Over the AFL Grand Final weekend we were pretty busy doing a Twitter data visualisation for an article by the Herald Sun but there's so much more in this data set that we decided to write a follow-up post as well.

Have a look at the below video or the interactive Tableau dashboard behind it, showing volume of Twitter mentions for the different AFL teams over a period of time leading up to the Grand Final. Further down are some screenshots of the Ross Lyon (St Kilda coach) incident, in which he was accused of stabbing his predecessor Mark Harvey in the back for the position of coach at the Freemantle club - the whole story was actively discussed on Twitter as you can see.

The video (and dashboard) present what we call 'Twitter Time', all Tweets broken up into chunks of 400 and then analysed. As the chunks of Tweets (blue lines) are visualised, time will slow down at moments when the volume of Tweets was high, particularly at match dates (green circles) or at the Grand Final (dark green circle). The jersey size is proportional to the number of Tweets mentioning a particular team, and you'll notice that they go gray once they are eliminated from the series. The analysis was run on about 35,000 Tweets provided by Alterian's SM2 social media analytics platform.

Below is the breakdown of the Twitter chatter and mentions of key players, and most importantly coaches, from the time Ross Lyon announced that he was resigning and rumours started about his move to Freemantle (15th and 16th September). All Tweets were analysed for instances of player or coach names. Rumours pop up and die as Twitter volume rises and falls.

Above: Hawthorn vs Sydney & West Coast vs Carlton normal match chatter on Twitter.

Above: rumours of Ross Lyon's move to Freemantle and sacking of Mark Harvey explode on Twitter after Ross Lyon officially resigns. His move to Freemantle is confirmed on the 16th of September.

Above: Neil Craig becomes the topic of conversation as well as it is rumoured he might go to St. Kilda.

Above: AFL fans move on and resume normal match chatter ...

When we saw the new Tableau 6.1 dark map background feature and given today is Australian Census day, we though this is ideal to visualise the best nightlife hunting grounds for single men and women in Australia looking for a romance using some of the older Census data from 2006! But before we go into that, let's have a quick look at the sizeable Tableau 6.1 upgrade the company released last week. 

You can check out a full demo of the new Tableau Desktop 6.1 features online but let's have at look at the last Census 2006 data on single men and women in Australia to demonstrate Tableau's new postcode functionality and sexy dark map background.

The basic theory is that there's a severe undersupply of single men for single women, particularly in their 30's. At first glance, this would seem a bit odd given that there's a roughly 50/50 split of men and women at birth. So let's see if this is true ...

At the national level, there are actually more single men than women for ages 20 to 34. And for the 35 to 39 age group there's only around 10,000 more single women than single men. Overall, the man drought doesn't really exist for 30-somethings.

However, the man drought may just be a regional phenomenon.

The below maps highlight the ratio of women to men in between the ages of 30 and 39. Depending on the map and your gender preference, a redder shade indicates a less favourable ratio and a greener shade a more favourable ratio. The size of the location represents the total number of people aged 30 to 39.

Northern NSW appears to have the largest scarcity of single men aged 30-39 (ratio of 1.07), while there's an abundance of men in Regional SA (ratio of 0.85). Although these ratio's aren't particularly high, there's some evidence of the man drought in particular regions of Australia.

Now, here's the best bit. If we look at ratios of single women to single men aged 30 to 39 in particular postcodes, then there are some places in Australia with an obvious scarcity of men. The top 3 places (of notable size) are:

1. 2559, Blairmount, NSW - 2.4 women for every man

2. 4509, Mango Hill/North Lakes, QLD - 1.9
3. 6770, Halls Creek, WA - 1.8 

It seems that there is a man drought, but it just depends on where you live. Similarly, there are places with a severe absence of single women in their 30s too. Here's the top 3:

1. 3008, Docklands, VIC - 0.4 women for every man

Note: There's a fair bit of data behind the dashboards so please be patient when zooming in to your location.

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I have been called (and pardon my French) a LinkedIn 'whore' before by a very good friend and I always denied it but now that I have visual proof I can't really any longer!

Check out my network map below and the new LinkedIn Maps application that visualises and lets you explore all your connections in one big interactive colour coded map (and connect to me and help me grow my map :)

Apart from being pretty cool, this is also an amazing tool to find out how well connected some of your friends and colleagues are and in what circles (i.e. colours) they move (job titles can sometimes be deceiving but the people you know and connect with not so much).

Having announced that I was heading up to Sydney to join Datalicious, I was often asked what I’d miss about Melbourne. Of course, not being able to go to the MCG to watch the footy was certainly high up on the list, as was Saturday mornings spent at the Queen Victoria Market. However, with winter fast approaching, I imagined that Melbourne’s weather was something that I could readily leave behind. Sunny days in Sydney, here we come. But how close was my perception to reality? Was the popularly conceived notion true, that the weather in Sydney is much better than in Melbourne. Time to have a look at the data.

The first step in any decent piece of analysis is to work out what needs to be measured in order to make a valid and reliable judgement of performance, which metrics are important, which metrics are not. As far as the weather goes, the primary and most obvious one is temperature.  Feeling too hot and feeling too cold, seemed to be the most common complaint from people. It's also important to consider that according to the Bureau of Meteorology, how people ‘feel’ the temperature is also dependent on how windy it is, the humidity and if the sun is out. Finally, whether it is raining, how much, and for how long, also seemed to me to be a major dimension of people’s judgement of the weather.

OK, now that we’ve established the important measures, we next have to see what data is available and how the metrics might be defined. Luckily, the Bureau of Meteorology are equally as nerdy as myself and have measured all these things over the past 150 years and they have made the past 12 months of daily data available to download from their website. This is how they are defined:

+ Min Temperature - minimum temperature in the 24 hours to 9am

+ Max Temperature - maximum temperature in the 24 hours from 9am

+ Rain - precipitation in the 24 hours to 9am

+ Sunshine - bright sunshine in the 24 hours to midnight

+ Humidity - relative humidity at 9 am/3 pm

+ Wind speed averaged over 10 minutes prior to 9am/3pm

I’ve then put this data into the Tableau visualisation engine - below are two screen shots of the reports I created but please also check out the interactive Sydney vs. Melbourne Weather dashboard on Tableau Public and change the definition of good weather!

While I can now easily describe and ‘see’ the weather for Sydney and Melbourne, I can’t yet test my hypothesis because it includes a normative term: ‘better’. This is the difficult part. What is the benchmark for success? Is there a universally acceptable definition of good or bad weather? I doubt it. In the absence of such a universal definition, I’m just going to go with my own:

+ Few days with temperatures below 20 and few with temperatures above 30

+ Few days with o’night temperatures below 10

+ Few instances of consecutive days of rain (a rainy day is 5mm more of rain)

+ At least 1 in 2 days of sunshine (a sunny day is 8+ hours of bright sunshine)

+ Low wind speeds regardless of temperature

+ Lower than 80% humidity (when it’s not already raining)

If you look at the charts, Sydney wins on most of these measures with daytime temperatures in a better range, no cold nights, lower wind speeds (especially at temperature extremes), more sunny days, and more consecutive sunny days. It does rain more frequently in Sydney and the humidity is higher, but it’s tolerable. Sydney does have better weather then Melbourne. Case closed.

Now, I wonder which city has worse traffic congestion … let me know if you’ve got any data!

Great video on information visualisation with some good examples: We are swimming in data, too much to comprehend at times. Matthias Shapiro walks through the visualisation techniques that can be used to figure out what a data set is trying to tell us.