Final Project: Albany 8th Militia

Visualization #1
My first visualization looks at the relationship between the Albany 8th County Militia, their place of birth, their age and the “complexion” that each man is marked as. The use of complexion in this graph is not to examine any person in the militia based off of looks or bias, but rather to understand better the racial identity of the men in the militia. The graph itself is a stacked bar graph that’s a mix of textual and visual data. In order to fit all these categories into one graph, I not only had to use both of these types of data, but I also required the graph to be broken down into sections. Initially the graph separates the men from each place of origin. Origins other than the United States are specifically labelled by their country. In comparison births in the United States are broken down into states in which they were born. This is further broken down if the birth was in New York, due to the majority of births in the United states being in New York. The New York births are divided into county or area, since the counties of New York have changed considerably since the American Revolution. From within the category of the location of birth, the graph is further broken down by complexion. These categories are divided by broad skin based or racial complexions. Indians, blacks and mulattos are labelled by race while the rest merely state generally skin tone. Also, e more than unlikely to make more than an educated guess exactly where he came from. Which is a shame, such an interesting addIn the case of dark and brown, it’s to point out hair color, not skin tone. It’s from this category that the bars on the graph are representative of, visually showing the number of men of each complexion. These bars break down into the last category of age. They are broken into color coded sections, each representative of an age range. These ranges are each roughly ten years, except for teens, as one had to be 16 to join a militia. A filter to the right edge of the graph allows the viewer of the graph to see specifically whatever specific category they want in terms of age and complexion.

Data Visualization
My story of the men of the 8th Albany Militia in this graph reveals some data that I wasn’t expecting. In the country of origin section I think we see the most interesting piece of the story. That being that a majority of the men fighting were not born in America, but rather Ireland, Germany and England. Three countries, that in one way or another, the patriots fought in the revolution. Although the Germans fighting on the British side were mostly from Hanover and the Irish were more utilized by the British rather than willingly fought us. Still it’s fascinating to see that the men fighting for the patriots were from the same places that they fought against. These men were generally not blue blooded American born patriots but recent immigrants. I cant vouch for there motives in fighting but I cant say its for freedom from tyranny.
The complexion section tells a different but equally interesting story. While many men were from these North-Western European countries, those men were generally same in their complexion. Essentially they were mostly fair and pale, brown and dark(again in this case meaning hair color, not skin tone). What I find more interesting is that when we look at American locations of birth, we now see the diversity. Indians, blacks and mulattos pepper the categories. While not in high numbers, the presence of minorities shows two things. One the willingness of minorities to fight and die for the patriot cause, and two the beginnings of the boiling pot culture America is known for. Even so early in America’s history, this country shows its blending of peoples together for a single vision.
The age category shows a trend that while not too shocking, is interesting. A majority of the men who are in the militia are young, with twenties being the highest age range. Also surprising is that teenagers are the third highest age range, surpassing forties and fifties. These two points of information may say something about the willingness of young people to fight for a cause they believe, but probably just points to the greater numbers of young people that are able to actually fight.
Finally there are a few interesting points on the graph that are much more difficult to answer. First off, there is only one lone black soldier that stated his origin of birth as being in Europe, and that’s in Germany. It most definitely is different than our image of people who where born in Germany, especially in 1762, when there were much fewer opportunities for cultures to intertwine. Second there is one lone soldier, also black, in his twenties, who comes from Guinea. Modern day Guinea, located in West Africa, was not even an official colony until 1891. So I cannot say he was from there but I can only guess he was from the area, somewhere in West Africa. This most likely means he came to America as slave in the slave trade, which brings into question how and why a slave from West Africa became a freedman so quickly and then decided to fight in the American Revolution. Then there is one more lone man, this time an Indian, with his point of birth being written down as Portugal. It is unlikely to say the least, that an Indian would have been born in Portugal, so I can only guess that when they say Portugal on the muster roll, they really mean Brazil. For Brazil is the location of Portugal’s sole major colony in the Americas and would be the only place an indian in Portugal could likely be from. I can’t be certain but it would be a surprise if it was something else.

I chose these three major categories of age, complexion and place of birth because as I said earlier, I wanted to show a broad picture of the men who were fighting for the patriot cause. While one city’s data can’t speak for a whole nation, I think it can at least show some interesting views of Albany’s Revolutionary history. It’s also a good starting point for further research on the war. I felt that based on the information given on the muster role, that these three categories not only best showed that picture of Albany’s fighting men, but could also all work together on one graph. My original plan was to use a map to show visually the locations of the company’s births, but I ran into some problems. Mainly that the geo-dimensions in Tableau made you pick either county/region, state/province or country. With my data including all three of those and feeling that none of them could or should be compromised for the sake of a map, I scraped that idea and tested the other visualizations. None of the other visualizations I felt could fit all this information I wanted to put in very clearly, so I chose the stacked bar graph. For all three categories I had to group together certain similar categories to make the graph actually readable. Location of birth was the hardest one to create groups for since there were so many different locations and I had to combine so many of them together just to make the graph a relatively readable length. There were a number of specific locations I wanted to include but I couldn’t because there were still too many locations. The graph would not have been easily understandable. Plus sometimes there were limits to how specific I could be. Most notably, many men were listed as being born in “New England,” which since I couldn’t accurately know where that was specifically, I had to leave it as New England. It also meant that I couldn’t include specific New England colonies as locations, such as Connecticut and Rhode Island, since it would probably be too confusing to have both New England and specific locations in New England. While easier than the numerous locations of birth, for complexion I had to group similar tones or hair colors. For age, I decided to divide it into ten year intervals. From there I decided that since the locations were the most numerous category, that it should be the first to be divided. This allowed the less dividing complexion and age to both share the bars on the bar graph. With age having the fewest possibilities I decided to divide each bar by age to make it easily readable. Choosing the colors for the age divisions, I decided to make each division one color interval separate from the last. That way it would be easy to know what age range your looking at. The older you were going, the closer to green (and then finally yellow) from blue you were going.

The story or point of this graph was to show the general story of the men of the 8th Albany Militia. Specifically to answer who were these people who decided to answer the patriot call to arms for their ideas of freedom and rule. My biggest hope was to find data showing a dissenting idea of the Revolution. Since from what I’ve read on the Revolution I know it was not the grand, united, nationalist, patriotic fight against tyranny that we are generally lead to believe. Or better put by E. Wayne Carp of Pacific Lutheran University, “In this nationalist version of history, a united, freedom-loving people rose up in righteous anger at the King’s tyrannical actions, grabbed their trusty flintlocks, hid behind trees and walls, defeated the dull British soldiers who were sitting ducks in their scarlet uniforms, and established the United States of America.” Instead the ideas and acts of the Revolution were widely contested across the Colonies and estimations hold that as high as 20 percent of Americans joined the Loyalist cause. Combined with Native American tribes that were pulled in and forced to pick a side, a certain civil war over the question of how the colonies should be run and who will run it was created. Contemporary historians of the Revolution, such as David Ramsay and Mercy Otis Warren, even agree with that sentiment, declaring the Revolution “Originally a Civil War in the estimation of both parties.” This sentiment has only grown in recent times and the view of the Revolution as America’s first Civil War is widely debated among historians. Continually increasing numbers of modern historians, such as Alan Taylor agree the the Revolution was America’s first civil war. Writers like Thomas B Allen write about the extensive and underestimated Tory contributions to the British Cause. Others like Richard Berth tell the stories of the frontier, where communities and families amongst whites and Indians were broken up to decide the answer. Some historians, such as Thad Tate and Peter Albert, argue that during the war, some places devolved into violent anarchy reminiscent of what Thomas Hobbs wrote about. It’s a hard question to answer, if it can be accurately described as a Civil War, with the idea of what a Civil War is changing since the time of the Revolution. Either side you chose, generally it was about normal people, many with little to no opinion on the matter, caught in the ideas and fights of who could run this land better. This visualization I believe goes a long way in showing a story of the men who fought in the Revolution. It’s not quite the initial image of Civil War I was intending to portray, as it rather depicts England’s own people fighting against them. Still it makes a certain amount sense. With America and England extensively linked at this point, one could imagine that if Americans’ were fighting each other over who could rule it best, then England and its holdings would be fighting each other over the same idea. Also this only shows the patriot side, not telling us how many citizens of Albany disagreed and fought against the Patriot cause. With so many of Albany’s fighters being from these european countries, its not hard to imagine that others born in England or Ireland may have joined the other side.

Further Research Questions
There are a number of further questions I would ask after examining the graph. First there are the several outlying pieces of data that I described earlier. Where was the Indian from Portugal really from? Same goes for the Black soldier from Guinea? and why was there one (or only one) black soldier born in Germany? I think two of these questions are fairly easy to answer. Looking back at maps or writings of the area at the time, one could find out what place or area was called Guinea at or around the time of the Revolution. One could also look into Germany’s history in the slave trade and in Africa to see the prevalence of slaves or black workers in Germany in order to see how common black immigrants from Germany really were. As for the Portuguese Indian, I feel it’ll be harder to find out where exactly he came from. Unless they referred to Brazil as Portugal at the time or there is specific writings about that man, it’ll bition to the Militia and his story I can only guess is fascinating. Beyond that It would be great to look into the number of Loyalists that came from Albany to compare to the Patriot Militia. It would be tough to find this out, but possible. One would have to look into various loyalist companies in New York and possibly beyond. Also one would have to look into writings in Albany to see who left and who was suspected or known to be a loyalist to find those who fought with the British but didn’t join a loyalist company. Finally I would like to know how similar the place of birth was for other cities and militias in comparison to Albany’s. Were most Patriot militia’s made up of manly immigrants, or was Albany’s an outlier? It’s easy enough to say you would look in to the other muster roles to check this data, but I don’t know for certain if other muster roles would contain the same information as Albany’s.

Works Cited

Allen, Thomas B. Tories: Fighting for the King in America’s First Civil War. New York: Harper, 2010.

“Guinea Country Profile.” BBC News. December 15, 2015. Accessed May 2016.

Berleth, Richard J. Bloody Mohawk: The French and Indian War & American Revolution on New York’s Frontier. Hensonville, NY: Black Dome Press, 2009.

Carp, E. Wayne. “The Wars of the American Revolution.” Am Rev Essays–Carp. Accessed May 2016.

Cutter Ham, Tom. “Was the American Revolution a Civil War?” The Junto. 2014. Accessed May 2016.

Minty, Christopher. “Seriously, Though, Was the American Revolution a Civil War?” The Junto. 2015. Accessed May 2016.

Visualization #2
The second visual I created based around the Albany 8th Militia centers around the occupations of the men of the militia and relating it back to their age. The graph itself is a highlighted table graph, and is largely textual and numeric, with added visual elements to help display all the information present. The vertical axis of the graph presents the relative ages of the men of the company. Much like the first visual, the ages are grouped together by decades. The horizontal axis presents the relative occupations that were listed on the muster role. Some occupations that are similar in nature have been grouped together. In each table in-between these axis are numbers designating how many within an age group were listed as a certain occupation. To better show those numbers in a way that’s more visual and immediately understandable, the tables are also color coded. Each table on the graph is shaded in a hue of green depending on how many people in an age group perform a certain occupation. The more people that perform that occupation, the more green the table is shaded. The color scale is from 0 to 222, with that being the highest number in the graph. At 0 the table will have no green and be almost tan in color, while at 222 the table is a solid olive green. This is so the viewer can have a generally idea of which occupations or age groups (or both) were higher without having to read the number of each table.

Data Visualization
This graph isn’t initially groundbreaking in its revealing of the story of the city of Albany through the lives of the Albany 8th Militia, but none the less it presents some interesting points of data. First of all, by and large the biggest occupation amongst the men of the Albany 8th Militia is laborer. Not only do we see the highest single table on the graph at 222 for laborers in their twenties, but at each age group laborers are the highest by a wide margin. On one level that’s not too unexpected, most people can’t be specialized labor and there must be people to do the hard manual labor. On the other hand this may say something about the growth of Albany. Such a large amount of laborers would only be necessary if there was enough work in the city for that many laborers to be needed. Combined with such a small amount of men in other occupations, with further research it could be argued that this is showing signs of growth in the city of Albany. The high number of laborers would then be indicative of doing the immense amount of manual labor required to expand a city.
Also particularly fascinating about the graph are the types of occupations some of the men of the militia performed. For specialized work, it’s largely delegated to the making of clothes and materials. Following up laborers in number of men are wood workers, cordwainers(shoemakers) and tailors. These jobs make sense, as the people of Albany would have needed these commodities before the days of manufactured goods. Today though, technology and corporations have made these goods sought by the citizens of Albany easily available, making these jobs much less necessary. While these occupations do exist in cities to this day, they are by no means high employing jobs.
A number of jobs show how far removed from the days of the American Revolution the city of Albany and the World have become, since these jobs have long since become obsolete. Specifically the job of apothecary, which could only exist in a world were modern medicine and science have yet to accurately cure diseases and ailments. Also that of a hatter, which of the time meant making top hats from beaver pelts. This not only is extinct due to the decline in top hat sales, but also due to the health and mental effects making the hats gave to the hatter(“mad as a hatter”).
Other jobs, while they exist today, would be uncommon to find in any major urban area due to the proximity of the jobs to a city. Specifically farmers, which while there are not a lot in the militia, is enough to make it an obvious way of living in 18th century Albany. Today the immense size of cities as well as suburban sprawl would make the job of a farmer near impossible in a city. Also the job of a miner, as there are two in this militia. It’s not known what they mine for but any place that could’ve been mined in Albany would have long since been built over to make way for city expansion. Finally there are a rather high number of sailors among the militia. While Albany indeed was built on the Hudson River for the advantages of traveling up and down the river, river traffic on the Hudson has long since been relegated to recreational boating. Technologies like planes and cars, and the development of highways has made the occupation of sailor obsolete in Albany at least.
Lastly of note are the 10 men with a null occupation. Im not sure if this means they didn’t want to put down their occupation for some reason, or that they had no job to put down. It would be interesting if there were 10 unemployed or even homeless men signing up for the militia.

When making this visual, I wanted to make sure I didn’t repeat my first one and create a bar graph. After exploring the various other graph and visual options, the highlighted table graph seemed to work the best. Not only is it a visually unique graph, in comparison to the graphs that I’m familiar with, but I particularly enjoyed the gradient shading of the individual tables. It made it a more visually appealing graph while presenting the information I wanted in way that made it quickly understandable. You didn’t have to specifically read the numbers presented, the green shaded tables immediately show you what was common and what wasn’t.
The hardest part of making this visual was condensing the immense number of jobs so that the graph could actually be readable. There were so many occupations listed that keeping all of them would’ve taken a viewer far too long to take in all the information. Deciding which jobs to group together was tough, as deciding which jobs were technically close enough to be the same category was a decision I didn’t feel qualified to make. I chose to include a couple “and this” labels in the categories because I felt the occupations were unique enough to be included. Each grouping I made weakened the overall view of Albany so I wanted to keep as much unique information in the graph that I could while still being visually readable. There was at least one occupation that for the life of me I couldn’t find anything about. I’m not sure if they spelled it wrong or if I didn’t look in the right places, but I have no clue what a “bellis maker” is, so I was forced to include it in the null category. Fortunately it was only one job so it didn’t askew my story too much.

I had expected the data to present a very different image of Albany than it ended up presenting. Albany had initially been established by the Dutch as a trading post, and while that was in 1621, the city had long after been a place that attracted fur traders and other merchants. On the edge of the frontier, even as the Revolution arrived, the thought was that the men of the militia would represent this image of a wilderness trading hub. It was much to my surprise when only one merchant was on the muster roll. Now its very true that this does not give a definitive look at the occupations of the people of Albany. It may be that the merchants did not sign up for the militia, perhaps in fear of losing goods or money. At the same time though, its a rather large sample size of Albany’s men, one would expect at least a few would enlist. After some quick research to show if this trend was in fact true, one source claimed that the fur trade did decline around this time in Albany, but wood and grain were then traded in Albany. While that would make it still expected for merchants to be present, the trading in lumber would go a long way in explaining the larger number of woodworkers in the city. Woodworkers did make up the second highest category, so perhaps these men were engaging in trading as Albany shifted its source of wealth.
As for the incredibly large numbers of laborers, I had earlier guessed that based on supply and demand, they were needed for jobs required to keep up and create an rapidly expanding city. It could also be that low income laborers merely made up the militia since they had the least to lose by going to war. When looking at the history of Albany though, the idea of an expanding Albany seems less farfetched. The end of the French and Indian War in 1763(1761 for North America) brought peace to the frontier and allowed Albany to grow without fear of raids. George Baker Anderson’s Landmarks of Rensselaer County, New York, claims that after Burgoyne’s failed invasion of Albany, the loss of a serious threat brought in a large influx of settlers from New England.The growth became so great that by 1786, Albany was the 6th largest city in America. Albany’s safety on the frontier brought it growth in the Revolution and it’s more than likely the same occurred after the French and Indian War. So it seems more than possible that the exorbitant numbers of laborers are in Albany to meet the influx of settlers to Albany and the growth to the city that such an influx entails. If that’s not the real reason for such numbers of laborers, it most likely became the reason, as such an expansion to a city could not be accomplished without a strong labor force. It may even be that the militia shows inadvertently the city of Albany on the precipice of its emergence as an important urban center that would in time be large enough to become New York’s Capital.

Further Research Questions
There are a number of small questions about the data that I think would require further research to answer. I personally would like to know what a “bellis maker” is, and I guess it could be done by looking into records of Albany to see if such an occupation existed. Or perhaps by doing better searches online to see if anyone actually knows that answer. Also I would like to know what the nulls in occupation mean. Do they really mean that they’re unemployed or is there another reason behind it. That could be answered by one looking at other Albany censuses or records to see if they tell what these men did. Finally I would like to see if there were more merchants in Albany than the muster rolls shows, and if their were, why they didn’t join the militia. Again you’d have to look into Albany censuses or Albany records to see if they have other merchants in the city. As for why they didn’t join the militia, save for a personal journal from a merchant or from another Albany resident that explains why, it would unfortunately be very difficult, maybe impossible to find the answer to that question.

Works Cited

Anderson, George Baker. Landmarks of Rensselaer County, New York. Syracuse, NY: D. Mason &, 1897.

“The Meaning and Origin of the Expression: As Mad as a Hatter.” As Mad as a Hatter. Accessed May 2016.

“The Official Site of the City of Albany, NY.” City History. Accessed May 2016.

