Visualising the changing density of Australian cities

Mon 1 October, 2012

Following on from my last post on Melbourne density, I thought it would be worth creating animations of the change in population density in other large Australian cities.

Below are animated maps showing density using estimated annual population on the ABS Statistical Area Level 2 (SA2) geography for the period 1991 to 2011. You’ll need to click on them to see the animation (and you may have to wait a little if you have a slow connection).

I’ve used SA2 geography because it is the smallest geography for which I can get good time series data. Please note that some SA2s with substantial residential populations will still show up with low average density because they contain large parks and/or industrial areas, or are on the urban fringe and so only partially populated (the non-urban areas bringing down the average density).

Sydney

You can see the growth out to the north-west and south-west, the rapid population growth in the CBD and to the south of the CBD, and general densification of the inner suburbs.

Perth

Perth is a little less dramatic, but you can see strong growth to the far north in the late 2000s, populating of the CBD area, and increasing density in the inner northern suburbs. Many of the middle suburbs show very little change. A lot of Perth’s growth areas don’t seem to show up, probably due to low average densities of fringe SA2s that include non-urban areas.

Brisbane

You can see rapid population growth all over Brisbane, particularly in the CBD are inner suburbs.

Melbourne

In case you missed my last post, here is the map for Melbourne.

I had a bit of a look at Adelaide, but the changes between 1991 and 2011 were not very pronounced due to slow population growth. The process of creating these maps is fairly labour intensive so sorry Adelaide, no map for you (unless I get lots of requests).

I hope this is of interest.

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A first look at 2011 Melbourne residential density, and how it has changed

Fri 21 September, 2012

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

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

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

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

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

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

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

How has density changed between 2006 and 2011?

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

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

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

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

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

There are increases in many areas, particularly:

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

How has density changed between 1991 and 2011?

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

Note in particular:

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

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

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

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

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

Geography Area 
(km2)
Population Density 
(pop/ha)
Areas on map
“Greater Melbourne” Greater Capital City Statistical Area 9990.5 3,999,982 4.0 white + yellow + green + red
SA1s, within Greater Melbourne, with population density >= 1 person/ha 2211.4 3,903,450 17.7 yellow + green + red
SA1s less non-residential land, within Greater Melbourne, with population density >= 1 person/ha 2295.2* 3,906,680 17.0 yellow + green
SA1s less non-residential land, within Melbourne Statistical Division, with population density > 1 person/ha 2199.7 3,862,387 17.6 yellow + green within purple boundary
SA1s less non-residential land, within Greater Melbourne, with population density >= 5 person/ha 1740.1 3,787,610 21.8 green

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

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

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

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


Melbourne urban sprawl and consolidation

Wed 4 April, 2012

[Last updated April 2016 with revised June 2015 population estimates. First posted April 2010]

How much is Melbourne sprawling, and how much is urban consolidation happening?

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

Note that this analysis uses local government areas (LGAs) within the Melbourne Statistical Division (although with all of the Shire of Yarra Ranges), rather than the new Greater Melbourne Statistical Area.  ABS now publish annual population estimates at an SA2 level (essentially suburb level). I’ve had a look at this data and the trends are very similar to the results for LGAs, so I am continuing with LGAs for now in this post.

Population growth

The first chart shows net annual population growth by regions of Melbourne. “outer-growth” refers to the designated growth LGAs on the fringe of Melbourne, namely Wyndham, Melton, Hume, Whittlesea, Casey and Cardinia (see the end of this post for definitions of regions and note that the areas have different sizes and starting populations).

As you can see, Melbourne’s population growth accelerated in the years up to 2008-09, slowed down dramatically for a couple of years but has since bounced back to strong growth. The big slump in growth in 2010 and 2011 was largely a reduction in urban consolidation in established areas, while the outer-growth areas continued strongly.

There were an estimated net 89,856 new residents in 2014/15, an average of 1728 per week (annual growth rate of 2.1%).

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

In 2009-10 there was a significant shift in the balance of growth towards the outer suburban designated growth areas as population growth in established areas slowed dramatically. However we appear to have reverted to the previous pattern, and now 47% of population growth is in the outer growth areas.

The following chart compares the estimated actual share of population growth in the outer-growth areas with the 2008, 2012 and 2014 Victorian Government’s “Victoria In Future” population projections (which DTPLI stresses are not targets or predictions).

Apart from 2010-11, the share of population growth in the outer suburbs has been significantly below all projections, mostly because established area population growth has been much higher than projected. The 2008 projection was for the share of population growth in the outer-growth areas to decline slowly over time, the VIF 2012 projection was for the share to be steady around 55% for the next 15 years, while the new VIF 2014 forecast is for an increasing share in the outer growth areas, peaking in 2028. The 2015 estimated actual is closer to the VIF 2014 projection.

Note:

  • these figures don’t include Mitchell which is now partly within the Melbourne Urban Growth Boundary.
  • not all greenfields sites are in “outer growth” LGAs – smaller greenfields developments occur in established LGAs (eg Keysborough in Greater Dandenong).

