Looking at other forms of Data Visualization.

I'm here to review a fellow colleague's own project for a Data Visualisation/Infographic project. His can be found here and it differs quite a lot from mine.

I used a highly analytical data set. Monitoring stress levels among students, quantifying them, trying to correlate them with other school factors, and trying to see what can be done about it was rather laborious if I am to be honest. With that said my colleague did not use such an analytical data set but rather decided to explore something more lax and 'fun'.

Using his own music library on iTunes he decided to graph and trend his musical preferences throughout 6 major genres over a number of years. The layout and design of the project is simple and therefore effective. It makes for easy readability and invites the user to traverse the piece comparing between different genres of music that were played.

The project was created with using the Processing platform (as was mine) and so he coded aspects of interactivity within the piece. If a user mouses over one of the white dots they are able to see more information on the genre ie: how many times songs that are categorized within that genre are played. It is easy to see the musical transition that occurred throughout the years that are presented. For example if  one were to click on the Lounge dot for 2012 it would notify the user of the genre, year and play count. In the particular case it would be: Lounge 2012 120.0. Which means that in 2012 120.0 Lounge-esque songs were played.

This however begs the question: "What event occurred that triggered such drastic changes in music?" 

Which leads me to some critiques. The only major issue (which really isn' too bad as the graphics do a good job of the portrayal) is the lack of information that appears to a user who does not have access to Processing. Considering that the stipulation of the project was to develop in Processing, (in this respect) the project is a complete success as the interactive features would be fully realized by a user. However, if even the slightest amount of information was presented as static text then the infographic would be able to transcend the Processing platform and be successful on the same level but without any background knowledge.

With that said there are some positive comments to be made. The use of colour and text is, as aforementioned, simple. So even without the incorporation of information an unfamiliar user can get a sense of what is meant by the infographic. The contrasting colours allow for comparability and it much resembles my colleague's concept art for the piece available here. He followed through with his idea and made it work. I also find the use of the circular backdrop unique and interesting. This contrasted against the angular shapes also presents in interesting artistic license.

On the whole I feel the piece is aesthetically pleasing to look at, it is a fun/interesting and self-discovering topic. The way the information is presented makes it, with little effort, easy to understand and compare between the data.

I give it 8 apples out of 6 oranges.
(But seriously it was good).

Processing Data Visualization.

Here is the completed version of my data visualisation piece using the Processing platform. As stated earlier my intention was to monitor stress levels among students in my high school Gr. 9 - 12 contrasting both males and females. Some results were surprising.

Figure 1: Showing the pie chart with levels of stress weighted against all of the grades. As well we see the 8 scatter plots to the left depicting the correlation of stress (X) vs. academic performance (Y) from Gr. 9 - 12 contrasting males and females. There is some text which explains the findings. This text is provided below as well. 

Findings 1:

Within this infograpgic there is an attempt to depict the levels of stress among students raniging from Gr. 9 - Gr. 12 at Rober F. Hall C.S.S. In 2010 'cluster sample' sample was taken by multiplying the number of students (male and female) in each grade by .10. This gave me 10% of the population of the school. This resulted in resulting in: Females: Gr.9 (22), Gr. 10 (24), Gr. (11) and Gr. 12 (26). Males: Gr. 9 (23), Gr. 10 (20), Gr. 11 (25), and Gr. 12 (28). The data, once put into a varrying amount of charts and graph reviled some interesting information. It was evident that the stress level of a student tests their ability to perform academically. Starting from Gr. 9 a student is less likely to be stressed if they are performing well at school. However, this changes as the student matures, perhaps most noteable amongst females as it is hypothesized that girls mature faster than boys. As the students progress through high school there is a notable shift from week negative correlations to strong positive correlations. This hints at the fact that as students mature the stresses of the 'real world' being to set and may actually have an effect on ones performance.

Figure 2: A similar view of Figure 1 with a few changes. The pie chart doubles as a bar graph depending on the section clicked. This redraws the pie chart and allows for a second perspective looking at how the different grades measure up against one another. Also the text has changes to provide even more findings. Again this is provided below. 

Findings 2:

The pie chart depicts the distribution of stress between grades throughout high school. This shows an exponential increase of stress as an individual transitions through grades 9 – 12.The capacity to handle and cope with stress tests a student’s capability to mature and perform well academically. However, pie charts are not th best axample of representation and so, having it transition between a pie chart and a bar graph enables the view to gain a much better understanding of the data. In the chart it depicts an exponential increase in the stress factors between grades 9 - 12 with a factor of:  18%, 25%, 28% and 29% respectively. Similarly, this can be compared alongside itself in the bar graph mode by clicking an individual slice. This gives even a more consise version of the same data as the viewer can see how each grade measures up with the other. To the right of the infographic are 8 scatter plots which are comprised of both sexes (male and female) for each grade. This shows the distribution of each student sampled in contrast with their fellow stud. This shows how the students' reaction to stress transitions over the their higsschool career. Moving from a strong negative to a moderate/high positive linear correlation. What this means then, is initially students have a high stress rate for performing poorly in highschool. However, as depicted in the scatter plots, the average grade level in creases over the period of four years. This is indicative of students' determination to 'do well' in school resulting form the criteria and prerequisites placed upon them via post-secondary institutions. Ergo recieving high grades produces a high stress rate which explains the shift to a strong linear correlation among upper-level students high school....or at least my highschool anyways.

Hope you enjoy.