Olpalka, Anthony. “Albany: One of America’s First Cities.” One of America’s First Cities: Colonial Albany – Oldest US Museums. Accessed May 2016.

“History of Albany New York.” Wikipedia. Accessed May 2016.,_New_York#cite_note-mceneny56-34.


Data Description: Slave Sales 1775-1865

Data set Info:

The data set includes information about each individual slave, information useful for their upcoming sale. The data has numeric information, text and geographic information. The spreadsheet consists of nine columns. The geographic information is the state and county column, the states shown are Georgia, Louisiana, Virginia, North/South Carolina, Mississippi, and Maryland. The county column lists a variety of counties within each state where a slave was sold to give a more accurate account of where in the state the slave was sold. The numeric information is split up in the date of the entry of slave information column, the age of the slave in years and month’s column and the appraised value column. For the year column the dates start at 1775 and continue to 1865 but the spreadsheet doesn’t go in order by date so the numbers jump around quite a bit. The other two columns describe the slaves age in years which tends to vary from old to young but more often doesn’t have any information at all and months column is completely empty I believe due to the fact that very few infants were sold. The last numeric information is regarding the appraised value of the slave which is varied based on the age and skill and defect of the slave. The final text information is in three columns that include the slave’s sex, skills and defects. The sex of the slave is broken into male and female. The next text column is the skills column, in which the men had skills listed as cabinet makers or gardeners and women would be cooks or midwives. The final column is the defects column. This column shows slaves that were noted with flaws. These could be as simple as too old or too young, any type of sickness they might have or if they were disruptive.


This information draws a lot of connections between the rows and columns. Many can be found and expanded upon. I believe the most notable relationship is the appraised value and the rest of the columns. The amount of money willing to be paid on a particular slave is changes often depending on the other columns information; gender, age, skill and defect can alter the price in any given state or county. A young male with a skill would be much higher price than an older female with a defect. Another relationship found is the one between the date in which the slave was sold and the location. Possibly revealing that in certain states and specific counties experienced a much later or earlier slave trade. Could be from slavery expanding to other states more aggressively or slowing down much later in other states. Maryland and South Carolina have some of the earliest dates on the spreadsheet, then every other state tends to pick up during the 19th century. Could be due to policy changes that America was facing that effected slave trade. The next relationship found is an obvious one between the male skills and the female skills. The males had skills that were using their hands like cabinet maker and gardener while the women had more domestic jobs like cooks or caring for children. A relationship I would be interested in discovering is one that would relate defects to either age or gender, specifically a defect that dealt with disobedience.

Process documentation:

When choosing which visualization to do I wanted to have almost all of the data set represented as best as I could for my argument. The first graph I used was a bar graph. This was to show the different skills used by male and female in each state.

First the states were split up with the state name at the top of the graph, there were seven states represented in the data set. Showing each state starting with Georgia moving forward alphabetically ending with Virginia. At the bottom of the graph each skill set is included based on which state there were records in. Some states like Louisiana have numerous skills listed fifty nine in total. The state with the least amount on skills on record is Tennessee only have two skills listed and only three records, cartman with two male enslaved occupants and one male blacksmith enslaved person, the appraised value for those records wasn’t listed. Once each skill was listed the number of records each skill had is placed inside the bar graph. Knowing the number of records helps with the appraised value averages within the state. After they are given recorded numbers for each skill that number is further split by male and female. The skill is listed at the bottom the male bar is labeled with a green color, and the female bar is placed directly on top of it labeled with an orange bar. The then number of records for each skill is placed inside or just outside the bar. It was important to split male and female records for my argument that is based on how males and female enslaved peoples values are varied based upon skill, especially when males and females have the same skill set.

The second visual I chose was a tree graph based on appraised value for enslaved persons based on gender and defect. To start the graph I split up the enslaved persons by gender men, then added the defects they were labeled with excluding the records that had no defect mentioned. Then I added appraised value as an average instead of a count. This made the tree graph to get rid of the outliers that were taking up big spaces. The color was then chosen from dark red to light pink to show a decrease in appraised value. When choosing to how to label the graph I wanted to keep it similar to the first graph. The first label is the gender of the enslaved person, the next is the defect they were given and finally the number of records that were given for that specific enslaved persons defect. In the first box it is labeled male, broken back and the number three, all those are then used to determine the appraised value that can be shown once the cursor is over the box. The dark red indicates that is the highest average appraised value, in this case having a broken back was the labeled defect for enslaved males and being deaf for females. While the light pink shows the lowest appraised average value, enslaved males and females both show being crippled as the defect.

Both graphs show the best visualization for my argument. From the design of each to labeling and color. My argument becomes more clear with these distinct visualizations.

Story for Visualization:

Comprising the Slave Sales data set, I found the visuals that best represented my arguments by using a tree and bar graph.

The first bar graph shows the break up of each state. By splitting the data into separate states you get a better understanding of the differences that occurred in each state. Although all states mentioned partook in the buying and selling of enslaved people there were clearly differences in each. The differences were shown in the appraised value how much each state valued and enslaved male or female with a particular skill. Then the graph is split into each specific skill within the state. Some states had multiple skills, Louisiana had fifty nine skills that an enslaved person could have, among those skills were brick mason or carpenter usually occupied by men or cook and house servant usually occupied by women however, those skills did see it’s fair share of men. Then other states had very few skills. North Carolina had only six skills listen among those skills there were only sixteen records shown, that wasn’t even the lowest state with skills recorded. Tennessee only had two skills listed and three records of enslaved males with those skills. The story you get from this bar graph is how enslaved males and females were valued. During this barbaric time in American history the data set showed how men and women were bought and sold. My first graph explored the differences of the value of enslaved males and females when they were given a particular skill. By adding skills and gender I sought to give more human traits to just numbers on a paper. When listed on the excel sheet you see price but no mention of a name. Gender and age was listed but when adding a skill there is a more human trait added. This person was capable of doing something. Some of the enslaved males and one enslaved female were mechanics. That’s not a skill just anyone can pick up, it has to be taught and understood and then to be given that skill and it effect a value you must be decent at the job. For enslaved females across the listed states a common skill that affected their appraised value was that of a midwife. Again this is another skill that comes with a great deal of responsibility certainly more than working with machines these women worked to care for other pregnant women and then care for the mother and the child post pregnancy. A highly valued skill and very human trait.

The first graph I wanted to show optimism in a dark place. By showing the skills you see the human traits instead of just numbers. With the second graph the story becomes much harder to understand. In the second graph again there is split between male and female enslaved people. This time it isn’t the skills that are highlighted but the defects an enslaved person might have been given. Unlike the first graph there is no mention of states. An average appraised value is then given to the defect mentioned and the number of records are shown for each defect. The highest appraised value for an enslaved male to have is $870 with a defect of a broken back, the lowest is $5 with a defect of being crippled. For enslaved females the highest appraised value is $800 with the defect being deaf, the lowest appraised value is $.50 wit the defect also being crippled. The story of this shows more of the cruelty of slavery. Enslaved males and females were given defects and still bought and sold. Some of the defects mentioned were just the mere fact of being a child. Enslaved boys and girls were not able to escape the cruelty of being bought and sold. Other defects that were especially painful to come across were talking back, one can only imagine the suffering that was imposed upon a male or female in slavery when they disobeyed orders given. There was also a defect of pregnancy, the thought of being moved around while carrying a child isn’t easy. There were other defects that one couldn’t help, like having cancer or being blind. What these enslaved people had to endure must have been harsh and I cannot imagine having a sickness on top of living the life of an enslaved person. I highly doubt they were treated kindly. The second graph shows more of the cruelty given during these times. How these people that were just look at like property suffered more than we can imagine.

When deciding the two graphs it wasn’t enough to just highlight what the data set showed. The story that I was hoping to express was the people behind the numbers. When the data was converted to an excel sheet the story of the individual was still not mentioned. Forming the data to show more human traits shows more than the numbers could. Seeing a person being appraised for $500 is one thing but to know that the enslaved person listed was deaf adds a deeper component. The same goes for skills shown. Seeing an enslaved woman being appraised at $460 might not make anyone think twice about who she was, but giving her a character trait that she was a tailor puts more humanity to her. These aren’t just numbers they are people that suffered with things we still suffer from today and had skills that we still value.


Slavery plagued America like so many other countries in the past. Today slavery still isn’t completely eradicated from the world however, we are still examining the troubles that occurred on our own soil. With the help of the data set of slave sales used you can get a clearer idea of what happened during the times of 1742 to 1865. Arguments can be made based on the way one chooses to construct the given numbers, dates and text shown. Although it hardly paints a full picture some conclusions can be discovered. The way I choose to arrange the data set was split between male and female and their value based on skills and defects.

The data shown in the two tree graphs labels each enslaved person with a particular skill on one and a defect in the other. From there the data is then broken down into male and female showing each skill men have and on the other side women this is also shown in the defects chart as well. Next the tree graphs shows the appraised value a male or female enslaved person would receive upon having a particular skill or defect. Although values for enslaved people rises during the years there is a connection between having a particular skill or defect that can also affect the value. Skills are shown to commonly be separated by gender; this might make sense in a southern society that believes in gender roles even when it comes to enslaved people.

Males tend to have more of the common masculine skills, from mechanic, blacksmith, shipbuilder and cartman or someone who drives a horse carriage. Based on these skills their values varied accordingly, the average appraised value for a mechanic was around $1,200 which is the highest appraised value for a male enslaved person with a skill. The lowest appraised value for a male enslaved person with a skill was $250 as a rope maker. Both the highest and lowest appraised valued skill tends to be a more male oriented occupation.

The females had skills that varied in more of the famine roles and occupations. There were skills in hairdressing and seamstress. As well as cooking, baking and other household skills including raising children and laundry. The highest appraised value for a female with a skill was $1,000 as a hairdresser, the lowest appraised value was a spinner at $200.
Already there is a pattern where the men enslaved persons are getting a higher appraised value then the women enslaved persons the argument can be made that the skills were different from male to female but that isn’t always the case. There are particular jobs that both men and female enslaved people share that aren’t distinct between male and female gender roles as well as certain skills that are shared among the two genders that do cross gender roles. Jobs that have no gender role for an enslaved person in the south would be that of a labor or field worker. When thinking of slavery in the south the image that appears in the mind is that of both men and women doing hard labor on a plantation. Labor work has no gender role and the male enslaved are valued at $630, while women enslaved have an average appraised value of $550. There were also skills that crossed across gender roles. Enslaved females also worked as mechanics. There was one recorded record of an enslaved female during the data set timed period in the small number of states listed that had the mechanic skill. Her appraised value was $600 in the state of Louisiana where there were thirty five male mechanics all valued at twice her rate.

Looking at skills there is a pattern of women enslaved people being appraised higher than male enslaved people. Of course there are some outliers like in the case of South Carolina, a more male gender role skill of a driver, the enslaved male tends to be appraised higher. This is due to the fact in the other states mentioned there isn’t a female enslaved skilled driver. However, in South Carolina there is one recorded of an enslaved female skilled driver and she was appraised at a much higher value of $1000 than the enslaved males appraised at $600. The same goes for enslaved males in female gender roled skills. In most other states labeled the female enslaved person with skill of baker or cook they are appraised at a higher rate than the enslaved males with the same skill. Again in South Carolina you see a different pattern where the enslaved males with the baker or cook skill are appraised higher than the females. In this case the male enslaved person has the average appraised value of $300 while the enslaved female is appraised at $125.

Although there are peculiar cases when the gender role skill has the opposite effect on the average appraised value enslaved males still tend to be appraised higher. When you take out skills completely and focus on the tree graph you see the effect that defects had on the appraised value of enslaved people.

The second tree graph shows the effect of average appraised value when it comes to defects that enslaved males and females were given during the time period. For almost all defects labeled, it is shared between male and female, the only difference being enslaved females being pregnant with nine records shown and the average appraised value of $475. This would be impossible to compare to an enslaved male defect. But for the rest of the defects there is a record for both male and female enslaved persons. The highest average appraised value for an enslaved male with a defect is a broken back, with three records shown the appraised value is $870, while the enslaved female with the same defect is appraised at $320 with five records shown. That pattern of enslaved males being appraised higher with defects continues for almost all defect labeled. The only difference being enslaved females with the defect of being deaf are appraised much higher than the enslaved males with the females being appraised at $800 and the males appraised at $550.

Today there is still a gap in equality among men and women and appraised value. The case shown in the graphs below are just a more barbaric viewing of still present inequality. But the argument remains the same and the data set further proved a point of how men and women are still not equal and how much work we still need to do.

Research Questions:

When diving into this particular data set it was hard to comprehend exactly what I was looking at. Shown nameless it was often forgotten that the numbers on the excel sheet were actually people that were bought and sold in the same country I call home, and a practice that just ended a few hundred years ago. The data set had to be take apart and manipulated to give these numbers traits that went beyond an appraised value. However further questions still remain when trying to get more human traits out of these numbers.

When the information was put into excel I have doubt that the original data set had names. That would have been a useful tool. Giving more humanity to the numbers and values and skills would have brought more meaning to the data set. It would have been interesting to know where these enslaved individuals moved to and where their linage ended up. By gaining names along with the skills it would have been interesting to see how far a particular skill lasted in the linage or if the skill was a helpful tool when slavery had ended. Same can be said for given names to follow along with the defects. If the pregnant women had successful child births and did the children have to suffer like the parents did.

When looking at skills I wondered how the skills were obtained. If it was taught from enslaved person to person or from another source. And then how useful that skill was in helping that enslaved person from day to day. If it meant they were given a less harsh treatment or if they were given a harsher treatment because having a skill meant they could do more work. I’d also be interesting in knowing how the enslaved people with defects managed their day to day life. Some had cases of missing fingers, that’s not easy to deal with but it is not as bad as being enslaved with cancer or being blind. I wonder If they were able to see doctors or if they died was family given bereavement time, did they have family at all.

Finally my question would be of skills and defects in northern states. The data set shown is only the southern states which already has the awful slavery reputation but the north still partook in the terrible act. I’d wonder if the skills were the same or if there were skills not needed for the south. I’d like to know how skills and defects affected appraised value in the north and if the enslaved people with a sickness were even considered a defect or was life for an enslaved person in the north more humane.

Adding more information to the data set would have helped my argument especially when comparing and contrasting the differences between the males and females. It would also help when giving more human traits to the enslaves people.

1883 Pensioners Final Project

Data Description

For this project I chose to look at the 1883 Pensioners. I wanted to explore a data set that involved some aspect of war but with a different twist. When we look at war data it is usually casualty figures that are the main topic of discussion. People forget that for those that are wounded and the families of those killed in action, some sort compensation, a pension, is given as a way of saying thank you for your service and to help alleviate the difficulties that may result from their time in the service. The reasons for the pension can be anything from visible wounds, to psychological trauma, to the loss of the head of household—the breadwinner.

When looking at any given data set, you are almost certainly going to be given numeric data, textual data, and/or geographic data in some quantity. In regards to the 1883 Pensioners data set we are given all three. But, each is represented in a different amount. The textual data is on a very large-scale, as is the numeric figures. There are dozens and dozens of different categories for wounds. From gunshot wounds, to diseases and illnesses, to amputations, to being widowed, the list is extensive. We are also given the names of each person and the month and year of the first pension claim.

For each category that is listed, a number of sub-categories of numeric data is also given. The most interesting of which comes in the form of the monthly rate of payment received by the individual. With this number you can then twist it around to see the average monthly rate, the median monthly rate or the monthly sum. What this allows someone reading this data to do is to compare and contrast the various pension claims to see how they stack up in severity, in terms of monetary compensation. Another aspect of numeric data is seen in the number of records of each pension claim. This too allows us to see what the largest pension claim was.

Geographical data is present in this data set. However, an address is not given. Instead, it is referred to as “Post Office.” This is the city or town that the individual is currently living in at the time of pension submission. This does not provide much in terms of useful information as this list includes individuals strictly in the Capital region. If an address had been provided, we could then create a map of the city of Albany along with the various outskirts and examine just where these veterans or their families are living. Are they generally in poorer neighborhoods as is the general trend with war enlistees? Is it an area that is higher concentration African-American or Caucasian? Unfortunately, when this list was compiled they were not expecting someone to look at it decades later and attempt to create patterns and trends from it. No, it was instead meant to organize those that were receiving government pensions for their role in the Civil War.

Data Visualization 1

For my first visualization, I wanted to take a look at the number of records for claiming a pension. In this visual we will be looking at the “Number of Records” tab.

At first glance it does not look like much. I wanted to create a story that correlated with the figures that show that most of the deaths associated with the Civil War were in fact, not combat related. When I say combat related I am talking about gunshot wounds, hand-to-hand combat and artillery barrages. Instead, the number one killer of soldiers, on both sides, was dysentery ( Most of the soldiers that fought during the Civil War were from the rural countryside. They lived their lives on small mom and pop farms and interacted with only a handful of other people outside their community. When you do not have much contact with significantly larger groups of people, your immune system becomes more susceptible to contracting diseases and illnesses that others, from a city for example, would not come down with. For this reason, a substantial number of the deaths during the war were from disease as these farmers interacted with hundreds and thousands of other men for the first time. Other illnesses include smallpox, malaria and chicken pox. Because the medical field had not advanced to the point of having proper medicine to treat this diseases and viruses, large outbreaks were not uncommon with both the Union and Confederates.

While it was my initial goal to show this pattern in my data set, the opposite, in fact, developed. Now before going any further I need to emphasize something about this data set. This data is not an accurate representation of the makeup of soldiers in the Civil War, on either side. It is a rather small sample size of just under 1,000 claims. A claim does not necessary entail that they played a direct role in the Civil War, rather it could be the family applying for a pension for a deceased family member. In looking at the data in the “Number of Records” tab, we see that the largest number of claims fall under the category of Mother/Father/Minor, followed by General Wounds, Gunshot Wounds, Loss of Limb due to Combat, Disease, Injuries, Other, and lastly Amputations. According to this graph, there were a total of 918 records. 408 of these fall under the category of Mother/Father/Minor and only 51 falling under Disease. This data set is telling a different story than what the national story is. Instead of having most of the cases be relegated to be disease related, they are instead a second-party claiming the pension, for example a widow. The General Wounds are second with 215 respective cases, these being injuries as a result of chopping wood or breaking a bone. While there is a significantly large difference between claims as a result of disease or illness and being widowed, it can be safely assumed that a portion of the widowed claims are a result of their loved one dying from some sort of disease.

