Misread the due date so i’m late by about 23 hrs


The Albany militia 1760 dataset is a composite dataset. It includes textual numeric and geographic types of data. The geographic part of my data is the birthplaces of each individual. These birthplaces are in a single column in the data chart.  Most of the men on the militia roster are from European countries. As in most cases ones complexion combined with other physical traits can be used to pinpoint ones origin. Being able to find out where someone’s ancestors are from is classified as geographic data in my eyes. Therefore I hesitantly include this point.

As stated there is a numeric aspect to my data contained within columns one and five. The numeric data would be the age and height of the militia men. The age of the men recruited is actually very important. People who are experienced can make organizing zealous younger recruits easier, at the same time, older men with no experience may find it harder to pick up new skills once enlisted in the militia.

The rows of my data set are quite archaic in nature. There is no particular purpose to them besides the fact that columns and rows are needed within the table. It is possible that whomever was recording this data simply listed people information as they showed up. This data could utilize the rows in more sophisticated ways such as listing off the names in alphabetical order. This would make the data much more pleasant to look at. Not a single row corresponds to a column in a way that makes it organized.

With slavery still being a thing at this point in time, the 1760 Albany militia dataset was shocking due to the sheer number of colored men that were enlisted. Upwards of 43% of the men listed were people of color. With slavery still being a thing, cowardly slave owners had the option to send their slaves to the front lines in their stead. This way they would be contributing to the militia efforts.

Textual data comes into play when the companies and commanding officers are observed. The militia can’t have a large mob of people running into battle. This would be extremely inefficient. As previously stated the rows of this graph are relatively useless. That being said the columns make it simple for someone looking at this data to find the location of an individual after some searching for their name.

This dataset covers the years 1760 to 1763. The American Revolution started in 1763 making the fact that this dataset cuts off at the start of the revolution interesting. The data stopped being recorded because at that time it is unlikely someone can keep track of all these men and potential recruits in the midst of constant fighting and travels. Also based on the time frame of the data it can be inferred that the data recorded is preparation for the impending war

<script type=’text/javascript’ src=’https://public.tableau.com/javascripts/api/viz_v1.js’></script><div class=’tableauPlaceholder’ style=’width: 982px; height: 742px;’><noscript><a href=’#’><img alt=’Sheet 1 ‘ src=’https:&#47;&#47;public.tableau.com&#47;static&#47;images&#47;Tr&#47;TradeGraph3432432&#47;Sheet1&#47;1_rss.png’ style=’border: none’ /></a></noscript><object class=’tableauViz’ width=’982′ height=’742′ style=’display:none;’><param name=’host_url’ value=’https%3A%2F%2Fpublic.tableau.com%2F’ /> <param name=’site_root’ value=” /><param name=’name’ value=’TradeGraph3432432&#47;Sheet1′ /><param name=’tabs’ value=’no’ /><param name=’toolbar’ value=’yes’ /><param name=’static_image’ value=’https:&#47;&#47;public.tableau.com&#47;static&#47;images&#47;Tr&#47;TradeGraph3432432&#47;Sheet1&#47;1.png’ /> <param name=’animate_transition’ value=’yes’ /><param name=’display_static_image’ value=’yes’ /><param name=’display_spinner’ value=’yes’ /><param name=’display_overlay’ value=’yes’ /><param name=’display_count’ value=’yes’ /><param name=’showTabs’ value=’y’ /></object></div>

Story 1

My first graph takes a look at the migration patterns into Albany. My looking at the homelands for each individual it becomes apparent that most of the people getting ready to fight in the American Revolution are originally from a foreign country. The number of “home grown” Americans makes up a solid forty three percent. That makes it so more than half of the people in the militia are not native to this land. This graph also shows the occupation of each person. Not a single one of these people were labeled as a soldier. These people went into the militia not knowing anything about combat. This graph is set to calculate a moving average of age. The ages of these men are mostly in their twenties with very few men in their 40’s. Out of pure speculation, since most of these people came from overseas they obviously had a problem with how something was being run over there. These people enlisting was a way for them to protect the idea of stereotypically “pursuing the American dream”  It is interesting to notice that out of the occupations listed no one was a type of nobility. Not a single person could be considered high ranking. These people were mostly your common laborers. With most of the people who came here being of a humble background (labourers, blacksmiths. Etc.) This made it easy for a sense of comradery and community to form.

<script type=’text/javascript’ src=’https://public.tableau.com/javascripts/api/viz_v1.js’></script><div class=’tableauPlaceholder’ style=’width: 982px; height: 742px;’><noscript><a href=’#’><img alt=’Sheet 1 ‘ src=’https:&#47;&#47;public.tableau.com&#47;static&#47;images&#47;Sm&#47;Smallcompanieshavethecoloredppl&#47;Sheet1&#47;1_rss.png’ style=’border: none’ /></a></noscript><object class=’tableauViz’ width=’982′ height=’742′ style=’display:none;’><param name=’host_url’ value=’https%3A%2F%2Fpublic.tableau.com%2F’ /> <param name=’site_root’ value=” /><param name=’name’ value=’Smallcompanieshavethecoloredppl&#47;Sheet1′ /><param name=’tabs’ value=’no’ /><param name=’toolbar’ value=’yes’ /><param name=’static_image’ value=’https:&#47;&#47;public.tableau.com&#47;static&#47;images&#47;Sm&#47;Smallcompanieshavethecoloredppl&#47;Sheet1&#47;1.png’ /> <param name=’animate_transition’ value=’yes’ /><param name=’display_static_image’ value=’yes’ /><param name=’display_spinner’ value=’yes’ /><param name=’display_overlay’ value=’yes’ /><param name=’display_count’ value=’yes’ /><param name=’showTabs’ value=’y’ /></object></div>

