clusters are a little long and thin and so are literally stretching things a little (eg Bella Vista – Norwest – Castle Hill, and Alexandria – Mascot), but it’s hard to cleanly break up these areas.

I think my criteria is a fairly low threshold for suburban employment clusters, but raising the criteria too much would knock out a lot of clusters. I should note that some potential clusters might be excluded simply because they did not contain small destination zones concentrated on more dense areas.

Belmont in Perth was the lowest density cluster to qualify (weighted jobs density of 42 jobs / ha). Here’s what it looks like (in 3D Google Maps in 2018):

Chatswood in Sydney was the highest density cluster – with a weighted job density of 433 jobs / ha. Here’s what it looks like (in 3D Apple Maps in 2018):

Apologies if your favourite cluster didn’t make the criteria, or you don’t like my boundaries. You can look up the 2016 boundaries for each cluster here, or view them all through Google maps.

Where are these clusters?

On the following maps I’ve scaled the clusters by employment size and used pie charts to show the modal split for journeys to work in 2016. All pie charts are to the same scale across the maps (the size of the pie charts is proportional to the number of journeys to the cluster in 2016).

Note that North Sydney is excluded because it is within 4 km of the CBD.

All of Melbourne’s clusters are east of the CBD, with Clayton the largest. Places just missing out on the cluster criteria include parts of the Tullamarine industrial area (5271 jobs at 55 jobs/ha), Doncaster (around 5000 jobs at 40+ jobs/ha), Chadstone Shopping Centre (5375 jobs at 105 jobs/ha), and La Trobe (around 7700 jobs but low density – and even if there was a destination zone tightly surrounding the university campus I suspect it would still not qualify on density ground).

Only three suburban clusters qualified in Brisbane.

Note: the Nedlands and Murdoch clusters are essentially the hospital precincts only and do not include the adjacent university campuses.

Adelaide only has one suburban cluster that qualifies – Bedford Park – which includes the Flinders University campus and Flinders Medical Centre.

The Canberra clusters cover the three largest town centres, each containing at least one major federal government department head office.

What proportion of jobs are in these dense suburban employment clusters?

The following chart shows that Sydney and Canberra have been most successful at locating jobs in suburban employment clusters (well, clusters that meet my arbitrary criteria anyway!):

The proportion of jobs not in the inner 4km or a suburban employment cluster increased between 2011 and 2016 in all cities except Sydney (although the shift was very small in Melbourne).

Here’s a summary of private transport mode shares for the clusters, versus the inner city versus everywhere else:

Inner city mode shares vary considerably between cities, in order of population size. Total job cluster private mode shares are only 4-7% lower than elsewhere in most cities, except for Sydney where they are 17% lower.

Sydney’s clusters combined also have a significantly lower private mode share of 68% – compared to 84-89% in other cities.

How do the clusters compare?

Here is a chart showing their size, distance from CBD, and private transport mode share for journeys to work in 2016:

Next is a chart that looks at weighted job density, size, and private mode share for 2016. Note I’ve used a log scale on the X axis.

(Unfortunately the smaller Kogarah dot is entirely obscured by the larger Alexandria – Mascot dot – sorry that’s just how the data falls)

There is certainly a strong relationship between weighted job density and private mode shares (in fact this is the strongest of all relationships I’ve tested).

Sydney has many more clusters than the other cities (even Melbourne which has a similar population), it has much larger clusters, it has more dense clusters, and accounts for most of the clusters in the bottom-right of the chart.

And there’s just nothing like Parramatta in any other city. It’s large (~41,000 jobs in 2016), has relatively low private transport mode share (51%), is about 20 km from the Sydney CBD, and has a high jobs density.

Melbourne’s Clayton has about three-quarters the jobs of Parramatta, is around the same distance from its CBD, but is much less dense and has 90% private mode share for journeys to work.

Curiously Sydney’s Macquarie Park – which on my boundaries has about the same number of jobs as Parramatta – is closer to the Sydney CBD and has a much higher private transport mode share and a lower job density. However it’s rail service is relatively new, opening in 2009.

Perth’s Joondalup and Murdoch are relatively young transit oriented developments with relatively new train stations (opening 1992 and 2007 respectively), however they also have very high private transport mode shares, which I think highlights the challenge of creating suburban transit-adjacent employments clusters surrounded by low density suburbia.

Also, many of Sydney’s suburban clusters have a lower private mode share than that of the overall city (67.6%). That’s only true of Hawthorn and Camberwell Junction in Melbourne, Fremantle in Perth, and Woden and Belconnen in Canberra.

Some outliers to the top-right of the second chart include Heidelberg (in Melbourne), Liverpool (in Sydney), and Nedlands (in Perth). The Heidelberg and Nedlands clusters are relatively small and are dominated by hospitals, while 37% of jobs in Liverpool are in “health care and social assistance”. Hospitals employ many shift workers, who need to travel at times when public transport is less frequent or non-existent which probably explains their relatively high private transport mode shares. Heidelberg is located on a train line, and is also served by several relatively frequent bus routes, including one “SmartBus” route, but still has a very high private transport mode share of 85%.

Outliers to the bottom-left of the second chart include Randwick, Burwood, and Marrickville (all in Sydney). While these are less dense clusters, I suspect their relatively low private transport mode shares are because they are relatively inner city locations well served by public transport.

As an aside, if you were wondering about the relationship between job density and private mode shares for inner city areas, I think this chart is fairly convincing:

Of course this is not to say if you simply increase job density you’ll magically grow public transport patronage – there has to be capacity and service quality, and you probably won’t get the density increase without better public transport anyway.

How well connected are these job clusters to public transport?

Arguably the presence of rapid public transport is critical to enabling high public transport mode shares, as only rapid services can be time competitive with private transport. By “rapid” I consider services that are mostly separated from traffic, have long stop spacing, and therefore faster average speeds. For Australian cities this is mostly trains, but also some busways and light rail lines (but none of the clusters are served by what I would call “rapid” light rail). Of course there is a spectrum of speeds, including many partly separated tram and bus routes, and limited stops or express bus routes, but these often aren’t time competitive with private cars (they can however compete with parking costs).

I have classified each cluster by their access to rapid transit stations, with trains trumping busways (note Parramatta, Blacktown, Westmead, and Liverpool have both), and some clusters sub-classified as “edge” where only some edge areas of the cluster are within walking distance of a rapid transit station (although that’s not clear cut, eg Murdoch). Here are the public transport mode shares, split by whether journeys involved trains or not:

It’s probably of little surprise that all of the high public transport mode share centres are on train lines (except Randwick), and that most public transport journeys to these clusters involve trains. However the presence of a train station certainly does not guarantee higher public transport mode share.

Only four clusters have some degree of busway access (Chermside and Randwick are not actually on a busway but have a major line to them that uses a busway). Only Upper Mount Gravatt has a central busway stations, and it has the third highest non-train (read: bus) access share of 12%.

Randwick is an interesting exception – the University of New South Wales campus in this cluster is connected to Central (train) Station by high frequency express bus services which seem to win considerable mode share. A light rail connection is being constructed between Randwick and the Sydney CBD.

Non-rail (essentially bus) public transport mode shares are also relatively high in Bondi Junction (15%), Parramatta (11%), Belconnen (10%), Brookvale (10%), Woden (10%), Fremantle (9%), Macquarie Park (9%). These are all relatively strong bus nodes in their city’s networks.

Clayton and Nedlands are not on rapid transit lines, but both have high frequency bus services to nearby train stations which results in slightly higher train mode shares (4% and 5%). For Clayton, only the Monash University campus is connected by a high frequency express bus and it had a 17% public transport mode share, whereas the rest of the cluster had public transport mode shares varying between 3 and 7%.

The Bedford Park cluster is frustratingly just beyond reasonable walking distance of Tonsley Railway Station (12 minutes walk to the hospitals and almost half an hour’s walk to the university campus) – so only about 10 people got to work in the cluster by train in 2016. However that’s going to change with an extension of the train line to the Flinders Medical Centre.

The train-centred clusters with low public transport mode shares are mostly not in Sydney, and/or towards outer extremities of the train network (except Box Hill and Heidelberg in Melbourne). So what is it about Sydney’s trains that makes such a difference?

