My final project is going to be the story of the slave trade from 1775 to 1865. I chose this topic because I feel it was an important part of American history that helped shape America into the super power that it is today. The visual that I chose was the appraisal value of slaves both male and female by state as well as county. I chose to go with the bar graph because I felt that it’ll be more easier to read than some of the other options that were available and is one of the most common ways to display data. By breaking down the value of slaves by counties and states it can pose several questions such as “why does this county have a higher appraisal value than another county within the same state” or “Why does this state appraise slaves at a higher value than the next state”. Each county has a different appraisal number for their slaves. The sex of each slave seems to be the only factor according to this visual as far as how much the price will be is concerned (although I’m sure age, skills, defects, etc are a factor as well). As most people would expect, the male slaves are appraised at a much higher average rate than the female slaves. One can only infer that this is due to the diversity of work that a male slave can do that a female slave simply cannot do. Male slaves aren’t hindered by the psychical barriers like the females may be subjected to. It appears that the slave states further down south have a higher value average than those that are located closer to the north. This can caused by several reasons. One of those reasons is that the slave states located more up North have a bigger liability in the fact that the slaves are more likely to be tempted to run away and succeed than the Southern counterparts. With this potential scenario occurring, it could be deterring the price of the slaves making them too high of a risk to be worth trying to get top dollar for them. Another possibility for the states further down south appraising the average values of slaves more than their Northern counter parts is because of the amount of labor that is center in the more Southern states. The slave states that are located more south have an abundance of work that the slave states further up North just don’t have. With the amount of work that a plantation for example requires it will be the only practical thing for a person with money to purchase some slaves to be able to sustain the plantation and lessen the workload and make life easier. Knowing this, the slave sellers are able to raise the price of the slaves because they are aware that the slaves are a very big asset to the people who buy them in the more Southern states. The demand for the slaves seems to be higher in the more Southern states than the demand for slaves in the states that are closer up North. Once purchased by the individuals in the Southern slave states they are valued more because they are viewed more of an asset than liability, which may not be entirely true for the slave states that are located more up North.
For my final project I will taking a look at the Slave Sales from 1775 to 1865 data set. Specifically the values of slaves in regards to a skill they possess or a defect they might have. When looking closely at the data set it contains information detailing what you’d expect to find; like age, gender and appraised value of a slave. What I find to be interesting about the data set is it includes skills and defects something I would not have considered. With this information there are more factors that are incorporated into the sale of a particular slave. I believe there are connections regarding the prices of slaves within a particular state possessing a particular skill and which gender the slave happens to be. The females and males tend to have gender specific roles in terms of skills they were labeled with. The men have skills like mechanic, and field laborer and with that their appraised value shows, women have skills such as cooking and baking as well as housework and hairdressing and their appraised value shows as well. While some of the men and women have share skills there are differences that find it harder to compare value based on the skill. However, the defects tend to run along more similar lines. Both men and women share similar defects like, height whether it is too short or too tall, loss of hearing, loss of sight, and any type of sickness including hernias, cancer or just a general label of “sick”. There is also common trends of labeling defects as run away, drunk or fits meaning they are difficult to deal with. From these defective labels you see a shift in appraised value. A drunk man is appraised around $425 where an appraised drunk female has and average value of $300. Maybe not such a shocking revelation seeing as today there is a gender wage gap, however, it became interesting with such defects like, without fingers, a male without fingers is appraised nearly twice as much as woman without fingers, same as a man with cancer compared to a female with cancer.
What I found most interesting or should I say troubling with this data set is the way I read it. At first glance it was troubling to see. These are people and these are children given a price and given a skill or defect and then sold. We all know the horrors of slavery but when you are asked to put it in a bar graph or pie chart you become removed from the fact that these were people and not just numbers on a page. That’s why it’s so important to remember what the numbers really are, and tell the stories of what the numbers are telling us.
The dataset that I chose was Slave Sales from 1775 to 1865. I decided to focus more on the sale of children during that time period. I decided to do so because I want to learn more about what it was like being a child growing up during slavery, or being born into slavery. It is estimated that approximately 1/4 of slaves that crossed the Atlantic into slavery were children. Many were forced to move, unwillingly, from plantation to plantation, never truly having a home after being taken from their mothers at a very young age. When learning about slavery in many classes that I have taken, there has never been an emphasis on the children that were involved. My objective is to use this dataset, as well as research, to put an emphasis on children and their experiences during this time period. I took the dataset and condensed it to what I’m more interested in and found some very interesting facts. The first thing I found was that while many children of different ages came from the same states, each individual age tends to come from a specific county in that state. For example, 8, 9, and 10 year olds in this dataset all originate from the county of Anne Arundel, Louisiana. Meanwhile, most of the 15 year olds on the dataset come specifically from the county of Edgecombe, North Carolina. I’m not sure as to why this is, but am interested in finding out more. I would think that every age would originate from every county.