Process Documentation 1:

All of the groups that are represented in all my visualizations have numerous sub-categories within. For example, if you look at the Gunshot wounds bar, what you have is different types of gunshot wounds making up the 119 total claims. There are gunshot wounds to the head, face, jaw, left leg, right foot, etc. This visual is fairly new. When I first set out on this journey, this particular example was not composed of a neatly grouped list. Instead, I had every single different type of gunshot wound, disease, illness; you think of it and I probably had it listed. Now you would think that the process to create categories to house all the various wounds would be fairly simple. Well, you would be dead wrong. You see, when each reason for filing a pension claim was originally listed, there was no universal language or code to organize things. It was all dependent on the person writing at that moment. Each person had their own unique abbreviations and wordings for various items. This meant that some reasons could be grouped under more than one category. After I was able to discern what should be placed where, I chose to use the default colors that were given to me each time I dragged the “Grouped Wounds” Dimension into the table. These colored groups represented my columns. Next, for my rows, I decided to utilize the “Number of records” measure to show the amount of claims filed in each bar. It was not complete but to further show the differences between each column, I chose to show bars with higher amounts of pension claims as larger in width than those with much smaller amounts. While this makes sense, in practice it can create a problem in viewing such thin bars.

Argument 1:

As I mentioned earlier, the main argument for this particular visualization is that disease and illness were not a particularly large contributor to pension claims, in terms of this data set on the Capital region. Poor hygiene along with interacting with large groups of other soldiers resulted in numerous cases of malaria, dysentery, etc. Outbreaks within regiments were not uncommon and men would often be forced to leave the service to due being sick. This is backed up the need to apply for a pension. Because the people listed in this census are from a highly populated region in the northeast, for the most part at least, their immune systems have been able to build up some sort of tolerance to the various illnesses out there. I would argue that this is the main reason for the low numbers of disease and illness claims filed by these men and their families. This assumption is supported by looking at other pension records that are available (Google Books: 291-303). For example, if we were to look at the city of Utica in Oneida county, we would see a rather large list of wounds that pertain to injuries sustained in battle or by other means. While there are cases of diseases and illnesses, they are trumped by the various wounds. We can also look at the town in which I grew up in, Clinton, also in Oneida County. By today’s standards, Clinton is a very small village. At last check, the population was just under 2,000 so we can only imagine how much smaller it was over 150 years ago. Out of the list of pensions for Clinton, there is only one entry that claims an illness: dysentery which was the number one killer during the Civil War. But the remaining entries are either combat related or because a widow or family member is claiming the pension because they lost a family member in the war. The north was much more populated than the south during the war, same goes today as well, so it makes sense that most of the pensions were in response to wounds other than disease and illness. If you look at South Carolina, (Google Books: 184-189) a startling trend emerges. Here we see the opposite of the north. Instead of mostly gunshot wounds, we see an overwhelming number of widows and illnesses. This can be explained by the sheer number of southern males that were lost during the war as a majority of the fighting took place in the south. The south is also nearly exclusively rural, hence the hundreds of plantations, so the increase in illness cases can be explained by the weaker immune systems of the Confederate Army.

Data Visualization 2:

For my second visualization I chose to examine the role of women during the Civil War. While women have always played key roles in every conflict the United States has fought in, the exact extent to how much they can be involved has always been controversial. Only in the past few years has combat roles been opened up for women to be involved in though they are required to pass the same physical requirements as their male counterparts. But during the Civil War women played a much more behind-the-scenes type of role. Most often referred to as Camp Followers, these women did exactly as their title suggests: they followed the train of soldiers. These were women who, generally, did not have much at home after their husband, brother, father, etc., went off to war. If they had no means to sustain themselves and make money, they often followed their loved ones as they went off to war. They acted as cooks, nurses, cleaners, and prostitutes. Some even went as far as dressing up as a man (Sam Smith) and fighting on the front lines, some even died ( While they no where were close in numbers as the men, it is believed that around 500 women secretly fought in the war. Probably the most famous woman fighter is that of Jennie Hodgers, better known as “Albert Cashier” (Civil War Trust). She enlisted in the 95th Illinois Infantry on August 6, 1862 and would go to fight in over 40 different engagements. It is also believed that at one point she was captured by the Confederates but she broke out of prison and returned to duty. She served three years before her unit was discharged for heavy losses due to combat and illnesses. But her story does not end here. She went on to continue living under the guise of a man, collecting a pension, and would only be discovered in 1910 when she was hit by a car, though the hospital kept her secret quiet. However, 3 years later, as dementia set on she was discovered and forced to live the remainder of her life as a female. She would die 2 years later and be buried in her uniform with full military honor.

Process Documentation 2:

The creation process for this visual is nearly identical to the first. The main differences here are the different dimensions and measures that were used. I chose to have a horizontal graph for this one in order to break the data up into male vs. female. In terms of data, I included type of injury sustained, the average monthly payout and the number of cases for each category. As with the first visual, I created larger bars for the categories that included larger number of records and smaller bars for the categories that included a small number of records.

Argumentation 2:

As I have mentioned earlier in the first visual, this data set is a rather small sample size. To accurately create a significant argument, we would need to also be graphing other 1883 pensions from nearby counties. Luckily, Albany county seems to have had its fair share of females taking part in the fighting during the Civil War. Whether they were injured while fighting or were simply in the wrong place at the wrong time as a camp follower is impossible to know for they may have never been discovered. In any case, what we have in our data set is 2 records of females sustaining gunshot wounds and then gaining a pension as a result. In scouring the nearly 1,000 entries, I was unable to find their names which may have led to further research and possibly finding out if they had in fact disguised themselves. Nevertheless, these two women received an average pension of $4.00 a month compared to her male counterpart whom received an average of $5.93. The sample size is way too small and distorted in favor of the men with 117 records of gunshot wounds so it is difficult to say whether they received such a low amount because they were women or because their wounds were not as severe as some of the men. In any case, they did in fact receive a pension for gunshot wounds. Even if we take out the gunshot wounds from the data, historians know for a fact that women played a key role in the Civil War. Clara Barton herself, founder of the Red Cross, was a nurse during the war. Women took on every aspect of the war that the men did including fighting and performing dirty tasks such as amputating limbs. While historians acknowledge their contributions, I do not believe classes teach how much women played a direct role in the war. Up until the Fall of senior year in college, I was unaware of the camp followers. To show that they played this large role in the war, we should be teaching more about them.

Further research Questions:

  1. Were pension claims during the Civil War and claims 20 years after the end of the war greatly differ in terms of monthly payments for similar injuries?
    1. To figure this out I would rearrange the data set in chronological order. I attempted to do this but my efforts were futile as I could not figure out how to correctly get Tableau to do it. With a lot of time on my hands I suppose I could create an Excel spreadsheet that is in chronological order and could even uncover more patterns in the data that we are unable to see otherwise.
  2. Did the pensions differ based on whether you served as a Union soldier or a Confederate?
    1. In a preliminary scour of the Arkansas, Missouri, and South Carolina pension rolls, this would seem to not be the case. No matter the wound, you received nearly the same amount whether or not you were a northerner or a southerner.
  3. Were African-American veterans afforded the same treatment by the Pension Bureau?
    1. When I first chose this data set, I was under the belief that this was a list of African-American soldiers and their families that were receiving pensions. I quickly learned the opposite, that it was probably whites that represented the bulk of the data. That is not to say that African-Americans are not present on the list, however I would imagine if they were a significant contributor to the war effort (which they were), wouldn’t the Pension Bureau have a separate column for race? Every 1883 Pension roll uses the same layout with the name, record number, cause for pension, etc. None of them include the race of the individual. So this begs the question of if they just did not deem that important enough to distinguish in the records or did they just not offer any pensions to African-American veterans. This would, of course, violate the 14th Amendment. I would bet that with some more digging, one could uncover the records of African-Americans receiving pensions, I just do not know where to look for that.



“620,000 Soldiers Died during the Civil War, Two-thirds Died of Disease, Not Wounds: WHY?” Civil War Trust. Accessed May 11, 2016.

Council on Foreign Relations. Accessed May 12, 2016.

United States Pension Bureau. “List of Pensioners on the Roll January 1, 1883.” Google Books. January 1, 1883. Accessed May 11, 2016.

United States Pension Bureau. “List of Pensioners on the Roll January 1, 1883.” Google Books. January 1, 1883. Accessed May 12, 2016.

Smith, Sam. Council on Foreign Relations. Accessed May 12, 2016.


Data Description: Slave Sales 1775-1885

For my final project I decided to discuss the slave sales between 1775-1885. While working on this project, I noticed several correlations as well as other factors that I hadn’t thought of before. My data for this project is a combination of numeric, textual, as well as geographic data. The numeric perspective of the data stems from the appraised value that the slaves are given in the data set. Another way that numeric data is used in this data set are the years. There is a gradual increase in the years from 1775 all the way to 1885. In addition to the years throughout slavery there is a date of entry and age groups of the slaves are included in the data. On the textual aspect of the data set there is an abundance of information that is conveyed through the text. There are text data on the sheet such as gender of the slaves, any defects that these slaves may or may not have, and any skills that some of these slaves may or may not posses. An example of the text data portion of the sheet would be something like “Deaf” or ” house servant” that is included on the data sheet. And the last aspect of data conveyed on the data sheet is geographic portion. The way that the data sheet puts this information out is through the locations in the United States that the slaves are residing in and where they are working at. The geography portion of the data from the sheet is the smallest data set of the three. The geography only has two sets; State and county. Despite only geography having two sets, it is involved in most of the visual sheets within the project. The columns in the data set mostly consists of slave gender, state code, age, skills, and date entry. The gender is the sex of each slave that is accounted for within the data provided. A popular trend between sex and value is heavily noted in the data set. Perhaps the biggest relationship between sex and value that someone with little prior knowledge can infer would be that the male slaves would be appraised at a higher amount than their female counterparts. The state code is which state in which whom the slaves belong to while the county is which part of that state the slaves belong to. There are an array of states that are included in the data sheet. The states range from those states that are deep south to those that are more up north border lining slave state and free state. The age column consists of the different age ranges between the slaves. The skills portion of the columns are used to state if the slaves have a special skill which may add to their value. Lastly the date of entry are the dates that have been recorded in which the slaves are enslaved or born. The rows are mostly made up of the average appraisal value of the slaves. Sometimes the row would include one of the options that are in the columns as well.

2 data visualizations 

For my first visual, I have chosen to use the data that I created using the skills and values of the slaves depending on what state that they live in. The story told in this data visual is how much of an asset that these slaves were to the productivity of not only the states in the south, but to the United States as a whole and therefore made how much they were considered to be worth increased based on said skills. Each of these states in the south had different appraisal value of their slaves depending on what skills that the slaves had. Some of these skills were more challenging than others such as a slave with the “construction” skill will be at a higher appraisal value than a “cigar maker”. It’s only natural that the slaves who are more diverse in the jobs they can do and the level of difficulty of the job makes them worth a little more than their counterparts who aren’t as well rounded or who don’t have any skills that can be deemed as challenging. An observation that I made in the data set is that the states of Louisiana and Mississippi have the highest appraisal value of there slaves each skill category that they are in. When the states of Louisiana and Mississippi where in the same category, the state of Mississippi had a higher appraisal value than Louisiana. Perhaps the reason that the states of Mississippi and Louisiana have their slaves at a much higher value than the other states is because there was a heavy reliance in these slaves and their challenging skills that they possessed. These two states are known for their giant plantations and needed the man power to keep them up and running successfully. The slaves who can do the challenging task such as “construction”, “blacksmith” and “carpenter or cabinet maker” are viewed as a bigger asset to the slave owners and the well being of the plantation as a whole. Unlike Mississippi however, Louisiana has slaves of every skill set. With this observation, the state of Louisiana has the biggest diversity of slaves with skills. Louisiana finds it necessary to have slaves of every skill set because they may find it important to keep their economy up and running. The state of Maryland however is comprised of slaves that posses easier skills such as “Butler” and “house servant” . With slaves who posses these skills, Maryland has them set at a lower value since the skills aren’t as desirable. States such as Maryland that are located more towards the north perhaps didn’t need the same slaves that their southern counterparts needed as far as skills are concerned.  The northern states aren’t going to need slaves with the skill set of “field worker” since there aren’t any plantations in the slave states that are more up north like the ones located in the south. The slaves located more to the northern states appear to have skill sets that are more like “chores” as opposed to hard labor.

For the second visual that I decided to go with is the average sale appraisal of slaves. What I did with this visualization is separate the slaves by sex as well as what state that each sex resided in. As expected, the male slaves are valued at a much higher price than the female slaves on average. The male slaves were valued at almost a hundred dollars more than the female slaves were valued at by most of the states that were included in the data sheet. One thing that I found that was surprising while looking through the data was that female slaves are held at an higher appraisal value than male slaves in one state. while doing this project I didn’t expect to see female slaves being valued more than male slaves at all. The one state that female slaves are valued more is South Carolina. Not only are the female slaves valued more in this state than the males but they are also more by a significant amount. The female slaves in South Carolina are well over a hundred dollars more than the male slaves. This could be due to the fact that there isn’t as many labor inducing jobs in this state. Another factor for this higher appraisal could be a scarcity of female slaves in this state for some unknown reason.  As far as male appraisal goes, it should come as no surprise that the state of Mississippi has the higher appraisal of male slaves. It is a known fact that Mississippi is one of the two plantation giants of the south during this time frame along with the state of Louisiana. As depicted on the visualization on top, these two states are diverse in the skill sets of their slaves as well as having slaves that can perform difficult tasks which reflect the appraisal value of these slaves. But what I also found was that Mississippi also has the highest appraisal value of female slaves as well. Mississippi relied heavy on slaves so this can be he reason why they are valued so much.  Another finding in the data sheet that I observed was the low appraisal rate of both male and female slaves that the state of Maryland has. Again, as depicted in the visualization above the low appraisal rate of slaves in Maryland shouldn’t be surprising. The state of Maryland has a slave appraisal value under two hundred dollars. The story of Maryland seems pretty straight forward and simple. Maryland didn’t own any plantations or any fields that required slaves to be in and maintain; but what Maryland did have was wealthy white people who would purchase these slaves to perform the task and daily chores that they themselves didn’t want to do such as gardening or household chores. Maryland doesn’t need slaves that are as skillful as those that are located more towards the south which explains for the low appraisal. It seems to me that the state of Maryland has slaves just because it is legal to have them and do not rely heavily on them and they seemed more as a luxury.




Process documentation 

In order to make my visualizations I had to take a few different approaches. Some methods were more effective at conveying the information than others. I didn’t want to focus on one particular style of visualization because I felt that it would bore the reader and therefore the overall message that I’m trying to put out will get lost. I used several different kinds of graphs and charts in my final project but I’m going to focus on the two prominent methods that appear more frequent than the others. The two that I’m going to focus on are the bar graphs and line graphs. The first visualization type that I chose to use was the simple bar graph. I believe the bar graph is one of the best methods in showing data. It’s simple to read and easy to create. I use the bar graph to compare several factors such as differences in sexes between the slaves in the data set, the appraisal value of the slaves as well as what state or counties these slaves reside in, and that’s only in the first appearance of the bar graph.

Another variation of the bar graph that I used was the stacked bar graph. Slightly odd at first but turned out to be very effective. With this graph I was able to compare the skills of each slaves and determine an average value of them by location. The stacked bar graph option made it easier to display this information than another visual type may have been. In regards to what color of the bar graphs I chose and decide to use depends on the information from the data sheet that I plan on highlighting. For the ” Slave appraisal” data chart I decided to go with black with the intentions to have the reader notice the color of these enslaved men and women looking at the prices that they were being auctioned off. For the “skills & values by state” it may be hard to read at first glance because there are so many colors but it gets easy to understand after a minute. I used multiple colors because there is more information that has to be displayed. The skills are color coated by state to and the skills are located on the X-axis. The bar graph may be the best method for comparing data that I have ever used. The stacked bar graph helps argue that slaves are appraised differently based on sex as well as skill and location.



The causation of the appraisal for female and male slaves in the slave data is the high demand for slaves between 1775 and 1885 due to the economic opportunity that the southern states had at the time in which were created through the means of producing goods such as cotton and tobacco. Slaves have fluctuated in value over that hundred year span, mostly increasing in value as the years went on. It has become an ordinary way of living for those in the south to own slaves and benefit from their free labor. As the popularity of owning slaves grew, so did their value. In the beginning of the years in which the data was recorded, having slaves was legal and a way of life for the white men which mainly were located in the south. It is a pretty known fact that tobacco plants as well as other jobs were located in the southern states and that they took a lot of labor to maintain. With the legality of slaves, those who were able to afford slaves bought as many as they could to do these jobs that required an abundance of labor. The appraisal value of male slaves are more than that of female slaves. This is caused due to the amount of labor as well as type of labor that male slaves can do that female slaves can’t physically do. Females don’t have the same body type or muscle mass that males do, therefore they are more limited to the work they can do. Male slaves also tend to be more skillful with their hands and can do jobs such as being a blacksmiths while women who are skillful will most likely do the jobs such as basket making and other simple jobs. While males are doing more physical labor out in the field or blacksmith work, female slaves can do less physical work such as watching children, making items such as baskets, and other activities. With that being said, their value isn’t going to be as high because that labor isn’t as intense or challenging and isn’t as profitable as the males line of work is overall. It is pretty standard that all male slaves will be worth more money than their female counterparts. The direct causation of the value difference between the male and female values seems to be due solely to their potential productivity and how much money they can bring in.

