This post is drifting a little away from transport, but I hope you will find this interesting…
How has the spatial distribution of socio-economic advantage and disadvantage changed over time in Melbourne? (oh, and Geelong too)
The animated maps below are fascinating, but of course there’s lots of important caveats regarding the data.
About the data
Since 1986, the Australian Bureau of Statistics (ABS) has calculated Socio-Economic Indexes For Areas (SEIFA) based on five-yearly census data. These include indexes of relative socio-economic disadvantage (IRSD), and – since 2001 – an index of relative socio-economic advantage and disadvantage (IRSAD). For 2006 and 2011, SEIFA was explicitly designed to measure “people’s access to material and social resources, and their ability to participate in society” (with similar intent for prior years).
This post looks at the spatial changes over time in these index values. I must be upfront: ABS explicitly cautions this type of analysis. This is mostly because the component census variables that make up SEIFA scores and their respective weightings vary between each census, but also because statistical area boundaries change, the number of areas has increased, and indexes were calculated on usual residents from 2006 onwards (as opposed to people present on census night for 2001 and earlier). ABS also notes that middle range scores are very similar, so time-series analysis should focus more on the top and bottom ends of the spectrum. More discussion on this issue is available from ABS and .id consulting.
However, I’m going ahead noting the above (as readers also should!), on the following basis:
- The intent of the indexes has not changed over time, although the quality has (perhaps one day ABS will recalculate SEIFA values for previous census using better measures where possible)
- I’ve used percentile ranks within Victoria to get around the issue of the changing meaning of particular index values (although this might cause some issues if there has been a relative difference in changes between Melbourne and regional Victoria)
- I’ve included a summary of the component variables that have changed between censuses (documentation is available from 1996 onwards)
- I’m mapping this at a metropolitan scale with a view to looking at regional variations, rather than very local changes. In the following maps you’ll see fairly strong regional patterns
- My analysis will focus only on substantial shifts (which have indeed occurred)
- Excessive caution may mean that we never do any interesting analysis!
Changes in Index of Relative Socio-economic Disadvantage (IRSD)
This index has been available from 1986 onwards.
More significant changes in the make up of this index in recent years include:
- 2011 added: families with jobless parents
- 2011 dropped: indigenous persons, renting housing from government authority
- 2006 added: household overcrowding (replacing multiple-family households), low rent payments, lack of an internet connection, low skill community and personal services workers, people who need assistance with core activities
- 2006 dropped Elementary Clerical, Sales and Service workers and tradepersons
- 2006 changed the evaluation of household income to consider ‘equivalised household income’ replacing a number of measures that try to capture income levels relating different household make-up scenarios. It also stopped using gender specific measures of people with certain occupations or unemployed
- 2001 saw no changes to the included variables from 1996
- Variables for persons who did or didn’t finish year 12 at school have changed slightly in both 2006 and 2011
check the SEIFA documentation for full details.
Click on this map to enlarge and see an animation of IRSD percentile values for the years 1986 to 2011.
You can see some quite dramatic changes over time. Two big trends of note are:
- Most inner city suburbs have gone from being some of the most disadvantaged to much less disadvantaged. It’s hard to imagine suburbs such as South Yarra and East Melbourne as being highly disadvantaged, but the data suggests that was the case in the 1980s. During this transformation, pockets of high disadvantage have remained, probably reflecting older government housing estates. There appears to have a been a fairly large change between 1986 and 1991. This could represent dramatic demographic change and/or reflect changes in the calculations of SEIFA index values.
- Areas with the highest disadvantage have generally shifted away from the city centre (including some middle suburbs such as Carnegie), perhaps reflecting the growth in high-end CBD jobs driving the attractiveness of near city living.
- New urban fringe growth areas often begin with low levels of disadvantage, but have become more disadvantaged over time. This is particularly evident in areas such as Hoppers Crossing, Werribee, Melton, Deer Park, Craigieburn, Keysborough, Karingal, Epping, Hampton Park, Cranbourne, Altona Meadows and Keilor Downs. Perhaps this is because when these areas were initially settled there were many double-income-no-kids households that now have more kids and less income? It could also be a reflection of a turnover in the resident population.
- The maps only show geographic units with a population density of 5 per hectare or more, so you can also see the urban growth of Melbourne (more on that in a upcoming post).
Changes in Index of Relative Socio-economic Advantage and Disadvantage (IRSAD)
This index was first calculated in 2001 and aims to also measure advantage, not just factors that suggest disadvantage. In 2011 it included all but one of the IRSD variables, plus a number that describe levels of advantage (eg high income, higher education, occupations such as managers and professionals, high rent or mortgage payments, spare bedrooms).
The component variables of IRSAD have changed in line with the changes to IRSD, plus some other variables:
- 2011 added people with occupation classed as managers, houses with spare bedrooms, households with 3 or more cars
- 2006 added people paying low/high rent, high mortgage payments, renting from government authority, households with no car, households with broadband internet connection (replacing persons using the internet at home)
Again, check the SEIFA documentation for full details.
An aside: SEIFA associates higher car ownership with advantage, but I suspect some inner-city types might consider not needing to own a car an advantage.
Here is an animation of the Index of Relative Socio-economic Advantage and Disadvantage for years 2001 to 2011. Again, click to enlarge and see the animation.
The changes between 2001 and 2011 are much less dramatic, probably because of the shorter time span. Some observations:
- The Melbourne CBD drops in 2011 – possibly because of a change of demographics (more students?) and/or a change in the component variables.
- Many parts of the middle eastern suburbs (particularly the Whitehorse area) appear to drop from the upper to the middle percentiles in 2011.
What’s also interesting to see is some socio-economic fault lines in Melbourne, such as:
- Altona North versus South Kingsville/Newport (north-south divide along Blenheim Road/Hansen Street/New Street)
- Skeleton Creek between Point Cook (including Sanctuary Lakes) and Altona Meadows
- A north-south line being the boundary between the Shire of Melton and the City of Brimbank in the north-western suburbs
- Along Hume Drive / Lady Nelson Way (an east/west line in northern Brimbank)
- Greenvale versus Meadow Heights (split by the proposed north-south Aitken Boulevard)
- A north-south divide through Heidelberg Heights, roughly parallel to the Hurstbridge rail line
- Along the Dingley Arterial between Dingley Village and Springvale
How different are IRSAD and IRSD values?
IRSAD contains a lot more variables and uses different weightings. See the ABS website for full details.
For those who are interested in the correlation between the two, here’s a scatter plot for both 2006 and 2011 data comparing the two index values (as percentile ranks) for all CDs and SA1s (respectively) in Victoria:
You can see the relationships between the two indexes is stronger in 2011 (R-squared = 0.96) versus 2006 (R-squared = 0.89). This might reflect the make up of the variables in each year and/or the smaller geographic units in 2011 (SA1s) which may reduce diversity within each geographic unit.
I’m sure others could spot other interesting patterns, and/or offer explanations for the changes over time (comments welcome).