The second interesting finding was that from the ages of 2 years old to 17 years old the prices vary. I thought that the older that the child was, the more valuable that the child would be, and therefor buyers would pay more money. You would believe that the more work that can be done by the child, the more expensive they would be and have more value. Matter of fact, the children that were valued the most were from the ages of 2 years old to 7 years old. After that, 14 and 15 year olds were valued the highest. At the age of 14 many of the children had picked up useful skills, like being a laborer or fieldworker. With skills that were useful to buyers, the age group of 14yrs old was the highest valued, at an average rate of being sold for $540.23. By the age of 16, the average value of children went back down to $199 based on the dataset. The county of Charlestown, South Carolina doesn’t have any listed prices, so that may add to why the average is so low. The visualization below shows all of the average values of different ages of children from the age of 1 to 17 years of age.
All of this information came from the dataset alone. What’s very sad is that many children are at risk for becoming very ill when they’re made to work in terrible conditions. At first, many people avoided having children slaves because they felt at risk because they didn’t want them to become ill. When the demand for more slaves in the Unites States increased, so the beginning of child slavery. The dramatic increase in the need for children slaves didn’t happen until the late 17th/ early 18th century.
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.
The data set that includes information on the slave sales from 1775 to 1865 contains valuable insight into the slave trade in the United States that many people may not immediately think of when they think of the history of slavery in America. It provides insights into information such as how much slaves would be sold for when they possess a particular skill set compared to slaves that possess other skills as well as no skills at all. It also allows you to compare the types of skills that female slaves possessed as opposed to the skills that were possessed by male slaves. In the visualization shown for example, you can see that female slaves that were sold possessed more domestic based skills such as “hair dresser, house servant, pastry cook, laundry, etc.”. In terms of these skills women that possessed hair dressing skills were appraised to have the highest value, being appraised at around $1,000, as compared to a female spinner that was given an average appraised value of $203. The skills that are possessed by the male slaves appear to be more skilled labor types of jobs. These jobs include skill sets such as “mechanic, brass molder, painter, cigar maker, blacksmith, construction, etc.”. By looking at the visualization we are able to see that the mechanic skill is valued the highest among the male slaves with the average slaves with skills as a mechanic were appraised to be worth around $1,258. This would be compared to a male slave possessing the skills of a pusher which is appraised at a value of $150. The visualization also allows us to compare the appraisal values of male slaves compared to the value of female slaves during the slave trade in the United States. By looking at the graph we are able to see that on average, male slaves were valued at a higher rate than female slaves. This included times when they possessed the same set of skills. For example, a male mechanic was valued at an average rate of $1,258, while a female mechanic was on average appraised at a value of $600. We are also able to see that more males possessed skills than females did.
The date set of the Slave Sales between 1775 and 1865 contains information that can provide insight on how the slave trade operated. By simply looking at the information presented, you can see that some of the information correlates with one another to tell a story. The story that my first visualization focuses on is the appraisal value between gender and the states they were sold in. Before I could start my story I needed to accurately identify if there was a relationship between value and gender. The data showed that male slaves were significantly valued at a higher price point than female slaves. The average male slave would be sold between the average price of $172 and $777, depending on the state. Women average price was between $113 and $639. Although the data does not directly state why males valued more, we can infer that males would be of more use on plantations. If a master was looking for someone who was physically capable, could withstand long tired-some hours, work fast and carrying heavy loads, he would obviously go for a male slave. However, this does not mean that women slaves were disregard. Women slaves were still valued because they were capable of bearing children, being house servant, and possessing skills that made them useful.When age played a factor, my data showed a relationship between the age and value of the slave. The data showed that whether it was male or female slave, the average slave sold would be about 18 to late 20’s (prime age) and would be valued high than someone of a later age. The last aspect is how the different states play a role in my visual story. Incorporating the state gives a different perspective of how the selling of slaves differed in the territories. For example state, state such as North/South Carolina, Tennessee, Virginia, and Maryland showed their average age of slaves being between the ages of 4 and 12, rather than 18 to 27 like other states. It is possible that theses states were more interested in selling children or they didn’t keep a better records of the ages of slaves and therefore some slaves would be unaccounted for. Another example is how states played a role in the value of slaves. For states such as Georgia, Louisiana and especially Mississippi both women and male valued significantly higher than the other states. It is possible that these states were more popular plantations. Therefore, more slaves were migrated into these states and migrated out of the states with fewer slaves.
For my final project I would like to focus on the data set of Slave Sales between 1775 and 1865. This data set includes numeric,text,and geographic information on slaves up for sale. This data set features a couple of numeric columns, one regarding a slaves age from infant to as old as 99, another appraised value of that slave, and another was the year listed for sale ranging from 1742-1865. As for the geography of this dataset, a column is included for the state the slave was sold. Ironically the only states mentioned are in the south including Georgia,Louisiana, and Mississippi. The other geographic column of counties is used to narrow in on a certain location a slave was sold at. This dataset also has columns for the sex of slave, defects of a slave and skills a slave possesses. For a column like the sex of a slave there are only 2 options male of female, while the defects and skills of slaves can hoist a bunch of options ranging from old to lame. I did notice that for the defect and skills columns both had significantly more blank spaces then all other columns.
As for relationships between the different columns and rows in the slave sales dataset there are several. I noticed their is a correlation between the defects, age,and sex of a slave and the appraised value. Old and disobedient slaves were sold for way less than a young male “prime” slave. Young slaves were often valued very low as well. Also most of the skills types are connected to just one race. Women were seen as child bearers and caretakers, while men were seen more as laborers.