Six months ago I used Wordle to create a visual representation of my blog (see October's Wordle Wordle Wordle). By looking at the word cloud of my labels that was generated by Wordle, it was easy to see that I wrote quite frequently about public history, digital history, UWO, museums, human rights and the Holocaust. None of that is a big surprise when you consider that I'm a student at UWO in the Public History MA Program and that I am taking courses which deal with public history, digital history and museums and that I have a strong interest in human rights issues and the Holocaust.
In that entry, I wondered what a similar visualization would look like in six months or a year. Would it change drastically and reflect new interests and experiences? Or, would it remain the same, reflecting a constant interest in certain topics?
As you can see by looking at the word cloud at the top of this blog post, the visualization has gotten considerably more complex and diverse over the last six months (I have added dozens of new labels to the cloud). However, I would argue that it still largely reflects the initial trends of my blog. The single exception to this relative continuity is the inclusion of new labels which show an increased awareness of and interest in topics relating to local history and heritage (who knew Durham Region had such fascinating history?!).
That's a mildly interesting observation to make, but so what? What can a visual representation of my blog tell you that a quick skim of my labels list cannot? Sadly, I'm afraid the answer is probably nothing.
In October I had a cautious optimism for the use of digital visualization tools as techniques to help with the data mining process. Now, I am thoroughly unconvinced as to their usefulness. It might take me 2 or 3 seconds longer to scroll through the alphabetized list of labels that I have used in my blog since its inception than it does to glance at word cluster like the one above, but it tells me exactly the same thing. In fact, scrolling the list of labels actually saves me considerable time when you consider that I had to first go to the Wordle website and input my data before I could create the word cloud in question. Also, I can't help but notice that the picture that was produced by the generator is now so crowded with new, low-level labels that it has become an eye strain to try and extract any but the most obvious data from it.
In my humble opinion, Wordle and similar word/tag cloud generators may create pretty pictures, but they are a bust as far as their usefulness to the study of history goes.
But what about other visualization tools? Surely I can't paint all tools with the same brush, can I? You wouldn't think so. The esteemed Dan Cohen certainly doesn't. He used different visualization technologies in his research to map whether Americans prayed or watched CNN after hearing about the terrorist attacks on September 11th, 2001. Cohen stands by the fact that it was only through mapping the results of his survey onto a satellite image of the USA provided by Google Earth that he was able to observe that people in rural areas of the United States were far more likely to pray after hearing of the attacks than they were to watch CNN. Conversely, people in urban areas were far more likely to turn to CNN for information and solace than they were to pray.
So it was with this conflicting ringing endorsement by Dan Cohen (digital history guru) and growing personal skepticism that I attended the Historical Geographic Information Systems (Historical GIS) lecture given several weeks ago by Don LaFreniere, a colleague from the Geography Department. Don is a huge fan of GIS and the objective of his lecture was three-fold:
1) To teach us the basics of navigating GIS software
2) To introduce us, as historians, to the potential applications of GIS programs to our research questions and future careers in public history
3) To explain how he is currently using GIS visualization tools in his research process
Extremely knowledgeable and enthusiastic, Don did a fantastic job of patiently leading even the most technologically challenged of us through his tutorial. But even Don's enthusiasm couldn't convince me of the usefulness of visualization techniques. In order to get to the point of visualization Don and a team of undergraduates had to spend more than two years compiling a database of census materials from which they drew the information they mapped using the GIS software. For my money, it was the database that was truly the incredible feat, not the mapping we were able to do with the data points. Using the spreadsheets that he had created, we were able to search for specific criteria and filter the overall findings according to our desired specifications. At this point we were able to see all of the relevant details and make (in my opinion) the same observations that we were able to make after applying the visualization tools to the data set.
I don't know. Maybe I'm missing something. Maybe, as Sara Sirianni suggests in her blog post about the workshop, visualization techniques are the greatest thing since sliced bread. Maybe.
For now I remain skeptical.
