Assignment 3

slav-wordle-7

I chose to visualize data from a discourse analysis of Mount Carmel Daily Item newspaper articles containing the word “Slav” from 1892-1910.  I created the dataset in order to make a word cloud (above) representing the perception of Slavs in the coal region during the turn of the 20th Century.  The analysis initially included the data categories: date of publication, location, article title, epithet (specifically the words in the article used to describe Slavs, split into the three categories: modifiers, verbs, and nouns), and people.  Refining the dataset for visualization in Palladio, I added geographic coordinates, removed people, and reorganized the epithets into epithet frequency categories: race, class, and total.

graph location.epithet

Although it was scrambled and confusing at first, I found Palladio’s graph view to be most interesting for this dataset.  The visualization above is a result of inputting total epithet frequency (highlighted) and location name (un-highlighted), and sizing the nodes based on article frequency.  Since the labels are too small to read without zooming in and losing the effect of the big picture, this view is most effective for seeing which entities occur in the most articles.

graph location.date.epithet 1

In an attempt to enhance the graph view’s most helpful feature, I input location and date, and sized the nodes based on total epithet frequency.  The resulting visualization (above) is cluttered, but more easily readable

graph year

Simplifying the visualization, I organized the “year” nodes in chronological order like a time line.  This might be the most effective visualization I created in Palladio because it is simple and clear.

graph race.year

After organizing the nodes for year (highlighted) and race epithet frequency (un-highlighted), the visualization (above) is more revealing.  The organization is inexact because the nodes were dragged by hand, but the viewer is able to see in which years the most articles containing the most race epithets were published.

map zoom

Using the geographic coordinates in the map view, I plotted the location of each article on a world map.  I wanted to use different shades of red to represent the density of entities in articles of a particular location, but Palladio would only show one color at a time even though there is an option to add multiple layers to the map.

map title 1  map title 2

Instead I used the node size option to represent the racial epithet frequency.  Zoomed out (above left) the viewer can see all the articles scattered across the world, but the upper east cost of the United States is taken over by a single blob of color because the view is not detailed enough to show the individual article representations.  Zoomed in (above right) the Coal Region is visible in more detail.  Since I am not familiar enough with Pennsylvania geography to be able to identify the town each of the nodes is located in without a label, this visualization is not very useful to me.  If this visualization could be laid over a road map of Pennsylvania, it would be interesting to see which town’s newspaper articles contained the greatest frequency of racial words.

table date.race.class

The table view, although it has the ability to group the data by a chosen row dimension (I chose “year” for the visualization above), it has almost the same function as the spreadsheet the dataset was originally organized in.

gallery

Similarly, the gallery view does not seem to provide any more insight into the data than a spreadsheet.  It is frustrating to use because only a small fraction of the data is visible at once, and the format is uninteresting because none of the articles I used to compile the dataset had accompanying images.

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