FACS 3935: Assignment 2: Artists' Statement

During the final weeks of the 2013 winter semester as a part of a media and databases course students were issued an individual final project as a culminating task/experience. The project took place in the form of an info-graphic composed out of a dataset of the students’ choosing. The info-graphic itself was open-ended and its composition and presentation left up to the interpretation of the particular student. For me personally, my project focuses more on the process work of translation of information rather than the aesthetic appeal or readability of the work.

I decided to use my iTunes library as a dataset for the project. Why, simply because I love and compulsively collect music. It is a strange thing I’m sure, to think of a person who collects music in the form of physical CD’s especially when such things are so easily available as downloadable files on the Internet. However, with my collection I feel that even if I were to download/torrent something it would not rightfully belong to the collection unless I physically bought it and added to the ever-overflowing shelves of my room. In essence I wanted to work with a dataset that I was familiar with and fond of hopefully being able convey some sort aspect of myself through the work and through the music I listen to.

The construction and composition of my work may be seen as a little unorthodox as I have created two separate aspects of the piece through two separate mediums. I feel that there are two sides to my music collection: the digital and the physical. The digital being whatever I obtain through the Internet and the physical being what I purchased.

For the digital aspect, representing the collection my music that is done online, I created a Photoshop file and exported it as a .png. The file contains images of the album artwork from the last 289 albums I’ve downloaded amalgamated into what I call, a super-collage. The images of the albums are organized by similar colour to create a sort of gradient-like mosaic effect. I duplicated this image and created one clear version and one blurry version to represent the anonymity of the torrent culture as well as the grey areas when discussing the contemporary issue of ownership of online content. Similarly I made the files available for download here, which also represents internet culture.

For the physical aspect, representing the collection of my music that is made by the purchase of merchandise, I decided to display on a 3-pane presentation board, the top 50 most-listened-to albums that I actually physically own. These are organized meticulously in alphabetical order (concerning artist’s name) and then further by chronological order (concerning the albums of that artist). This represents my obsessive compulsiveness to constantly organize my music libraries ensuring that every possible detail that I am aware of is added to the library. Within the CD sleeves that the CD’s are housed in, on the reverse side is a QR code that, once scanned, links to Google Doc spreadsheet containing information about the particular album. You can find a copy of all 50 spreadsheets here.

A photo taken of the physical aspect of the piece contain 50 CD's in protective sleeves. There are individual QR- codes on the reverse side of each CD jacket. 

I feel that there is no better way to illustrate a collection to some one than to showcase it. I am proud of my collection and so using in projects is exciting to me. This illustrates the motivation behind the work.

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. 

And Now for Something Completely Different....

Previously my intention was to use some rather dark data sets (dealing with U.S. mortality rates) however as of late, I feel that I have had a disconnect with that data as well as the process of visualizing it.

Despite my best intentions I feel that there was a growing indifference between the dataset and the work I was hoping to compose. There was a large struggle in coming up with a creative, engaging and artistic representation of my original dataset as I found it difficult to visualize my final composition. I also must say that I feel that dealing with death in 1st World Countries has been dealt with and is a sort of cliche. My original goal of the end result of my piece being, of course, a discussion of any possible improvements or solutions to the high death rate in 1st World Countries has also been said and done.

So it is with this in mind that I am completely disregarding my previous proposals and introducing a new concept with a dataset that I am interested in and excited to begin working with.

Some Back Story:

When I was in high school I took a Social Studies course in which the culminating project was to create a questionnaire for which to survey the students of the school. The survey had to tackle a problem that I saw within the school's student community...considering that my school is located in the middle of two farms and a small graveyard it is no stretch of the imagination that I saw that issue dealing with the school's morale. Yes I know, because it's such an important thing for a high school to have spirit well listen, I liked the topic so I stuck with it. Here's a picture for proof. The survey was to be compiled and looked at for any "revealing information". I was instructed to write an essay on how I, as a student, could improve or use whatever information came out of the survey in an productive way. You can download a copy of my survey here. Feel free to take the survey and add to the data set. It may be possible (with your help) to do a compare and contrast between the stress levels among High School vs University/College students.

Now I know I just finished saying that discussions on improvement are very typical, however, considering that the survey was comprised of a high school student body and not a population of 350, 000, 000+ I feel that it is a discussion that is much more plausible as well as fruit bearing.

I had a sort of epiphany late last night where I realized that I still have all of the information from the survey. So instead of writing a laborious and wordy essay, why not just visualize the surveys for a much more consistent and tangible piece of information? I feel that in creating a visualization for the surveys taken, I can bring more signification to understanding the self-esteem of high school students. It might even be the case where my school can exemplify and be represent other schools across the different boards in Ontario.

Above is a simple Excel graph of all of the average stress levels between males and females ranging from grade 9 - 12. This is quantified by a stress number scale (valued 1 - 10) where 1 is hardly stressful and 10 is an anxiety attack!! =O

With this information in the graph as well as the information given to me in the surveys I can extrapolate the stress levels among students and plot them on a comprehensive dotted line graph. I was intending on having all of the averages of males and females in tables off to the side of the visualization along with any other extraneous information I can gather to further giving depth to the visualization and encourage its viewers to compare between grades and sexes. 

Above a concept of the different elements I will be using within my visualization. Everything from colours, to fonts and shapes are displayed. 

Above is a rough, very rough visualization of the concepts I will be attempting to bring through to the composition.

