Story Draft

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. 

Final Project Story

In this visualization I decided to research the differences in the payment of pensions for people that had the same injuries but started receiving pensions at different time. From this visualization I organized the data into years and injuries and then went from there. My original question for this visualization was to find the differences in amounts of pay for each individual. As you would guess many of the payments are equal, however I had also thought about how there would be differences in pay, and I was right about that as well. When you look at this data the person who created it did not include how severe certain injuries were, like there are labels for various gunshot wounds but the severity is not specified. This was the basis of my question. Why was there discrepancies? If there wasn’t a reason for there to be a difference in classification why is there different pay? The amounts also fluctuated by time of issuance. It wasn’t like there was a correlation going up or down the numbers were just random, again, a reason to include the severity of the injury that each person had received. This could’ve all been cleared up by a couple extra words on the sheet.

There are some very different stories that come from this chart as well. Like the many people who are affected by gunshot wounds, presumably from the Civil War. This shows the variety of injury that come from having such a bloody war. There appears to be a a gunshot would for every appendage possible. I suppose that there is the possibility that the injury happen during some incident outside of the war but I think it’s reasonable to say that the most of injuries occurred as a result of the war. When you look at some of the other ailments You see some injuries like an axe to a particular appendage and see that they only see that they receive like $4 a month. That number is very surprising to me,even in those days where four dollars was a nice amount to have for some income but I feel like that wouldn’t be enough to support someone who was severely hobbled by an injury. There were some cases like a dependent mother who might have multiple kids and use that as their only income and the pension was eight dollars Again that seems incredibly low. The pension for women, especially in those times would most likely be the only opportunity for women to make money so this seems low in the sense that these women could have multiple kids. Conversely, there were a couple people who were blind who received $70, this was interesting to me because that seems like it would be a reasonable amount for people who probably don’t have a lot of expenses.

Overall I feel like that given these circumstances, in my opinion people could’ve been paid more, but I don’t know what a good salary per month was for the time, so this could’ve been fair.

Draft

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.

Final Story Rough Draft

The nineteen forty Albany census provides us with resident’s basic personal information, but it contains a much deeper story in between the lines. When simplifying the information and the numbers, many judgments can be made about the circumstances of this time period. The unemployment and employment rate of each gender, income of specific households and citizenship based on birthplace are just a few of the pieces of data that can be discovered within the census. My data visualization displays the average age of employed persons based on gender. On the female side, the average age of unemployed women was over forty years old, just about forty-two, and the average age of employed women was just over thirty-five years old. This tells me that the average women in the nineteen forty’s worked for pay for a shorter span of their lives than males did. With that being said, Men in the nineteen forty’s saw an average age of unemployment of just under forty years old, only a few years younger than their female counterparts. The average age of males who were employed for pay is greater than the average of those who are unemployed, being just over forty. This represents that males may have been struggling to find jobs when they were younger, but as they aged and gained experience then they were able to find a job and work there until they retire. Persons whose gender is nullified on the census demonstrates a higher average age of unemployed persons than employed persons. This relationship is similar to that of females so this leads me to believe that most of the persons whose gender is nullified on the census are women. For those whose employment status was nullified in the census they saw a very low average age for both females and males, about 15 and 18 respectively. These persons were most likely nullified when it comes to employment status because they are so young and may not have been expected to work just yet because of academic reasons.

Yo this is a story all about how Albany’s 1915 census got fliiped-turned upside down

I think there’s a few interesting points to take away from the graphic and data behind occupations in Albany in 1915. There’s a few jobs that no loner exist or are called different names now, plus a surprising difference in the number of women and men who worked (or at the very least, were listed as working) at the time. I purposely chose “gendered” colors for the bar graph, but I think the story behind that has to go in a separate part of the final post. But there are a few interesting stories behind the data itself.
I’m assuming no one here really knows what a “teamster” is (or maybe you do, being history buffs and all). Today we’d use the plebeian term “truck driver.” But it’s hard to imagine an 18-wheeler peeling down the dirt roads of Albany a hundred years ago; a little more research shows that these were the men who delivered goods to stores, usually on an animal-driven vehicle. A whopping 12 men in the city of Albany were blessed with sitting behind the back-end of an ox or horse for a living, twice the number of the next most popular listed job in the city. But I’m sure they weren’t only delivering groceries to the whopping 2 grocers in Albany at the time; out of 64 employed males at the time, about 5 of them weren’t directly related to selling or handling delivered goods (and that’s taking a good guess at what some of these other jobs are). So I’m sure these 12 men were nice and busy staring at these animals’ backsides all day as they delivered books, ice, and masonry tools.
Of course, when you first look at the graph the first thing you probably notice is the big pink bar shooting up highest. With only a dozen less listed workers than men, more than half of the women listed as employed are employed in “Housework.” What sort of housework? Not homeschooling, as school teachers are listed in another category, nor seamstressing, as dress and shirtmakers are categories unto themselves. Perhaps, even though it was work that was expected of them as women, house work within their own homes was considered an occupation? No, I think that since it was listed as an occupation, it was something that they were paid for, and not something they did in their own homes. Or rather, it wasn’t something they were acknowledged for doing in their own homes. They just had to come home from cleaning someone else’s home all day, and then clean their own.
There are some jobs which are surprisingly gendered (as there is not one single listed occupation that has both men and women working in it) occupations, ones that might expect from the other sex. The only listed gardener in the census is a male, while the only person in charge of manufacturing boxes in all of Albany is a woman (at least, according to the census). Weren’t they afraid that her wandering uterus would make her unable to deal with such a heavy workload?
All in all, I think the most important and interesting piece of information to take away from the graphic is that there were an almost-equal amount of men and women listed as being employed in Albany a hundred years ago, when we usually imagine that the men went out to make the money for the family while the women sat at home and either tended to their children or made sure the house was comfortable for their husbands, fathers, and brothers to come home to.