Sydney’s train network is distinctly different to all other Australian cities in that there are many more points where lines intersect (outside the central city), creating many “loops” on the network (for want of a better expression). In all other cities, lines only generally intersect in the central city and where radial lines split into branches, and cross city trips by public transport generally only possible by buses (in mixed traffic). In Sydney lines do branch out then but then often bend around to intersect other neighbouring lines. This provides significantly more connectivity between stations. For example, you can get to Parramatta from most lines directly or with a single transfer somewhere outside central Sydney. Indeed, Sydney is the only city with a regular non-radial train service (T5 Leppington – Richmond, although it only runs every half-hour).

I’ve roughly overlaid Sydney’s dense suburban job clusters (in red) on its rail network map, and then marked the train mode shares:

While some clusters can only be accessed by a radial train line (or are off-rail), many are at intersection points, and most can be accessed by multiple paths along the network. The 29%+ train mode shares for Chatswood, Parramatta, St Leonards, Burwood, and Rhodes might be partly explained by these being highly accessible on the train network.

Here are Melbourne’s dense suburban employment clusters and train mode shares overlaid on Victoria’s rail network map:

The clusters connected to more train lines (Hawthorn and Camberwell) have higher train mode shares, although they are also closer to the city.

The Spatial Network Analysis for Multimodal Urban Transport Systems (SNAMUTS) methodology (led by Professor Carey Curtis and Dr Jan Scheurer) uses graph-based analysis of public transport networks to develop several indicators of network performance. One indicator that measures network accessibility is closeness centrality, which looks and speed and frequency of services to connect to other nodes in the network (it actually uses inter-peak frequencies and speeds, but they probably correlate fairly well with services in peak periods). A lower score indicates better accessibility.

I’ve extracted the closeness centrality scores for public transport nodes in each employment clusters (from the nearest available data to 2016 at the time of writing, some as old as 2011 so not perfect) and compared this with private transport mode shares to these clusters:

Some clusters were not really centred on a public transport node in the SNAMUTS analysis (eg Osborne Park in Perth, Clayton in Melbourne) and hence are not included in this analysis. These clusters have very high private transport mode shares, and would likely be towards the top right of the chart.

There’s clearly a relationship between the closeness centrality and private mode shares, with low private more shares only occurring where there is high accessibility by public transport. But it’s not super-strong, so there are other factors at play.

Some of the outliers in the bottom right of the distribution include Upper Mount Gravatt (based on a large shopping centre but also on a busway), Murdoch (dominated by hospitals a moderate walk from the station), Nedlands (also dominated by hospitals), Chermside (a combination of hospital and large shopping centre, with the bus interchange remote from the hospital), and Bedford Park (where 63% of jobs are in health) . Again, the pattern of higher private transport mode shares to hospitals is evident.

So do you need strong public transport access to support higher job densities? Here’s the relationship between closeness centrality and weighted job density:

There are no clusters with poor public transport access and high job density, which is not surprising. But this does suggest it could be difficult to significantly increase job densities in clusters currently in the top left of this chart without significantly improving public transport access.

Interestingly, Box Hill in Melbourne does have a similar closeness centrality score to Parramatta and Chatswood in Sydney, suggesting it might be able to support significantly higher job density. However, it only has rapid (train) public transport from two directions. It might be more challenging to maintain bus and tram travel times from other directions if there is significant jobs growth.

Melbourne’s largest cluster – Clayton – is not on the chart because it is not centred on a public transport node. There is however a bus interchange on the southern edge of the cluster at Monash University, which has a relatively low closeness centrality score of 64. I suspect the main employment area would probably have a higher closeness centrality score if it were to be measured because it not connected to the train network by a high frequency express shuttle service and has fewer bus routes. That would place it in the top-left part of the above chart (2016 weighted job density being 63 jobs/ha).

Do higher density clusters have fewer car parks?

The higher density centres certainly tended to have lower private mode shares, but does that mean they don’t have much car parking?

Well I don’t know how many car parking spaces each centre had, but I do know how many people travelled to work by car only, and from that I can calculate a density of car-only journeys (and I’ve calculated a weighted average of the destination zones in each centre). That’s probably a reasonable proxy for car park density.

Here’s how it compares to jobs density (note: log scales on both axes):

There is a very strong correlation between the two – in general centres with higher job density also have higher car density. The strongest correlation I can find is for a quadratic curve that flattens out at higher job densities (as drawn, with R-squared = 0.77), which simply suggests you get lower private mode shares in higher density clusters (in general).

The clusters on the bottom side of the curve have lower car mode shares, and so have a lower car density. Many are inner city locations with better public transport access, but also many nearby residents.

Heidelberg (a hospital-based cluster in Melbourne), has the highest car density of all centres and a high job density, but isn’t a large centre.

Do walking mode shares increase when there are many nearby residents?

If there are many residents living within walking distance of a cluster, relative to the size of that cluster, then you might expect a higher walking mode share, as more employees of the cluster are likely to live nearby.

I’ve roughly summed the number of residents who travelled to work (anywhere) and lived within 1km of each cluster. I’ve then taken the ratio of those nearby working residents to the number of journeys into the cluster, and then compared that with walk-only mode shares for 2016:

Yes, there’s definitely a relationship (although not strong), and this may explain some of the outliers in the previous charts such as Randwick, Marrickville, St Leonards and Bondi Junction.

Is there a relationship between parking costs and mode shares?

It’s quite difficult to definitively answer this question because I don’t have parking prices for 2016, and many car commuters might not be paying retail prices (eg employer-provided free or subsidised parking).

I’ve done a quick survey using Parkopedia of parking prices for parking 8:30 am to 5:30 pm on Monday 2 July 2018, and picked the best price available in each cluster. Of course not everyone will be able park in the cheapest car park so it’s certainly not an ideal measure. An average price might be a slightly better measure but that would be some work to calculate.

But for what it worth, here is the relationship between July 2018 all day parking prices and 2016 private transport mode shares:

You might expect an inverse correlation between the two. Certainly clusters with very cheap or free parking had very high private transport mode shares, but other centres are scattered in the distribution.

Looking at outliers in the top right: I suspect Bedford Park (63% health workers), Heidelberg (hospital precinct), Tooronga (with one major employer being the Coles HQ), Chermside (including Prince Charles Hospital), and Rhodes will have significantly cheaper parking for employees (with visitors paying the prices listed on Parkopedia). Indeed, I could not find many parking prices listed for Rhodes, but there are clearly multi-storey parking garages near the office towers not on Parkopedia.

Looking at outliers in the bottom left: Relatively cheap $15 parking is available at multiple car parks in Bondi Junction. The $10 price in Chatswood was only available at one car park, with higher prices at others, so it is probably below the average price paid. Maybe traffic congestion is enough of a disincentive to drive to work in these centres?

For interest, here’s the relationship between weighted car density and parking prices:

The relationship is again not very strong – I suspect other factors are at play such as unlisted employer provided car parking, as discussed above.

So does job growth in suburban employment clusters lead to lower overall private transport mode shares?

Here is a chart showing the effective private mode share of net new trips in each job cluster, plus the inner 4 km of each city:

(Fremantle, Dandenong, Burwood, and Woden had a net decline in jobs between 2011 and 2016 and so have been excluded from this chart)

The chart shows that although many suburban jobs clusters had a low private mode share of net new trips, it was always higher than for the inner 4 km of that city.

Here’s a summary of net new trips for each city:

So every new 100 jobs in suburban employment clusters did generate many more private transport trips than new jobs in the inner city, particularly for Sydney (45 : 10), Melbourne (68 : 13), and Canberra (84: 18). But then new jobs in suburban employment clusters had significantly lower private transport mode shares than new jobs elsewhere in each city.

So arguably if you wanted to minimise new private transport journeys to work, you’d aim for a significant portion of your employment growth in the central city, and most of the rest in employment clusters (ideally clusters that have excellent access by rapid public transport). Of course you would also want to ensure your central city and employment clusters were accessible by high quality / rapid public transport links (not to forget active transport links for shorter distance commutes).

One argument for growing jobs in suburban employment clusters is that new public transport trips to suburban employment clusters will often be on less congested sections of the public transport network – particularly on train networks (some would even involve contra-peak travel relative to central city). On the other hand, new jobs in the central city have much higher public transport mode shares, but relatively expensive capacity upgrades may be required to facilitate the growth.

New active transport trips to the central city and employment clusters probably requires the least in terms of new infrastructure, and there are probably very few congested commuter cycleways in Australian cities at present.