If you’d like a more detailed idea about where changes in density is occurring see my posts showing changes in Melbourne density over time and a comparison of 2006 and 2011 at meshblock level.

Population growth compared to projections

The following chart shows the variations between the VIF 2008, 2012, and 2014, and estimated actual population for Melbourne:

The 2015 estimated result is remarkably close to the VIF 2014 projection – out by only 1085 people or 0.024%!

The next charts shows the VIF2008 projected population growth 2007 to 2015, compared to the estimated actuals:

Actual population growth in the inner and middle suburbs was more than double the 2008 projections, growth in the centre and outer regions was above projections, whilst population growth in outer-growth areas was slightly less than projected. That’s a lot of urban infill that was not accurately foreseen in the 2008 projections (the VIF 2004 projections foresaw even less of the urban consolidation in established areas).

The VIF2014 projections for 2014-15 are much closer to the estimated actuals:

The next chart shows estimated actual annual population growth by region to 2014, along with VIF2014 projections for upcoming years:

Growth in dwellings

Two readily available dwelling-based datasets are dwelling approvals (data available to a fine geography level) and dwelling completions (unfortunately these area estimates available at state level only). There will always be a time lag between approval and completion, and many approved dwellings don’t end up getting built. The ratio of dwelling completions to dwelling approvals in Victoria for the last 15 years is 92%. Comparing the two datasets for whole of Victoria, I found a 12 month offset provides the strongest correlation between approvals and completions:

dwelling approvals versus completions

Further complicating the analysis, the RBA has estimated that around 15% of dwelling approvals replace demolished dwellings, and around 8% are second homes or holiday homes.

There isn’t a strong correlation between Melbourne dwelling approvals and Melbourne population growth either, but for the purposes of this post I’ll look at dwelling building approvals because that is the only data I can get in any geographic detail.

The following chart shows a recent acceleration in dwelling approvals across Melbourne, with 55,303 new dwellings approved in 2014/15, more than double the 2007 figure.

Of particular interest are the recent surges in approvals in central, inner and middle Melbourne. The number of dwelling approvals in “inner” Melbourne almost match the outer growth areas in number. If these dwellings actually get built and occupied, then perhaps we will see a surge in population growth in established areas.

Comparing dwelling and population growth

The following chart shows the ratio of population growth to dwelling approvals, which provides indicators of average household size. In 2008-09, there was one new dwelling approved for every 3.2 new residents, but this dropped to around one new dwelling for every 1.7-1.8 new residents in 2009-10 and 2010-11, thanks to a surge of dwelling approvals combined with slower population growth. From 2012 to 2014 population growth picked up relative to dwelling approvals, but the surge in dwelling approvals in 2015 has sent it down to 1.6.

The chart also shows the VIF 2008 projection of average household size (of occupied dwellings), the forecast ratio of population growth to dwelling growth, and the average household size based on census data for 2006 and 2011. The forecast was for slowly declining average household size (following a recent trend). The census-derived average household size in 2011 was 2.445 persons, essentially unchanged since 2006.

Curiously, the ratio of new residents to dwelling approvals was only 1.5 in the early parts of the decade, much lower than average household sizes. Does this reflect small dwelling sizes approved in those years, or maybe a large number of dwelling demolitions?

Measuring progress against the Melbourne 2030 urban consolidation target

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

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

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

The dashed red line is a straight line interpolation of the Melbourne 2030 target for greenfields dwelling share. The outer growth LGA’s share of dwelling approvals had been higher than the target until the end of 2012, but has fluctuated a fair bit.

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

Now the 2014 Victoria in Future projections (released with the final version of Plan Melbourne) have around 45% of dwelling growth occurring in the outer growth areas between 2011 and 2031. The Plan Melbourne share of dwelling growth in the outer growth areas to the year 2051 is 39%, which suggests more urban consolidation between 2031 and 2051.

In reality, we seem to be tracking much closer to the original Melbourne 2030 target.

(Note: The outer-growth LGAs’ share early in the 2000s was much lower. This may reflect urban growth that was still occurring in areas I have classified as “outer” as opposed to “outer-growth” before the Melbourne 2030 plan was released in 2002.)

Appendix: Definitions of regions

I have allocated local government areas to regions as follows:

Centre = Melbourne, Yarra, Port Phillip

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

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

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

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

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

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


How does travel vary across Melbourne and regional centres in Victoria?

Sun 19 June, 2011

What differences are there in car use by geography, income, household type, and age?

And could you do more to reduce car use by pushing population growth to regional cities instead of the fringe of Melbourne?

I thought I’d take a closer look at travel and trip distances using massive 2007-08 VISTA dataset, and see what factors lead to variations.

In this post I look at travel distances (total and by car) and mode splits across geographies, trip purposes, incomes, ages, and household types. And more.