Final Story Rough Draft

There are several stories that can be told from the Albany Militia Muster Roster. From how the small militia looked, to the jobs that they had in their normal lives. It’s very interesting to see where the individuals come from and how old they were when they originally enlisted. There is certainly a lot of personal information provided in the roster that paints a very descriptive story. The story that one of my visual focuses on are the racial backgrounds where these soldiers come from. I sorted and grouped the different complexions provided and assumed the description of the competition is correlated with their race. For example I grouped “Black, Brown, Negro, Dark and Swarthy” assuming that they are all African Americans. A also grouped “Fair, Pale, Ready, Reddy, Ruddy, and Sandy” with the assumption that they all are Caucasian. And then I left Indians alone because they are Native to the land. I also excluded “Freckled” and “Pockpitted” because they both can be used to describe Indians, Blacks and Whites. I thought that this would be an interesting way to sort this data because all of these individuals made the voluntary decision to sign up for this Militia, and they come from all walks of life. Especially during this time period, there was a very large gap in social equality between blacks, whites and Indians. One would assume that a majority of the soldiers on this muster roster would be White because they have a whole lot more to lose and they were treated a whole lot better by society. I would have thought that since society has been so oppressive towards African Americans, there would be a tremendous dividend between the two with the Whites in the majority. Much to my surprise, the African Americans were in the majority leading by well over 100. I found this very interesting and once I saw this, I had to ask myself was this all voluntary? Maybe it was and they just had a love for the country and a desire to defend it. Another interesting fact is that there was only individuals that fell under the category of “Indian”. However I will admit that I was not entirely surprised. Foreign powers were coming into their land and trying to control it. Why would they want to sign up for a fight that really has nothing to do with them because either way, there will be a foreign country controlling their way of life.

Final Story Rough Draft

When looking at all of the data from the Albany census of 1860 a few stories begin to emerge. I think the most immediate story that jumps out is the racial diversity of Albany in 1860 according to this census. The race column uses a one-letter label and when scrolling through the hundreds of names you see the letter W, for white, almost exclusively. When you take a magnifying glass to the census and search for more you are able to find some small diversity. The letter B, as in black, and M, as in mulatto, are also listed on this census, but only for a total of eight records between the two races. There are three recorded black members of the community and eight-recorded mulatto members of the community. Due to the time period we are looking at this may not sound too surprising, but when you consider that all slaves living in New York were freed in 1827 it becomes slightly more surprising. When removing the scope of Albany, New York from our data you can see that the number of white persons in the United States still greatly outweighs the number of free persons of color. Slaves make up thirteen percent of the population while freed persons of color make up only one percent of the population. So while it is not surprising that only a few persons of color lived in Albany in the grand scheme of America in 1860 it is slightly interesting due to New York’s dense population of almost four million and the abolition of slavery years earlier. When comparing the totals of the United States to New York’s totals in population, New York has about one percent of freed persons making up their population similarly to the entire country. Out of all of the free persons in the United States about ten percent were living in New York during 1860. That is a lot considering many states still were under slavery during this time. Out of all of these free people living in New York at this time only 0.01 percent of them were living in Albany. A majority that were had presumably been here for a long time because the average age of a black person living in Albany in 1860 was seventy years old. Presuming that not many people would travel to a new location after being freed at seventy years old and I would also take into account that both black males living in Albany during this time both were working jobs that were both also worked by white males during this time, one as a preacher and one as a waiter. When you consider that Albany is the capital of New York I might have guessed that more freed persons would be living there, but you have to take New York City into account. Out of the forty-nine thousand free persons of color in New York during this time period twelve thousand people, or twenty four percent, were living in New York City during this time. These numbers had been on the decline though and continued through the 1863 Draft Riots in New York. From 1870 on the numbers showed a steady increase.

