Visualisation – Lab Week 3

For my exploration I wanted to see how much the names varied over the course of the years, in terms of the count they had, as my initial thought when looking at the data was “well if a name is popular then it is bound to experience some sort of repetition over the next couple of years at the very least.”

The reason why I believe that this graph is appropriate is because (if we do not have colour blindness as is the case with me, I apologise if it is not more clear for people with that disability as I know the greens and reds are the most common type of colour used in the graph and the most common type of colour blindness) the colours allow us to see the trend in which the different names, with some staying pretty even until one year where they suddenly reached a peak in use, for example in the case of Ella that was going pretty steadily until 2007 where it reached the highest value in all of the female names.

I changed a 2 things (with the first having some nuance), this first change being the colours, originally there were only 2 colours, but I decided to use the bigger scale as it would allow for a better idea of the individual use cases of the names as originally one would have thought that Sarah for example did not get any use in 2003 and then kept going, but instead it is the case that Charlotte was used almost as much as Sarah was once it became a part of the ratings and Sarah stopped being a part of them. The nuance is that I changed the name of Ella and Joshua to be the most prominent since they were the female and male names (respectively) that got the most use at some point, ergo they had the highest count in their gender which I found out by sorting the data into genders and then doing the max function on it. I did that because it seemed like the most interesting data to have for me since you get to see how much the trend of them was affected before and after their fall, with both names having lower use cases towards the end of the graph than some of the other names in their gender (like Sophie and Liam respectively). 

The other thing I changed was the fact that I added the dots to the graph where the datapoints for the count are, because in my opinion this makes it so that when combined with the colours you are able to better discern between which name is being used and by how much since some of the colours are kind of similar to one another, having a specific reference point helps with that.

While the main thing that I got from Lin’s lecture was how to create a proper chart (by her point of how we are awful at viewing pie charts and measuring area for example), I thought her point on how by cleaning data we sometimes need to create a graph to see something and then go back to cleaning data again and modifying it in ways that allow us to better see the thing we saw, was fascinating because it’s in a way how I see Digital Arts in a way I think that while we might start by trying to study a particular thing (like the trends of the most popular names over the 2000’s) we might end up wanting to research a different thing (like the socio-economic background that made it so that some names are more popular than others at a given time) and I think that this is critical in the Digital Arts for we are always trying to show the world in different and more interesting ways than previously thought of before, while also trying to provoke questions in the viewer (such as the one I have about how the socio-economic background of the time affected the use of the different names in the graph).