Another argument for suburban employment clusters is to bring jobs closer to people living in the outer suburbs.

Are new private transport trips to suburban employment clusters much shorter than new private transport trips to the central city, and therefore perhaps not as bad from a congestion / emissions point of view?

Certainly many of these clusters will have congested roads in peak periods, but the distance question is worth investigating.

So how far do people travel to work in different employment clusters?

The 2016 census journey to work data now includes on-road commuting distances (thanks ABS!).

Of course for any jobs cluster there will be a range of people making shorter and longer distance trips and it is difficult to summarise the distribution in one statistic. Averages are not great because they are skewed by a small number of very long distance commutes. For the want of something better, I’ve calculated medians, and here are calculations for Sydney job clusters:

(I’ve added a “Sydney” jobs cluster which is the “Sydney – Haymarket – The Rocks” SA2 that covers the CBD area).

There’s a lot going on in this data:

  • Median distances for private transport commutes to most employment clusters are longer than to the CBD (particularly the big clusters of Macquarie Park and Parramatta).
  • The clusters of Brookvale, Bondi Junction, and Randwick near the east coast have lower medians for motorised modes, probably reflecting smaller catchments. Randwick and Brookvale also do not have rail access, which might explain their low median public transport commute distances.
  • Public transport median commute distances were longer in the rail-based near-CBD clusters of Bondi Junction, Alexandria – Mascot, and St Leonards, but also in some further out rail-based clusters, including Parramatta, Westmead and Penrith.
  • Penrith – the cluster furthest from the Sydney CBD – curiously had the longer public transport median commute distance, which probably reflects good access from longer distance rail services (but public transport mode share was only 14%).
  • Active transport medians vary considerably, and this might be impacted by the mix of shorter walking and longer cycling trips. For example, North Ryde saw more cycling than walking trips, but also had only 1% active transport mode share.

Here’s the same for Melbourne (with a cluster created for the CBD):

Clayton, Dandenong, and Melbourne CBD median commute distances were very similar, whereas median commutes to other clusters were mostly shorter.

Here are results for clusters in the smaller cities:

In Perth, Joondalup had shorter median commuter distances, while Osborne Park and Murdoch (both near rapid train lines) had the longest median public transport journey distances (but not very high public transport mode shares: 7% and 15% respectively). Half of the suburban clusters had a longer median private transport distance than the CBD, and half were shorter.

In Brisbane, median private commute distances were shorter in Chermside, but similar to the city centre for other clusters.

Coming back to our question, only some suburban employment clusters have shorter median private transport commute distances. I expect the slightly shorter distances for those clusters would not cancel out the much higher private transport mode shares, and therefore new suburban cluster jobs would be generating more vehicle kms than new central city jobs.

But perhaps what matters more is the distance travelled by new commuters. New trips from the growing urban fringe to a CBD would be very long in all cities. While ABS haven’t provided detailed journey distance data for 2011, some imperfect analysis of 2011 and 2016 straight line commuter distances between SA2s (sorry not good enough to present in detail) suggests average commuter distances are increasing by 1-2 kms across Sydney and 2-3 kms across Melbourne, and these increases are fairly consistent across the city (including the central city). This may reflect urban sprawl (stronger in Melbourne than Sydney), with new residents on the urban fringe a long way from most jobs.

So did private transport mode shares reduce in suburban employment clusters?

Yes, they did reduce in most clusters, but some saw an increase of up to 2%.

The cluster with the biggest shift away from private transport was Rhodes in Sydney (relatively small and only moderately dense), followed by Perth’s fastest growing hospital cluster of Murdoch.

But perhaps more relevant is how fast each cluster is growing and the mode share of new jobs:

If you want to reduce private transport travel growth, then you don’t want to see many clusters in the top right of this chart (growing fast with high dependency on private transport). Those centres could be experiencing increasing traffic congestion, and may start to hit growth limits unless they get significantly improved public transport access.

Of the cluster in the top-right:

  • Bella Vista – Norwest – Castle Hill will soon have a rapid rail service with Sydney Metro.
  • Murdoch’s high private transport mode share might reflect the fairly long walking distance between the station and hospitals (up to 10 minutes through open space with no tree canopy), but also hospital shift workers who may find private transport more convenient.
  • Clayton might reflect most jobs being remote from the train line (although it is served by three SmartBus routes that have high frequency and some on-road priority). Note: my Monash cluster unfortunately does not include the Monash Medical Centre that is closer to Clayton Station and very job dense. The hospital precinct had its own destination zone in 2016 with 88% private transport mode share, but was washed out in a larger destination zone in 2011 which made it difficult to include in the cluster (for the record, that 2011 destination zone also had an 88% private transport mode share).
  • Joondalup is a large but not particularly dense employment area, and I suspect many jobs are remote from the train/bus interchange, and some local bus frequencies are low.

Can you predict mode shares with a mathematical model?

I have put the data used above into a regression model trying to explain private transport mode shares in the clusters. I found that only weighted job density, walking catchment size, and distance from CBD were significant variables, but this might be for want of a better measure of the quality of public transport accessibility (SNAMUTS Closeness Centrality scores are not available for many centres).

I also tested the percentage of jobs in health care and social assistance (looking for a hospital effect), the surrounding population up to 10km (nearby population density), median travel distances, and the size of clusters, but these did not show up as significant predictors.

Can you summarise all that?

  • Compared to other cities, Sydney has many more clusters and they are larger, more dense, and generally have much lower private transport mode shares.
  • With the exception of Canberra, less than half of all jobs in each city were in either the inner city area or a dense suburban jobs cluster. In Perth it was as low as 32%, while Sydney was 45%, and Canberra 54%.
  • Higher density clusters correlate with lower private transport mode shares.
  • Only higher density clusters centred on train stations with strong connections to the broader train network achieve relatively high public transport mode shares of journeys to work.
  • High quality bus services can boost mode shares in clusters, but the highest bus-only mode share was 15% (in 2016).
  • High-frequency express shuttle bus services can boost public transport mode shares in off-rail clusters.
  • Walk-only mode shares for journeys to work are generally very low (typically 2-5%) but generally higher in clusters where there are many nearby residents.
  • Private transport mode shares are generally 90%+ in clusters with free parking.
  • I suspect there is a relationship between parking prices and private mode share, but it’s hard to get complete data to prove this. Subsidised employer provided parking probably leads to higher private transport mode shares, and may be common at hospitals. However unexpectedly cheap parking in Bondi Junction and Chatswood needs to be explained (perhaps an oversupply, or just horrible traffic congestion?).
  • There is some evidence to suggest hospitals are prone to having higher private transport mode shares, possibly due to significant numbers of shift workers who need to commute at times when public transport service levels are lower.
  • Private transport shares in suburban clusters are much higher than central cities, but lower than elsewhere in cities. The private transport mode share of net new jobs in clusters is much higher than for central city areas, but generally lower than elsewhere in cities.
  • High density clusters still have large amounts of car parking.
  • Median commuter distances to suburban employment clusters are sometimes longer and sometimes shorter than median commuter distances to each cities CBD.
  • The clusters of Joondalup, Clayton, Murdoch, and Bella Vista – Norwest – Castle Hill have grown significantly in size with very high private transport mode shares. These centres might be experiencing increased traffic congestion, and their growth might be limited without significant improvements in public transport access.

What could this mean for Melbourne’s “National Employment and Innovation Clusters”?

One motivation for this research was getting insights into the future of Melbourne’s National Employment and Innovation Clusters (NEICs). What follows is intended to be observations about the research, rather than commentary about the whether any plans should be changed, or certain projects should or should not be built.

Firstly, the “emerging” NEICs of Sunshine and Werribee didn’t meet my (arguably) low criteria for dense employment clusters in 2016 (too small). The same is true for the Dandenong South portion of the “Dandenong” cluster (not dense enough).

Parkville and Fishermans Bend would have qualified had I not excluded areas within 4km of the CBD.