A discussion on The Visual Display of Quantitative Information

When quantifying data (with the intent of displaying it) there are many things that one should consider. First and foremost It is important to know the data set you are working with as well as how that data can be properly and efficiently visualized for the sake of gaining information. The Visual Display of Quantative Information by Edward R. Tufte details some of these topics in his discussion of the matter.

Initially he starts off by stating some general rules about Data Visualization (DV). It is simply not enough to show the data, showing the data meets the bare minimum of what is required for the effective use of displaying the information. Yes you need to show the data but it is more the question of how you go about doing so that is significant. First of all, only the relevant data should be consider and any outlying or extraneous variables should be discarded lest the visualization becomes skewed. Secondly you should display the data in many layers and from many angles. This ensures that you're viewer may come to understand the data fully and completely. The dataset should be well described as well to eliminate any questions or confusion.

Tufte then goes on to talk about the graphical essence of how the data might be displayed. When considering something like DV there are numerous methods to achieve a relatable aesthetic. Visualization such as graphs and charts are sometimes too traditional. Tufte suggests something called data maps and he believes that quantifying datasets (especially large ones that may otherwise seem confusing) can be elegantly represented. He exemplifies this through the map of the United States' counties which details the mortality rate due to cancer/other diseases.

Another method for representing rather complex datasets is (what he calls) through a time series. This enables the viewer to recognize patters or trends within the dataset. Similarly time series' are also very effective in establishing change. This is perhaps most notable in the graph depicting the weather in New York in 1980. another example would be the train schedule in Paris in 1880 and changes that occurred to the map nearly 100 years later with the addition of a new train line. When put into context with a visual image this becomes even more effective. This is expounded upon through the many examples of how animals and humans move kinetically.

Lastly, some of the most effective graphs are what through the narrative graphics of space and time. These combine some of the most important aspects of DV. Utilizing space, time as well as visual representing, and linking them all together in a consists data form allows for a simple intake of the information presented. This creates little confusion, is somewhat simplistic but at the same time it does not hinder the graphs relevance. An example of this in his chapter is in the Man and Insects example.

It is clear that there are many ways of choosing how to represent a dataset. In part, your dataset may determine what sort of DV you use, however as much as the display of information is necessary it is important that you not loose sight of the message that is trying to be conveyed.

A Quick Graph

Recently I made| a simple bar-graph made in Processing  with a few numbers taken from a dataset created by the Toronto Transit Commission's (TTC) calculated riderships and revenues.

You may download the files here from Dropbox.

Data Set

For my first major project in FACS 3935 I have decided to work with the U.S. mortality rates in recent years ranging from 2008 - 2012. I knew initially that I wanted to work with such a dataset because it felt appropriate to work with. I was initially interested in the number of people that have passed away as well as what various causes might be.

I wanted to play around with these numbers and try to extract any trends that might have occurred over the years. I think it will be interesting to see in what years the mortality rates have risen, dropped off, plateaued etc etc. Similarly I would like to try and identify any correlation between the cause of death as well as the year. My hope is to

I have not yet decided on any finite data set as I have two to consider. One possible dataset I will consider using is the (CDC) Center for Disease Control statistics page. I feel that this will give me a fair insight to the causes of death of among Americans. The second source of data is the United States Census Bureau in the section concerning births, deaths, marriages and divorces. Both of these sources contain easy to read/easy to download PDFs as well as Excel spread sheets.

Source:

• Center for Disease Control
• U.S. Census Bureau

My hope for this project is to create enough insight into mortality rates among US citizens. In an attempt to create and offer reasonable discussion and possible solutions, I aim to (if nothing else) discover why mortality rates are so high as well as postulate ways of lowering the mortality rate.

Citations (in Chicago):

1. Center for Disease Control and Prevention. "Deaths and Mortality". Accessed January 20, 2013. Last modified January 11, 2013. http://www.cdc.gov/nchs/fastats/deaths.htm
2. United States Census Bureau. "Births, Deaths, Marriages, & Divorces". Accessed January 20, 2013. Last modified June 27, 2012. http://www.census.gov/compendia/statab/cats/births_deaths_marriages_divorces.html

Something Like This...

Data Visualization

FACS 3935 First Post

Hi there!

For those of you that are new to my blog allow me to take this moment to formally introduce myself. My name is Jeff. I am currently a 3rd year Digital Media student at York University in Canada. I have vast interest in Art, Music, Design, Camping and a whole bunch of other stuff! But I digress...

I have been asked by my prof to write a brief post on a new course I'm taking called FACS 3935: Art, and the Database. So along with maintaining this blog I will also intermittently add new information and work that I've created in the context of the class.

So with the "grand-scheme" in mind this new course tries to analyze databases as they are used within New Media Art. And as always before gaining an understanding of what that all means we have to define what a database is. Well, a database (in essence) may be considered as a structure which simply collects and stores data. Libraries, Transit Systems, Banks, Phones, Music Players...all of these things incorporate databases in order to function one way or the other. So I guess some questions are: are there  New Media artforms that exist which incorporate databases? If so, how can we analyze/better analyze such forms of art? How can contemporary artists begin to create such forms of art?

Some of the concepts that will be tackled in class include (but are not limited to):

• Gain a deeper understanding of the use of database technology in the creation of infographics, data visualizations, and data art practices.

• Consider the roles database technology plays within a larger cultural, social, political and  economic context.

• Critically engage with data in the creation of both an infographic and a data art project.

Overall I think that if used efficiently and represented properly data can become very beautiful in art. one such artist taht achieves this is David McCandless. Visit the website Information is Beautiful to see more of his work as well as more of the work, concepts and ideas I will hopefully be conveying in this class.