An argument that can be noted from the data set that may come as a surprise to some people is the average price of slaves throughout time. Most people will automatically assume that as the years go by that the value in slaves only increases until it is abolished in the 1860’s. According to the data however, the value trend with slaves throughout time haven’t been as consistent as some people may think. The trend of slave values started at a decent number and then proceeded to decrease in value before picking back up. It isn’t clear why the value of slaves decreased during this time but it can be argued that the causation for the slaves to steadily increase in value after it’s descent can be caused by the growing popularity of slaves. As the years went by, news of how effective the slaves were could have been spread throughout the south. The businesses started to thrive and become more successful than they may have been previously. It is noted that the value of slaves really started to get high in price in the 1800’s. The causation of this spike can be credited to the industrial revolution. The south was a big manufacturer of goods during this time period and provided these goods to the northern states; states in which slaves were outlawed. With the slaves providing free labor, the south was able to benefit exponentially. This of course made the south even more wealthy and this in turn caused the appraisal of slaves to increase since they were viewed as a great asset to the owner of the plantations and manufacturers of goods. Even though the value of slaves were at a higher average than it was previously, the value of slaves still drop in price certain years before increasing again. This trend of erratic values continues throughout the decades. The causation for this trend is unknown and there aren’t any signs that may explain why. The value of slaves however hits its peak in 1864 in which is a time in American history when the civil war was taking place. The average appraisal price for the slaves during this time period was over a staggering 1000 dollars. The price of slaves during the time of the civil war jumped up by over three hundred dollars.  The reason for this insanely high price can be argued to be a direct result of the civil war that was happening at this time. Since one of the main reasons of the civil war was over the liberation of slaves, this caused the value of the slaves to soar to new heights. This is the highest value that slaves have been since the data has been recorded. It can be inferred that slave owners were attempting to get top dollar for their slaves at this time since the possibility of slaves being outlawed all together was in the minds of the owners during the time of war if the south suffered defeat to the north. Slave owners figured that it was the best time to increase the value of their slaves in order to have some type of monetary gain in case the south lost the war. In the year 1885, the value of slaves appears to begin its plummet at a fast rate. This is a direct causation of the south losing the civil war to the north which resulted in the outlawing of slaves. Since having slaves were illegal, there was no longer an appraisal value on the now former slaves.

Further research questions 

The research conducted within the project brought about some research questions that may have not been thought about before hand.  One research question that was a result of the first visualization “Slave appraisal” would be since male slaves are worth more than female slaves, are they treated less harshly than female slaves; or is there no difference between the treatment of the slaves.  Perhaps the best way to go about answering this research question would be an attempt to find pictures as well as any documented treatment of both male and female slaves during the time of the census.

For the second visual “Skills & values by state” I learned a few new things and thought of some questions that this visualization forced me to think about. Before this project I had no idea that slaves had any skills. Before this data set I was under the misconception that all slaves did was pick cotton and serve as butlers ( for the lighter shade slaves). But according to the data there are an array of skills that these slaves possessed. But what exactly did these slaves do with their skills? For example, what did a slave with the skill of “construction” build? Do the slaves with the same skills work with each other or do they work on separate projects individually?  Trying to get answers for this research question may be a little challenging. It is unsure if any documents of what the slaves build will be recorded since slaves are generally viewed as “dumb”. During this time frame I find it unlikely that slaves would be credited with anything they have done.

My last visualization “AVG of slaves throughout the years” may be the most intriguing and thought provoking visual that I have used from the data set out of all the charts and graphs that I made. The reason I believe this to be the most interesting visual is due to the inconsistency of the data on the graph. This phenomena prompts several research questions due to the vast inconsistencies. One research question for this graph would be “Why doesn’t the price of slaves only increase since slaves were so widely used during this time period?” It seems a little strange that the price in slaves has periods in which it decreases in price before making it’s way back up. Several other research questions are “What causes these prices to drop?” “What was the cause for the prices of these slaves to increase again?” Just to focus on one year specifically for example purposes, the year “1783” increased dramatically from the previous year by well over a hundred dollars. But then the very next year the average value of slaves drops severely all the way to about sixty dollars. To go about answering these research I would have to visit a library or database hat has records of what may have occurred during this time span. Then focus more specifically on the year in which the data makes a dramatic change and see if there is any correlation within Americas history and the decrease or increase in the value of slaves within that year.


Data Description

The data that is provided mostly consists of numeric and textual data however when the data is all put together, it paints a very detailed image. It contains very general categories that don’t get into the best detail of the individuals.
The first two columns include the soldier’s full first and last name. From just looking at the first two columns, you can gather a few pieces of information. One being that everyone in this militia were men. This doesn’t come as a surprise to me because of the time period. A second piece of information that sticks out comes from their last names. You can tell some of the individual’s backgrounds just by reading their last names. The next three columns are their enlistment dates. This is an interesting piece of interesting because we can now find out when a majority of these men decided to sign up for militia. We can use this data to see if something happened around the date of their enlistment that motivated them to sign up. We can also tell get an assumption of who is in charge of who by the seniority of the enlistment date. Generally speaking that is how the military works, there is a strong emphasis on seniority. The next column includes when the men were born. The maximum age given in this document is 58 years old while the youngest being only 16. The average age of this militia is 28.87766 which would be rounded up to 29. The next column tells us where they were born. I feel that this is one of the more interesting and important columns in this data set. We now know where these men come from and there’s a surprising amount of individuals who come from European countries who signed up for fight for their new home. Another surprising piece of information is that there’s quite a few men from Britain who have signed up to fight their home country. The following column is equally as interesting and that is their current jobs. For the most part, the men who have signed up come from bluer collar type jobs. There aren’t any many men signed up for the militia that have a prestigious type job where they would be making a lot of money for the time. This doesn’t come as a surprise though because they would most likely be against a change in government because that would most likely result in them having to change their business. The following two columns tells us who their officer in charge and which company they were part in. Unfortunately I didn’t find much use for this information mostly because for many of the enlistees, there were no information provided for their company and also I couldn’t find much information about the officers that were in charge of them. The remaining columns give details about their physical appearance. The following columns are very informative because it really helps paint a picture of what these men looked like for us. For example, the smallest height on this chart was recorded as 4 foot 11 while the tallest is 6 1/4 feet. The remaining three columns deal with their complexion, skin color, and eye color. For the complexion column, the men were generalized into a few general categories being dark, brown, fair, and a few others.
By reading this piece of data, one could get a very good feel about the current condition of the Militia during that time. Giving the sense that maybe they weren’t the largest, most intimidating group of guys. However they were brave enough to sign up to fight and defend the people that they care about.

Visualization 1

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. The story behind this visualization is to illustrate what the militia looked like during this time with focus on the skin color of these men. With a majority of this militia being black, I was curious as to why that was. Were these men forced into signing up or were they truly volunteering to fight for freedom that they weren’t even promised? And also what were the negative impacts of having men who were forced into signing up. This visualization provides us with a few possibilities as to how they go their numbers and how effective they were. If the men categorized as “Black, Brown, Negro, Dark and Swarthy” voluntarily signed up for this militia, then I would say that they were a pretty effective militia. My reasoning behind this is because if all these men chose to sign themselves up, they must feel that they have something to fight for and protect. Sadly, I don’t feel that this was a reason why a majority of these men signed up. On the other hand, if these men were forced to sign up because they were slaves and they were told to, then I would say that it would lessen their effectiveness. This is because they are a majority of the militia and since they are being forced to fight for something they may not believe in, then these men may not be entirely motivated to die for the cause. On the other hand, there is a possibility that these men were promised their freedom in exchange for enlisting. I feel that this is the sad reality of what happened, however I’m not sure if this would mean that they will readily put their life on the line for something that they were just “promised”.

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Process Documentation

I sorted and grouped the different complexions provided and assumed the description of the complexion correlated with their race. For example I grouped “Black, Brown, Negro, Dark and Swarthy” assuming that they are all African Americans. I 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 either made the voluntary decision to sign up for this Militia, were forced, or given some type of deal to join 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.
I decided to use a simple bar graph to illustrate the different groups because it’s a simple yet powerful image where you can tell which group of people made up the majority of the militia. I also chose to compare the groups by just the numbers so that people can easily see the numerical difference between the groups. I feel that there is a different impact on the viewer of the graph when they see the actual amount of people compared to it being a percentage.


During this time period, there were tensions growing between the colonies and their imperialistic government governing them from across the Atlantic Ocean. With things starting to heat up, it became clear that there was soon to be some bloodshed. With this in mind, both sides started to enlist troops to strengthen their forces. This was the main reason why there was a formation of the Albany militia because they knew very well that the fighting would soon come to them. A major event that made the Americans realize that they should prepare for something was the French and Indian War. Although the Americans fought along side the British against the French and Native Americans, things were still not right between the two and this feeling grew larger after the war. For one, the war had been incredibly costly. It had a sever impact on the British economy and the Brits believed that the Americans should help pay for it. [1] This idea would result in ridicules taxes like the Stamp Act, and the Sugar act. The stamp act was enacted in 1765 and required that almost everything printed be printed on stamped paper, which would be a direct tax on the people. This is one was in which the British Parliament felt that they could get the colonist to help pay for the war. [2] It was acts like this that finally put the colonists over the edge. It was also taxes like these that gave birth to the infamous “No taxation without representation”. However there’s one big question that is still not answered, why the African Americans made of a majority of the Militia? Both the British and the Colonists took advantage of enslaved men by promising their freedom in exchange for fighting for their armies. This explains why there were a large number of enlistees of Blacks in the Albany roster. During the course of the revolutionary war, an estimated total of 25,000 to 30,000 both free and enslaved black men fought in numerous battles for both sides. [3] Although many of these men have been oppressed and were slaves, they played a vital role in the gain of our independence and had very pivotal roles on the military that resulted in our freedom. “Blacks on either side served as infantrymen, spies, couriers, cooks and guides. Some were rewarded with their freedom.” [3] For example there was a slave named James who served the revolutionists as a spy during a very climatic time in the revolution. He pretended to enlist in the Loyalist army and then he relayed important military information to the Americans, which gave the Americans the advantage in battle.
Although many of these men didn’t truly volunteer to join the ranks, they still fought with true effort in order for the revolution to succeed. They were given a deal to for their freedom and many took that opportunity which explains why the number of black enlisted men outnumbers the whites.

Visualization 2

Where a person is born defines a lot about them, especially a soldier. If a Soldier feels that his homeland is under threat, he will be willing to sacrifice himself in order to defend it. That is usually what separates Soldiers from people who get paid to fight. There is a sense of pride, country and a need to defend it. A second part of the data set provided that I will be focusing on will where these soldiers come from. Whether they come from a foreign country, or they list that they come from one of the states, it tells a whole lot about them and their story. Where these soldiers come from and where they say they come from are very different and tells us a lot about themselves and their sense of nationalism. For example, if an individual says that they are from New York when they sign up, that person could be from another country however he has a strong sense of pride about his new-found home there for he is willing to fight for it. On the other hand, an individual who declares that he is from another country may not feel the same sense of pride. But it should go without being said that either way, any individual who signed up for the militia is brave.

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As you can see in the image, a majority of individuals from this roster identified themselves as either coming from Ireland or Germany followed by England. This doesn’t come as a surprise because a majority of the people who came to New York in the first place were from Europe. All of the following militiamen come from New York and the surrounding areas. The huge difference between New York born troops and European born militiamen has mostly to do with the age of the newly formed government and country. The younger population mostly make up the American born militiamen and on the other hand, the older are the majority and they are the first generation to colonies the new land. I would predict that in the following years as the war progressed, more and more New York born men would enlist because they would eventually be old enough to fight. Some people may ask why sign up to fight for a country and land where they aren’t even from? Surprisingly, 71 individuals were actually born in England and would be fighting their own homeland. Initially I would have thought that it would have been 2nd generation men signing up for the Militia to fight the British. I was pleasantly surprised when I saw that a majority of the soldiers were born in Europe. They all signed up to accomplish a common goal and were tired of being oppressed by an imperialist government governing them from across the Atlantic Ocean. I think that it’s very impressive that these people from all different backgrounds were able to come together in order to defend what they called their new homes.

Process Documentation

I created this visualization by grouping the men by areas where they say they come from. For example the men who were born in New York, I combined them into one group even though they may have said that they come from different countries. I did it this way because it shows a powerful image of where these men come from. And it’s important to see where a majority of these come from. It also shows how all of these men come from all different walks of life and it also paints an image in our mind of what the militia looked like. The larger and darker the boxes get means that more men come from what area. And it’s important to note that the larger, darker boxes are almost all European countries.


The Albany militia mustered men from all walks of life. The information provided paints a picture of a misfit group of men. They came from all different countries and came together to fight a common enemy. The formation of this militia was very important. Because this was a government ruled by the British parliament, they were unable to form their own military. Therefor they had to form this militia in order to protect themselves. Many of the men, especially the younger ones had little to no military training. On the other hand, there were few who were veterans from the Indian wars. [4]
Contrary to what I said earlier asking why men would sign up to fight against their own home land of England and whether or not it would hinder their commitment to the fight for freedom, the men who signed up for the militia were the most patriotic. As stated previously, there was no army for these men to enlist in to fight in yet however when this changed, many of these militiamen enlisted in the Continental Army. And since these militiamen were fighting since the beginning, they were filled with patriotism and truly had a desire for freedom. [4]
There are several events that led to these men from all different backgrounds to come together for a mutual cause. In general, all of the events were moves made by the British government that frustrated the 13 colonies and eventually resulted in them being fed up and enlisting in the ranks of the militia. One of the first things that the British government did was the proclamation of 1763. In this particular situation, King George lll essentially told the settlers that they cant settle west of the Appalachian Mountains without guaranteed protection (which they promised). This made the settlers unhappy with the government because it was their first attempt at interfering with the colonies. [5] The other acts by the British government were mentioned before. The sugar and stamp acts were both taxes on the settlers, which made them, pay more and they had no say in it. All in all, the colonies began to get fed up with a government governing them from across an ocean. These acts drove more and more men to enlist in militias. We could feel the tension by looking at the number of men who enlisted who originally came from England. 80 men enlisted in the Albany militia because they were fed up with the current government and their unfair taxation and laws.

Further research questions

The visualizations provided tell a lot about the militia and allows us to understand the circumstances during that time. Although the visualizations helped us answer several important questions, there are still some outliers that I would love to understand better.
One question that I still have is, why is there a Native American category under the complexion row? And why are there so few of them? I posed this question earlier but was unable to come to reasoning behind it. With assumption that there were indeed Native Americans fighting in the militia and it wasn’t a typo, it would definitely bee interesting as to why there were so little of them. I would have to research their names by looking for them in the excel spreadsheet. Once I find out the names I would continue my research and find out who they were and where they’re from. I think that there are two reasons as to why they may have enlisted. One being that they would love for a chance to fight the British again considering they had just lost a war against them. Another reason could be that they were offered some type of deal similar to what the slaves were offered in exchange for enlisting. As for the question regarding the amount of Native Americans who enlisted, I would think that they wouldn’t want to enlist in a fight that wasn’t really theirs. And also they had just suffered from a loss against the British so I would image they wouldn’t want to enlist so quickly.

A second question that came from the data regards the promise that the African Americans who enlisted in the militia were given. I would like to find out whether or not they got their freedom once they came back from fighting. Especially on the British side because they did end up losing the war. It’s possible that the salves who became on the soldiers side became prisoners and even slaves again. It’s also possible that because there was virtually zero equality or representation for the Blacks, that they never got what they were promised. In order to obtain the answer to these questions, I would maybe have to look at some bibliographies of slaves during the war. More specifically those who enlisted in both the Loyalist and the Militia. I would be able to see if they have similar or different outcomes and if the effects of the was impacted if they ever received their freedom or not.



Over the years much information has been collected on the citizens of the United States. Many people are not aware but each time a questionnaire of sort is given out, the information is collected and possibly put into a census. A census is an official survey of a population; it records various details about individuals. Many different categories are put into a census. Some of those categories include name, race, age, birthplace, eye color, hair color, occupation and much more. A census provides government officials with information that becomes useful when deciding on things such as the distribution of public funds and on a broader scale, a census helps show how the country is changing. The use of censuses has been around for quite a while and has provided useful information from the past that proves relevant to historians and those alike today.

Data Description

When a census is looked at, it is often of a particular city to find specific answers to questions about the people and what their lives may have been like. The 1940 census data set provides viewers with information about people that lived in Albany at this time. The census includes a lot of demographics in comparison to some other data sets; when looking at the data, the person’s name, age, race, address, marital status, education level and things of the sort can be found. The data set includes numeric, text and geographic information. The numeric includes age, estimated birth year, income, and the value of the person’s home. The text includes whether the person rented or owned their home, their relationship to other people in their home, gender, race, marital status, whether they attended high school or college, highest grade they completed, their employment status, birth place of their parents and their native language. In comparison to the numeric and text information given, the geographic information is not much; it includes the individual’s birthplace, residence and street name. In each column that has numeric data, the data varies. For example, the column that pertains to the age of those listed in the data set ranges from the age of 1 to 87, the value of the homes range from 20 to 10,000, the income of each person varies between not having an income and making as much as 7,500 dollars. The geographic range of the data shows that many people lived around the same areas; although some of these people are members in a family, there is a significant amount that seems to have no relation to one another but live nearby.

Most of the people lived in the downtown area of Albany; the locations ranged from Hamilton Avenue, Stanwix Street, Delaware Avenue, Barrow Street, Second Avenue and other neighboring places. The rows in the data set present us with a variety of information; the subheadings for the rows include race, address, age, employment status and other information. All of these subheadings either describe a person (the individual’s age, race, marital status etc.), a place (address) or a thing. The columns in the data “answer” the questions that the rows ask. In other words, the columns provide the information that is missing. For example, if the rows are named age, race, occupation, address, employment status and other demographical information, the columns fill in the gaps and provide that information. Most census work the same way with the type of information that is provided and how the information is given; the rows and columns are set up in a way that makes it easy for the viewer, whether it is a historian or just someone that likes looking at census data to parse out the information and find what it they are looking for.



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. Although, in most cases, hard work does breed success, this what not necessarily the case for some people that are a part of the 1940 census. The census provides a wide range of information including the types of jobs that were held, the race of those that held these jobs, their level of education and the difference between men and women when it comes to the work force. It is evident that 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. In addition to more men being in the work force than women, the men began to work at an earlier age than the women. There are numerous jobs held by the men and women in the 1940 census; some of the jobs include accountants, barbers, bartenders, book keepers, lawyers, carpenters, cooks and much more. The different jobs show the level of education that those in field held 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-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. This shows that being a lawyer was a great accomplishment, one that not many could achieve for one reason or another.

Although it is known that there were different ethnic groups living in Albany at the time, an initial look at the census makes it difficult to figure what ethnic group or race on a broader scale held what job. A visual needed to be created to parse out the information that could not automatically be seen when looking at the census. The visual used is a scatter plot; the scatter plot breaks down the different races that the census displayed, which for this census is African American, Caucasian and Filipino. Although the scatter plot does not show what types of jobs people from these races held, it does show that there was still the unfortunate circumstance of white supremacy in the 1940s. The plot shows that the African Americans and Filipinos were able to acquire a college education whereas the Caucasians of this census did not but yet, they still managed to remain superior. Their homes were worth more and they made more money in the workforce. As previously stated, this shows the pattern of white privilege and white supremacy that has been evident in history since the conquering of nations and lands began. Education, although important, is not the only factor that goes into someone having a decent job, decent place to leave or making a decent salary as the scatter plot shows. Education levels differ gravely between these races but they also differ between ethnic groups which can be seen with the second visual.

