Observations

Tuesday

I was in the A-K document. These observations were recorded there.

  • A total of 14 people edited the document
  • Only two people made use of the chat feature
  • There were 10 people signed into the chat box at the end of the editing period. This number fluctuated throughout.
  • The chat was left a total of 10 times.

Thursday

  • It was raining on Thursday. A total of 13 people wore rain jackets to class, 6 of whom kept them on during class, 7 deciding to take them off.
  • The four large screens were on(including screen saver) for a total of 1:07:33.
  • Dr. Sample was talking to the entire class for approximately 29:33. This accounts for 39% of class time
  • Students were addressing the entire class for approximately 17:20. This accounts for 23% of class time

Choosing Data Visuals

I found Johnathon Harris’ choice in portraying the timeline of the photos of his work “The Whale Hunt” to be very interesting. The pulse-like structure was certainly very misleading. When I first was on the site, I thought the the spikes in the timeline, similar to the spikes of a heart beating, represented exciting things happening in the series. This was enforced by the large amount of spiking at the end of the timeline, which I assumed coincided with the actual killing of a whale. However, I started to question this notion when I arrived at the large lone spike in the middle of the timeline.

Eventually, I realized that the spikes represented a larger amount of photos taken at the same time. In a way, this still did reflect my original thoughts, as a spike in photos would suggest an event worthy of lots of photos, more than likely more interesting than the events that were given only one photo. In this way, Harris’ timeline is a clever way of portraying the events, as the pulse line does in away suggest which events are more interesting then others, like the whale butchering vs the men standing around at camp.

Seeing this work made me think on what authors take into consideration when choosing a type of visual to represent something. Audience surely has to be the main factor- I thought back to how confusing the Kissinger visualizations were before I had a more thorough explanation. Those were meant for people more experienced in fields trying out different visualizations to gain new insights into something. Harris, as a photographer, was most likely looking at his work as art, and the pulse-like timeline added an extra element of creativity to his work.

Observations

Tuesday

Four students were wearing Bean boots

Three students brought coffee

Phones were used nine times while Dr. Sample was talking

Seven people were visibly wearing their misfits

Thursday

Six students were wearing Bean boots

One student brought coffee

Phones were used three times while Dr. Sample was talking

Eight people were visibly wearing their misfits

 

Does your location affect how much information is gathered on you?

While reading about the “cave dwellers” and the staggering amounts of information the Obama campaign generated on potential voters, I thought back to an article I read about swing states and how focused presidential campaigns are on them. This got me thinking about how people living in these states were likely targeted much more often than voters in other states that were almost guaranteed to go one way or the other.

The presidential candidates focused all of their public campaign event efforts into only 12 states during the 2012 election- why wouldn’t they focus all their data mining efforts to these states? This would give them less data do sift through and perhaps build even more detailed files on individuals as well as models predicting election results in those states.

Thinking about how geographically targeted political data mining is lead me to wonder about the effects geographic location has on your susceptibility to data mining as a whole. Are there certain areas whose residents see more information being gathered on them then others? Or is it a more arbitrary matter?

I would think that you would see a positive correlation between certain areas and the amount of information gathered on individuals in it, and that this correlation would have everything to do with the type of individuals who live in it. Population and other factors probably wouldn’t show much relation, but a very technologically savvy city like Champaign, IL would see more data mining efforts than a less connected city like Cheyenne, WY.

Perhaps there is no correlation at all and almost everyone is subject to big data mining, but I think it poses an interesting question.