Visual Analyst 3D – Time and Distance

Time and distance are telling great stories.

Geo-located data sometimes is best presented when geo-location is forgotten, at least partially, for a brief moment of time!

For example, the following screen-shot depicts the average age of people involved in accidents depending on the distance from the city, over time. For clarity, it has been smoothed to emphasise the larger areas of similarity, rather than individual peaks and troughs. The chart is telling us that accidents closest to the city centre (left) involve the young generation of people, and further away from the city (right) the age of those involved increases. It is also noticeable that over the period of six years, the average age of those participating in the inner suburbs accidents (up to 20km from the centre) is slowly increasing (the colour shift from purple, through orange and into yellow, from back to front on the left-hand edge of the chart).

Quite a pretty picture but not revealing many more significant insights, except perhaps for the puzzling existence of the accident free zone spanning the area 340-440km away from the city, which can be easily explained with the following map of Victoria. It seems that the accident-free zone almost entirely falls outside the borders of the state and those small areas which are part of the Victorian data set include: in the west – Little Desert National Park, Wyperfield National Park, Big Deserd Wilderness Park, Murray Sunset National Park, and in the east – Snowy River National Park and Croajingolong National Park.

The next chart presents an alternative view of the same information and illustrates that the severity of accidents is gradually increasing with distance from the city, which could perhaps be attributed to the lack of speed restrictions outside the metropolitan area and the increasingly more difficult terrain and driving conditions away from the city center. Interestingly, the worst – while the most sporadic – accidents occur just on the boundary of the accident free zone. It is worth noting that the big accident spikes in the middle of that zone are perfectly aligned in time with the Cann River bushfires in the years 2009, 2010 and 2011.

At the same time, it is quite comforting to see that overall the severity of accidents seems to be reducing with time.

The next chart presents exactly the same accident severity data, however this time, the data is plotted timeĀ against longitude. In this case, the central belt of insignificant car dents is clearly aligned with metropolitan traffic, which slowly transforms into raging colours of more serious accidents away from the city, eventually adopting dramatic spikes of car crash severity, even though sparsely scattered across the time-space.

This final chart demonstrates that the simplest of the plots often convey the clearest message. A column chart may seem most rudimentary, however, in the following data visualisation the columns, representing the number of accidents involving cyclists, clearly concentrate in the city area. When compared with other accident types (not shown in this blog), cycling accidents are also the only accident category where the numbers of related injuries is steadily growing in time, so cyclists beware!!!

For more information see the Immersive and Collaborative Analytics.

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