Final Project Story Draft Due 4/14

The visual data that I chose to use to describe the slave sales data set is a graph. Graphs with entities separated by color are more appealing to a person’s eye in general, and their mind automatically notices the difference in volume of each color, or lack thereof. For example, if a person sees a pie chart that is 75 percent red and the remainder is green, they’ll automatically wonder what the red area represents and why it’s so plentiful. On the other hand, colors in bar graphs create distinctions, but the length of the bars is what tells all. Where the z-axis is placed (on the bottom, side, or top of the graph) also has an impact on what viewers’ perception. An x-axis that’s on top as oppose to on the bottom typically has an adverse effect at the first glance compared to if it was on the bottom because it looks as if numbers are decreasing as the bars decrease in length.
I chose the bar graph lay out because it makes it seem as if certain states were forging ahead of others. Essentially, leaving them in the dust of the money they spent on slaves. This scale isn’t the typical graph, but I do think that it gets the point across visually without having to see the prior spread sheet to analyze the data. I chose the deep burgundy color because it wasn’t alarmingly red, but the burgundy resembles blood and this tugs on views heart-strings –especially in the context of slave sales.
The context surrounding the slave sales data set is the rise of the cotton kingdom. The spike in Louisiana slave purchases may be due to the expansion of slavery and cotton production, which makes sense. The raw data set itself shows that men in their prime are bought for higher prices (keep in mind that man’s prime is longer than a woman’s). Women, on the other hand, are of more value when they are of age to bear children and their value probably deprecates so in a time when the goal is to increase production, men are probably the more ideal choice. Though child-bearing and reproduction is important, this timeline probably seems longer to a person that wants to capitalize off of cotton production high while it’s hot –wait nine to ten months for a mother to give birth and a few more years for that baby to be mature enough to pick cotton themselves. Women were still being bought at an increasing rate, while men, as we see in Louisiana, were in higher demand.
In terms of sequence, the range of the slave sales data set covers the rise of the cotton kingdom which was vaguely 1830-1861. Therefore, the increase in millions spent by the states is associated with the rise in cotton demand. Aside from natural reasons, the cotton revolution is the main reason that states in that time period spent hundred off dollars to buy quality slaves because they’d prove vital in capitalizing off of the cotton kingdom.

2 thoughts on “Final Project Story Draft Due 4/14

  • April 18, 2016 at 8:59 PM

    Good reasoning with your colors and the intersection between visual and argument.

    Some things to consider for revising this or adding additional visuals: You make a compelling argument about the meaning for Louisiana’s high valuation of enslaved men, but you might also want to look at A. the difference between the number of enslaved people bought and the amount spent on them–I think you’ll see some surprising differences. And B. you might want to think about change over time, either by adding an interactive filter for date (drag Date Entry into the Filters pane and then right click>show filter), or by doing line or area graph broken apart by gender and/or state. A line graph will imply that the lines are independent of one another, while an area graph will imply that the stacked sections are part of a larger whole, so take that into consideration.


  • April 18, 2016 at 9:01 PM

    An example of a possible set of line graphs.

    I forgot to mention re: your maps, it looks like your maps by defect are thrown off by the nulls; you can take care of this by either A. excluding the nulls entirely, or B. like the interactive filter for time I described above, you can do the same thing with your defects category, and let your reader select certain defects they want to look at.


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