Final

Slave Sales 1775-1865.

Data Description

The data set for the Slaves sales from 1775 to 1865 holds the information of individual slaves, their gender, and information considered important for potential slave buyers. The data can be considered numerical, textual, and geographically information. The numeric information for the data set is columned by the age of slave in years and month, the date of the entry of the slave, and the slaves appraised value. The two columns that describe the slaves age in years, the youngest being 0 years old, to the oldest being ninety nine. The column for age in months is empty throughout the entire data set. I can argue that the months column is completely empty because infants were rarely bought, and sold with in the slave trade itself. For the date of entry column the beginning date is 1775 and continue through 1865, although the data set itself is all over the place with which particular year a slave was sold. The column regarding the appraised value of a specific slave has variation based on the age, skill sets and defect of the slave. I can also argue from studying the data set that gender also played a major role in the appraised value for a specif slave, as well as the geographical location that a particular slave was sold in. The textual information for the data has text data with in the columns that include any defects that each particular slave may or may not have, and any skill set that some of the particular slaves may or may not acquire. The defects column of the data set included terms such as “runs away” or “deaf”, and the columns for skill sets include terms such as “house servant” and Laundry”. The textual information tends to be historical accurate in proving how real racism was, not just between the years of 1775 and 1865, but through out American history. The geographical information only has two columns; State and county. The states include Georgia, Louisiana, North Carolina, South Carolina, Tennessee, Virginia, Maryland, and Mississippi. The columns for county include all the counties with in the specific state. All the areas associated with this data set are southern states, some of which would succeed at the beginning of the Civil War to protect their institution of slavery, that they strongly believed was a given right.

Visualization one

The first of my data visualization is meant to show the correlation between the age, and gender of a slave, and their average appraisal price. This visualization makes me question not only the institution of slavery, but also sexism with in slavery. The varying prices between age can be, although horrifying, expected, but the variation between the genders of a particular slave seem unjustified.
The average peaking value for a female slave from the years of 1775 to 1865 was at the prime age of twenty two, with the average price of $566.9. The value of a female slave drastically declines after the age of twenty nine, and again at the age of thirty nine, although there are a few unexplained outliers. The lowest average value for a female slave from the years of 1775 to 1865 was at the old age of ninety, with the average price of $9.4, females at the age of one were valued more then elderly females at the average price of $71, although one elderly female slave was appraised at the average value of $800.0 at the age of 79. I can argue from reviewing the data set of the Slave sales that females slaves were ideally in their prime at the age of twenty two until thirty nine for work purposes, and more likely breeding purposes. Women were not value as high as men though, for obvious reasons such as labor, and strength.
The average peaking value for a male slave from the years of 1775 to 1865 was at the prime age of twenty nine at the average price of $795.1. The value of a male slave begins to drastically decline after the age of forty three with the average appraised value at 609.6, although like the female data, there are a few outliers within the data sets. For example, eighty four year old man was appraised at the average value of $227.5. The lowest average appraised value for a male slave was a ninety nine year old whose appraisal value was $19.6.
The data set of the Slave Sales from 1775 to 1865 shows the range of values for a set of slaves sold in the states of Georgia, Louisiana, Maryland, Mississippi, North Carolina, South Carolina, Virginia, and Tennessee. A closer study of the Slave Sale data set from 1775 to 1865 revealed a pattern between the value of a slave, their gender, and their age, only after I excluded the unknown ages of specific male, and female slaves within the data set. I found the average value for specific slaves varying on their gender, male or female, and their age ranging from age one to age ninety nine.

Process documentation

For my first visualization I created I decided to use a dot graph to show the variations between the price of a gender, and the age when slaves were being bought and sold in the years of 1775-1865. I chose the dot graph because it clearly shows the decline in average appraisal price as a slave in the slave trade aged. I chose the color blue to represent the male slaves, and the color pink to represent the female slave because these specific colors are always associated with the genders. I thought the colors would intensify my argumentation of how racism, and sexism went hand and hand in the years prior, and after 1775 to 1865.