While the results might not be too surprising, I hope you’ll find the evidence interesting.

How do travel distances vary by geography?

In a previous post I showed that people in the outer suburbs generally have a longer median travel distance:

The patterns were not uniform in the outer suburbs. Nillumbik is the second highest on 35.1 kms per person, while Hume is much lower on 19.6. Factors such as incomes and household types might explain this variation (more on that later).

Most of my analysis will deal with six geographic zones – four rings of Melbourne, Geelong and other regional cities in the VISTA sample combined (Ballarat, Bendigo, Shepparton and the Latrobe Valley). Here’s a map of the Melbourne zones:

Note: I’ve used “city” as shorthand for the central area, and “inner” as shorthand for the inner suburban ring.

Based on those zones, here is a simpler view of daily travel distances (total and by car):

This suggests little difference in total travel distance, but significant differences in car travel distances.

I’ve not used averages because some trips were extremely long (the longest trip by an inner city resident was 833 km) which can skew the averages.

But is median the right measure of travel distance? Probably not, if you look at the following chart of the cumulative distribution of all day travel distances:

How do you read this chart? A point on each line means Y% of people travelled up to X kms per day. Essentially the lower the curve on the chart, the longer distance those people travelled.

You can see differences between distributions are not straight forward:

  • The lower half of travel distances were quite similar.
  • The differences manifest in the top half of distances. You can see that people in outer Melbourne were much more likely to clock up longer travel distances that those in the inner city. For example, 30% of people in the outer suburbs travelled more than  , while only 15% of people who live in the inner city travelled more than 40 kms.
  • In fact, there were more long distance travellers in outer Melbourne than in Geelong or the other regional centres.
  • 24% of people in the outer suburbs of Melbourne did not travel at all, while only 15% of inner city residents did not travel on the survey day. This causes the distances to cross around the median.
  • There is greater diversity in travel distances of people in the outer suburbs, including about the quarter who did not travel.

Here is the same again for car distance travelled (probably the most important chart in this post):

The differences are much clearer here, with car use and travel distance increasing through Melbourne by distance from the city, and the outer Melbourne suburbs having the longest car travel distances. Distances in Melbourne’s outer suburbs are generally longer than in Geelong and the regional centres.

Interestingly, 48% of inner city residents made no car travel at all, hence the very low median. While the city, inner and middle lines converge at a longer distance, the outer suburbs still had 10% of people doing more than 80 kms in cars.

How does mode share vary by geography?

The distributions on car distance travelled reflect mode splits across the regions. Here is a chart of mode split for trips (using the ‘main’ mode for the trip, which means car+PT trips are counted as PT):

Active and public transport mode shares fell away with distance from the centre of Melbourne. I expect this will be a product of poorer service levels, and a smaller proportion of people travelling to Melbourne’s CBD (the main market where public transport dominates).

But here’s a slightly different take, the mode share of person travel distances:

There is much less variation in public transport mode share of kms travelled. This points to people in the outer suburbs of Melbourne, Geelong and other regional centres making much longer trips when they travelled by public transport. I expect many of these will be long distance rail trips to Melbourne.

The clear difference is that people in the outer suburbs and regional cities did a lot less walking/cycling and lot more travel by car.

What about mode share of very short trips?

Walking is a significant mode in the inner city, and many destinations are within walking distance. You might think that the regional centres are similar, because they are more compact in general.

Well, it appears not. Here is a chart of mode shares of trips under 1km (probably a walkable distance for most people).

Around half the short trips in the outer suburbs , Geelong and regional centres were made by private transport – essentially cars! Why did people drive for such short trips in these areas? Is it a lack of safe places to walk/cycle? Or is it a lack of disincentives to drive?

Digging deeper, even for recreational trips of less than 1km in the outer suburbs, 30% were made by car!

Does the number of trips made vary by geography?

The following chart shows the distribution of the number of trips made. In VISTA, a trip is defined as travel between two activities.

People in the inner city generally made more trips, and those in the middle and outer suburbs made fewer trips. This will also be influencing the total distance travelled per day.

Note: very few people make only 1 trip in a day because it essentially means you start and finish you day in different locations (within the VISTA definitions of a day at least).

How do trip lengths vary?

Here is a distribution chart of lengths of trips (for any purpose):

By almost any measure, those in the outer suburbs of Melbourne made the longest trips. They were followed by people who live in the middle suburbs of Melbourne and Geelong. This means that either people choose to partake in activities that were further away, or (more likely) those activities were further away from home.

What about trip distances for different purposes?

First up, median trip distances by purpose:

Work related trip distances were clearly the longest, especially in the outer suburbs. The “median” person living in the outer suburbs of Melbourne travelled 16 kms for work (note that not all “work related” trips are to/from home).

Here’s a closer look at the distribution of trip lengths between home and work:

The differences when looking only at home to work and work to home trips is much more stark, with the outer suburbs of Melbourne fairing worst by a long way.

