The data set I have chosen to use for my data analysis project is “Slave Sales 1775-1865”. The data included in this set includes, state and county in which a transaction took place and the gender and appraised value of an individual being sold from the Revolutionary period to the close of the Civil War and after emancipation. Other information included in this data set is skills and any physical “defect” found in a particular person. As far as types of data, there is numeric, text, and geographic information included, which makes this a particularly detailed study. The geographic landscape shown within the data places the transactions were recorded in southern states,a fact that is not surprising. This is a substantial set of data, with over 76,000 entries, so it will definitely require more narrowing of scope and number. I think that perhaps using a sample of just the 1810s-1860s, or maybe an even narrower range, perhaps 1850-1865 would be the project more cohesive.
Each column paints a particular picture of a time and place in the development of the institution of slavery in the south, and the literal human cost. Each of the rows plays an important role in forming the narrative of the dataset. From each state and county within the state, the people being bought and sold are described in the very simplest of terms, as they are no different than any other commodity bought and sold on a daily basis. We get an age, in years and months both and gender. We also know if any of these persons had special skills that could make them particularly more sought after. These include cooks and cabinetmakers, even drivers. Finally, the defects of a certain person are also recorded. Many are marked as “crippled” or old. Perhaps most interesting and disturbing is that “child” is considered a defect.
There are some relationships that come to the surface in analyzing this dataset. One that immediately comes to mind is that between skills and appraised value. It seems that those with skills did seem to be more expensive than someone who was unskilled, or perhaps a woman. Which brings another relationship worth exploring-the relationship between gender and appraised price. Did women necessarily fetch a much lower or a comparative price during sales transactions.
A third relationship that could yield interesting results and research questions is that between states and number of slave sales. In the south, did one state conduct more sales than others, or were they all near average? In looking at the data, the years and decades of the sales could also prove to show interesting conclusions. Of course a drop off in slave sales could be seen in during the Civil war, but what about before than. What sales were made during the early decades of the 1800s? I think that these relationships could effectively be shown graphically, but also using a progression map. By making this dataset interactive, the viewer can more effectively make use of the information presented.
Some difficulties that can arise when analyzing and answering these would probably come from the data itself. Though this is quite a large sample, it isn’t as detailed as other sets.There is only basic information available (ie. gender,age) that does not, on the surface create a well rounded narrative.I feel that information such as where these enslaved people came from or even a name could tell us more and create better visuals. Another difficulty is in the fact that some of the entries are missing, perhaps a problem of transcription or simply loss of records.
I corrected some of the errors you and others have found with this data set; the next time you open your project, download the csv from course resources, open your project in tableau, and right click the name of the dataset in the upper left, then select “edit data source” and select the newly downloaded file. This will correct some of the weird outliers in the negative numbers etc that turned up.
You have a good point in your final paragraph about the limitations, but think about the levels of detail that you do have that can make a narrative: what does age and gender tell us about perceived value of an enslaved woman, and what’s the narrative of why a younger woman might have been perceived as more valuable than an older woman? What’s the narrative of why this might have changed over time (for example, if you filter to just women 16-30 and make a line graph of their average value over time, what’s that narrative? Are women valued more in different geographic areas? What’s that narrative?) DH questions require a bit of sideways thinking from how we usually work as historians to work within the constraints of the data we have rather than fighting against it.
The impulse to narrow your data is a good one, but make sure you don’t filter out too much–if you narrow your date range too much, you may end up with very few records in certain categories to base your analysis on. If you do choose to narrow your frame in which ever way, be sure that you take that into account in making generalizations (ie, a statement about women’s average appraised value based on an 1830-1865 filter may not be true for women’s appraised value across the whole time range).