Final Proposal

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.

1940- Visual 1

Education and the type of occupation one holds are often correlated with one another. From the time a child is put in school until the time they finish, it is drilled in the head of the individual that a good education is needed in order to obtain a well-paying job. It is taught that hard work breeds success and young men especially are taught to be providers for their families which comes into play with the types of jobs they seek and level of education they want to achieve. This proved true for those living in Albany in the 1940s; the 1940 census helps to show that those that received a decent education held jobs that not only benefitted them but their families as well.
The census provides a wide range of information including the types of jobs that were held, the gender of those that held these jobs and their level of education. It seems as though many women did not work and often stayed at home to care for their children and other household duties. Although many of the women did not work, some did and they held quite prestigious jobs. The women that worked seems to not begin work until they are well in their late teens if that but most are in their very late twenties, early thirties and older. The census does show that a greater amount of men worked in comparison to women and they began to work quite early. As previously stated, although mostly men worked, some of the women that did had higher education levels then that way which made it possible for them to work higher paying jobs. The census shows that a few of the women received college degrees or higher and began lawyers and doctors whereas that was not quite the case for the men.
There are numerous jobs held by the people in the 1940 census; some of the jobs include accounting, barber, bartender, book keeper, lawyer, carpenter, cook and much more. The different jobs shows the level of education that the people that held these jobs had and how many people the criteria applied to. For example, one of the jobs held is a file clerk. Some of the people that were file clerks had different backgrounds in education. The census shows that twenty-four people completed high school and eleven completed elementary school and this shows that being a file clerk is not a job that may qualify as being of high standing or one that a person needed to have much experience in. In comparison to those that are lawyers, three people completed college and nine people went beyond a college degree and that shows that being a lawyer was a great accomplishment, one that not many could achieve for one reason or another. This pattern could be seen throughout the entire census; there are jobs that people hold which require little to no education and then there are the few jobs that require a higher education level. This comparison between the education level and occupation is an interesting one because it is evident that a good education gets you a better job but it is clearer on paper.

Data Visualization Readings and Analysis

(Mentioned in Post)

Each of the articles that I’ll be discussing are all connected by one thing –visual data. Since we’re in a digital history and class and most of us don’t have the longest attention spans –visualizing data can be an easy way out as oppose to looking at spreadsheets. However, is the grass really greener on the other side?
The main point in “How to Lie with Data Visualization” was that regardless of what the cold, hard numbers are, people and corporations can lie through the visuals associated with statistics –as its title insinuates. Though people are obligated to post the true statistics, they make negative statistics work in their favor through the way it is presented visually. For example, turning the y-axis on a graph upside down, making it seems as if numbers are decreasing while they’re doing no such thing –as in the gun control example. As a result of this tactic, it would seem that at a glance after Florida’s ’Stand Your Ground Law’, the amount of gun deaths plummeted dramatically. However, the exact opposite happened but in moving the y-axis the creators of this graph succeeded in deceiving viewers.
Ben Jones’ article (based on William Zinsser’s book) touches on 7 different points that concern non-fiction writing tips, as well as those regarding visual data. The first point that he makes regarding “The Transaction”. In other words, this is the reflection of how a creator of a visualization feels about the set of data onto the set of data itself. This was illustrated very vividly in the video included in the article. I found that the creator of this visualization is very focused on the impact of deaths as a result of guns. The creator didn’t use a conventional graph, but single, slim straw like curves so that the impact of the amount of gun deaths will truly be seen by its viewers. Not only are the amounts of gun deaths and age ranges made visual, but the years of those lives that were lost as well. This provides a different perspective as oppose to the conventional bar graph. That wouldn’t show how many years are lost in such deaths.
One of the most profound points made in the “On Visualizing Data Well” was exhibited in “How to Lie with Data Visualization”. According to Ben Jones, the humanity of the visualizer and their views are reflected in what they create. For example, in Ravi Parikh’s article, one of his examples included how people are deceived by bar graphs –such as the one attached displaying baseball stats. In this case, what John Theibault was saying regarding visualization is proven true: it’s used to quickly identify patterns in large datasets during the research process. However, what happens when data visualization is deceitful? According to Parikh, “We’re wired to misinterpret the data”. For example, in a deceitful pie chart with slices of 60%, 63% and 70%, clearly the person behind this data set used the wrong graph because these three amounts do not amount to 100% collectively. This makes viewers think that candidates (in this example) are closer or further in the race than they appear.

Why do you think some people/companies use deceitful visual data?
Would you rather to simply see statistics as oppose to visual data?
What are some examples of visual data that we see in every day culture? (Commercials, for example)