The second visualization created is a symbol map. The map shows different countries around; the countries are a representation of the birthplace of the people in the 1940 census. Most of the countries have a pie chart affixed to them. The pie chart breaks down the different education levels seen in each country and the number of people that records show have that level of education. When the charts are looked at, Italy and Germany have the biggest pie charts, which indicates that they may have had a larger population living in Albany in 1940. In recent demographics, the same seems to still hold true. “In 2004, estimates of foreign born population was looked at. The top ancestry groups in New York State are Italian American making up 15.8%, African American at 14.4%, Hispanic making up 14.2%, Irish at 12.9% , German at 11.1% , English with 6%, and Polish at 5.27%. According to the data, 1.5% of the state population is multiracial.” The Italian and German population still holds strong years later. The map shows the highest education levels achieved by Italians and Germans, which is an elementary school level education. There are high school and college level education reached by people from these countries as well as other places such as Turkey, Austria and Poland. Although Turkey, Austria and Poland have education levels reported, their charts are not as big which can be due to a smaller number of people from those places living in Albany.

The differences in education levels pertaining to the different ethnic groups is an interesting one. The first visualization shows the broader picture of how the white race has a lower level education than African Americans or Filipinos but this visual allows the viewer to see things closely. The breakdown of the birthplaces shows how little of an education these people received but yet still came out on top. This can still be seen today; a great deal of businesses such as restaurants, shopping places and small convenience stores are owned by Italians, Spaniards and other people from Europe in general. Seldom are there stores that are strictly African American owned or owned by individuals that did not qualify as being Caucasian. Both visuals show the benefits of being Caucasian and what it means to be superior to others.


As previously stated, the 1940 census is made up of a variation numbers such as dates of birth and texts such as names and whether an individual owned their home or was educated. This information allows viewers of the census to piece together lives of the people in the census and to get a sense of what these people did daily back then. From the information provided, many conclusions can be drawn. One of the conclusions is that mostly men were the head of the household, most women did not work but some did, and men and women went to school but men received higher paying jobs than women. These conclusions are just some of many that can be gathered from the census by taking a quick glance. Although initially the census seems to provide a great deal of information, there are some other relationships between the data that needs more looking into and requires past knowledge.

Education, as mentioned, is always seen as an important aspect of how well off an individual will be, the type of job they will hold and if that job will be able to provide for the person and family members. The level of education someone is able to reach holds much value and the value of it directly correlates with many other aspects of someone’s life. However, often times, there are a group of people (usually a particular race or ethnicity) who receive a good education and are still unable to provide for their family or are working lower paying jobs than others. They are looked over when it comes to promotions and often have their work ethic attributed to something other than them simply working hard. This can be seen in the 1940 census; the census shows that most of the people on it received some sort of education. The educational levels ranges from elementary school to a four year college degree or beyond. As previously stated, most of the men and women had an education but the men received higher paying jobs and this was also the case when it came to whites and blacks.

Looking at the census with all the different data, it is difficult to see what correlates with one another and what does not. Creating different visuals allows the viewer to see if there are causations, patterns or correlations between the information provided. The census divides into three races: Filipino, Negro and White. Upon taking an initial look, the division of the census into three races is unclear. The data shows that there are races but being that there are many names, it is hard to parse out the different races. The census itself also does not show the division of the educational level that each race has reached. The correlation between race, educational level, average income and the value of the homes they lived in is not clear until different visuals of presenting the data were created. In order to make these relationships clear a scatter plot was created. The educational levels are broken down into three groups with different colors so that the differentiation can be made; red is college or higher, purple is high school and green is elementary.

Carefully looking at the plot, the assumption that white privilege has its place in the relationships seen between the previous categories noted is made. The plot shows that there were a group of whites that received an elementary school education and the average value of their homes was about 2,074 dollars and average income was 238 dollars. There were also group of blacks that received a college education or higher and the average value of their homes and average income was less than that of the whites. The same conclusion is made pertaining to Filipinos and whites, the Filipinos highest educational level is college or higher and the average value of their homes and income is less than whites and less than the blacks as well. For years, other races have had to work twice as hard, if not harder, to get decent paying jobs, whereas whites are sometimes allocated the privilege of not having to go through as much hardships but still being able to reap the benefits.

To ensure that white privilege was indeed at play, the scatter plot was looked at again and a second set of information in the white section was provided. The highest level of education that whites received was a high school education; this means that both blacks and Filipinos went on to receive college degrees in different fields whereas whites did not. Based on previous information, the assumption that although the highest level of education reached by whites was high school, they would still have a higher income and their homes would be of a higher value was made and was correct. For a white person with a high school education, the average value of their home was 1,585 dollars and average income was 227 dollars. The scatter plot also provided an interesting find. A white person with just an elementary school education had a lower average income than one with a high school education, although not by much, but their homes were worth more than another white person with a high school education. The census does not make why that is clear but further research may be able to provide an answer to that. Although that interesting observation was there, the fact still remained that their homes were worth more and incomes were higher compared to the other two races despite of their minimal level of education.

The 1940 census shows a trend of white privilege that have been there since the beginning of time. Whites across the world have felt superior to others and their superiority complex has led them to acquire lands, wealth and even people. The census shows that for these other two races, although they have worked hard and have reached high levels of educational achievement, it means almost nothing in the end. They worked jobs such as cooks and laundry personnel, and are being passed on the jobs that they may be able to use their degrees in. Whites were able to acquire jobs such as administrators, treasurers and accounting clerks despite their educational shortcomings all because they were not black or Filipino.

Process Documentation

In order to ensure that the viewer understands the correlation between education and race and how that plays a role in how much someone’s home is worth or how much money they are paid, the visuals used need to properly connect with the stories told and the argument made. In the beginning, a bar chart was created to show the different occupations that people held and the level of education that went hand in hand with these occupations. However, a further study of the census and prior knowledge showed that there was more to the story than simply schooling and work. The bar chart, although useful, did not tell the entire story. In order for patterns to be seen and correlations to be made, the two visuals (scatter plot & symbol map) were developed.

The scatter plot was created in order to really break down the information given in the census. The census is filled with an extensive amount of information, which makes scrolling all the way through difficult. Due to this, it is hard to see right away that there are different races in the census. The scatter plot was able to make that visible. After the races were determined, I then decided what kind of correlations I wanted the viewer to see. I decided that I wanted to make a connection between education and race and how those two things played a role in the lives of the people in the census. I proceeded to group the levels of education together. Elementary was from grades one through five and a little beyond, high school from freshman to senior year and college from freshman year to senior year and beyond. I chose three colors to differentiate between the levels of education: green (elementary), purple (high school), and red (college). The color differentiation allows the viewer to know what level of education they are looking at without having to read or try to figure it out for themselves. I then decided to try to see if there was a correlation between the level of education an individual had and how that may or may not play a role in how much money they made and how much their home was worth. I placed the value of home in the columns section, and race and income in the row section. This gave me all the information I needed and helped create the correlation I was looking for. The end result was race determined how these people lived. Although education was important, their race made or broke their wealth and well-being.

For my second visualization, I decided to make a symbol map. The map was needed to break things down even further. The scatter plot broke up the information in the census by race but I wanted to see if there was any correlation in ethnicities. The questions I wanted to answer was whether or not different ethnic groups within the Caucasian race received different levels of education and which ethnic groups were more educated. To create the map, I needed to have a geo dimension, which in this case would be the birthplaces of the people in the census. I then proceeded to add the highest grade completed to my map. As with the scatter plot, I grouped the grades together and used the same colors (green, red and purple) to differentiate between the three levels of education. I used the same colors because I did not want to cause the viewer any confusion and also to give the viewer the option of drawing his or her own conclusions from these two visuals. After placing my measure and dimensions where they needed to be, the map was created. Upon completion, I was able to see the different ethnic groups that were higher in numbers in Albany in the 1940s and their levels of education. The map allowed me to see that there some whites that received a college education but there were very few. This new information caused me to wonder about other things the census and the visuals I created left unanswered.

Further Research Questions

The two visuals and the overall census helped answer a few questions about the people that lived in Albany in 1940. The questions they help to answer are the basic demographic information, the race of these people, the average value of their home and average income according to level of education and race. These questions are important because these people played a role in the history of Albany. The visuals shows how race plays a role in other aspects in someone’s life and that this correlation is a pattern that people are noticing more as further research is done. A few things that the census nor the visualizations answer are why the scatter plot does not show the college education that Caucasians received, even though this information was visible on the map. Are these ethnic groups considered something else or was this something that the software failed to pick up on? Another question that is not answered is whether race played a role in determining where the people lived, did race or ethnic group separate them subconsciously and, lastly, did an individual’s level of education determine what job they held. In present time, someone’s education level helps to determine if they will receive a particular job or not or what level of the job the person will be in. In the 1940 census, it is determined that race played a role in many aspects, but does race also play a role in a specific field someone is in?

Although the census does not answer these questions, there are ways that the answers can be found. In order to find the answers I am looking for, I will have to dig deeper into the lives of the people in the census as well as the city of Albany. I may be able to find the answers if I do further research on the different groups that lived in Albany and find patterns on the types of jobs they held in order to discover whether or not the different groups lived close to one another by choice or by force. I proceed on finding this information by visiting the New York State archives, which can possibly provide me with additional censuses and material about people in Albany throughout the years. I can also use google to find this information as well as research sites such as JSTOR to find whether articles have been written about Albany during this time period. Another way I can go about finding the answers I need is speaking with Albany natives. Often times, I find that asking people questions leads me to learn information that I would not find in a book, article or on the internet. Many individuals have families that came to Albany from all over and may have information and knowledge about events, places and other people that has not been transcribed. Talking to be people sometimes proves to be the most valuable asset needed in order to find what is being looked for.

Censuses provide information that is needed to help a city prosper and continue on. It provides government officials with valuable information to make decisions but, most importantly, it provides historians and other researchers with information that we may not be able to collect ourselves. History is based on past events and people. Therefore, without data, such as those provided from censuses, some of the research we do would be more difficult. The 1940 census provides information that led to questions about race, education, ethnicity, well-being and other aspects of people’s lives being raised. It answered some questions and left some unanswered, which prompts further research. The further research that is needed can provide us with answers to additional questions and allow us to look closer at the lives of people in 1940 and additional years. The visuals created helped to break down the information, which was needed for further understanding. Overall, the knowledge gained from the census and visuals is one that is important and proves true and relevant to aspects of things that take place today. It shows how true the saying “history repeats itself” is.







“Demographics of New York.” Wikipedia, the Free Encyclopedia, April 30, 2016.



Data Description

Anytime we hear the word “slavery”, we tend to think back to a time of great controversy within America. Although slavery has long been abolished, it is still important to look back at information about the slave trade to better understand such a complex system. The Slave Sale (1775-1865) data-set is an example of how we can use historical facts to create stories and arguments about  how the slave system operated. The data presented in the spreadsheet are about the basic information used when selling a slave. Within the spreadsheet there are nine columns: state, county, date entry, gender, age, appraisal value, skills and the defects. Unfortunately, the data is only specific to seven southern states: Georgia, Louisiana, Virginia, North and South Carolina, Mississippi, and Maryland. However, the data-set still provides a variety of information, such as  numerical, textual and geographic data. For instance, the age, entry date and the appraisal value of a slave would represent the numerical information. While gender, skills and defect would be textual. Finally, the states and counties would represent the geographical information.

Despite the data set providing a variety of information, within the data there are some incomplete aspects. For example, there were many slaves whose age were unknown. However, from the information given we can infer that  the oldest age was about 80 and the youngest was about 3. Another example of incomplete information was the appraised value section. Each master expected to receive the recorded amount for his slave(s) whether he bought a male or a female slave. Based on the data, an appraisal value ranged from the hundreds to the thousands range. Many factors went into determining how a slave would be valued. As a matter of fact, the idea of viewing slaves as property and recording them by their value and not by their names was just a way to further de-humanize slaves. The last two columns skills and defects provides insight on what factors might have played a role in appraising. For instance, the men had skills such as cabinet makers or gardeners and women would be cooks or midwives. As for defects, being old or too young fell under this category. Also, deformities and disabilities were considered a defect.

The main question behind the slave sale was How can a slave benefit its master? The Slave Sale data-set does not tell viewers how each column may connect with one another, it only gives the basic recordings. However, once you begin to find these connections then you can formulate stories and create arguments. For this data set there were many columns that correlated. For example, gender vs. appraisal, gender vs. skills,and  age vs. appraisal. Sometimes more than two columns can correlate to create a more complex story. It is also helpful to use the data as a platform to ask questions that the information my not directly answer. Such as, Were these slave masters looking for women who could bear children or looking for men who were physically well? Questions are a way to generate answers, hopefully so you can have a better understanding of the slave trade.

Data Visualization

In order to create a story based on my first visualization, I had to find a connection between each group in the data set. My first visualization focuses on the appraisal value between gender and the states. Before I could start my story I needed to accurately identify if there was a relationship between the value of a slave and gender. The data set determined that male slaves valued at a much 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 were between $113 and $639. However, because the data set shows correlation but not causation, it does not give the reason why the gender difference affected value. However what stood out the most from my visualization were the three dominant state: Mississippi, Louisiana, and Georgia. It also important to consider that these three dominant states were also the closest to down south. According to the visual, it did not matter the gender of a slave, if they were sold to one of these three states it was guaranteed that these slaves’ average appraisal was higher. For instance, if we take a look at the visualization Mississippi stood out the most for appraising women at an average of $639 and men at $777.  This is also important to notice because we are observing a commonality between the state and genders.

Another aspect of the story was the correlation between age and appraisal value. The visualization showed that is was more common for a male or female slave to have a higher price value if the were about 18 to 27 years of age. This gap between ages was a slave’s “primal years”, where their bodies were capable of withstanding more labor than someone who was just a child or middle to elderly years. 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 distinct 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 to 12, and not 18 to 27 like Georgia, Louisiana, and Mississippi.  This is not to say that only children were sold to those specific areas or adults to the other. In some ways a master will always consider his slave(s) a worthy “property”.

For my second visualization I continued my earlier story topic, focusing on gender and  appraisal differences. However, by incorporating skills as a new variable I was able to create a different story about the slave trade. Skills played an important role in the value of a slave. This goes back to the idea of How can a slave benefit its master? The types of skills slaves possessed varied, sometimes by the gender of the slave. Focusing on women vs. skills, the most dominant recorded skills women had were hair dresser, driver, leather dresses, mechanic, fieldwork and house servant. The less dominant skills were gardeners, plowman, cigar makers, dairy, laundry and cook. The average appraisal value of a skill determines what was dominant (common) and what was not. For example, the visual showed that a woman’s hair dresser and mechanic valued an average of $1,000, and a leather dress-maker valued 800. Comparing these three skills, to being a gardener or plowman who valued 450 or a cigar maker who valued 350, you can figure out which skills would be considered more common.

The next aspect of the story was male vs. skills. Based on the visualization, the most common skill for a male slave to have were either being a mechanic, brass molder, constructor, seamstress, sugar refining, leather maker, blacksmith, and silk trawer. Out of these eight skills, there were four skills that were the most common based on the appraisal value given. For instance, being a male mechanic was the best skill because it valued an average of  $1,283. Following a mechanic, was a brass molder who valued $1,013. Finally, being a constructor and a seamstress averaged a price of $1,000 each. The less common skills were tool-maker, brick mason, shoemaker, cattle minder and butcher. The average price for these skills were about between $500 to $300. It’s also interesting to notice that even though there were records of slave with skills, there were also those with no talents that still had substantial value.

Often times when we compare men vs women, we revert to stereotypes to classify the genders. For instance, women are often times viewed as being delicate and fragile. Therefore, we can infer that women slaves’ skills would be more domesticated. While a male slave who was considered more physically fit would work in the fields or have hands on skills. Looking at the data-set from a stereotype aspect, the visualization does show the idea that each gender had their own type of skill. The woman had domesticated skills as well as care giving skills. For example, women were nurses, midwives, sewed and tailored clothes, did laundry, and cooked. While men had more labor work such as, military, butcher, shipbuilder, butcher, and cart-man. Even when a skill overlapped between the genders if it was a more “domesticated” skill then a female slave would be appraised higher. This is clear is the laundry skill, men average value was $408 while women were $461. Even a woman tailor was worth more than a male tailor . The skill that stood out the most was a hairdresser, a female hair dresser surpassed a male $1,000 to $275. The same concept applies to overlapping skills that were more hands-on. For example, fieldwork had a value of $634 for men, but $551 for women. Another example was male mechanics whose average price was $1,238 compared to the $600 value of female mechanics. The last aspect of my story that I also found interesting was the breaks in stereotypes. Having a driving skill is considered gender neutral. However, we would expect that male drivers would be priced higher, but on the contrary, it is women whose price was $1,000 compared to $657 for men. Next, was seamstress which would be more of a woman’s skill to have. However males average value were $1,000 while women were $522.


Process Documentation

When creating my visualizations, it was important that the information I displayed also connected with my stories and arguments.  Before I created my visual I had to find which variables connected with one another. For me the variable that I focused on was appraisal value. Within the spreadsheet I noticed values increasing and decrease, and concluded that there were other factors that could possibly be affecting the appraisal value. From there I created  my first visualization, the bar graph. I chose a bar graph as a visualization because it organized my information well, and made it easier to compare each category with one another. The information displayed on the visualization also connects with my arguments. For example, I argued why there were gender differences in appraisal value, why age was an important factor, and why states differed in prices.  My bar graph separates into two sections, the top based on women and the bottom based on men. This way viewers can easily compare how gender affects appraisal value. Next, each bar is identified by a state, and there are seven in total. The purpose of the states was to show how appraisal value can change depending on the location.  The last aspect is age vs. appraisal value. In order to show the age variable I decided to incorporate a color into my visualization. The intensity of the color determines the age of the slave. For example, if the age was closer to 4 it would display as a light green, but if the age was closer to the 20’s range it would display as a darker green. The information displayed on the bar graph also connected with my arguments.