Significant sections of the Parkville, Fishermans Bend, Dandenong, Clayton, and La Trobe NEICs are currently beyond walking distance of Melbourne’s rapid transit network. Of these currently off-rail clusters:

  • Parkville: a new rail link is under construction
  • Fishermans Bend: new light and heavy rail links are proposed. In the short term, paid parking is to be introduced in some parts in 2018 (which had commuter densities of 47-63 per hectare in 2016). The longer term vision is for 80% of transport movements by public or active transport.
  • Clayton: New light and heavy rail links are proposed. The Monash University campus has had paid parking for some time, but there appears to be free parking for employees in the surrounding industrial areas to the north and east. It will be interesting to see if/when paid parking becomes a reality in the industrial area (commuter densities ranged from 48 to 74 jobs/ha in 2016, not dissimilar to Fishermans Bend). The Monash Medical Centre area is relatively close to Clayton train station, has very high commuter density (329 per hectare in 2016 before a new children’s hospital opened in 2017) and had 88% private transport mode share in 2016. No doubt car parking will be an ongoing challenge/issue for this precinct.
  • La Trobe: No rapid transit links are currently proposed to the area around the university, which had an 83% private transport mode share in 2016. There is a currently a frequent express shuttle bus from Reservoir station to the university campus, and a high frequency tram route touches the western edge of the campus.
  • Dandenong South: The area is dominated by industrial rather than office facilities, and the job density ranges from 7 to 33 commuters per hectare, which is relatively low compared to the clusters in my study. There are no commercial car parks listed on Parkopedia so I assume pretty much all employees currently get free parking. No rapid transit stations are proposed for the area. The area is served by a few bus routes, including one high frequency SmartBus route, but 98% of new jobs between 2011 and 2016 were accounted for by private transport trips. This suggests it is difficult even for high-frequency (but non-rapid) public transport to complete with free parking in such areas.

Another potential challenge is connectivity to Melbourne’s broader train network. Parkville (and Fishermans Bend should Melbourne Metro 2 be built) will be well connected to the broader network by the nature of their central location. The area around Sunshine station has excellent rail access from four directions (with a fifth proposed with Melbourne Airport Rail). Dandenong, La Trobe and Werribee are on or near 1 or 2 radial train lines.

You can read more about Melbourne’s employment clusters in this paper by Prof John Stanley, Dr Peter Brain, and Jane Cunningham, which suggests there would be productivity gains from improved public transport access to such clusters.

I hope this post provides some food for thought.


What might explain journey to work mode shifts in Australia’s largest cities?

Mon 28 May, 2018

[Updated 29 June 2018 with further analysis of parking levies and their impact]

Between 2011 and 2016, journey to work public transport mode shares went up significantly in Melbourne and Sydney but dropped significantly in Perth and Brisbane. Private transport mode shifts did the opposite. Can this be explained by the changing distribution of jobs within cities, or other factors such as changes in transport costs?

In a recent post focused on Brisbane I found that stronger growth in suburban jobs relative to central city jobs could explain around half of the city’s mode shift towards private transport, with other factors (mostly the changes in relative attractiveness of modes) explaining the rest.

So how is job distribution changing in other Australian cities? How much of the mode shifts can be attributed to changing job distribution and how much could be attributed to other factors like changes in transport costs, or increasing employment density?

(for details about how I define public, private and active transport, see the appendix in this post)

How is job distribution changing in Australian cities?

Here’s a view of the changing distribution of all jobs within each city by workplaces distance from the city centre.

(Unfortunately I only have 2006 data for Sydney and Melbourne)

The changes are relatively subtle, but if look at how the bands shift between years, you’ll see increasing centralisation in Sydney but a decentralisation in all other cities between 2011 and 2016.

The strongest decentralisation was in Brisbane and Perth, which also showed the biggest increases in private transport mode share.

However Melbourne saw both a slight decentralisation of jobs and a mode shift away from private transport between 2011 and 2016.

So we need to dig deeper to find out what’s going on here.

How does private mode share vary by distance from the city centre?

The following chart shows private transport mode shares by distance from the city centre for the last two or three censuses for each city. The darkest line for each city is for 2016, with lighter lines being previous years (I only have 2006 data for Melbourne and Sydney).

There’s a clear pattern in all cities that private mode shares are lower in areas closer to the city centre, with Sydney the lowest, followed by Melbourne, Brisbane, Perth, Adelaide, and Canberra (which is also the order of their population size).

Notably Sydney private mode share averaged lower than 90% out as far as 24km from the city centre, whereas Adelaide sees 90% mode shares as close as 2km from the city centre.

If you look carefully you can see that Brisbane increased private transport mode shares in the central city between 2011 and 2016, while private mode shares dropped or were stable in all other cities at most distances.

You can also see that the central city mode shifts away from private transport were largest in Melbourne, something I’ll come back to.

Here’s the same again but for public transport:

Sydney and Melbourne saw mode shifts to public transport at most distances from the city centre, unlike all other cities.

What mode shift can we attribute to changing job distributions?

A city’s mode share (measured by place of work) will be fundamentally impacted by two types of changes between censuses:

  • Changes in the volume of jobs in each SA2 – because different SA2s generally have different mode shares due to factors like proximity to the city centre and public transport access. If there is stronger jobs growth in areas that already had lower private mode shares, you would get a mode shift away from private transport, all other things being equal.
  • Changes in the mode share in each SA2 – because different modes became more or less attractive for commuters between census years. This might be due to changes in public transport service quality, transport infrastructure provision, and relative changes in the cost of public transport, private motoring, and commuter parking. It could also be influenced by broader demographic changes.

For each city I have calculated what the city-level private transport mode share would have been in 2016, had mode shares in each workplace SA2 remained exactly the same as 2011, but the job volumes in each SA2s had still changed. The city level mode shift due to SA2 volume changes is then the difference between this hypothetical 2016 mode share and the 2011 mode share. The remainder of the city-level mode shift between 2011 and 2016 results can then be attributed to mode shifts at the SA2 level.

Here’s a chart showing the mode shift impact of both volume changes at the SA2 level, and mode shifts at the SA2 level:

As we noted above, Sydney saw a slight trend to centralisation of jobs between 2011 and 2016, and it had the largest volume change attributed reduction in private mode share (-0.4%). However other factors were responsible for a further 2.5% of the mode shift away from private transport.

The story is similar in Melbourne but to a smaller magnitude in both aspects. Both of these cities also saw increasing inner city job density – which matters – and I’ll back come to that in a moment.

In Brisbane you can see that the total mode shift towards private transport was roughly equally attributable to SA2 volume changes and SA2 mode shifts (as I discussed in my earlier post).

Perth had an overall 1.3% mode shift to private transport, and the majority of this was due to significant jobs growth in the suburbs compared to the CBD (in fact, the SA2 with the largest jobs growth was Murdoch in the southern suburbs). But there were also other factors that led to a mode shift to private transport.

In Canberra – Queanbeyan, volume changes by themselves would have seen a mode shift to private transport, but other factors were larger and led to an overall mode shift away from private transport (although it is actually complicated because the 2011 census day was in a federal parliamentary sitting week, while 2016 was not).

Nothing much changed in Adelaide.

Next I’m going to explore what could be behind the mode shifts at SA2 level, in terms of job density and real transport costs.

Can increases in workplace density impact mode shares?

As discussed in my Brisbane analysis, if the relative attractiveness of modes hadn’t changed, you might still expect a mode shift to public transport in high density employment areas with increasing jobs numbers because you would expect the cost of parking provision to increase with increasing land use density (i.e. more competition for space).

Indeed, in Sydney and Melbourne a number of inner city SA2s became significantly more job dense between 2011 and 2016, and also saw mode shifts away from private transport:

(inspect this data in Tableau)

A similar thing happened in Civic (the main centre of Canberra).

But Adelaide and Perth saw both declining job density and declining private transport mode share, which suggests something else is at play.

Job density didn’t really go down in Brisbane – see my Brisbane post for an explanation (basically, ABS redrew the SA2 boundary along the Brisbane River).

Could changes in the real cost of transport be causing mode shifts?

The following chart shows the real change in urban transport fares in Australian cities since 2000, as measured by the ABS as part of the Consumer Price Index series (which unfortunately includes public transport, taxis, and “ride share” but is for a representative sample of journeys so hopefully mostly dominated by public transport fares):

The lines are somewhat saw-toothed because public transport fares generally only rise once a year, and become better value in real terms over the course of the following 12 months.

Many cities have seen above-CPI public transport fare increases at various times, most notably Brisbane in 2010-2014. Melbourne has had above CPI fare increases, but also reduced zone 1+2 fares in 2015 which lead to a reduction on the ABS measure (the fare reduction only really applied to people travelling across zones 1 and 2 – which roughly summarised means travel between the outer and inner suburbs). Brisbane fares peaked in 2014, which was followed by a freeze and then a large reduction in 2017.