The median distances in Geelong and the other regional centres were actually less than the inner suburbs of Melbourne, however they have a long tail with over 10% of trips in Geelong more than 50km.

Back to the previous chart, social trips also get longer as you move to the outer suburbs of Melbourne, which suggests that outer suburbs are not as self-contained for social destinations.

Most other trips purposes had a median around 3-4 kms, although this was more like 2-3 kms in the inner city, and distances increase in the outer suburbs. Chauffeuring trips (pick up or drop off someone) show the least variability (many of these would be taking kids to/from school).

Trips to education were longest in the inner suburbs, possibly reflecting children from wealthy families attending private schools that are further away.

How does travel time vary by trip purpose?

You can see:

  • Work trips take the most time in Melbourne, but there isn’t a lot of variation. This supports the hypothesis that people have a commuting travel time budget, and generally find work within that budget.
  • Work travel times were highest in the inner suburbs (perhaps related to slower road speeds) and outer suburbs (much longer distances).
  • Education trip times were longer in the inner and middle suburbs (perhaps related to congestion and/or longer trips to private schools by children in wealthy families)
  • 10 minutes was the most common median trip time – which actually shows up as 9 minutes in the chart, owing to the way I calculate medians in Excel (sorry, not perfect, but Excel doesn’t do medians in pivot tables).

Here’s a closer look at work-home trip time distributions:

You can see big steps at the multiples of five minutes, as people tend to round estimated trip times to the nearest 5 minutes. Median trip times in Melbourne are all around 30 minutes, and much lower in Geelong and regional centres. People in the inner suburbs were least likely to have commute trips less than 20 minutes, while the outer suburbs were most likely to have trip times over 30 minutes.

How does travel speed vary by trip purpose?

As you might expect, trips were faster in the outer suburbs, probably because a combination of less congestion and more roads designed primarily to move vehicles quickly (freeways and divided arterials).

Education trips didn’t speed up as much in the outer suburbs, perhaps because they were more likely to be on public transport. Which brings us to…

How does mode share vary by trip purpose?

Around half of education trip kms were by public transport overall, although this was curiously lower in the inner city and outer suburbs.

Work trips had the next highest public transport mode share, which fell away towards the outer suburbs.

Other trip types mostly had slightly higher public transport mode shares closer to the centre of Melbourne. Note: I have not excluded very long trips from this analysis, so they might throw the figures slightly.

Here is another view, private transport mode share:

You can see more significant trends across Melbourne, as people in the inner city and suburbs were more likely to travel by active transport.

What other factors influence travel distance and mode split?

Different households will have different travel needs, and the distribution of household types across Melbourne is not even:

And median per person travel distance varied by household type:

You can see that the household types more prevalent in the outer suburbs (couples with or without kids) have the highest median car travel distances. So this will be impacting longer travel distances in the outer suburbs. You can also see that couples with kids have the highest car mode share, which is no big surprise!

Here’s a look at the distribution of total travel distance for people living in households that were couples + kids, one of the most common household type:

Couples with kids in the inner city certainly travel less distance, and while the bottom half of people were similar for other regions, the travel distances were much longer in the upper half of such people, suggesting geography still had a big impact.

Equally household incomes were not consistent across Melbourne:

Equivalised household income is a measure that allows income comparisons across different household sizes. It is calculated as household income divided by a measure of householders: the first person is assigned a value of 1.0, subsequent persons over 15 years are 0.5, and any children are 0.3.

Curiously, the inner city has the lowest income profile in Melbourne (note that VISTA 2007 did not include Southbank and Docklands residents), while the wealthy live in the inner suburbs.

It will come as little surprise that household income is a driver of total – and car-based – travel distance:

Do rich people shun public transport?


No, only the very-rich seem to shun public transport. According to the VISTA numbers (which are weighted to census 2006 demographics), only 10% of people live in households with an equivalised income over $2000.

The highest concentration of wealthy people is in the inner suburbs, travel distances are generally shorter (although income might explain longer travel distances in relatively wealthy Nillumbik).

To isolate household income, here is a distribution chart for people in households with an equivalised income of between $500 and $750 per week (the largest $250 bracket overall):

Again the lower half exhibits very little difference, while the outer suburbs of Melbourne has much longer distances in the upper half. (note the inner city line is quite jagged, because the sample size in this instance is only 204).

What about age:

People aged 20-64 certainly travelled longer distances. Looking at the distribution of ages, there were more people aged 20-74 living in the inner city and inner suburbs, compared to middle and outer Melbourne. There is very little difference in the percentage of the population between 25 and 64 across the regions (those with the largest car travel distances).

And yes, public transport mode share is lowest amongst very young children and the middle-aged (the later group often being the decisions makers!):

And finally (without going through all the detail here) people who work full-time tend to travel more, but they become less prevalent as you move away from the centre of Melbourne.

So, should we encourage population growth in regional centres instead of Melbourne’s outer suburbs?