Visualization two

For my second visualization is meant to show the “defects”, the gender of the slave with the “defect”, and the average appraisal value for this specific “defect.” I am using quotations around the word defect in this sense because certain “defects” in this visualization are not considered defects by today definition. The range of “defects” in this data set include thing such as: very tall, short, crippled, one handed, missing fingers, broken back, and etc. Other “defects” include things such as: lame, idiot, dirt eater, dumb, deaf, drunk, nursing a child, and etc. The highest appraisal value for a female in this data set is $800.00 with the “defect” of being deaf. The highest appraisal value for a male in this data set is $866.70 with the “defect” of having a broken back. The lowest appraised value for a female in this data set is $30.00 with the “defect” of being sick with cancer. The lowest appraised value for a male is $5.00 with the “defect” of being deaf. I can only argue that the female with the defect of being deaf is appraised at a higher value because she is either younger then the male, or is still able to communicate while being deaf because typically throughout the data set male slaves are always value higher then females. I chose to show the “defects” with in the range of genders to reiterate my first visualization of how sexism, and racism went hand and hand prior, during, and after the years of 1775 to 1865.

Process documentation

For my second visualization I created a bar graph that is brightly colored. The bar graph is again separated into the genders of male, and female to shows the difference in appraised value between “defects” and genders. The brightly colored bars within the graph are meant to show the wide range of varying “defects” that the slaves being sold were labeled with. The bars also show the appraised value of each slave with the “defect” and how each “defect” was compared prices wise to another.

Argumentation

The argument for my first visualization, as well as my second visualization is centered around how sexism, and racism went hand and hand in the years of 1775 to 1865. For both male, and female slaves slavery was an absolutely devastating experience, but the circumstance of enslavement were different for both the male, and female slaves. Although most planters in colonial North America favored robust young men as slaves, the bulk of these were shipped to the West Indies, so early on, slave buyers turned to purchasing female field hands, who were not only more readily available, but also cheaper. In fact, because skilled labor, such as carpentry and blacksmiths, was assigned only to male slaves, who were also more expensive because of the skill set, so the pool of black men available for agricultural work was further reduced. During the time period of 1775-1865 Women slaves were considerably cheaper, than a man that was their exact same age for what I believe to be attributed to strength, and after further research different types of work. One thing the data set does not tell me is what specifically each slave was being brought for whether it be plantation work, a house maid, or a stable hand. Appraisal value could have most likely varied between the job each slave would be doing, and the geographical location of that job. No matter the circumstances sexism was embedded into the context of slavery, and racism. Whether a female was considered less appealing for a job because of strength reasons, or job details in my opinion in that era even if a woman was equal to a male, the male would still have been sold for a more considerable profit simply based on his gender.

The argument for my second visualization again revolves around sexism, and more so for this data set the historical racism that it presents to use. The “defects” column of the data set ranges from what would be considered disabilities in today’s world such as: deaf, blind, broken back, one hand, cancer, or crippled. The “defects” with in the data set that show the historical racism that was involved in the slave trade include: dirt eater, dumb, idiot, complaining, bad character, runs away, steals, and insane. These “defects” show how lowly the slave trades thought of their slaves, but shows me that during an excruciating time some still had the urge to fight for their independence. In today’s time the idea that slave had a “defect” because they run away, steal, complain, or have a bad character means that specific slave had the will to stand up against their owner. In the years between 1775 and 1865 the slaver traders did not see things so positively though, these “defects” seriously declined the price of a slave. Along with the first data set, females with “defects” were considerably less costly then a male with the same or more extensive “defect.” Again showing how in the years prior, during, and after 1775-1865 that sexism, and racism were two institutions that coexisted together.

Further Questions:

The first question prompted by my data visualization is what time of work each slave was going into. I would like to further research this so I can properly compare the appraised price of each slave, and further learn why two of the same aged, and skilled slaves could be appraised for different values. In my opinion this would help my complete understanding of the slave trade, and how in the years of 1775 to 1865 it worked. The second question prompted by my data visualization is if a “defect” such as a dirt eater was racially driven, or there is a historical explanation behind why a slave would be actually be eating dirt. I would like to know if it was a religious exercise, or something culturally driven. The third question prompted by my data set is why specifically some elderly slaves were valued higher than some slaves considered to be in their prime of life. Is it because a slave at the age of eighty has been a slave for a long time and was setting an example for the younger slaves, or simply for educational purposes for the other slaves? The final question prompted by my data set would be why in specific geographical locations did the appraised value of slaves vary so much? Were slaves valued higher in different states in the United States because of different work loads, or during this time period was one state booming more then another?

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