Day 3 brought us a number of tables regarding everything and anything about car crashes in Victoria with each row in the table representing a single crash and corresponding info about the cars involved, the environmental conditions, and the affected persons or parties. The crux of the challenge boiled down to using Tableau Prep for data exploration, data cleansing, and data prep – and having not used the program since we learned about it two months ago, I was pleasantly surprised at how it streamlined the data exploration process. At the end of the challenge, I produced a dashboard that focused on animal-related car incidents and relevant information to inform animals (If they have a copy of Tableau and an internet connection…) about the who, what, and where of accidents in Victoria. Luckily this format seemed to work for people too, so I’m happy with how the story translated from an interest planted while exploring the data in Tableau prep into a fleshed out dashboard. It’s this rapid data exploration that I feel is the most significant differentiator from other data prep tools so read more to find out how and why!
Tableau prep
Tableau prep helped a lot with profiling the data and understanding the story we wanted to present while still in data exploration and preparation – it was a result of being able to see the proportional number of fields in Tableau prep that I was able to see that the information regarding accidents involving animals was quite robust and I became fascinated by how an animal would have to live their life in constant fear of the road. The highlighting of bars was like seeing a dynamic bar chart for each dimension within a column and lead to all of us approaching the data from different angles. I used prep to join the tables together, create a column with a calculated field, and then output the file as a hyper to visualise in Tableau.
Tableau
Within Tableau I focused on the who, what, and where of accidents involving animals in Victoria. A bar chart showing the most common car models associated with crashes covered the who, a sankey and heat map showed the animals most affected and the time of accidents, whereas the map showed the where! I also created a seismogram split across the centre of the viz to show a monthly timeline of accidents occurring between 2006 and 2018.
Although the end result was a bit cluttered I was happy that I was able to convey all the information and to hit the brief of my original plan. I feel like a bit more formatting could take this viz a long way, but at the end of day 3 I walked away with a newfound appreciation of Tableau Prep and a dashboard packed to the rafters with information.