By contrast, here is the (negative) growth in the cost of “private motoring” (which includes vehicles, fuel and maintenance):

Private motoring costs have declined in real terms since 2000, although they increased a little during the second half of 2017.

The next chart shows the change in ratio between the two costs. Urban transport fares have become less competitive than private motoring over time in all cities:

But if we are looking at changes between census figures, we should probably also look at cost changes between the times of each census. Here’s how prices changed in real terms between the September quarters of 2011 and 2016 (which cover the August census dates):

The real cost of private motoring dropped in all cities, but so did the real “average” cost of urban transport fares in Sydney and Melbourne (the Melbourne drop being mostly around large fare reductions for travel across zones 1 and 2).

The biggest differences in cost changes were in Brisbane and Perth (around 18%), which I think will go a fair way to explaining why these cities had the biggest shifts to private transport attributable to SA2 mode shifts.

Brisbane saw a rapid increase in public transport fares between 2011 and 2014 which is likely to have changed many commuting habits, but those habits may or may not have changed back when fares were subsequently reduced (e.g. if someone bought a car due to fare increases, they may not have subsequently sold their car when fares reduced). Perth certainly had less mode shift at the SA2 level compared to Brisbane, which might support this hypothesis.

What about changes in car parking costs?

The ABS CPI’s private motoring cost index does not include car parking costs – which would be difficult as they vary considerably with geography.

However we do know about central city car parking levies that governments charge in a bid to reduce road congestion and fund inner city transport initiatives. Sydney, Melbourne, and Perth apply levies to central city non-residential car parking spaces, and ultimately these levies will need to be recovered through parking prices.

I’ve calculated these levies in 2017 dollars (adjusting for inflation as measured in June quarters), and here’s how they have changed since 2000:

Melbourne increased its central city parking levy by 40% per space in 2014 (category 1), and created a new lower-priced levy area in some neighbouring areas to the north and south in 2015 (category 2, see map). This is likely to have contributed to the larger mode shifts away from private transport in the central city area of Melbourne compared to most other cities (particularly considering there were similar changes in average private motoring and urban transport fares in Melbourne between 2011 and 2016).

Sydney’s category 1 fee applies in the Sydney CBD area, Milsons Points and North Sydney. It was $2390 in 2017, and has only risen with indexation since 2009 (when it was doubled). A lower category 2 levy applies in the business districts centres of Bondi Junction, Chatswood, Parramatta, and St Leonards.

Perth has an annual licence fee per bay which ranged from $1039 to $1169 in 2017.  The Perth fee was increased by around 167% in 2010, and there were also above-inflation increases from 2014. The fee increased 63% in real terms between 2011 and 2016 for “long stay” spaces, and 69% for “tenant” spaces.

I am not aware of any such fees or levies in place in Brisbane or Adelaide (a proposal for Adelaide was voted down).

So how are CBD parking prices changing?

Unfortunately good data is a little hard to find, but this Colliers Car Parking White Paper provides “average daily rates” for CBDs for 2009-2015, and early bird rates for 2015. I expect most commuters would pay early bird rates – which average between 28% and 62% of daily rates depending on the city (quite some variation!). I’ve adjusted the pre-2015 figures for inflation to be in 2015 dollars:

In real terms, “average daily” parking costs have declined in Melbourne, rocketed up in Brisbane and Canberra, and moved less in Sydney and Perth. I don’t know whether these reflect trends in early bird prices. And we don’t know how prices changed between 2015 and the census year of 2016.

So how much are parking levies contributing to parking prices?

I have to make some assumptions (guesstimates) here. Regular weekdays represent about 60% of the days of the year. If we assume say 80% of the levy is recovered from weekday commuter parking (there generally being less demand for parking on weekends), we can calculate the average weekday commuter cost of the levy to be 27% of the Sydney early bird price, 25% of the Melbourne early bird price, and 15% of the Perth early bird price. Certainly not insignificant.

Here’s a summary of the levy and “average daily” price changes and mode shifts in the central city parking levy areas:

Changes 2011 to 2016
Parking levy area or CBD SA2 Levy real increase Average daily real price change (2011 to 2015) Private mode shift New private trips Private share of new trips
Perth 63% -5% -0.8% -60 -3%
Melbourne – category 1 40% -11% -5.3% 3200 5%
Melbourne – category 2 (new) n/a -6.4% 5315 30%
Sydney CBD 0% +1% -2.6% 6204 9%
Brisbane City SA2 n/a +64% +1.7% 3135 68%
Adelaide SA2 n/a -11% -1.5% 2567 35%
Canberra Civic SA2 n/a +71% -3.2% 746 30%

Firstly, “average daily” parking prices don’t seem to be following the changes in parking levies in Perth and Melbourne (category 1 area). Other factors influencing parking prices will include supply (influenced by competition for real estate and planning rules) and demand (influenced by employment density) with the market ultimately determining prices.

Car park operators appear to be absorbing the increased cost of the levy (although we don’t know the trends in early bird prices so we cannot be entirely sure). But that’s not to say that the levy hasn’t had any impact on prices – for example, the price reductions might have been larger if the levies had not increased.

Secondly, price changes do not appear to be correlated with mode shifts as you might expect (except Canberra). Brisbane prices increased dramatically, but so did private mode share! Price reductions in Perth, Adelaide, and Melbourne did not result in increased private transport shares.

Maybe other factors are driving mode shift away from private transport in those cities. Maybe early bird prices are trending differently to “average daily” prices. Maybe increased traffic congestion persuaded people to shift modes. Maybe there were significant price changes between 2015 and 2016. Maybe most existing public transport users were not aware of reductions in parking prices.

I don’t know what happened to parking prices in the new category 2 areas of Melbourne but there was a large mode shift away from private transport (-6.4%), and they may well be linked. Indeed, Infrastructure Victoria has recently recommended the category 2 area be expanded to include the inner-eastern suburbs of Richmond, South Yarra, Windsor and Prahran. And the Grattan Institute has recommended increasing the levy to match Sydney’s rates.

Curiously, when I look at City of Melbourne Census of Land Use and Employment (CLUE) data, the category 1 area (approximated with CLUE areas) had an increase of only around 367 non-residential parking bays between 2011-12 and 2015-16 (a four year period), a lot less than the additional 3200 private trips, which might suggest increased average occupancy.

Also, it is likely that a significant portion of people who drive to city centres are not paying for their parking costs (eg employer provided car parking). Employers may simply be absorbing price increases.

For more interesting discussion and research about car parking in the City of Melbourne, see a recent discussion paper and background report prepared by Dr Elizabeth Taylor.

Did changes in population distribution impact mode shares?

While this post has been focused on changes by workplace location, it is possible to separate the overall mode shifts into the two components by home location. Here are the results:

In Sydney, Melbourne, and Canberra, stronger population growth in areas that already had low private mode shares in 2011 made a small contribution to overall mode shifts away from private transport. These cities have all seen densifying population in inner city areas better served by public transport.

The distribution of population growth in Perth and Brisbane had a small effect in the opposite direction.

And again, nothing much changed in Adelaide.

What about active transport?

Cycling-only mode share was pretty stable in most cities (except Canberra up 0.2%). Walking-only mode share declined in Sydney (-0.2%), Brisbane (-0.3%), Adelaide (-0.4%), Perth (-0.3%) but was steady in Melbourne and increased in Canberra (+0.2%). So Canberra has the biggest shift to active transport.

Can you summarise all that?

If your head is spinning with all that information, here’s a summary of what some of the major factors could be in each city between 2011 and 2016. I say “could be” because I’ve not looked at every possible factor influencing mode share.

Sydney: the 2.9% mode shift away from private transport was probably mostly to do with increasing job density in employment centres (more on that in my next post), but was also partly by a shift to more centralised jobs, and increasing population density in places well served by public transport.

Melbourne: The 1.8% mode shift away from private transport probably had a fair bit to do with increasing central city job density, the significant spatial expansion of the central city parking levy area and rates (although we don’t know if early bird prices also rose), a reduction in some public transport fares, and strong population growth in areas well served by public transport.

Brisbane: The 1.9% mode shift towards private transport appears roughly half about the decentralisation of jobs, and half the reduced attractiveness of public transport – particularly following significant fare rises between 2010 and 2014, and possibly/arguably declines in service quality.

