Want to know more?
This is a data journalism project, which means we love it when people have questions! We've answered some of the most common questions below.
No it does not. But a low overall quality of life score could mean that your region is underserved relative to other regions, or that it's facing specific social or economic barriers.
If you live there, you may already be aware of some of those challenges.
We hope the Happiness Project gives some of the regions with lower scores a way to understand social and economic disadvantage, and thereby start discussions about resource need.
That's a really tricky question to answer. What we can say is that the economic, social, political and historical conditions surrounding the quality of life markers are really complex.
If your town has a low score, it doesn't mean it's a bad place to live — but it probably suggests your community could, in various ways, be better served.
We decided housing affordability is about more than just cost.
There are areas in Australia with extremely high cost of housing, but when you examine the data, you'll see that those regions have high home ownership rates, the houses aren't overcrowded, there are very few low-income earners and mortgage and rental stress rates are comparatively low.
This usually happens when there is high cost housing, high income and stable employment prospects.
If we only looked at the raw cost of housing (what the average Joe or Jane is paying in rent or mortgage), we'd fail to address housing affordability in regions where there are high levels of rental or mortgage stress, where residents live in cramped conditions and where the cost of housing is high, proportionate to residents' wages.
The project draws on data from the ABS (Census 2016, SEIFA Indexes); data from the Public Health Information Development Unit's Social Health Atlas series; Australian Health Policy Collaboration data in Australia's Health Tracker Atlas (2017); and public access data from each of Australia's state police forces.
We're really grateful to these organisations for making data publicly available, in formats that make projects like this possible.
If you're interested in diving deeper into some of these topics, we recommend the ABS's Census QuickStats tool, which gives you easy access to some of the raw data for your area, and the PHIDU's Social Health Atlases.
You're right! Happiness is complicated, and a bit intangible: you recognise it when you feel it, but it's slippery when it comes to defining it.
Really, we're measuring quality of life, which is easier to quantify, and which is the foundation for all the more personal factors that make up happiness.
The scores we've come up with are not to be confused with definitive facts, but they're good indicators of where a LGA might be sitting in each of the categories, and the basis for initiating a national conversation about the geography of advantage and disadvantage.
That said, there are plenty of things we can't (easily) get data on: how beautiful a region might be, whether there are people there who love you, what kind of entertainment and recreational activities are on offer, whether your job is intrinsically rewarding and even whether or not you have a pet.
We're obviously limited to the quality of life ingredients that can be measured, and those with reliable regional-level data available.
One area we would have really liked to include is environmental data but we weren't able to find that for Australia's LGAs.
In future, there is scope to refine and develop the project further.
If you've got data or suggestions you think we should include, we'd love to hear from you.
There's no conspiracy here. The staff and students who worked on this come from all over Australia.
Actually, a lot of us come from regional and remote communities, which is part of why we think this project is important.
We care about regional and remote Australia and want the challenges of these communities to be broadly understood.
Yes, but we've tried to mitigate that possibility. Wherever necessary, we've considered population when constructing the Happiness Project.
For example, crime is measured in rates because 100 assaults in a town of 1000 people is quite different to 100 assaults in a city the size of Brisbane.
That said, small towns might experience greater fluctuation in their results if we continued this research over a period of time, and some towns are so small that we couldn't get accurate data in certain categories without grouping several LGAs together.
Yes, some LGAs are so small that data is only partially released, to protect the confidentiality of residents. There are also LGAs that are so remote, small or hard to access that data on some factors was not available. For most regions, we've been able to construct a score for each category in the project, but in rare instances, we've had to list a score as NA (not available).
One can never eradicate the possibility of errors completely, however the study implemented a range of techniques to manage the risk of error. Data analysis was supervised by staff with statistical expertise, and any data coded for analysis purposes was cross-coded (which basically means, all data entered by the study team was done so by at least two people).
If you think there's an error, please let us know.
About 17,000 pieces of data make up the project, plus a whole lot of additional, contextual data.
Believe it or not, all of the raw data is comfortably housed in a single Excel spreadsheet.
We really believe in open-source data, and we're also passionate about making the methodology and frameworks for data-driven journalism projects available to newsrooms and reporters who might be embarking on projects — either through publication, or sharing within our networks.
If you're a reporter who'd like to see behind the scenes of this project, get in touch.
Likewise, if you've got a public-interest data journalism project you'd like to pitch us or your newsroom would like to do one but doesn't know where to start, we might be able to help.
The sourced data was provided to the Bachelor of Interactive Media and Design students Alice Royster, Olivia Meredith and Yuchen Ou as part of their project work in the subject MMDE11-150 Interactive Web Design. Students conceptualised and designed the interactive visualisation under the guidance of their lecturer Dr James Birt, tutor Nikolche Vasilevski and industry client Caroline Graham.
The search algorithm uses existing government areas data and Google maps geocoding to establish the searched term within the local government area.