Well, it’s probably the wrong question to ask! People in inner Melbourne do a lot less car travel than anywhere else. This analysis clearly shows that encouraging people to move into inner Melbourne would probably do the most to reduce car travel per capita.

People currently living in the outer suburbs of Melbourne travel more and do more car kms than those in regional cities. The main problem is that their work and social trips are much longer.

The evidence suggests putting people into regional cities would generate less car travel than putting people on the fringe of Melbourne.

However, there are several points worth considering:

  • If you can generate jobs in the outer suburbs of Melbourne, you might be able to reduce work travel distances. Easy to say, but it defies agglomeration economies that cause jobs to co-locate in the inner city and suburbs. If Melbourne’s Central Activities Areas (formerly Central Activities Districts (formerly Transit Cities)) can become significant employment destinations then that will certainly help.
  • If you do encourage people to settle in regional cities, will they have the same transport profile as existing residents? I would guess that there would be a significant difference between people living in the centre of regional cities, and those living on the fringe. The reduced car travel advantages of regional cities are probably largely eroded on the fringes of the regional cities. However, encouraging higher density in the inner areas of the regional cities would probably generate less car kms.
  • If you increase the population in regional cities without also increasing employment opportunities, you’ll create unemployment problems and/or force people to travel further to get to work. This would cancel out some of the benefits of locating people in regional centres. It may also increase demand on long distance V/line commuter trains into Melbourne (which currently consume valuable metropolitan train paths with low passenger density).

It doesn’t seem like there is much difference between the outer suburbs and regional cities. But there is a much bigger difference when you compare these with the inner suburbs of Melbourne.

If we really want to reduce car use, we’ll need to do relatively easy things like:

  • Locate people in inner city and suburban areas, where travel distances are short and there is viable high quality public transport (though it will probably require capacity upgrades)
  • Increase public transport service levels in existing outer suburbs and regional cities, with a particular focus on efficiently connecting people to employment areas by public transport.
  • Break down the barriers to walking and cycling in the outer suburbs and regional cities. Footpaths and safe places to ride would be a good start!

Notes about the data:

  • Wherever possible I have used person weightings in VISTA, which are for all week travel and align VISTA data with 2006 census data on demographics.
  • I have determined trip purpose by looking at the destination purpose of each trip. If the destination is not home, then I assign the destination purpose as the trip purpose. If the destination purpose is home, then I assign the origin purpose as the trip purpose. This gets around the common problem of nearly half of all trips having “go home” as the trip purpose, which costs you half your data when analysing by trip purpose.

What does Melbourne’s urban density look like? (2006)

Sat 2 April, 2011

Transport planners love to talk about urban density, but what does Melbourne’s urban density actually look like? Google for a Melbourne urban density map and you won’t find much.

The ABS publication Melbourne.. A Social Atlas has a density map (see pages 12-13) at the Census Collection District (CCD) level, but only has five colour graduations so subtleties are quickly lost.

So I’ve decided to draw one myself.

Arguably the best source of data for housing density is the ABS’s experimental mesh blocks, which are smaller than Census Collection Districts (CCD). Mesh blocks are designed to have more uniform land use, which gets around the problem of a CCD which might contain a mix of residential, parkland and commercial land use showing up as low density. But I’ll come back to this.

So here is a 2006 population density map of Melbourne at the mesh block level:

(I’m using people per square km, which is 100 times larger than people per hectare if you need to convert).

You’ll need to click to zoom in, and you might want to then zoom in again with your favourite image viewer to see the detail.

Some observations:

  • Many areas on the very fringe show as low density, but this might be because that area was under development at the time of the census, and only some people had moved in.
  • Everyone talks about low density sprawl on the fringe, but even back in 2006 there was evidence of higher density development in the outer suburbs. Have a look at the Craigieburn area in the north or around Narre Warren and you will see many patches of green. New blocks on the urban fringe are now actually quite small in places compared to those in the middle suburbs. Two storey townhouses are actually not uncommon in new estates.
  • In the north-west (around Delahey/Sydenham), you can see a north-south divide where there is higher density on the eastern side. This corresponds with the municipal boundary between Brimbank and Melton. Presumably they’ve had different urban development policies.
  • The biggest clumps of density are in the inner city, particularly Carlton and Carlton North, Fitzroy, St Kilda, Richmond, and Kensington (the western side of which enjoys a 5½ days per week route 404 bus service).
  • Looking at the Central Activities Districts (CADs), there are clumps of density near the Dandenong and Box Hill CADs. But nothing to speak of inside Ringwood, Frankston, or Broadmeadows CADs (in 2006).
  • Other curious pockets of density in the suburbs include west of Highpoint Shopping Centre, Sunshine, Glenhuntly/Carnegie, and Glen Iris.
  • The lowest density suburbs in Melbourne are found in the middle and outer eastern suburbs (particularly Upwey/Belgrave), and in the north-east around well off areas such as Eltham, Toorak and Eaglemont. North west Reservoir seems to be a problem area – high socio-economic disadvantage and low density (not to mention a bus route that runs 5½ days a week).
  • Interesting to see relatively higher densities south of the Dandenong rail line.