Perth: The 1.2% mode shift towards private transport was probably mostly due to a decentralisation of jobs, and partly due to public transport becoming less cost competitive with private transport (despite an increase in the central city parking levy). Urban sprawl is probably also a factor.

Adelaide: The 0.2% mode shift to private transport is probably mostly due to public transport becoming less cost competitive with private transport. Changes in job and population distribution, and employment density do not appear to have had a significant impact.

Canberra:  The 1.0% mode shift away from private transport was probably the result of competing forces of higher jobs growth in car-dominated workplace areas with increasing job density in dense employment centres, increasing central city parking prices, higher population growth in areas better served by public transport (and possibly cycling facilities), and also the fact census 2016 was not a parliamentary sitting week while 2011 was (so really, it’s hard to be too sure!).

You might want to add your own views about changes in the service quality of public transport and cycling infrastructure in each city. I also haven’t looked at the impact of major new public transport infrastructure and service initiatives (such as the opening of new train stations), which we know does impact mode shares at a local level (maybe that’s for a future post).

I hope you found this interesting. My next post will look at suburban employment centres, and their role in changing mode shares in cities.


Can Airbnb explain falls in dwelling occupancy in Melbourne and Sydney?

Thu 10 May, 2018

According to census data, private dwelling occupancy has been declining in most Australian cities (refer my earlier post on the topic). Could an increase in private dwellings dedicated to Airbnb rental – but vacant on census night (a Tuesday in winter) – explain much of this decline? Let’s look at the data.

Firstly here’s a reminder of private dwelling occupancy trends in Australia’s 16 largest cities.

Dwelling occupancy rates declined in all large cities (but rose in some smaller urban areas, particularly on Central (NSW), Sunshine, and Gold Coasts).

The ABS have advised me that their field officers would have no way of telling whether a dwelling is on Airbnb, and would therefore count them as private dwellings. So vacant Airbnb dwellings could account for unoccupied private dwellings.

How many of the additional unoccupied dwellings might be dedicated to Airbnb?

The fantastic site Inside Airbnb provides data scraped from the Airbnb website about listings in various cities. I’ve used available data extracted on 4 September 2016 for Melbourne and 4 December 2016 for Sydney (the closest data sets available to the August 2016 census). I’ve then filtered for entire home/apartment listings that had more than 90 days availability in the 12 months ahead and had been reviewed at least once in the last six months, to estimate the number of “active and dedicated” Airbnb dwellings. Definitely just an estimate.

For the area for which Melbourne Airbnb data is available (I’ve approximated Inside Airbnb’s unpublished boundary as SA3s with any listings in 2016) these Airbnb dwellings account for 0.19% of total private dwellings. For that same area, dwelling occupancy dropped 0.71% from 92.61% to 91.90% between 2011 and 2016.

According to a Melbourne University study using data from commercial Airbnb data site AIRDNA, around 62% of entire home/apartment Melbourne listings (that were not blocked out by owners) were unoccupied on Saturday 27 August 2016.

I’m guessing the Airbnb vacancy rate might have been higher on census night (a Tuesday). If say 70% of the Airbnb dwellings were empty on census night (just a guess), then they would account for 0.09% out of the 0.71% decrease in dwelling occupancy in Melbourne between 2011 and 2016, which is about 19%.  Note: Airbnb barely existed in Melbourne in 2011 – there were only 161 Airbnb listings.

If somewhere between 60% to 80% of active/dedicated Airbnb properties were vacant, then they might explain between 16% and 21% the decline in dwelling occupancy in Melbourne.

For the equivalent area of Sydney, these Airbnb dwellings account for 0.22% of private dwellings, and there was a drop in private dwelling occupancy of 0.58% between 2011 and 2016. If somewhere between 60% to 80% of active/dedicated Airbnb properties were vacant, then they would explain between 23% and 30% of the decline in overall dwelling occupancy.

However I must stress these are rough estimates and might be over or under the actuals for several reasons:

  • It’s possible that some of these “entire home/apartment” listings are not counted by the ABS as dwellings (eg granny flats or segmented buildings that don’t have separate addresses) which would lead to over-estimates.
  • Some Airbnb listings that have less than 90 days availability in the 12 months ahead might just be very popular – leading to underestimates (my guess is that is unlikely).
  • The Sydney figure might be an overestimate because the Airbnb data was extracted three months after the census, and the total number of Airbnb listings almost doubled in 2016.
  • The actual Airbnb vacancy rate on census night might not have been in the 60-80% range.
  • I don’t know exactly where the city boundary was drawn for the Inside Airbnb data, but my approximation is more likely to be larger – which would lead to slight underestimates (probably very slight as the differences would be in peri-urban areas with few dwellings).
  • There may be other reasons – please comment.

That said, it looks like Airbnb might explain somewhere in the order of a fifth of the drop in private dwelling occupancy in Melbourne and Sydney between 2011 and 2016. Certainly not all of it, but probably not none of it either.

What proportion of dwellings are dedicated to Airbnb in different parts of Melbourne and Sydney?

Here’s a map of active/dedicated Airbnb dwellings (as per filter above) as a percentage of total dwellings in Melbourne at SA2 level:

It maxes out at 2.5% in the Melbourne CBD (that’s 1 in 40 dwellings), followed by Southbank and Fitzroy at around 1.9%. Mount Dandenong – Olinda is the orange patch to the east which measures 1.3%. View the data in Tableau.

As you would expect, Airbnb properties appear to be more prevalent around the inner city and tourist areas (eg St Kilda and the Dandenongs). These are also the areas with generally lower dwelling occupancy, and certainly some of the unoccupied dwellings in the census will be Airbnb dwellings.

Here is Sydney:

The highest rates are 2.6% around Bondi Beach, and 2.5% at Avalon – Palm Beach. Manly comes in at 1.5%, Surry Hills is 1.5%, Potts Point – Woolloomooloo is 1.4%, while the CBD area is 1.3%. Again, you can explore this Airbnb data in Tableau.

Could Airbnb properties explain the spatial differences in dwelling occupancy?

Here’s a plot of dwelling occupancy and Airbnb percentages for SA2s in Melbourne and Sydney.

I’ve done a linear regression on each city, and while the relationships are significant, they are not strong, and the correlation coefficients are -4.9 in Sydney and -1.5 in Melbourne. The signs are as expected (ie more Airbnb, lower occupancy), but the magnitudes are much higher than would be the case if Airbnb was the main explanation for lower dwelling occupancy (otherwise they would be around -1 or smaller). Which essentially means Airbnb presence is correlating with other drivers that would explain lower dwelling occupancy.

Indeed inner city and tourist areas had lower dwelling occupancy in both 2006 (when Airbnb didn’t exist) and 2011 (when Airbnb only had a tiny presence in Australia).

Therefore I think we can conclude Airbnb properties are more prevalent in areas where dwelling occupancy is lower for other reasons – one of which is likely to be popular places for visitors. Unoccupied Airbnb properties are almost certainly part of the pattern, but cannot explain the majority of the decrease in dwelling occupancy.

Can you do those Airbnb maps at higher resolution?

It’s getting a bit beyond the topic of transport, but yes I can go down to SA1 level. It’s not particularly important, but certainly interesting.

A disclaimer: Airbnb introduce randomised errors on property locations of up to 150 metres, so there will be some mis-attribution of properties to SA1s, but hopefully not too much. Also, I’m still only counting properties that match the above criteria.

Here’s inner Melbourne (note: different colour scale to last map):

Airbnb maxes out at 11% for three city blocks around Swanston Street and Collins Street, plus a large SA1 in East Melbourne that actually only contains a small residential area close to the CBD (including an apartment tower at 279 Wellington Parade). There’s also an SA1 in Southbank behind Crown Casino that is 10% Airbnb.

Curiously, there were only about 6 Airbnb listings in the New Quay apartments in Docklands that had 65-70% occupancy (refer earlier post), so Airbnb is definitely not to blame of the low occupancy of those towers.

Outside the central city, other hot spots are St Kilda around Acland Street (6%), just east of South Yarra Station (6%), and a patch of Olinda (5%). Explore in Tableau.

Here’s Sydney:

It tops out at 11% in the southern part of the CBD, with 10% in part of Pyrmont and 7% in pockets near Manly and Bondi Beach. Other hotspots include Coogee (6%) and Whale Beach further north (5%). Explore in Tableau.


