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.