For my second visualization I decided to use a tree chart to display my information. However, I left the same green theme that was in my previous visualization. The reason for doing this was because I continued with the idea of gender differences. However, I replaced the age and state variables from my bar graph with skills, in order to create a different story and argument for my tree chart. The chart separates between male and female, but this time the genders are displayed side by side. As a result, it makes comparing gender and skills much easier. The tree chart also identifies each box with a specific skill, making it easier to find similar skills within the gender. Overall, the chart was a good visualization choice because it connects with my argument that skills were valued by gender. In some parts of the chart, you see a darker green meaning the skill was valued the highest. On the other hand, there might be some skills with a lighter green. Color plays an important role, especially when skills overlap. Sometimes a skill found in both genders displays itself in different shades depending on the gender, proving that skills were sometimes valued by gender.


Before we can truly understand the way the slave trade works, we must first understand the slave trade process. The information given in the data set tells the story of how different information can correlate, but does not imply causation. According to Historian Herbert Gutman, “once every 3.5 minutes, 10 hours a day, 300 days a year, for 40 years, a human being was bought and sold in the antebellum South”[1]. Most slaves were primarily sold to work and maintain their white master’s plantation. Other times, when a master experienced a decrease in profits they would sell their best slaves to help with their economic struggles. It also did not help that as America began moving westward the opportunity to own land increased. As a result, a higher demand was placed on slaves, which encouraged the slave sale.

Based on the bar graph, there is a distinguished difference between male and female appraisal values. Whether a slave was sold into a big or a small plantation, a master’s goal was to acquire a slave who was able to work quickly, withstand gruesome hours, and carry heavy loads all for the sake of producing the most products. Most times male slaves were more appealing because of their physical ability to work on the fields which gave them an advantage over women. Even with a difference in gender value, both men and women shared a commonality when it came to average age. For example, if there were three men whose ages were 25, 15 and 35 and each had the same set of skills, the 25-year-old slave would be priced at a higher value. This meant that if a male or female slave were in their prime age (18-27) they would be priced at a higher value. However, some states show an average age of (4-12) which does not necessarily mean only children were sold, but a result from insufficient records. The masters as well as the slaves did not keep good records of their ages, some slaves’ names were barely acknowledged. This was a way for masters to keep their slaves oppressed, it was all about considering slaves as less than human beings and more about considering them as property. The better the master’s “property” the better the chances that he will make a greater profit.

The last aspect of the graph is the different states that divide each column. Out of the seven states, Georgia, Louisiana, and Mississippi are the three dominate states. From a geographical aspect these three states were located further south, where slavery was more prominent. The cotton gin invention could also explain why many plantations increased in size. For example, in Georgia the slave population by 1800 doubled to 59,699, and by 1810 the number of slaves had grown to 105,218 meaning that more slaves were being sold into the state [2]. In Louisiana, by 1840 – 1860 Louisiana’s annual cotton crop rose from about 375,000 bales to about 800,000 bales [3]. By 1860 Louisiana produced about one-sixth of all cotton grown in the United States, creating a higher demand for slaves to work the fields in this area [3]. As for Mississippi, it was the state with the highest appraised value for both male and female slaves. This is due to the fact that by the first half of the 19th century, Mississippi was one of the top producers of cotton in the United States. As the white settlers’ population  increased so did the slave population and by 1859 Mississippi made a name for itself, producing over a million pounds of cotton [4].

Based on the information displayed in the tree chart, men and women were given different skills as well as different values for each skill. Sometimes if a slave had a demanding skill or performed his task well they might be preferred by their master because they were beneficial. To understand why there was a gender difference in skills, we must look at the social order on a plantation. First there is the white male master then, the few women slave owner and finally, the slave. However, even within the slaves a male slave was superior to a female slave. This is why we see higher values on the male side of the tree chart than on the female. Despite, how women were portrayed they still held some privileges such as having children and raising them. Pregnancy could be a reason why women had more domesticated skills. If a female slave became pregnant, her body would not be able to withstand the gruesome fields like a physically strong man. Therefore, this was an advantage to have skills like nurse, laundry or cook. However, women still had many responsibilities. Aside from taking care of their own children, women slaves might be in charge of taking care of the master’s wife children. Also, they could be responsible for fieldwork, but still have tasks to do in the master’s home.

Despite living in a life of oppression and dehumanization, a slave with a skill was a slave who had value. In a way, giving a slave the opportunity to learn and do a task was allowing them to have some control. Earlier in my tree chart story I observed that there were certain skills in both genders had the highest appraised value out of all the skills. For example, a driver which at first I thought meant an actual driver, but after research I found another definition for driver. A slave who was a driver was crucial to the flow of a plantation. He or she had to help other workers and also know the crops. A driver should know what was the necessary task to do to produce a successful crop [5]. As a result, anyone who could do this task would have a high value. Other highly priced skills were mechanics, construction, brass smolder, and blacksmith. All these talents made a slave an artisan, someone skilled at making things by hand. It also made these skills essential to having on a plantation so that it could run smoothly. For many masters having a slave with these demanding skills came with little cost because a slave was expected to do these tasks without expecting a pay. Even though skills might have allowed slaves to do other work than just field labor, they still continued to face exploitation.

[1]. Berry, Daina Ramey. 2007. “”in Pressing Need of Cash”: Gender, Skill, and Family Persistence in the Domestic Slave Trade”. The Journal of African American History 92 (1). Association for the Study of African American Life and History, Inc.: 22–36. Accessed May 8, 2016.

[2]. Young, Jeffrey R. “Slavery in Antebellum Georgia.” New Georgia Encyclopedia. September 28, 2015. Accessed May 8, 2016.

[3]. “Antebellum Louisiana: Agrarian Life.” Antebellum Louisiana: Agrarian Life. Accessed May 11, 2016.

[4.] Dattel, Eugene R. “Cotton in a Global Economy: Mississippi (1800-1860).” Mississippi History Now. Accessed May 8, 2016.

[5.] Littlefield, Daniel C. “The Varieties of Slave Labor, Freedom’s Story, TeacherServe®, National Humanities Center.” The Varieties of Slave Labor, Freedom’s Story, TeacherServe®, National Humanities Center. Accessed May 8, 2016.

Further Research Question

We may truly never gain a full understanding how the slave trade operated, due to insufficient or lost information. Unfortunately, the Slave Sale dataset is an example. The information presented can only allow an understanding of slave trade in seven states. Something that I wish the spreadsheet recorded were names.  Although I am sure there might be other records with names of slaves, it would have been beneficial for this recording. Introducing names also give a different aspect to analyze. For example, slave sales often times broke apart families, with more names recorded we can see if any family member were sold to different states. Were there children being separated from their mothers?

Once variables from the dataset began to connect with one another, I was able to better understand the process behind how slaves were being valued. For instance, the records show how slaves were being appraised and what factors might have contributed to their increased or decreased value. However, it does not explain the processes behind how a master sold his slave. Did masters make announcements on posters about slaves being sold? Were slaves placed on display and auctioned off to the highest bidder? Or was each slave inspected and given a non negotiable price?

Another interesting aspect where skills. Slaves were always oppressed from learning knowledge. Laws were made to prevent slaves from reading and writing. However, giving a slave a skill or task meant that you were giving the slave some type of control and responsibility other than just field work. Based on my visualization some of the skills that slaves possessed were construction or shipbuilding. For the artisan skills who were teaching the slave how to perform those hands on task? Or was this something the slave knew how to do already? Did these skills allow slaves to truly have more freedom?

There are many questions that this spreadsheet nor my visualizations can answer. In order to answer some of the questions it would require more outside research. For example, I would try to find other records of slave sale from different states to compare whether my observations are true for other southern plantations. As well as comparing northern and southern plantation. Based on the geographic location of the plantations there were some differences between more northern states like Maryland and Virginia   and southern states like Mississippi and Louisiana. Therefore I would like to further explore whether there were different values, different skills or different types of defect depending on the plantation’s location. I also want to find other general  recordings about African-American slave trade. Slave stories and non-fictional novels are also important factors to look at because they are first hand accounts. The stories can come from a slave who was sold away from his or her family or a slave who had multiply skills on a plantation or even a slave with a defect like old age or a bodily defect and how having a defect affected their life on a plantation.




Kaufman Final

1915 Census:
Data Description
The 1915 census sure was diverse, with lots and lots of different shades of white. With numeric, textual, and geographic data, it sure was a riot to look through! It includes the names of the citizens of Albany in 1915, their birth year, their birth place, age, sex, relationship to the rest of the house, their skin color, and their address. 1,216 rows of this, to be exact! A hoot! A holler! Fun for the whole family! The names and genders include men and women, because that was all that existed back then. Ages ranged from 0 years to 4 years to 20 years to 70 years, showing that it was not just old white people in Albany at the time- there were plenty of young white people, too! Relationships range from head of household to the wife to the children, to apprentices and patients laid up (presumably, hopefully) in a doctor’s house. Race ranged anywhere from white to white to white, with a good handful of black or brown people thrown in there, because that means it is not racist. Citizens came from a wide range of European places, including Germany, Holland, and Finland. I guess these were the immigrants that were juuuust white enough to be allowed into the city. (Though, this was also during the Great Migration, which we did cover in class- so if there were any new black citizens moving to Albany, they were listed as being born in the United States.)

Although occupations is included in the descriptions, not many people seem to have a listed occupation. This might be because there was a lot of unemployment at the time, or it might be because we simply do not have the data to put into the tables. About 20% of the census has an occupation listed with them (although quite a few occupational inputs also list “no occupation”), but I have a hard time believing that Albany had an over-80% unemployment rate at any point in its history, much less when other cities were likely starting to experience some sort of economic boom related to preparing for World War I. Everyone who does have an occupation listed, however, is listed as living in only a handful of places- namely, McCarthy Avenue, South Pearl Street, and Kenwood Road. (This is especially interesting in regards to whoever did their midterm walking tour on Pearl Street, and maybe they could better answer why this was one of the few designated places where employed people live. Is it part of the push towards the suburbs? Moving the employed, usually white people into their own “safe” areas?) Surprisingly, every person who is listed with the “relationship” of being a student is a female. I would assume that this is because if a male was listed as being studying under a certain subject, he was listed as an apprentice of that subject. So though a woman and a man might both be studying medicine, the woman would likely be listed as just a student (or maybe, just maybe, a nurse), while the man was likely listed as either a doctor or an apprentice in a house whose head of household was a doctor. Similarly, everyone listed as a servant is female- i.e., there were no male maids, only female. There was also a surprising number of older people living in Albany, and a lot of older people living in a lodging house. Maybe this term was used differently and meant something more like what we would think of as a nursing home or an assisted living home? It’s just hard to imagine a bunch of old 70- and 80-year-olds living two or three in a room in any other situation. They are all listed as being patients, so I guess that explanation makes sense.

Speaking to the New York Factory Investigating Commission in 1914, Pauline Newman stated that “a working girl is a human being with a heart, with desires, with aspirations, with ideas and ideals and when we think of food and shelter we merely think of the…necessities…Have we thought of providing her with books, with money for…a good drama?…Have you thought about a girl providing herself with a good room that had plenty of air, proper ventilation in a somewhat decent neighborhood. Do you think of all these things when you think of a minimum wage? Let us not think of a piece of bread. Let us think of a working woman as a human being who has her desires to which she is entitled.” With the Fight for $15 still on-going today, it can be easy to see how the struggle for a sustainable minimum wage is something that has been fought over for a century or more. However, more so, the quote highlights the plight of working girls, the wages they were allowed to earn, and, intersected with my data, the jobs that they were even allowed to work.

When one thinks of the jobs that women typically worked before the boom of equality that came in the 1960, very few and very gendered occupations come to mind, from telephone operators, to secretaries, to housemaids. If a woman left the house to work, it was because she was young, and helping her household by earning a wage for her father, or her brother, or her grandfather, or any male relative that she lived with. Women were thought of as existing in the private sphere, within their own homes and perhaps in the homes of their friends and relatives. Never did they venture into the public sphere for their own advantage, nor would they dare to venture out in the hopes of earning an education or a wage for their own advantage. If a woman left the house to earn a wage for her household, we typically think of gendered jobs such as secretarial work, house work, or school work. While the beginning of World War I saw a boom in the occupations that were acceptable for women to work in, this was mostly limited to European women- America didn’t join the fight until 1917, and as we all know, it was pretty pointless for us to join at that point.

However, the early 20th century was still a point of revolution for women in America, and the 1915 census shows the starting point of changes in gendered American society. The 19th Amendment was only a few years away, and the Seneca Falls Convention, over 60 years earlier, had produced numerous succeeding generations of supporters of women’s rights, the same way that today we would think that the hippie movement has led to a more liberal generation, having been parented and grand-parented by previous hippies. The 1915 census sees an increase in occupations employed by both male and female workers, from semi-“genderless” jobs such as song writer and painter, to surprisingly diverse jobs, such as horse dealer and ironworker. These latter occupations might typically be thought of as more male-orientated, being business-driven and more opt to physical labor. While there is an equal amount of male and female ironworkers (i.e., one of each), there are six female horse dealers in the data set, as opposed to only one male worker.

Going off of this surprising difference in expectations and reality, 69 males are listed as having an occupation at this point, while 60 females are listed as occupied- a surprising difference of only 9 out of a total of 129 employed. However, out of the 60 females that were listed as occupied, only 25 of them are listed in an occupation that isn’t listed as “housework.” What exactly does the census mean as housework? While it would be nice to think that these are women who were still occupied in some fashion, such as leaving the house to go clean someone else’s house, it was likely that “housework” within their own home was still considered the woman’s job- i.e., it was her full-time job to stay home, take care of the house, cook meals, and take care of her kids. So while more occupations were beginning to be available to women, we still had a long way to go, if it was considered the job of the woman- most likely the mother- to take care of the kids of the house.
While it is all too easy to look at the differences in the preconceptions that we might have about this time period and the few dalliances that the data actually shows, women’s work was certainly cut out for them. While the spike in amount of women dealing with “housework” shows the expectations placed upon women in the private sphere, the majority of listed occupations in this dataset further speaks to the expectations placed upon women even in the public sphere, where, having proven that they were at least capable of stepping into sunlight and not bursting into flames, they were still given jobs that mostly would have subjected them to little to no physical labor, or spoke to the expectation that women were homebodies whose main purpose is to nurture and care for others. Jobs like school teacher and nurse played into this, with the expectation that, while a woman simply could not handle the power of the headmaster of a school or (heaven forbid!) go to university to become a doctor, they could still subject themselves to lesser degrees of this workload.
So while the opportunities available to women were beginning to expand at this time, we still had a long way to go. European women were afforded opportunities that wouldn’t be available to American women until the beginning of World War Two, when American men left for the frontlines and women were left to take their places in factory jobs. The past few decades of women’s rights movements had led to a more open-minded approach for quite a few generations, and this likely led to a small opening in the types of jobs “appropriate” for women.

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 longer 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 13 men in this data set 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 this data set at the time; out of 71 employed males of the set, about 12 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 13 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?

There are some jobs which are surprisingly gendered 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? Only four occupations of the time had both male and female workers listed- horse dealer, painter, ironworker, and songwriter.

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.


Process Documentation:
There were plenty of options for mixing and matching which data sets to compare, and I had a lot of options with what to compare occupation to- birthplace, color, house number (i.e., which neighborhood they lives in, so which jobs lived in which neighborhoods/which neighborhoods had higher income and were “nicer”), age, or relationship within their household (i.e., sure, it’s technically mostly men who work, but are they the heads of their houses? Or are they in an apprenticeship?). But the relationship between sex and occupation had the most potential. As I said, I think most people imagine that the women of the time didn’t work at all or, as the data sort of supports, they did housework. But the census said differently, so I chose to compare these two sets. I at first not-so-deliberately chose the color scheme- I just instinctively felt that pink would work best for the female workers and blue would work best for the male workers. It took me about half a minute to realize this, and I started playing around with the colors a bit. But no two colors paired together quite as well, or had as much of an impact on the visualization. I tried red and green, and orange and blue, but nothing looked quite right; maybe I thought about it too much, but I imagined someone looking at the data and struggling to understand why those colors were chosen. And I wouldn’t have had much of an answer besides “well, it isn’t pink and blue, so that’s all that really matters.” Choosing these colors is a little easier on the eyes, and a little easier to get a quick idea of what the data is and what it’s trying to say. I even tried swapping the colors, but the point of data visualization is to make things easier to understand, not necessarily to make people question gender normativity. I lumped a few categories together, like “shirt maker” and “dress maker,” until I had an amount of graph area that you could look at without having to scroll around or zoom out too much. A bar graph, I think, best represents the visualization that I was trying to aim for- the idea that women weren’t quite as much of homebodies as we tend to think they were. The first thing that your eye goes to when you look at the graph is the big pink line rising up above everything else, pretty obviously stating that many women were occupied in this field. This pretty well supports the idea that women weren’t homebodies; hovering over the data and adding things up, there were only a dozen less female workers listed than males. The most populous female job, housekeeping, is three times more occupied than the highest male job, truck driving. Because it is such a small data set (out of 132 workers), a dozen is probably a little more significant than if it had only been a dozen out of two or three hundred; however, I still think it pretty strongly speaks to the female workforce of the time.


Place of birth of foreign-born Albany citizens
There are a few obvious spots that your eyes go to as soon as you see this visualization. Many immigrants came from Western Europe, with a stark and obvious lack of immigrants from anywhere south-east of Russia. Few to zero immigrants came from anywhere south of the American border, and over-all, just by looking, it’s obvious to see that most immigrants came from “safer” European nations.

By 1915, World War I was basically in full swing. The Austrian-Hungarian Empire had declared war on Serbia after (and supposedly because of) the assassination of Archduke Franz Ferdinand, and much of Europe was in the midst of the turmoil of The Great War. However, despite the large portion of German citizens living in Albany, few- if any- migrated because of the Great War. Out of 91 German immigrants, only 6 had migrated since the previous census. After Germany, England, Ireland, and Canada all have the most immigrants- safe, white nations who, while questionable to most Americans, weren’t quite as questionable as, say, Guatemalans or Cubans, who only had 1 and 2 immigrants in 1915, respectively. There are less- but still more than zero- immigrants from more “unsafe” parts of Europe and Eurasia, such as Italy, Armenia, and Russia. Despite the massive size of Russia- and the numerous satellite states that it included at the time, such as Poland, or Ukraine, or Belarus, or Estonia- only two immigrants from the city of Albany were listed in this data set, and I imagine that that’s a pretty good indicator of the actual population size of the time.
While not quite as many as its European counterparts, there are still quite a few immigrants from Canada in Albany. After Germany, Ireland, and England, respectively, Canada has the most immigrants, and its massive size makes the slightly-dark coloration a bit more obvious.