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

Wed 25 April, 2018

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

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

Greater Brisbane main mode shares

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

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

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

Mode shares and shifts by home location

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

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

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

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

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

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

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

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

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

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

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

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

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

Mode shares and shifts by work location

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

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

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

Here are the mode shifts by workplace location:

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

What about the relationship between job density and mode share?

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

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

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

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

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

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

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

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

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

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

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

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

And what about the spatial distribution of population growth?

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

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

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

Appendix: about the data

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

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

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

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

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

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

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

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

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


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

Sun 28 January, 2018

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

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

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

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

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

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

About the data (boring but important)

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

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

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

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

Things to note:

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

Mode share maps to/from a location

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

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

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

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

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

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

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

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

Which leads me to…

Journey volume and mode split maps

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

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

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

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

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

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

Here is the same for Parkville:

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

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

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

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

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

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

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

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

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

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

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

Which can also be shown as a sorted bar chart:

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

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

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

Percentage of commuters who travel to selected workplace SA2s

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

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

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

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

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

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

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

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

Where do people from a particular area commute to?

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

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

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

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

(these maps can also be filtered by mode)

Another way to look at that data is a…

List of top commuter destinations

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

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

Changes between 2011 and 2016

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

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

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

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

Contrast this with Parkville:

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

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

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

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

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

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

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

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

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

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

The larger declines were from suburbs like:

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

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

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

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

Find your own stories

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

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

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

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

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

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

Finally remember to be careful in your analysis:

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

Have fun!

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


Changes in Melbourne’s journey to work – by mode (2006-2016)

Sun 10 December, 2017

Post updated 11 May 2018. See end of post for details.

My last post looked at the overall trends in journeys to work in Melbourne, with a focus on public and private transport at the aggregate level. This post dives down to look at particular modes or modal combinations, including mode shares, mode shifts and the origins and destinations of new trips.

Train

Here’s mode share for journeys involving train by home location (journeys may also include other modes):

The highest train mode shares can be seen mostly along the train lines, which will surprise no one.

In fact, we can measure what proportion of train commuters live close to train stations. The following chart looks at how far commuters live from train stations, for commuters who use only trains, used trains and possible other modes, and for all commuters.

This chart shows that almost 60% of people who only used train (and walking) to get to work lived within 1 km of a station, and almost three-quarters were within 1.5 km. But around 8% of people only reporting train in their journey to work were more than 3 km from a train station. That’s either a long walk, or people forgot to mention the other modes they used (a common problem it seems).

For journeys involving train, 50% were from within 1 km of a station, but around a quarter were from more than 2 km from a station.

Interestingly, around a third of all Melbourne commuters lived within 1 km of a train station, but a majority of them did not actually report train as part of their journey to work.

So where were the mode shifts to and from train (by home location)?

There were big mode shifts to train around new stations including Wyndham Vale, Tarneit, Lynbrook, South Morang, and Williams Landing. Other bigger shifts were in West Footscray – Tottenham, South Yarra – East, Brighton, Viewbank – Yallambie, Yarrville, Footscray, Kensington, and Pascoe Vale (some of which might be gentrification leading to more central city workers?).

There was also a significant shift to trains in Point Cook, which doesn’t have a train station, but is down the road from the new Williams Landing Station. Almost 28% of commuters from Point Cook South work in the Melbourne CBD, Docklands or Southbank, and most of those journeys were by public transport.

We can also look at mode shares by work location. Here is train mode share by workplace location for 2011 and 2016 (I’ve zoomed into inner Melbourne as the mode shares are negligible elsewhere, and I do not have equivalent data for 2006 sorry):

Melbourne Train mode share 2011 2016 work.gif

The highest shares are in the CBD, Docklands and East Melbourne. Notable relatively high suburban shares include the pocket of Footscray containing State Trustees office tower (30.7% in 2016),  a pocket of Caulfield including a Monash University campus (29.5%), Box Hill (up to 19.6%), Swinburne University in Hawthorn (37.4%), and 17.5% in a pocket of Yarraville.

The biggest workplace mode shifts to train were in Docklands (+8.6%), Southbank (+5.5%), Abbotsford (+5.5%), Richmond (+5.3%),  Collingwood (+5.1%), Parkville (+4.9%), and South Yarra – East (+4.8%).

Bus

Across Melbourne, bus mode share had a significant rise from 2.6% in 2006 to 3.3% in 2011, and then a small rise to 3.4% in 2016. Here’s how it looks spatially for any journey involving bus:

The highest bus mode shares are in the Kew-Doncaster corridor, around Clayton (Monash University), in the Footscray – Sunshine corridor, a pocket of Heidelberg West, around Box Hill and in Altona North. These are areas of Melbourne with higher bus service levels (and most lack train and tram services).

Here’s a map showing mode shift 2011 to 2016 at the SA2 level:

Outside the Kew – Doncaster corridor there were small mode shifts in pockets that received bus network upgrades between 2011 and 2016, including Point Cook, Craigieburn, Epping – West, Mernda, Port Melbourne, and Cairnlea.

There was also a shift to buses in Ormond – Glenhuntly, which can be largely explained by Bentleigh and Ormond Stations being closed on census day due to level crossing removal works, with substitute buses operating.

There were larger declines in Dandenong, Footscray, and Abbotsford.

In terms of workplaces, Westfield Doncaster topped Melbourne with 14.4% of journeys involving bus, followed by Monash University Clayton with 12.8% (remember this figure does not include students who didn’t also work at the university on census day), 13.3% at Northland Shopping Centre, and 12.3% in a pocket of Box Hill.

SmartBus

“SmartBus” services operate from 5 am to midnight weekdays, 6 am to midnight Saturdays, and 7 am to 9 pm Sundays, with services every 15 minutes or better on weekdays from 6:30 am to 9 pm, and half-hourly or better services at other times. These are relatively high service levels by Melbourne standards.

SmartBus includes four routes that connect the city to the Manningham/Doncaster region via the Eastern Freeway, three orbital routes, and a couple of other routes in the middle south-eastern suburbs. All routes are relatively direct and none are particularly short. Seven of these routes serve the Manningham region.

To assist analysis, I’ve created a “SmartBus zone” which includes all SA1 and CD areas which have a centroid within 600 m of a SmartBus route numbered 900-908. These routes were all introduced between 2006 and 2011, generally replacing existing routes that operated at lower service levels (I’ve excluded SmartBus route 703 because it was not significant upgraded between 2006 and 2016).

Here are mode shares inside and outside the SmartBus zone:

In 2006 the SmartBus zone already had double the bus mode share of the rest of Melbourne, as existing routes had relatively good service levels, including Eastern Freeway services. Following SmartBus (and other bus) upgrades between 2006 and 2011, there was a 2.5% mode shift to bus in the SmartBus zone, and a 1.3% mode shift to bus elsewhere. The SmartBus zone had a further 0.5% shift between 2011 and 2016 while the shift was only 0.2% in the rest of Melbourne.

Here’s an animated look at bus mode shares for just the SmartBus zone.

You can see plenty of mode shift in the Manningham area (where many SmartBus routes overlap), but also some mode shifts along the others routes – particularly in the south-east.

Notes:

  • the SmartBus zone includes overlaps with some other high service bus routes – those pockets generally had higher starting mode shares in 2006.
  • The orbital SmartBus routes do overlap with trains and/or trams which provide radial public transport at high service levels, negating the need or bus as a rail feeder mode (still useful for cross-town travel).
  • I haven’t excluded sections of SmartBus freeway running from the SmartBus zone. Sorry, I know that’s not perfect analysis, particularly along the Eastern Freeway.

Train + bus

Journeys involving train and bus rose from 1.1% in 2006 to 1.5% in 2011 and 1.7% in 2016, which is fairly large growth off a small base and represents around half of all journeys involving bus. I suspect there might be some under-reporting of bus in actual bus-train journeys, as we saw many people a long way from train stations only reporting train as their travel mode.

Here’s a map showing train + bus mode share. Note the mode shares are very small, and I’m not willing to calculate a mode share where less than 6 trips were reported but they result in more than 3% mode share (I’ve shaded those zones grey):

Large increases are evident around the middle eastern suburbs (particularly around SmartBus routes), the Footscray-Sunshine corridor (which have frequent bus services running to frequent trains at Footscray Station), Point Cook (where relatively frequent bus routes feeding Williams Landing Station were introduced in 2013, resulting in 750 train+bus journeys in 2016), Craigieburn (again bus service upgrades with strong train connectivity), and Wollert (likewise).

