You don’t have to look very hard to see that gambling and gaming machines in particular (or pokies as they’re more commonly known) are a hot button topic in Australia right now.
After seeing information about the billions of dollars (sometimes billions in a single local government area) that are turned over by gaming machines in New South Wales (NSW). I wondered if there would be a way to look at the spatial patterns of gaming machines.
Unfortunately, this information is not provided online for NSW. However, in Victoria, it is here.
So after downloading this dataset and running it through the Google Geocoding API, I was able to easily plot all the locations on a map. What I decided would be interesting to look at is how long it would take people to get to their nearest pokie.
Using the excellent tools available at https://www.route360.net and with some very patient and helpful support from Managing Director Henning Hollburg, I managed to make a map that displayed the areas that fell within a 5, 10, 15 and 20 minute walk from a pokie.
That map is available here: http://wherepokies.com
Limitations/caveats: – This will only show venues listed on the Victorian Government website above at the time of downloading (Crown Casino is not included)
-Accuracy is totally reliant on Google Geocoding API
I feel like its hard for me to work out what to make of this map, on one hand as a person from NSW, where there are something like 4 times as many pokies I feel like it is maybe not such a terrible picture, then if I try to consider it from the perspective of a non-Australian observer I wonder if it seems odd that so many people live within a casual stroll of a gambling machine.
What I would REALLY love to do is make the same map for New South Wales, I’m trying to chase up the data from Liquor & Gaming NSW, so watch this space.
Data Source: Roads and Maritime Service NSW LINK
The Roads and Maritime Service provides data on numbers of driver licences issued and sorts them by the postcode of the licensee. This makes it relatively easy to observe spatial patterns.
The interactive map above displays the proportion of all licences issued in a postcode a particular licence category represents.
For more details on what sort of vehicles the licence categories represent click here.
A couple of the more interesting patterns I noticed are below.
Sydney Area, Rider Licences as a proportion of licences issued
Rider licences (R) seem to me to be grouped together in 2 groups in the Sydney area. One being the inner suburbs, around the inner west and eastern suburbs, this might be explained by the shorter distances required for trips into the CBD. The second is the Northern Beaches area.
New South Wales, Heavy Combination Licences as a proportion of licences issued
Heavy combination licences (semi-trailers and the like) seem to represent a greater proportion of licences in a band running north south through the mid to far west rejoin of New South Wales.
Note, there is no data for postcodes: 2755, 2734, 2624, 2411, 2387,2379, 2356 and 2331.
‘Leafy suburbs’ is a term that gets used a fair bit as as short hand for the economically advantaged parts of Australian cities.
I wondered if there would be a way to calculate which suburbs in Sydney, Australia are the “leafiest”.
I found the 2011 NSW woody vegetation extent data available here, which displays FPC; “FPC is the fraction of the ground that is obscured by green leaf, and is a measure of density.” Based on this data I was able to calculate the average FPC for each suburb in the Sydney area.
This gives a quantitative representation of the leafiest suburbs of Sydney. The map below displays the suburbs of Sydney broken up into quintiles, you can also click on your suburb to see its FPC value.
As you might expect the North Shore, Northern Suburbs and areas near the Blue Mountains are among the leafiest.
In future I’d like to do a comparison of this data against other social and economic indicators. House prices are something I’d be particularly interested in comparing. But for the time being I thought it’d be cool to share this.
Title image uses data from here and Stamen Toner/OSM layer in QGIS.