Still, the most “unsafe” (and I use that term sarcastically) place that immigrants in Albany came from was Armenia, if this data set is truly representative of the overall population of Albany. There is a whole lot of gray area around the rest of the world- absolutely no immigrants from Africa (at least, none that were listed as citizens, if you know what I mean), none from Asia, and very, very few from South America.

Process Documentation:
For the foreign-born citizens of Albany, I had two important decisions to make: what color to make the nations that citizens came from, and which nations would be appropriate to lump together. As with the occupation by sex, color gives an important message as soon as you see the visualization. Red seemed to scream that it was a bad thing that these nations had brought so many immigrants to our city, and unfortunately, try as I might, I couldn’t find a way to make the color of each nation that nation’s flag. So I went with green- any easy visualization that seems to say that more immigrants are better. I had to lump a few nations together, and had to decide if nations that were no longer standing and wouldn’t be labeled as they were in Tableau were appropriate to be labeled as they were. As of 1915, the Austrian-Hungarian Empire still stood, and would stay as such for another three years. But obviously it doesn’t at the point that this version of Tableau was published, so I had to decide if anyone born in the Empire ought to be listed as originating from Hungary or Austria. The same thing with Prussia and the Russian Empire. How many spheres of influence ought I to consider when throwing together Germany as one country, or Russia? How many “satellite states” that exist now were part of the Russian Empire in 1915? One person is listed as living in Lithuania- do I take that with a grain of salt, or list them as being from Poland? In the end, the only change that I made was grouping citizens listed as coming from England, Scotland, and Wales as being from only England, as this is the only country in the vicinity available for listing on Tableau.

Before you can look at specifics for immigrants- such as occupations or average wage or sex diversity- you have to know the actual number of immigrants from each country. If the average Russian immigrant makes $5 less a month (which is, you know, a whole lot in 1915) than his American counterpart for the same job, why may it have been or not have been such a big deal at the time? Of course, today we would say that paying anyone less for the same job because of any defining trait, such as birth place or, say, gender, is not only wrong, but, like, super illegal. However, in 1915, there was a lack of laws surrounding issues like this, and it might have been a whole lot easier to not listen to the protests of said Russian workers if there’s only 2 or so of them. However, it might be easier to understand a lower discrepancy in the pay of English workers if you understand that there a whole lot more of them than Russian workers, and it’s a little more difficult to tell an English-speaking immigrant that they aren’t making as much money as their American counterpart if they, you know, perfectly understand what you’re saying.

Further questions to consider:
Why were there so many Canadians in Albany? It seems weird today to think that so many Canadians would want to come to America. Perhaps there were better job opportunities in America at this point? Was Canada suffering from some sort of recession? I’m sure some digging around through Canadian economic records could answer this. If it wasn’t economics that brought them here, then what else was going on? Some research into Canada’s history in the early 20th century will probably answer that.

What about the Germans? Only a few of them were recent immigrants, and I assumed because of the beginning of the Great War. Most of the older immigrants had arrived somewhere between 30 and 50 years earlier, in the mid- to late-eighteenth century. Was there something happening in Germany at this time that would drive a mass migration? Some extra research would be nice, but through being a history minor I know that the Austrian-Prussian War was around this point. Austria was expelled from the German nation, so perhaps those who ended up moving to the United States listed Germany as their home country, despite being from Austria? I would be pretty upset if my nation had been at war and ended up losing; I might end up moving, too.
Why was housework considered an occupation for a woman? Wouldn’t it just be something that they were expected to do, instead of something that was their “occupation?” It probably wasn’t women whose job was to go take care of other people’s houses; as Dr. Kane pointed out, it was probably just stay-at home parents. But would that have been considered an occupation, housekeeping other peoples’ homes? There were a few wealthy families in Albany at this time, and I’m sure they had some hired help for around the house. How would these jobs be categorized? A better look at the total data set for the 1915 census, with the entire population of Albany listed, would probably answer this question. Who lived in the “richer” parts of Albany, and how many people did they have listed as living in their homes? What were these people’s listed relations/occupations? Did they simply work in the house that they were listed as living at, or were they actually related to the family who lived there?

What the heck was a paper box maker?? I feel like the obvious answer is TOO obvious. What did she make boxes for? Everyone? Only one company? Was she the only paper box maker in the entire city of Albany, responsible for putting together every paper box that the city needed? Again, I think a look at the entire data set of the 1915 census might at least help to answer some of these questions. Was it purely a female-orientated job, or was it more diverse? It sounds like a very factory-orientated job, I find it hard to believe that it was only one female who was working it.
The two song writers lived on the same street; were they in a relationship? What sort of songs did they write? Anything that I would have heard of? A look at alter censuses would answer at least some of these questions, and a search for their names might pop up songs that they wrote that became popular.

Final Project

Description of the Data

The data that is included in the data set Slave Sales 1775 – 1865 contains numeric, textual, and geographic data. The first piece of numeric data that is included in this data set is the year in which the data for the sale of the slave was collected. The range of the data for the column “date_entry” is from the year 1742 to 1865. The next piece of numeric data that can be found in the data set is the age in years of the slave that was sold at auction. The range of age in years for this data set is 0 – 99. After the slaves age in years, the next column of numeric data includes the slaves’ age in months at the time of the sale. The range for the column containing the slaves age in months is 0 – 11. The next column containing numeric data for this data set provides the appraised value of the slave that is being sold at the auction. This descriptive data has the biggest range and is also influenced by many factors as opposed to the rest of the numeric data that has already been previously described. The range of values for the appraised value of the slaves being sold at auction is 0 – 7000.

The first textual data that you see in this data set provides us with the sex of the slave that is being sold at auction. The data for this column can either be male or female. There are some cases where this column has been left blank. There is also textual data for the column labeled “skills”. This column provides us with information about what special skills the slave in question might have possessed that could make them more valuable to potential buyers at the auction. A few examples of what might be contained in this columns are skills such as “driver, mechanic, blacksmith, and fisherman”. Next to the “skills” column is the next set of textual data which is the column labeled “defects”. This column informs us of any defects, or perceived defects, that a slave might have possessed while it was being sold at auction. These defects may have had an impact on the slaves’ appraised value, usually slightly lowering the appraised value for the slave in question. Some of the defects described include things such as “blind, old, insane, cripple, and sick”.

The geographic data that’s provided provides us with which state the sale of the slave occurred in. This information is provided in the column labeled “state_code”. The potential different states that could be listed in this column are “Georgia, Louisiana, Maryland, Mississippi, North Carolina, South Carolina, Tennessee, and Virginia”. There is also one more column that provides geographic information for the slave sales. This data is provided in the column labeled “county_code”. This column breaks down the location of the slave sales farther than just by state. It tells you in which county in each state that the sale occurred. This gives us much more precise information about what might have been happening in each state by looking at where the majority of sales in each state occurred and where certain slaves with particular skills might have been sold more often.

Visualization 1: Skill Comparisons Between Males and Females

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 was 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.

One last point that we can conclude by looking at a comparison between the males and the females is that the male slaves appear to have had wider number of skills that they could have possessed in comparison to the female slaves. The males that possessed a skill in the data set collectively possessed 67 different skills. This is a much higher number than the number of skills possessed by the females in the dataset which possessed 27 different skills.

Visualization 1: Process Documentation

When I was first presented with the dataset I was not sure at all which comparisons I should make between the different rows and columns. Looking at a set of data that is filled with so much information that covers so many different areas can make you feel over whelmed at first. The only way that I decided that I would be able to see which data would make the most interesting comparisons was to look at different visualizations of comparisons side by side. To do this I began taking as many columns and rows as I could and made different visualizations that helped to comprehend exactly what it was that the information was telling me. After I did this I was much more able to make a decision about which data it was that I wanted to refine for my final visualizations and analyzations.

The first visualization that I decided to use for my final project compares the different sets of skills that were listed as the slaves having possessed, organized between males and females and as compared to the appraised value of the slave at auction. I thought that this data would be an interesting comparison because it helps to show what skills were valued higher in this time period. It also allows us to see the differences in what skills were possessed by male and female slaves during this time period. The first decision that I had to make was which type of visualization to make. In the end I ended up deciding on the tree map visualization. I made this decision because I feel that it does the best job of showing how much each skill was valued at in the time period when compared to the other skills that were compared with other slaves at the time. After deciding on using the tree map the next step was to decide how I wanted to go about organizing the data. The first decision that I made was to organize the data by the appraised value of each slave with that particular skill. The next decision that I made was to also separate the data between male and female slaves that possessed skills. I thought that it would be interesting to visually see the different types of skills that males and females would have possessed, as well as if males or females possessed a higher number of skills. In order to see which sex had a higher number of skills overall, I also had to organize the data by the total number of slaves that possessed each skill.

In order to best visualize this data it has been organized so that the biggest box in the visualization is the skill that was possessed by the highest number of slaves. Likewise, the smallest box in the visualization is the skill that was possessed by the least number of slaves overall. The next decision that I made to make this chart easier to read was to visually separate the male slaves from the female slaves. In order to do this as easily as possible I made the decision to make the skills that were possessed by male slaves orange, while the skills that were possessed by female slaves have been highlighted blue. Along with being separated by color, the sexes are different shades of color depending on the average appraised value for a slave with that skill. The darker the box is, the higher the average appraised value was for a slave of that sex that possessed that particular skill. An example of this is that the male laborer box is a lighter shade of orange than the male blacksmith box, because the average appraised value of a male laborer was 634 dollars, while the average appraised value for a male blacksmith was 903 dollars.

Visualization 1: Argumentation

When looking at the visualization there are some things that will begin to standout almost immediately given the nature of the graph. Certain colors and sizes of objects will immediately capture your attention and draw your focus to them. After noticing these parts of the data I began to see for some potential explanations for why the data may have ended up this way.
When looking at the data the first thing that jumped out to me was that on both the male side of the chart and the female side of the chart the skill with the highest number of slaves possessing it was the laborer or fieldworker skill. I think that this is because the demand for slaves that could work in the fields in the south was very high during this period of time. This most likely created a very high supply of slaves that were skilled in working the fields that were then sold farther down south. I think it is also important to note that on the female side of the chart you can clearly see that there was almost exactly as high of an amount of female slaves that possessed the skill of being a house servant as there were female laborer or fieldworkers. I think that this is because during this period in time females were most often thought of as being very domestic in nature and were often thought of as being in the house. I think that this is something that was very typical for this time period and speaks to the sexism that existed in America during this time period, some of which can still be seen in America today to a lesser degree. You should also note that female slaves were also appraised at a much lesser value than male slaves that possessed the same skills. An example of this is that the average appraised value for a male mechanic was 1,258 dollars and the average appraised value for a female with skills as a mechanic was only 600 dollars. This is another example of the high levels of sexism that were circulating in the United States at this point in time.

I think it is also important to note that the some of the highest valued skills that are represented in this data were the skills that had the least number of slaves that possessed the skill. For example, there was only one male slave that was recorded as being skilled in construction. This particular slave had an appraised value of 1,000 dollars. This can also be seen for the male mechanics; where there were only 45 male slaves recorded as having this skill, but the average appraised value for male slaves with this skill was 1,258 dollars. This would suggest that these skills were hard to come by in this time period and that slave owners would be lucky to have slaves that were skilled in these areas.

Visualization 2: Comparison of Defects by State

This data visualization is a map of the United States that displays which defects were located in which state. Beyond this, there is also an interactive sliding filter that allows the person viewing the visualization to shift the time period and look at the data 20 years at a time. The addition of this filter allows people to see the shift in trends over time throughout the history of the United States. The visualization shows the data for slaves with defects from as far north as Maryland, and as far south as Louisiana.
As one would expect, the earlier that you go in the history of the map, there are fewer defects in every state. In the early years of the visualization the only states that are listed as selling slaves with defects were Maryland, and South Carolina. As you begin to move the filter later and later you can see how the number of defects begins to increase. Along with an increase in the number of defects in these two states, you will notice how defects begin to appear in states farther and farther south until you eventually reach Louisiana. As you would expect, the later and later that you move the slider the number of defects, the more defects will begin to appear in every state. It is also important to note that the defects that begin to appear start to make more physically debilitating than the defects that were listed in the earlier years. These are injuries that are things such as missing a hand and crippled. As opposed to injuries in the earlier years that were mostly defects such as short, sick, and wench. It is also interesting to note that the defects in the Deep South are also much more physically debilitating than the defects that were recorded in the more northern states.

The visualization shows that as the use of slaves in the United States progressed, their use began to be used more and more intensely in the Deep South of the United States. The work became much more physically demanding and the injuries that resulted from the labor began to rise dramatically as the years went by.

Visualization 2: Process Documentation

Coming up with how to best represent this data in a visual way was a little bit of a challenge. The first step was to place all of the data on the map. This was done by taking only the slaves that were listed as having defects and placing them on the map in comparison to their state code. Tableau automatically placed them on the map and broke them up based on which state was associated with that particular slave. The next step that was taken was to tell tableau how to visually represent the data on the map. In order to best represent the data I decided that a pie graph would be the best way to show which states contained which defects. After determining that a pie graph would be used, I had to determine how to distinguish between the different defects in each pie. I did this by choosing a color template that associated each listed defect with a unique color that would be shown on the pie chart in states that contained the sale of slaves with that defect. One difficulty that I ran into with this was that tableau has a limit to how many different colors can be used at once. In order to try to get around this I decided to try to group together some of the defects that were similar in nature in order to limit the repeat of colors. An example of defects that ended up being grouped together were defects such as sick, unhealthy, and unsound. I also decided that the sizes of the pie charts needed to be adjusted. Originally, the graphs were so small that it was very difficult to distinguish between which defects were in each state. To correct this I simply adjusted the size with a slider that was provided by tableau and resolved the problem very quickly and easily.

The next step was to try to find a way that would limit the amount of data that would be displayed at once, since the charts were filled with almost every defect with all of the information being displayed at this. The solution to this was to add an interactive filter that would allow the person viewing the visualization to view the data by 20 years at a time. This limits the amount of information that can be viewed at one time and makes it much easier to identify which defects are found in each state. I discovered that by doing this it also allows the viewer to see the progression of the data over time, and it helps to tell the story of how slavery evolved in the United States over time.

Visualization 2: Argumentation

The visualization shows which types of “defects” different slaves possessed based on their geographic location in the United States of America between the years 1742 and 1865. The visualization is color coded by which defects appeared in the slave sales records for each state. It is broken down even further by the addition of a sliding filter that allows you to narrow down the data set in increments of 20 years. Without even looking at which types of defects were recorded in each state you can immediately begin to recognize a trend simply by moving the time slider up and down through the years. When it is at its earliest years you only notice defects in two states which reside primarily more north than south. However as you move the slider further along you begin to see more and more records of slaves with defects appearing in the southern region of the United States. This is most likely because as slave labor was more and more used by the southern states; more and more defects began to arise over time due to the severity and intensity of the labor that was required by slaves.

We can also begin to notice differences when we look at which types of defects appeared to be more common compared to defects in other states. For example, we are able to see that in the more northern states, the slaves that were listed as having defects appeared to be defects such as being old, or deaf, or as having bad character or even being free. This is compared to a southern state such as Louisiana where not only does it contain all of the previously listed defects, but it also primarily contains physical defects that are most likely attributed to the intense labor and living conditions that they were forced to endure. These defects included things such as being burned, without fingers, one handed, hernia, broken back, and crippled. All of these types of defects appear to be far more common in the south than up north and are most likely due to the much more intensive plantation labor that is known for being located in the most southern states in the United States.

We are also able to note that some of the more northern states are not listed as having any slaves with defects until the early to mid-1800’s. This could be because the types of labor that slaves were forced to endure were not as difficult or intensive as the labor that was endured in the Deep South. It is possible the punishments and the work its self was not as harsh, and because of this, slaves did not develop defects in the more northern slave states until later in the 19th century. It is also possible that perhaps slaves with defects began to appear more towards the north later in time because they were being sold with defects to the northern states. It is possible that they may have been sold simply because they possessed what some slave owners considered as a defect. It is possible that due to the nature of the work in the Deep South, that slave owners in the Deep South did not want to purchase slaves that already contained something that they thought was a defect, because they thought it would mean they would be less efficient at the work they would be forced to do. If this is the case, it could mean that slave owners in the Deep South might not have had any choice but to sell their slaves to the more northern states because they would be willing to buy them for less money, because the defects that they possessed would not severely impede their ability to effectively complete their jobs. This theory is also supported by looking at the defects that appear in the states mentioned above such as Mississippi and Tennessee. The defects that do appear in these states are not anything that would be to physically debilitating. These are defects such as being unhealthy or sick, lame, old, or unsound. These are all “defects” that could either resolve themselves over time such as being sick, or defects that would not affect their ability to work in a serious way, given that the labor in these states were less intensive.

It is also important to note how many defects are located in each state over time. Every state contains a considerable amount more of defects when you move the slider all the way to the later dates as compared to when they first appear on the map. There is no state where the number of defects in slave records goes down or stays the same. Not only does the number of defects increase, the types of defects also become much more diverse. This could be because over time, the labor that the slaves endured caused more and more of them to suffer from physical injuries that took time to develop. It could also be because over time slave owners began to trade slaves as they became injured or grew older and may have had to settle for a lower price from another slave owner due to the defects.

Further Research Questions
When looking at the data set and while making the visualizations previously discussed, there were some potential new research questions that I thought of. While looking at the visualization containing information about the defects of slaves, I began to wonder if there was a difference in the defects that were received by men as compared to the defects that may have been suffered by women. This question also led to me to wonder if the healthcare that was provided for slaves was different depending on the sex of the slave. Based on the previous sexism that I was able to see when looking at the differences in appraised value between males and females, I get the idea that male slaves were most likely to receive better medical attention than female slaves. In order to try to answer this question I would try to do research on the medical attention that was given to slaves during this time period and attempt to specifically locate scholarly peer reviewed articles about this type of information.

While looking at the visualization that was concerning the different skills between males and females, I began to wonder if the same differences would have been seen between the male and female salves in the Northern states. Given that the north was a more industrialized section of the United States than the south, I began to wonder if the skills of the females would be less domesticated and be more similar to the skills possessed than the males. I also wonder if the appraised value of these slaves would be similar to those of the males as opposed to the difference in appraised value in the south. In order to find this information I think that the first thing I would try to find would a census from several industrialized cities in the north and use this information to gather information on slaves living there. I would then try to find information on the sale of slaves in the northern states, similar to the dataset used in these visualizations.