Ormond – Glen Huntly shows up in 2016 because of the rail replacement bus services at Bentleigh and Ormond Stations at the time (as previously mentioned).

If you look closely, you’ll see higher shares in the Essendon – East Keilor corridor, where bus route 465 provides high peak frequencies meeting just about every train (service levels have not changed between 2006 and 2016)

Tram

Here’s a map of tram mode shares, overlaid on the 2016 tram network (there haven’t been any significant tram extensions since 2005).

Melbourne tram share

Higher tram mode shares closely follow the tracks, with the highest shares in Brunswick, North Fitzroy, St Kilda, Richmond, and Docklands.

It’s also interesting to note that several outer extremities of the tram network have quite low tram mode shares – including East Brighton, Vermont South, Box Hill, Camberwell / Glen Iris (where the Alamein line crosses tram 75), Carnegie, and to a lesser extent Airport West and Bundoora. These areas have overlapping train services and/or are a long travel time from the CBD.

Overall tram mode share increased from 4.0% in 2006 to 4.6% in 2011 and 4.8% in 2016. Here’s a map of tram mode shift 2011 to 2016 by home SA2:

The biggest mode shift was +13% in Docklands, followed by +10% in the CBD. This no doubt reflects the introduction of the free tram zone across these areas. Walk-only journey to work mode share fell 4% in Docklands and 6% in the CBD.

Abbotsford had a 9% mode shift to trams, which possibly reflects the extension of route 12 to Victoria Gardens, providing significantly more capacity along Victoria Street (the only tram corridor serving Abbotsford).

There were small mode share declines in many suburbs, although this does not necessarily mean a reduction in the number of journeys by tram. In Port Melbourne there was a shift from tram to bus and bicycle.

Here are tram mode shares by workplace for 2011 and 2016:

Melbourne tram share workplace

The highest workplace tram mode shares were in the CBD, along St Kilda Road south of the CBD, Carlton, Fitzroy, Parkville, Albert Park, South Melbourne, and St Kilda.

Cycling

Cycling mode share increased from 1.5% in 2006 to 1.8% in 2011 and 1.9% in 2016. These are low numbers, but the bicycle mode share was anything but uniform across Melbourne.

Firstly here’s a map of cycling mode share by home location:

There’s not much action outside the inner city, so let’s zoom in:

The highest mode shares are in the inner northern suburbs (pockets around 25%) where there has been considerable investment in cycling infrastructure.

Here’s a chart showing the mode shift at SA2 level:

The biggest mode shift were 2% in Brunswick West and South Yarra West. However aggregating to SA2 level hides some of the other changes. If you study the detailed map you can see larger mode shifts in more isolated pockets and/or corridors (including a corridor out through Footscray).

Here is the growth in bicycle trips between 2011 and 2016 by home distance from the city centre:

Significant growth was only seen for homes within 10km of the city centre. Here are those new trips mapped, with Brunswick SA2 showing the largest growth:

What about cycling mode shares by workplaces? I’ve gone straight to the inner city so you can see the interesting detail:

The highest workplace mode shares are in the inner northern suburbs, including Parkville (9%) and Fitzroy North (8%).

You’ll note the CBD does not have a high cycling mode share (3.8%) compared to the inner northern suburbs. But if you look at the concentration of cycling commuter workplaces, you get quite a different story:

This shows the CBD having the highest concentrations of commuter cycling destinations, although there were also relatively high densities at the Parkville hospitals and the Alfred Hospital. The highest concentration of commuter cyclists in 2016 was a block bound by Lonsdale Street, Exhibition Street, Little Lonsdale Street and Spring Street (it had a mode share of 4.3%).

However if you look at the increase in bicycle commuter trips between 2011 and 2016 by workplace distance from the city, the biggest growth was for destinations 1-4 km from the city centre:

Note: I am using a different scale for charts by workplace distance from the CBD.

How has walking changed?

Overall walking-only mode share in Melbourne as measured by the census has hardly changed, from 3.6% in 2006 to 3.5% in both 2011 and 2016. However there are huge spatial variations.

Here’s walking by home location:

The highest walking mode shares are around the central city with mode shares above 40% in parts of the CBD, Southbank, Carlton, Docklands, North Melbourne, and Parkville. Outside the city centre relatively high mode shares are seen around Monash University Clayton, the Police Academy in Glen Waverley, Box Hill, and Swinburne University in Hawthorn. Walking-only trips are very rare in most other parts of the city.

Here are walking mode shares by workplace location:

The highest walking shares by SA2 in 2016 were in St Kilda East, Prahran – Windsor, South Yarra, Carlton, Carlton North, Fitzroy, and Elwood. There were also smaller pockets of high walking mode share in Yarraville, Footscray, Flemington, Northcote, Ormond – Glenhuntly, Richmond, and Box Hill.

The biggest mode shifts away from walking were in the CBD (-7.3%) and Docklands (-4.0%), which also had big shifts to tram – probably due to the new Free Tram Zone.

Overall, the biggest increase in walking journeys was seen within 5km of the city centre:

For workplaces, the biggest growth in walking was to jobs between 2-4 km from the CBD (be aware of different X-axis scales):

Most common non-car mode

Here is a map showing the most common non-car mode in 2016*. Note the most common non-car mode might still have a very small mode share so interpret this map with caution.

*actually, I’ve not checked motorbike/scooter, taxi, or truck on the basis they are very unlikely to be the most common.

Train dominates most parts of Melbourne, with notable exceptions of the Manningham region (served by buses but not trains), several tram corridors that are remote from trains, and walking around the city centre.

The southern Mornington Peninsula is a mix of bus and walking, plus some SA1s where no one travelled to work by train, tram, bus, ferry, bicycle, or walk-only!

The next map zooms into the inner suburbs, showing the tram network underneath:

Generally tram is only the dominant mode in corridors where trains do no overlap (we saw lower tram mode shares in these areas above). In most of the inner south-eastern suburbs served by trams and trains, train is the dominant non-car mode.

If you look carefully, there are a few SA1s where bicycle is the dominant non-car mode.

In case you are wondering, there are places in Melbourne where train, tram, or walking-only trumped car-only as the most common mode. They are all on this map:

Mode with the most growth

Finally, another way to look at the data is the mode with the highest growth in trips.

Here is a map showing the mode (out of car, train, tram, bus, ferry, bicycle, walk-only) that had the biggest increase in number of trips between 2011 and 2016, by SA2:

Car trips dominated new trips in most outer suburbs (particularly in the south-east), but certainly not all of Melbourne. Train was most common in many middle suburbs (and even some outer suburbs).

Bicycle was the most common new journey mode in Albert Park (+56 journeys), South Yarra – West (+54), Carlton North – Princes Hill (+80), Fitzroy North (+162) and Brunswick West (+158).

Walking led Fitzroy (+147) and Keilor Downs (+15, with most other modes in small decline, so don’t get too excited).

Bus topped SA2s in the Doncaster corridor, but also Port Melbourne (+176), Vermont South (+30), Kings Park (+10) and Ormond – Glen Huntly (+275 with rail replacement buses operating on census day in 2016).

Tram topped several inner SA2s including the CBD, Docklands and Southbank.

A caution on this map: the contest might have been very close between modes and the map doesn’t tell you how close.

Want to explore the data in Tableau?

I’ve built visualisations in Tableau Public where you can choose your mode of interest, year(s) of interest, and zoom into whatever geography you like.

By home location:

By work location:

Have fun exploring the data!

This post was updated on 24 March 2018 with improved maps. Also, data reported at SA2 level is now as extracted at SA2 level for 2011 and 2016, rather than an aggregation of CD/SA1/DZ data (each of which has small random adjustment for privacy reasons, which amplifies when you aggregate, also some work destinations seem to be coded to an SA2 but not a specific DZ). This does have a small impact, particularly for mode shifts, and mode with the most growth.

This post was further updated on 11 May 2018 to include minor adjustments to DZ workplace counts in 2011 to account for jobs where the SA2 was known but the DZ was not, and to improve mapping from 2011 DZs to 2016 SA2s. Refer to the appendix in the Brisbane post for all the details about the data.