Are Australian cities becoming denser?

Tue 5 November, 2013

Please refer to a fully revised second edition of this post – published in April 2019.

[Updated April 2017 with 2015-16 population estimates. First published November 2013]

While Australian cities have been growing outwards with new suburbia, they have also been getting denser in established areas, and the new areas on the fringe are often more dense than growth areas used to be (see last post). So what’s the net effect – are Australian cities getting more or less dense?

This post also explores measures of population-weighted density for Australian cities large and small over time. It also tries to resolve some of the issues in the calculation methodology by using square kilometre geometry, looks at longer term trends for Australian cities, and then compares multiple density measures for Melbourne over time.

Measuring density

Under the traditional measure of density, you’d simply divide the population of a city by the metropolitan area’s area (in hectares). As the boundary of the metropolitan areas seldom change, the average density would simply increase in line with population with this measure. But that density value would also be way below the density at which the average resident lives because of the inclusion of vast swaths of unpopulated land within “metropolitan areas”, and so be not very meaningful.

Enter population-weighted density (which I’ve looked at previously here and here). Population-weighted density takes a weighted average of the density of all parcels of land that make up a city, with each parcel weighted by its population. One way to think about it is the residential density in which the “average resident” lives.

So the large low-density parcels of rural land outside the urbanised area but inside the “metropolitan area” count very little in the weighted average because of their small population relative to the urbanised areas. This means population-weighted density goes a long way to overcoming having to worry about the boundaries of the “urban area” of a city. Indeed, in a previous post I found that removing low density parcels of land had very little impact on calculations of population-weighted density for Australian cities. However, the size of the parcels of land used in a population-weighted density calculation will have an impact, as we will see shortly.

Calculations of population-weighted density can answer the question about whether the “average density” of a city has been increasing or decreasing. But as we will see below, using geographic regions put together by statisticians based on historical boundaries is not always a fair way to compare different cities.

Population-weighted density of Australian cities over time

Firstly, here is a look at population-weighted density of the five largest Australian cities (as defined by ABS Significant Urban Areas), measured at SA2 level (the smallest geography for which there exists a good consistent set of time-series estimates). SA2s roughly equate to suburbs.

According to this data, most cities bottomed out in density in the mid 1990s. Sydney, Melbourne and Brisbane have shown the fastest rates of densification in the last three years.

What about smaller Australian cities? (120,000+ residents in 2014):

Darwin comes out as the third most dense city in Australia on this measure, with Brisbane rising quickly in recent years into fourth place. Most cities have shown densification in recent times, with the main exception being Townsville. On an SA2 level, population weighted density in Perth hardly rose at all in 2015-16 (a year when 92% of population growth was in the outer suburbs)

However, we need to sanity test these values. Old-school suburban areas of Australian cities typically have a density of around 15 persons per hectare, so the values for Geelong, Newcastle, Darwin, Townsville, and Hobart all seem a bit too low for anyone who has visited them. I’d suggest the results may well be an artefact of the arbitrary geographic boundaries used – and this effect would be greater for smaller cities because they would have more SA2s on the interface between urban and rural areas (indeed all of those cities are less than 210,000 in population).

For reference, here are the June 2014 populations of all the above cities:

Australian cities population 2014

The following map shows Hobart, with meshblock boundaries in black (very small blocks indicate urban areas), SA2s in pink, and the Significant Urban Area (SUA) boundary in green.  You can see that many of the SA2s within the Hobart SUA have pockets of dense urban settlement, together with large areas that are non-urban – ie SA2s on the urban/rural interface. The density of these pockets will be washed out because of the size of the SA2s.

Hobart SUA image

Reducing the impact of arbitrary geographic boundaries

As we saw above, the population-weighted density results for smaller cities were very low, and probably not reflective of the actual typical densities, which might be caused by arbitrary geographic boundaries.

Thankfully ABS have followed Europe and released of a square kilometre grid density for Australia which ensures that geographic zones are all the same size. While it is still somewhat arbitrary where exactly this grid falls on any given city, it is arguably less arbitrary than geographic zones that follow traditional notions of area boundaries.

Using that data, I’ve been able to calculate population weighted density for the larger cities of Australia. The following chart shows those values compared to values calculated on SA2 geography:

pop weighted density 2011 grid and SA2 australian cities

You’ll see that the five smaller cities (Newcastle, Hobart, Geelong, Townsville and Cairns) that had very low results at SA2 level get more realistic values on the kilometre grid.

You’ll notice that most cities (except big Melbourne and Sydney) are in the 15 to 18 persons per hectare range, which is around typical Australian suburban density.