When most people think of a census, they think of it as a population marker. It is a mundane piece of mail with standard forms to fill out. The truth is that a census can tell you a lot. Trends found within censuses sometimes can show the bigger picture of the United States and beyond during the time period that they are taken. Each person on the census has a story and the census can be the beginning of piecing that story together. I looked at the 1860 Albany Census to attain some of this information.


Data Description:

The dataset for the 1860 Census in Albany, New York consists of a lot of basic information as well as a few more detailed pieces of information. In a row the information you get about someone is his or her first and last name, age, race, gender, birthplace, house number, family number, age and occupation. The data is numeric, textual, and geographic. The geographic data in this set is the birthplace of the person at hand. The textual data is first and last name, gender, race, and occupation. The numeric data is the age, page, house number and family number. It is majorly textual, but the other columns are just as important comparatively to the text only columns.

When looking at all of these columns and rows of data we should look at the ranges within each. The first column is page number. This column just contains what page number of the sample census the rest of the information in the row comes from. It is a numeric column ranging from one to twenty four. The next column is house number. I presume that the information in this column is like an address. There are multiple families within different numbers in the dataset. This column is numeric and ranges from one to one hundred and nineteen. The next column is family number. This column has each family listed under a different number on the census. This keeps all families organized. It is a numeric column ranging from one to one hundred and eighty-seven. The next column is the last name. This shows the last name of the person identified. It is a textual dataset and has a wide variety of last names. There are one hundred and eighty seven unique last names in the sample census. The next column is the first name of the person identified. This is also a textual column. This sample dataset contains nine hundred and twenty two people, but many first names overlap. The next column is gender. This is a textual dataset identifying which gender the person in question is. The options are either male or female. The next column is race. This is a textual column that identifies the race of each person. The options are white, black and mulatto. The census is predominantly white with about five persons identifying as either black or mulatto. The next column is age. It is a numeric dataset that consists of the age of each person. The ages contained within the dataset range from one month to ninety-five years. The next column is birthplace. It is a geographic column that depicts where each person was born. It contains states, countries and one continent.. The options are Canada, Connecticut, England, Germany, Ireland, Louisiana, Maine, Massachusetts, New Hampshire, New Jersey, New York, Rhode Island, Scotland, South America and Vermont. Finally the last column within the dataset is occupation. This is a textual column identifying the job of each person. There are seventy-four unique occupations in the dataset.

From this sample of the 1860 Albany Census we can garner a lot of information. You can begin to piece a story of a person together just by reading their row in the census. You can read about an elderly black preacher who was born in Pennsylvania and start to do more conclusive research and gather what his story may have been. You can find out a lot about Albany, as well as the rest of the world, during this time period with the little information from this sample census combined with a little research.


Data Visualizations:

When looking at the census some stories begin to emerge. When first digging through the census is just seems like it is standard information on a person from this time period. It does not really seem like there is anything you can do with this information. Once you start to do a little background research even one person’s row can start to emerge as more of a story in terms of the United States as a whole during this time period. You really begin to see stories of the area when you start to examine different columns and compare and contrast the information. That is what I began to do and I was able to come up with a few solid visual pieces of information based on the dataset.

This was not the first thing I began to dig into as far as making these visual pieces about the census, but it was one thing that jumped out early. This was the difference in occupation by gender. I was contrasting differences between occupation and columns like race or age to start. The only thing I really noticed was that there were many unique occupations. The filters of race and age were not really showing me anything besides standard facts. I moved on to gender. This is where I had my first finding. I realized that there was going to be a difference among the results, but I was surprised by some of the findings. Now there are over seventy unique jobs listed on the census and each of them has different information. Some show a lot more than others. When I began to look at the skilled versus unskilled argument that I assumed I was going to be making I was intrigued. I found that while it was true that most of the jobs that are considered skilled labor has mainly men I was finding a woman or two on many of these jobs.

Given the time period and my prior knowledge of history I did not think that many woman would have been in the workforce in 1860 in what was considered to be a skilled position during this time period. I researched the topic and found that it was actually starting to become common for women to join the workforce during this time (Banaszak, Shannon). I was surprised because to my knowledge women started joining the workforce in the United States either during wartime in the early twentieth century or during the 1920’s. Apparently starting in the mid 1800’s women began to enter the workforce commonly sometimes even in these positions that were normally reserved for men.

I thought that this was interesting so I made a filtered graph to show the differences between men and women in certain occupations during this time period. I chose a few jobs that were considered skilled positions that mainly consisted of men, but had a few women. These were the main piece of the argument that I was trying to display with this visual, but I also added a few jobs that were only occupied by men and a few only occupied by women. I also included a few that seemed to have a more even split just to show the diversity, but the main piece was the few women that were taking these skilled jobs during this time period. I felt like this was the best argument to make because I am pretty sure that I am not the only one who was taught all of this in my early education, but also up until a few years ago in high school. The teaching that always gets brought up is how women were not really a part of the workforce in the United States until World War I forced women to start taking roles that were formerly taken by men. I was taught some stayed in their positions, but a lot went back to their homes after the war only to re-enter years later. This information remains true, because many women did enter the workforce for the first time during this time period, but it is not exclusive to wartime or the early twentieth century. Women were actually joining the workforce, including positions like carpenter or laborer, dating back to the mid nineteenth century.

The first thing that really jumped out at me was the racial population. I was seeing so many more white people than any other race. There were only a total of five people that were not white so I wanted to do a little more digging. I was able to find some information, but in the end nothing seemed too off. I had a sample of the Albany population so there very well could have been more diversity, but either way this amount of diversity in the greater New York area for the time period was not really noteworthy. I made a few visualizations crossing the race column with others. I was saving my work as going, but ultimately moved on. I struggled to find another good visual piece after making a stacked bar graph of occupation by gender. I went back to the racial graphs I was making in the beginning of the project. I had one that crossed race with birthplace. Originally there was not much to go off, but when I was not just focusing on the racial aspects I noticed that besides New York the other birthplaces were considerably low except for Ireland.

Ireland had by far the next highest birthplace by over one hundred. I made a visual piece depicting this difference labeled Birthplace. This was interesting and showed the difference, but did not tell the whole story I was trying to depict with these visuals. It was just the beginning of the story. I did some background research and found that Irish people were immigrating to America a lot during this time (Irish and German Immigration). One of the main reasons for this immigration was due to lack of jobs in Ireland during this time period. It said that Irish people immigrating would take manual labor jobs mostly, but basically any job they could get would do. I decided to make a chart showing the occupations of Irish immigrants. It shows the difference between the types of jobs that Irish immigrants were taking. Laborer was by far the most popular job for these immigrants. A lot of general positions were the ones that I was seeing come into play. The story began to emerge. Irish immigrants were coming over to America due to lack of unemployment, among other things, and going to cities where jobs were available.

When looking at the information given to me in the sample of the 1860 Albany Census that I looked at, it was not clear what stories would emerge. I had to do a little digging and some background research on the United States and abroad to find the stories that were in this data. Once I began to compare and contrast with the information I had it was clear that these numbers and words within the dataset were really telling a larger story than they led on. There are many stories to unfold within a single sample census.


Process Documentation:

Many things started to jump out at me when I was looking through rows in the census. Each row consisted of a different person’s life and gave a short summary of what was going on with them during this time. I was trying to piece together some stories or general trends of the time period with these brief details I had.

I began to make many graphs on Tableau. Using different combinations of rows and columns sometimes I would make an interesting find between two pieces of data and sometimes it would amount to nothing. The first thing that jumped out at me was the race differential, but when realizing this was just a sample of the census and that the trends I was finding were nothing too crazy actually. Reading through the large sample of the census, I could not find a lot of other trends that really jumped out, but once I started to make a lot of findings. I was trying many different column options by gender to see any large differences or surprising finds between the genders.

Once I got to occupation I immediately noticed a few trends. I made the data into a stacked bar graph. I made this decision because I thought it would show any vast differences between the two genders by each occupation. I then sorted the genders by color to make any difference stick out slightly more. I used contrasting colors, naturally pink and blue. Once I had the graph made and was beginning to notice trends, I started to filter down the data because there was just too much. I wanted to keep the graph simple and there were over seventy occupations throughout the census. I knew that this might overwhelm any viewer and it is just a lot of data to process and some of it is really not necessary to any point that I was trying to get across with my graph. I filtered the occupations down to just eight occupations between the two genders. I chose the eight most essential that showed similarity, difference or just something surprising between the two genders within any given occupation.

Once I was done doing this I was lost for a little while. I had crossed many of the different datasets and was not really sure where to go. I tried to make a geographic graph, but after failing due to the data being very different (countries, continents and states mixed) I stopped using the birthplace dataset. After struggling to pick another graph due to some of their boring natures I decided to try the birthplace dataset without making it into a map. I first made a simple bar graph comparing birthplace by race. The few outliers from the white race in the dataset were almost exclusively from New York. I knew this was not much to work with, but I did notice that within the white population that so many people living in Albany during this time were from Ireland. New York was expectedly in first place, but Ireland was in second by far. I found this interesting.

I took the race element out of my graph and changed it to a TreeMap of birthplace. New York and Ireland were by and far the largest two squares and darkest colors on the TreeMap. The color choice is the standard of a TreeMap where the stronger the topic the darker the color, which in this case was green. After this I started to do some research on the topic of Irish immigration to the United States in general in 1860. I made a lot of discoveries. I found that there was a large influx of Irish immigrants to cities all over the United States during this time period and a major reason was for lack of employment in Ireland. I decided to make another graph to show to Irish occupation in Albany. They did a lot of jobs involving manual labor and I wanted to show the difference between the different jobs they held. I made a packed bubbles chart and filtered out any job with one or two Irish immigrants. I was left with eight bubbles. I filtered the bubbles by color by occupation. I was able to show that laborer was the largest occupation for Irish immigrants during this time.

My findings while looking through this dataset were all very interesting. Each thing I would find would get me to do some more research about general trends in the United States. This would result in me using any information I would find into another graph. My findings would open up more and more. I was able to pick the few most interesting and filter them down into simple graphs filtered down to get my point across.



When I began to look at my dataset and saw things begin to connect and stories begin to emerge I was not sure what direction to take it at first. After experimenting with a lot of different ideas for visualization it started to help me filter out the weaker ideas that I had on the table at the time. Two big stories of the time period emerged for me. Doing some research on the topics I was able to compare them to the United States as a whole and I made some interesting finds that I pursued. My two main points within the three graphics I created both revolve around occupation. One is occupation by gender and one is occupation by Irish immigrants.

The first graph that I made for my dataset show the difference in occupation between genders. Many occupations at this time were exclusive, or nearly exclusive, to a single gender. Males usually had what are considered to be skilled jobs, such as laborers or blacksmiths. Some of these jobs still had a woman or two though and this surprised me when making these graphs. Women had other jobs that were exclusive to them. These jobs were sewing, servant or something relevant to homemaking. The only listed occupations that were about even were attending school and schoolteacher. Being a tailor or tailoress was one of the only occupations that had a healthy mix of genders. Attending school makes sense because it consists of mainly children who were attending school during the time of the census, but tailor and tailoress and schoolteacher were more of a surprise. I do not know which gender I would have assumed as taking the gender role for a tailor or schoolteacher, but I was surprised to see that it was evenly distributed.

When looking through the jobs filtered by gender I was not surprised to see men had what were known as skilled jobs during this period and women had more traditional female jobs in the sense of serving and sewing. When you look into the history of women you really begin to see them emerge in jobs outside of the norm during wartime when they are necessary to the workforce.

During the 1920’s you also begin to see more independent women start to join the workforce in job roles that were not totally normal at the time. My understanding was that this was when women began to come into their own in the workforce and even during this time it was still tough for women to get jobs. This is why I was surprised to see that some women in Albany in 1860 were already assuming some skilled position jobs. When looking through the graph you will see that, while dominated by men, women have a small population in occupations like laborer, carpenter and boilermaker.

In a paper by Shannon Banaszak titled “Women in the Workforce: Before 1900” it is stated that although economic historians agree that there is a steady influx of women into the workforce between 1800 and 1900 that there is a drop from twenty percent to fifteen percent between 1860 and 1870. Due to my prior knowledge of wartime and the roaring twenties being the time for women to shine in the workforce paired with this data from Banaszak I was very surprised to find women appearing in skilled jobs during this time period in Albany history. Banazsak does go on to state that women actually were beginning to become a big part of the workforce, really beginning to take off in 1840, which was surprising to me. She does state the jobs they had were normally sewing or domestic service. (Banaszak) This information correlates with my graph and census. In the grand scheme of the workforce women played a larger role than I would have expected, but apparently it was becoming a normal occurrence during this time for women to enter the workforce.

The next graph that I would like to point attention to is birthplace by race. There are few records outside of white people in the sample of the 1860 Albany Census that I was looking at, but I made this graph originally to see if there was a difference between the five other recorded races and white people. A huge percentage of the white population was coming from New York and I was curious if this was the case with the people listed as black and mulatto. Besides one black man coming from Pennsylvania, all other persons of color in the census come from New York, which keeps with the trend of the rest of the census. It made sense that New York would be the number one place that people were coming from considering that it is an Albany census, but what came as a surprise to me was the amount of people that were from Ireland in the census. It is second to New York by a lot, but it is the next highest percentage by far. Out of the one thousand people in the sample of the census I was looking at six hundred and twenty four people came from New York and two hundred and eleven came from Ireland. The next highest number is twenty-four from England. This made me wonder if this was unique to Albany or if Irish immigrants were this popular throughout America in 1860.

Upon further research I found that during the 1800s more than half of the Irish population came over to America. The article called Irish and German Immigration states that this was true in Ireland and Germany due to many hardships and unemployment. Immigration to America would total in over seven and a half million coming to the United States between 1820 and 1870. About a third of that was from Ireland. This rush of immigrants from Ireland and Germany had major effects on every city in America. (Irish and German Immigration) After reading about the influx of Irish immigrants into America during the time period that this census was taken it made a lot more sense why the Irish population in Albany was higher than any other by far. After doing this research and finding out that a major reason that they immigrated was trouble finding any work in their native land I decided to make a graph to look at the Irish population of Albany’s place in the workforce. I had read in the same article I referenced earlier that Irish immigrants would do a lot of jobs that labor-intensive all over the United States. This was true in Albany as well. The most popular job among Irish immigrants in Albany was a general laborer.

I read a letter while doing research about the Irish immigration that was written from an immigrant to an Irish national stating that he was happy in America and although he loved Ireland he recommended everyone move during these tough times and come over to America for a better chance at life (Irish and German Immigration). He spoke of the famine. I did some other research and found that the main source of income for Irish nationals was too farm potatoes. Even when this business was doing well it was low income (Irish and German Immigration). When there was a five-year famine in the late 1840’s it caused starvation and killed many, which played the largest factor in driving many to immigrate to America. (Great Famine [Ireland]) Even when the famine was over in the 1850’s immigrants would write their family to join them for a better life. This is what led to the huge immigration numbers to major cities all across America.

Due to the huge influx during this time period there are currently more Irish Americans in the United States than there are Irish Nationals in Ireland. Cities all over America served as refuge for Irish natives and Albany was a spot where Irish could come to get a job and live out their life.

This sample census of Albany in 1860 really has many different big pictures behind the spreadsheet. Rooted within the census are stories of over nine hundred people. They are all unique and interesting. Their stories can tell the story of the United States or the world during the 1860’s. In 1860 we see a large influx of Irish immigrants to America looking for job opportunity. We also see women begin to enter the workforce in predominantly male positions. You can use a piece of information like this census to show the greater story of history during the time period.


Further Research Questions:

The census that I looked at has a lot of information stored within it. There are nine hundred and twenty two unique people and they all have a story behind them. While I was making my findings and visual depictions I ended up having to do some research. I would make finding such as Irish immigrants being by and far the largest amount of the population next to native New Yorkers. It sparked my interest about the United States and Irish immigrants as a whole during this time period. I would make a visual for the finding and do some research which would spark more findings or another visual. Research was a big part of my findings and relating them to the time period as a whole and there are many other research questions that I did not pursue within this census.

When looking at my first visual, which is occupation by gender, there are some other areas that can be pursued. I did some background research about women entering the workforce in the 1800’s and was able to find some good writings about my specific time period and women entering the workforce in the United States during this time period. I got information about women and their occupations, but it did not really give me any reasoning or specifics. This is something that would require further research. Researching why women began entering the workforce during this time period. There is likely reasoning behind it and I would be curious to what it is. When you look at wartime in the early 1900’s it makes sense that men had to leave for war so women would take over some of their occupations during this time period, but why were women beginning to enter in the mid 1800’s? I would have to dive into some more conclusive and extensive research on women in the workforce to find the answers I am looking for.

My other main visualization is Irish immigration to Albany and the occupations that they took. Getting to this point had taken some research. I was able to find that a large influx of Irish immigrants came over the United States during the mid 1800s for many reasons, but a large one was lack of employment opportunity in Ireland during this time. In my findings it was stated that Irish immigrants mainly were taking manual labor jobs (Irish and German Immigration). This prompted my visualization of Irish immigrant occupation. A question that I would say this brings up is, why were these immigrants taking mostly manual labor jobs? Is it because people did not want manual labor jobs during this time and these immigrants were desperate for jobs so they would take them? Was the reasoning that they had any past experience in manual labor? I would need to do further research on this topic to get these answers.

Overall research was a large part of this entire process for me. My visuals would not have been entirely possible without the background research I did about the topics. I would not have thought to make a graph about Irish immigrant occupations without doing some prior research and finding out that Irish immigrants were coming here for employment. There are even further topics within the census that I did not focus on in my project that I could have gone into. The census has many stories within it that begin to emerge with some background information.


Working for the past few weeks with this dataset has really opened my eyes to the use of working with datasets like censuses. Something as basic as a standard census can really tell you many different things. When you compare trends and patterns within your census to trends within the United States and abroad you begin to see the words on the page as people. Each person in this census had a life and story that went along with it. They were shaping the history of the time period.


Works Cited:

Banaszak, Shannon. “Women in the Workforce: Before 1900.” Oswego. December 6, 2012. Accessed May 3, 2016.’ Awards, 2013/Banaszak, Shannon.pdf.


“Irish and German Immigration.” US History. Accessed May 3, 2016.


“Great Famine (Ireland).” Wikipedia. May 14, 2001. Accessed May 3, 2016.