Tag Archives: spatial

Are there political differences between Sydney and Melbourne?

 

Even a casual observer of Australian politics will likely know that the Coalition tends to do better in some areas, and Labor in others. However, some important patterns are missed, either because geographic variation is often examined at the electorate level (sometimes obscuring important variation within divisions, which can be relatively large sptial areas) or because the mapping is not done as well as it could be.

For instance, this Australian Electoral Commission map leaves something to be desired. In particular, I don’t like how they divide up the Coalition parties by different colours (making the patterns between Coalition and Labor areas harder to identify), and that these are not shaded by the level of support, so a safe Labor seat looks the same as a marginal one. Combined with the focus on the division rather then smaller units, these decisions reduce the amount of information we can gain from the map.

Interested in how vote choice maps across different parts of Australia at a finer-grained level than the electoral division, I grabbed the two-candidate (Coalition v Labor) results from every polling place in the country from the 2013 federal election. These polling places were then spatially placed at the local neighbourhood level,* and mapped used ggmap() in R.

Here, for instance, are the results for the Sydney (left) and Melbourne (right) metropolitan areas. They are shaded so that an area shaded blue had a Coalition majority (the darker the blue the higher the Coalition vote) and red a Labor majority.

 

sydney.neighbourhood.vote.map.printmelbourne.neighbourhood.vote.map.print

Two-party vote by neighbourhood in the Sydney (left) and Melbourne (right) metropolitan areas. A blue area is one in which the Coalition won a majority. The darker blue a neighbourhood is shaded, the higher the Coalition vote. A red area is a Labor-majority area. Neighbourhoods are defined here as ABS Statistical Area 2 regions.

These maps make it clear there are important differences within and between the two cities. In particular, the Coalition appears to do better in Sydney and Labor in Melbourne.

In Sydney, the Coalition enjoys large majorities across much of the north, the inner-east, and the fringes of the south and south-west. The Labor vote is largely confined to concentrated regions in the inner west around Newtown and outer west around Liverpool, Cabramatta and (to a lesser extent) Mt Druitt. It’s not as bad as it looks for Labor, though, as their strongest areas of support tend to have higher population densities than many of the Coalition–supporting neighbourhoods.

Labor appears to be more successful in Melbourne, winning large majorities throughout much of the north, west and outer-east. There are Coalition majorities throughout large parts of the east, but these majorities are much slimmer than those enjoyed by Labor.

What drives these differences in vote choice? I suspect it’s likely a mix of economic and social differences. For instance, as the plots below show, neighbourhoods with older average residents and higher income households tend to have higher Coalition votes. This is particularly the case in suburban and urban areas, where neighbourhoods with median ages closer to 50 tend to have ~20 per cent higher Coalition votes than those closer to 30; and those with median incomes around $150,000 per year tend to have Coalition votes 30-40 per cent higher than those with median incomes around $50,000.

Of course, you cannot be certain that these are the causes of different electoral outcomes (we might be making an ecological fallacy), but a quick check of the Australia Election Study data makes it pretty clear these socioeconomic differences are fairly important to vote choice (maybe more on this is another post).

 

neighbourhood.vote_by_age.vote.print

Coalition two-party vote by neighbourhood location and median age of residents. Each open circle representative a neighbourhoods, defined here as state suburbs/localities. These are scaled by the resident population. The curve is a LOESS regression showing trends in the relationship between median age and party support.

 

neighbourhood.vote_by_income.print

Coalition two-party vote by neighbourhood location and median household income. Each open circle representative a neighbourhoods, defined here as state suburbs/localities. These are scaled by the resident population. The curve is a LOESS regression showing trends in the relationship between median income and party support.

 

*More specifically, the ABS Statistically Area 2 level, which are areas of approximately 10,000 people. There are several thousand of these neighbourhood areas across the country.