For comparison purposes, I’ve also created a version based on larger Census Collection Districts (CCDs):

(note: this map doesn’t show anything outside the Melbourne SD)

What’s the difference you ask? You cannot see a great deal of difference, though the CCD map makes Melbourne look a little less dense.

But if you zoom in you can spot differences in some areas where a CCD is part residential, part not. Here’s an example in the Black Rock/Beaumaris area:

The CCD map on the left shows a few darker red blocks next to the whitespace, but that low density is not visible in the mesh blocks on the right, because the mesh blocks split the parkland and houses. You can also see that the CCDs run to the shoreline, while the beach area has been split into separate mesh blocks.

The advantage of the mesh block map is that it pretty much shows housing density, as most pieces of land that are not residential have been removed (including suburban parks).

But the advantage of CCD density is that it includes local parkland, which is a measure of open space within and immediately surrounding residential areas.

A better way of looking at the density equation is a cumulative distribution chart, as created by Fedor Manin on his blog We Alone on Earth (also referenced on Human Transit).Rather than having to worry about whether low density areas on the fringe are “urban” or not, you can just look at density by population share, and the fringe areas will quickly tail out anyway. On this basis the problems of using an administrative boundary of a city (which often contains a large areas of rural land) largely go away, but then you don’t get a single number.

I’ve lined up all mesh blocks and CCDs in the Melbourne SD in order of density, and created a cumulative profile of density for each.

You can see a big difference between CCDs and mesh blocks (note the X axis is logarithmic). On a mesh block basis, about half of Melbourne’s population lives at a density of greater than 3200/km2, whereas on a CCD basis, only 30% of Melbourne’s population lives at a density greater than 3200/km2. Take note anyone doing a comparison between cities!

Here’s a chart on the same data showing a population distribution across densities, using mesh blocks and CCDs:

You can see the most common density for mesh blocks is slightly higher than for CCDs. The peak for mesh blocks is between 2818-3162 people/km2 on my intervals. That’s an funny sounding interval because I’ve used logarithmic intervals (if you use even intervals of 100 people/km2, the peak is between 2900 and 3300 people/km2)

So what is the average density of Melbourne?

What is Melbourne? Should we include satellite urban areas around the city? For example, is Sunbury part of Melbourne? It is within the Melbourne SD (Statistical District) but not within the Melbourne “Urban Centre” as defined by ABS. Do you want to include non-residential areas (urban density), or not? (residential density)

Here are six very different measures of the urban density of Melbourne, including some measures that have minimum density threshold to restrict the calculation to “residential” areas. The maps above use 1000 people/km2 as a threshold for colouring, and this appears to include all “residential” areas, except for some very large block estates.

Geography Area (km2) Population Density (pop/km2)
Mesh blocks within all Urban Centres/Localities within Melbourne SD 2,357 3,506,207 1,488
“Melbourne” Urban Centre 2,153 3,368,069 1,564
CCDs within Melbourne SD, with population density > 100 people/km2 2,151 3,514,658 1,634
Meshblocks within Melbourne SD, with population density > 100 people/km2 1,566 3,511,982 2,242
Meshblocks within “Melbourne” Urban Centre, with population density > 100 people/km2 1,350 3,358,317 2,487
Meshblocks within Melbourne SD, with population density > 1000 people/km2 1,084 3,316,516 3,060

You can quickly see why trying to calculate an average density is a fraught exercise! Though the first two are trying to measure “urban density”, while the later are attempting to measure “residential density” (and note the threshold for residential density makes a big difference).

A density profile chart (as above) is clearly a good way to get around the defined area problem. But you still need to be consistent in the land parcel size you use when comparing cities. Not easy when comparing cities with different statistics agencies.

Land use map of Melbourne

Before I finish up, the other beauty of the mesh block data is that it contains a land use classification for each mesh block.

So it is really easy to produce a land use map of Melbourne (and Geelong for good measure):

What are those two black blobs I hear you ask? Essendon and Moorabbin Airports. Tullamarine and Avalon airports are actually classified agricultural.

And you will see residential areas stretching a fair way east of Frankston, and north of Craigieburn – though these are not actually developed. So it’s not perfect.

In fact, according to the data, there is a mesh block in Melbourne with 358 people living in an area of 420 square metres (852,700 people/km2). That’s 1.17 square metres of land space per person. Really? No, what appears to have happened is that almost every resident of the Burnside Retirement Village was registered to one tiny parcel of land. I suppose that’s census data for you!


Car ownership and public transport

Sun 17 January, 2010

 

Is there a link between good quality public transport and car ownership rates? Will high density urban develop around good quality public transport lead to significant increases in car ownership?

Obviously not a new topic, but in this post I hope to at least illuminate the state of play in Melbourne (as per the 2006 census).