While the Hobart figure is higher using the grid geography, it’s still quite low (indeed the lowest of all the cities). You’ll notice on the map above that urban Hobart hugs the quite wide and windy Derwent River, and as such a larger portion of Hobart’s grid squares are likely to contain both urban and water portions – with the water portions washing out the density (pardon the pun!). While most other cities also have some coastline, much more of Hobart’s urban settlement is near to a coastline.

But stepping back, every city has urban/rural and/or urban/water boundaries and the boundary has to be drawn somewhere. So smaller cities are always going to have a higher proportion of their land parcels being on the interface – and this is even more the case if you are using larger parcel sizes. There is also the issue of what “satellite” urban settlements to include within a city which ultimately becomes arbitrary at some point. Perhaps there is some way of adjusting for this interface effect depending on the size of the city, but I’m not going to attempt to resolve it in this post.

International comparisons of population-weighted density

See another post for some international comparisons using square km grids.

Changes in density of larger Australian cities since 1981

We can also calculate population-weighted density back to 1981 using the larger SA3 geography. An SA3 is roughly similar to a local government area (in Melbourne at least), so getting quite large and including more non-urban land. Also, as Significant Urban Areas are defined only at the SA2 level, I need to resort to Greater Capital City Statistical Areas for the next chart:

This shows that most cities were getting less dense in the 1980s (Melbourne quite dramatically), with the notable exception of Perth. I expect these trends could be related to changes in housing/planning policy over time. This calculation has Adelaide ahead of the other smaller cities – which is different ordering to the SA2 calculations above.

On the SA3 level, Perth declined in population-weighted density in 2015-16.

When measured at SA2 level, the four smaller cities had almost the same density in 2011, but at SA3 level, there is more separating them. My guess is that the arbitrary nature of geographic boundaries is having an impact here. Also, the share of SA3s in a city that are on the urban/rural interface is likely to be higher, which again will have more impact for smaller cities. Indeed the trend for the ACT at SA3 level is very different to Canberra at SA2 level.

Melbourne’s population-weighted density over time

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

Note: I’ve calculated population-weighted density at the SA2 level for both the Greater Capital City Statistical Area (ie “Greater Melbourne”, which includes Bacchus Marsh, Gisborne and Wallan) and the Melbourne Significant Urban Area (slightly smaller), which yield slightly different values.

All of the time series data suggests 1994 was the turning point in Melbourne where the population-weighted density started increasing (not that 1994 was a particularly momentous year – the population-weighted density increased by a whopping 0.0559 persons per hectare in the year to June 1995 (measured at SA2 level for Greater Melbourne)).

You’ll also note that the density values are very different when measured on different geographic units. That’s because larger units include more of a mix of residential and non-residential land. The highest density values are calculated using mesh blocks (MB), which often separate out even small pockets of non-residential land (eg local parks). Indeed 25% of mesh blocks in Australia had zero population, while only 2% of SA1s had zero population (at the 2011 census). At the other end of the scale, SA3s are roughly the size of local councils and include parklands, employment land, rural land, airports, freeways, etc which dilutes their average density.

In the case of SA2 and SA3 units, the same geographic areas have been used in the data for all years. On the other hand, Census Collector Districts (CD) often changed between each five-yearly census, but I am assuming the guidelines for their creation would not have changed significantly.

Now why is a transport blog so interested in density again? There is a suggested relationship between (potential) public transport efficiency and urban density – ie there will be more potential customers per route kilometre in a denser area. In reality longer distance public transport services are going to be mostly serving the larger urban blob that is a city – and these vehicles need to pass large parklands, industrial areas, water bodies, etc to connect urban origins and destinations. The relevant density measure to consider for such services might best be based on larger geographic areas – eg SA3. Buses are more likely to be serving only urbanised areas, and so are perhaps more dependent on residential density – best calculated on a smaller geographic scale, probably km grid (somewhere between SA1 and SA2).

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Comparing the residential densities of Australian cities (2011)

Fri 19 October, 2012

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

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

Measures of overall density

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

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

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

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

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

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

Here are those densities in chart form:

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

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

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

Density by distance from the CBD

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

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

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

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

Distribution of population at different densities

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

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

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

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

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

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

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

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

Spatial distribution of density

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

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

Sydney

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

Melbourne (and Geelong)

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

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

Brisbane

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

Perth

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

Adelaide

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

Canberra (and Queanbeyan)

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

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

What about density and public transport use?

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

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

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

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

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

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

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

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

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

Other posts about density:


Visualising the changing density of Australian cities

Mon 1 October, 2012

[This is an older post. For more recent analysis, see: How is density changing in Australian cities?]

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.


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!