How it works  

1.  Tab - To compare towns, click on the left-hand tab that is just above at the left.  To compare trends in different indicators for one town, click on the right-hand tab.  

2.  Chart - Tick the boxes below.  Then, click on the ‘chart’ button at the bottom of the section.  You will see the indicator data for the towns plotted together.  

3.  Reveal - Roll your cursor over the charted lines to get exact figures.  

4.  Contextualise - Visit the 'Annual Report' pages (see menu at top right) for additional information from interviews & analysis on each town and local government area.  

5.  Download - Use the drop-down menu at the right to download a chart, such as for a PPT presentation.  

Western Downs
Crime Rate

Compare Towns

How has Chinchilla been going relative to Dalby?  How about a smaller town, like Wandoan, with a proposed coal mine and coal seam gas development nearby?  Or a larger town like Moranbah, which is dominated by coal mining?  Here you can compare towns.  Select 2, 3, or more towns to compare, such as Chinchilla, Dalby, and Wandoan.  Select one indicator, e.g., median rent on a 3-bedroom house.  

We are eager to add towns to the data set.  That makes the comparison function more powerful.  One can compare towns of similar size in different regions or different settings. Input from users to date highlights a need for benchmark towns.  For example, what town is similar to Chinchilla in a number of ways, but it has not had coal seam gas development occurring nearby?  

That enables contrasting purely agricultural towns with mixed agriculture and resources towns or with seaport communities or regional hubs.  At present, you can mainly compare different towns in neighbouring regions.  That provides insight into 'ripple effects' across an area.  One can also assess outcomes for different towns - some that residents might perceive as 'winners' from a resources boom versus those that might feel 'passed over'.   

The heading 'Benchmarks' enables comparing the towns where coal seam gas development has occurred with towns where it has not occurred.  That helps to address the question, "What would have happened to rents, income, businesses, or the crime rate if this development had not occurred?"  For a few benchmark towns, we were able to obtain a couple of data sets quickly.  For example, we have rents and the number of businesses in Kingaroy, Qld, but not the crime rates.  These partial snapshots are helping us to determine which towns are suitable 'benchmarks'.  

We have state-level benchmark data where it is available.  That would include areas like the Queensland median for rent on a 3-bedroom house.  For certain data, Brisbane appears to be a better benchmark.  That is the case for house sale prices, where an audience city-based staff in state government or industry would likely be more familiar with Brisbane prices than with statewide figures.  For certain income data, there is not a readily available state average, even though the Australian Tax Office reports such data at the community level.  

Compare Trends in different indicators in one town

To assess the relative magnitude of a change from one year to the next, like a growth in the number of wage earners, then plot just one town.  You can also plot multiple indicators for one town.  This function enables comparing an upward trend in rents with a drop in unemployment.  That is, more people enter a town.  There is more competition for rental property.  Rents rise.  Or, that is the theory …  

We can add indicators as needed.  It is important to keep the number of indicators used small.  Otherwise, comparison becomes too difficult and only 'nerds' will fathom the connections between factors.  So, if you want to add an indicator, consider which ones are less important to show.  We can alternatively 'clone' this part of the website to enable comparing a different set of indicators - such as 'new' indicators on environment, health, and mental health along with 'old' indicators on income and population, for example.  

What is this indicator?  

'Good Order Offences' relate to displays of aggression and offensive behaviour - such as impending 'dust ups' at a pub or on the street outside.  They include disorderly behaviour, offensive behaviour, threatening language, bad language, public nuiscance, public urination, obstructing a police officer, and contravening a requirement of a police officer.  For more information, see a site such as or  

'Non-resident workers in local government area' is an indicator of CSG industry activity in the region.  The term 'non-resident worker' (NRW) refers to company staff or contractors who normally live outside the area but commute to it - e.g., fly-in/fly-out (FIFO), drive-in/drive-out (DIDO), and those who are transported by bus, hot air balloon, and submarine.  The term 'local government area' reflects the fact that the tally of NRWs is across a set of towns and districts in a given local government jurisdiction, though not at town-level.  The preferred figure - a reliable number for NRWs near a particular town - is not available at this time.  The tally at local government level has been assembled for the past several years by the Queensland Government Statistician's Office (QGSO).  We are seeking a more localised indicator of the level of CSG industry activity near a particular town - such as changes in the number of phone calls or noticeable changes in electricity usage.  Samples of data suggest that such figures are available at the level of town and postcode.  

Where the data is from  

This data is from UQ's town data booklets.  You can download these data booklets from this website as PDFs.  The data booklets provide more detail on the indicators for each town.  That detail includes findings from interviews of approximately 10 key stakeholders in each town (our Delphi approach).  The data in the town data booklets was drawn from public sources. That includes the Australian Tax Office, the Australian Bureau of Statistics, and the Queensland Rental Tenancies Authority.  

It has taken a year to assemble this data and to "ground truth" it with interviews in the towns.  Making things complicated are changes over time in how the government tallies information and changes in boundaries within which data is measured.  There are also changes in the data itself, as individuals and businesses can submit income tax information late, and they can amend it.  Despite these challenges, the data provided here enables you to discern "the shape of the curve" - generally, how have things changed over time: gone up; gone down; cycled?    

Why do the charts for different towns have a similar shape? 

There are several reasons for charts to have a similar shape.  One has to look at (1) what might be affecting all towns and (2) what might be affecting the data set.  First, the towns that we studied are all affected by the growth in the CSG industry, particularly during its period of peak construction: 2011-2013.  Second, the Australian Taxation Office (ATO) or the Australian Bureau of Statistics (ABS) or whatever source might change how it counts something.  

For example, in 2010, the ATO started calculating average income in a different way.  It used to present an average that included just positive incomes.  Then, it started including zero and negative taxable incomes in the average.  This shift would pull down the average for the more recent data.  So, in the old days, a retiree on a tax-free income would not be included in the average.  Now, a retiree who declares zero taxable income is included in the average.  You will note in our data that when the method of calculating the average shifted in 2010, one can see that - from 2009 to 2010 on our charts - the curves all dipped slightly from their historical trend.  That is, the incomes did not climb as much as they had been in the previous years.

Based on these sorts of concerns, when we see a significant change across all towns in our data set, we check the data sources and we 'triangulate'.  The process of looking at other data sources might include comparing average weekly income with trends in wage and salary earnings (we show the total of wage and salary earnings for a whole town rather than an average per taxpayer).  To triangulate, we look at other sources of data that would suggest whether the trend that we are seeing is real or if it is the result of a change in how the data is tallied.  For average income, such triangulation might include looking at unemployment statistics, figuring that if wages rise, there is a greater demand for labour.  To augment these approaches, we are aiming to add 'control' towns, towns of similar size in Queensland, and potentially other states, that did not have CSG development occurring nearby.  That can tell us about national impacts - like the Global Financial Crisis or local drought or floods - or about changes in data accounting that we might not have been aware of, e.g., changes implemented by the ABS or ATO that may have escaped our attention.  

Looking for cause-effect relationships  

The point here is that the trends in the indicator data are meant to enable us to infer cause-and-effect relationships.  That is, does an upswing in business incomes look like a result of CSG development, or is it just due to changes in ATO data gathering?  When looking at potential causes, we need to consider a wide range - not just CSG development, but flooding, drought, good harvesting years, changes in exchange rates for export goods, arrival of new industries, etc.