The Australian Census provides very detailed data on car ownership to a high resolution. The data includes the number of 0, 1, 2, 3 and 4 or more car households in each Census Collection District (which average around 225 dwellings). Most spatial representations of car ownership show the number of households with 0,1,2,3, or 4 or more cars. This is fine, except it ignores household size. Single occupant households are unlikely to have 3 cars, while large family households with grown up children are more likely to have 4 cars.

So I have looked at the data differently in two ways:

  • Rather than cars per household, I’ve used cars per 100 adults for an area (I define “adults” below). This removes household size from the equation. Essentially what I did was add up the number of reported cars in each CCD, which for 0,1,2 and 3 car households is straight forward. 5.1% of households in Victoria reported “4 or more” motor vehicles and a further 3.7% did not specify the number of motor vehicles. In the absence of more detailed information, I have assumed an average of 4.2 motor vehicles for households reporting “4 or more” and zero motor vehicles present where households did not respond. While this means there is a small level of uncertainty as to the actual total number of motor vehicles in each census collection district, these represent small percentages of the total, and it is still possible to compare relative levels of car ownership between areas. Hence I refer to the car ownership rates as estimate.
  • I divide the number of cars by the number of “adults” – ie people who are generally of driving age. As the census reports age in 5 year blocks, I’ve used 20-74 as the age range where most people would be eligible and confident to obtain a drivers license. This is fairly arbitrary I agree. People under 20 can drive and own cars, as can those over 74, but they are perhaps less likely to do so.

So the calculations are not an exact science, but give a pretty good idea of the car ownership rates for different areas. It also allows me to show car ownership rates on a single map, rather than the need for multiple maps. In the maps below I have only shown urban areas that come within a minimum urban residential density, so the boundaries between urban and regional areas are clearer.

Superimposed on the map are high quality public transport routes that existed in Melbourne in 2006. I’ve used all train lines (stations are marked), all tram routes, and selected bus routes with “high” service levels – reasonable frequency and long span (at most a 16 minute headway on weekdays inter-peak). It’s hard to define a perfect threshold for bus routes as some have good frequency but poor span of hours in 2006. Again not a perfect science.

Here are the maps for greater Melbourne and inner Melbourne (click on them for higher resolution – you may need to click again to zoom in).

 

 

Observations and Analysis

Pockets of low car ownership rates are generally found:

  • In lower socio-economic areas (eg around Broadmeadows, St Albans, Dandenong)
  • Near large activity centres with public transport hubs (eg Ringwood, Box Hill, Dandenong, Frankston)
  • Where there is a dense grid of high quality public transport – ie you can catch public transport in multiple directions relatively easily to get to a range of destinations. This includes areas where the high frequency transport is provided by buses and not trams (eg the Footscray to Sunshine Corridor).
  • Residential colleges near universities (eg Clayton, Caulfield, Bundoora, Maribyrnong)
  • Army bases (eg near Watsonia)
  • Prisons (you can see a couple in the west)
  • Areas of higher income generally have higher car ownership. Higher than local trend car ownership can be seen in areas like (west) Kew, Toorak, Brighton and Greenvale.

Note that this exercise does not aim to fully explain the reason for rates of car ownership in Melbourne. There is extensive literature available about this subject. We have primarily set out to highlight car ownership rates in Melbourne to inform debate.

Conclusions and Commentary

  • The maps show low levels of car ownership in many places where there is a dense network of high quality public transport. That suggest that high frequency public transport routes operating from early to late in multiple directions is an enabler for people to choose not to own a car.
  • Increasing population in areas with dense networks of high quality public transport is therefore less likely to result in high levels of car ownership and use.
  • The tram network provides the radial links in most cases, while bus routes are needed to provide links across the tram routes. By upgrading a few more inner city bus routes, a larger area could support higher populations with low car ownership rates and high liveability.
  • Even those people who do bring cars with them will probably leave them parked most of the time as walking, cycling or public transport will be an easier option for most trips. High rates of car ownership do not necessarily translate into high rates of car use.

This analysis was the subject a media story in The Age, in November 2009.


Urban density and public transport mode share

Sat 16 January, 2010

Are all the statistics we see about urban density and transport reliable?

In in most recent book Transport for Suburbia (and his paper to ATRF 2009), Paul Mees highlights mis-use of urban densities figures by some researchers – the trouble being inconsistent determination of what exactly is the urban area of a city when you calculate density (= population/area).

To redress the issue of data quality, Paul has used calculations based on the actual urbanised area for Australia, US, Canadian and English cities (looking at entire greater metropolitan areas). He’s used figures based on urbanised areas as opposed to a statistical district, municipal council area, or other arbitrary administrative boundary which could contain large areas of non-urbanised land.

The calculations define urbanised areas using the following criteria:

  • US and Canadian cities: minimum 400 per square km,
  • Australian cities: minimum 200 per square km (meaning Australian cities might be slightly understated)
  • English cities: detailed mapping, likely to lead to slighty higher density figures.

So the calculations are not perfectly aligned, but they are more comparable than density calculations that use simple administrative boundaries. And they are also certainly consistent within each country.

He publishes tables of this data, talks about the relationships between them, but for some reason fails to plot the results on a chart. So I’ve decided to chart them (if you are after the data tables consult the ATRF paper above and/or the book).

The table of data is quite interesting in that it debunks some myths about the densities of various cities. Los Angeles is the highest density city in the entire table (the Freakonomics blog has a good series on Los Angeles Transportation: Facts and Fiction that is worth reading).

Firstly, car mode share in journey to work:

Is there a relationship between urban density and car mode share on journey to work? What do correlation coefficients say (closer to 1 and -1 means stronger) – something Mees didn’t calculate:

  • Australia: -0.74
  • Canada: -0.58
  • US: -0.46
  • England: -0.68

That suggests a relationship does exist, but it isn’t particularly strong. In reality, every city has unique characteristics and other attributes will explain the differences (the quality of services and infrastructure of alternative modes would certainly have a lot to do with it).

Looking at some outliers:

  • London has the highest density and lowest car mode share. It compares so favourably to all other English cities in car mode share, despite being only slightly more dense than Brighton/Worthing/Littlehampton (one combined urban area).
  • Canadian cities with the lowest car mode share are Toronto (highest density) and Victoria (second lowest density).

What about public transport mode share for journey to work?

This chart shows relationships stronger in some countries than others. Indeed the correlation coefficients are:

  • Australia: 0.79
  • Canada: 0.87
  • US: 0.42
  • England: 0.58

So much stronger relationships in Canada and Australia. Again there is a lot at work (particularly the quality and quantity of available public transport, which is one of Paul’s points).

In terms of outliers:

  • London is off the chart at 45.9% public transport.
  • Brisbane is perhaps an outlier for Australia – low density but pretty much the same rate of public transport use as Melbourne.
  • Los Angeles – which actually has the highest density of all the US cities but still relatively low public transport use.
  • The city with the highest PT mode share in the US is New York, even though it isn’t the most dense city in the US (there is lots of sprawl outside Manhattan).

The following walking chart might seem to suggest a strong relationship when you look at all cities, but remember that the density measurements aren’t quite the same, so it’s not fully conclusive. However, English cities still tend to have higher densities, particularly as many have green belts to prevent sprawl.

There is actually a negative correlation between density and walking (and cycling) for Australia and Canada. However I wouldn’t read too much into that as the sample size if small and there are lots of unique factors affecting each city.

But if you reckon there should be a positive correlation between walking and density, the outliers are:

  • Victoria (Canada) – low density but high walking mode share.
  • San Francisco and Los Angeles have low walking share.
  • Hobart – highest walking share in Australia, despite low density (and a big river dividing it in two).
  • Toronto – Canada’s most dense walking city, but least walking mode share
  • London – highest density but lowest walking share (9.2%)

Same again for cycling:

It looks like almost no one cycles in the US, despite having more favourable climate than Canada. Again higher cycling rates in the UK.

Cycling outliers:

  • Victoria (Canada) – high walking and cycling mode share
  • Kingston upon Hull (UK) 11% – off the chart’s scale (Mees suggests a large university may be the cause)
  • Canberra – which has a good network of bike paths (but still only 2.5% cycling mode share)
  • Sydney – with just 0.7% cycling – hilly terrain not helping.

What if you add up all the sustainable transport modes (PT, walking and cycling)? In theory, density should help all sustainable transport modes.

The correlations are:

  • Australia: 0.77
  • Canada: 0.62
  • US: 0.44
  • England: 0.70

The English result is actually stronger than PT (0.58), walking (0.32) and cycling (0.02). Do people respond to density using different, but sustainable modes?

Can public transport be effective in low density cities?

Paul’s main argument is that low transport density isn’t a barrier to successful public transport, and that it is easier to change public transport provision in a city, than it is to change urban densities (not that increasing urban densities isn’t a worthy goal).

Certainly urban density makes it easier to make public transport successful, but I’d agree that it is possible to make public transport work a lot better in low density environments.

Indeed, in Melbourne, relatively high quality SmartBus routes (that run every 15 minutes for most of the day on weekdays, very good by suburban Melbourne standards!) have been trialled in the outer suburbs, and the patronage response has been much stronger than typical elasticities (the subject of another post).

More generally, in Melbourne over the last three years we’ve seen a very strong correlation between growth in service provision (26% more kms) and growth in patronage (29%) – more than any other potential driver of patronage (again, topic for another post).

Comparable cities for population and density

Finally, by plotting population and density, you can see which cities are most similar – at least in these two respects (I’ve only looked at cities under 7 million and UK cities are off the density scale). I’ve labelled Australian cities and nearby equivalents. Note: the US and Canadian data is year 2000, while Australia is 2006.