Story 2

My second graph takes an odd approach. I was curious as to whether or not there would be some kind of racial divide within the militia. Therefore i took the complexion of each person and combined it with the data of each company. This allowed my graph to show me how many people of each race there was. Personally i believe my graph is misleading. I would have liked to have each company split up to show the divides but the way it is currently illustrated it can be misconstrued. The number of dark or negro men within the militia was unexpectedly high. They made up the highest concentration of one race withing the entire militia with over two hundred thirty men. Colonists and Indians were on pretty good terms but I never expected someone of native american origin to be directly placed in the militia. From my knowledge of tribal culture they would assist but would make it very clear that they were their own entity throughout the entire ordeal. For one to be in the militia shakes the foundation of the common understanding of Native American culture at that time.


Process Documentation

My choice of graph is called a side by side circle graph. This graph originally used circles to illustrate the data presented but I found that using squares gives it a different effect. By using squares which are made of straight lines the mind is able to extend the lines making it seem as though there is a horizontal bar graph. I attempted to use a horizontal bar graph but I found that there were too many bars to clearly see what was happening. Also the length of the graph was absurd.

My original color scheme was supposed to go along with the complexions that are listed within my data I thought that it would make it easier for anyone looking at it. Not only did this choice backfire because the colors made the graph very unattractive it also strained the eyes to look at.

This graph supports my points about people being forced out of their countries by internal factors. Immigration is clearly very high at this point in time. People don’t migrate in mass without a sufficient reason. Unfortunately there was no religion based data in the selected dataset. This data would have been useful due to religious persecution of Catholics and Jews was on the rise as the protestant religion gained power in Europe. According to this graph Irish immigration was extremely high even when compared to other areas on the graph. The main religion of Ireland is Catholicism and with catholic persecution rising it makes sense that migration also increases.

My second choice of graph is a packed bubble graph. This graph is more intricate than the other one because everything from the size of each bubble to the color means something. Based on this graph each company seemed to be made up of one major ethnicity. In one location this graph shows that the concentration of people of color is higher than any other ethnicity there. This makes it quite sad that at this time slavery is still in effect.

Within the soldier count on each bubble there is an uneven distribution of man power with one company actually having 1 person. This may be misleading data due to the officer they report to not being listed on this graph. When I tried to add this in the graph became severely distorted to the point I couldn’t make heads nor tails of what I was looking at.

The colors for this graph were selected purely on the basis that they are easy to look at. Even though they are easy to look at, the colors do a good job of making each area stand out on its own.


Thing’s I’d like to know

I would like to know about the different religions the people who came to Albany practiced. With the information on how many people of each religion came, I would be able to find out exactly how powerful the oppressive institutions were. As stated in multiple locations the protestant church was gaining power. I would be able to essentially give the church a power ranking so to speak.



My data directly overlaps with the protestant rise to power in Europe. The data presented also directly overlaps with the time period where Britain was extremely strong in comparison to the countries around it. Great Britain was in control of a large amount of land. They had stakes in Ireland, Germany, and most other European nations. After the reformation in 1534 Britain became protestant. By the early to mid-1700’s pressure is being applied to the citizens of various nations trying to get them to convert. The quality of life in these nations was also quite poor. People were forced to pay high rent to Britain in order to keep their land. This forces people to make sacrifices usually of the food variety. With people starving and religious tensions at a high people had two options. These options were to set out to America and attempt to start anew or bear with their current situation and hope that it gets better.

Another point of reference: based on what I stated above Britain was applying pressure outwardly on all the nations around it, But at the same time people are leaving England in large amounts. The English implemented a new sanction called inquisitions. These inquisitions were derived from the Spanish version where they took a lot of land. This made it so borrowing money from an English bank was placing your head on the proverbial chopping block. The inquisitions allowed for the English to confiscate your land in order to clear your debt. Once a man’s land was taken his life was effectively over. This made leaving for America a lot easier.

Power was shifting in the Americas and also in Europe.The Seven Years’ War ends. Britain, Spain and France sign the Treaty of Paris and Austria and Prussia sign the Peace of Hubertusburg in February. Austria gains nothing. France loses possessions in the Americas and cedes to Spain the huge territory of Louisiana, including New Orleans. France agrees to pull out of India, and it cedes its colony by the Senegal River to the British

Credit to: http://www.fsmitha.com/time/ce18-7.htm

Final (Part Two)

<script type=’text/javascript’ src=’https://public.tableau.com/javascripts/api/viz_v1.js’>

Second Data Vizualization/Story

The story behind the second data visualization for the slave salves data set is that as the civil war was on the horizon, slavery grew for various reasons. The civil war wouldn’t have come into play if the southern states that seceded weren’t economically stable on their own. This is a consequence of the growth of slavery. Though the slave trade ended by the time the spike in slave sales occurred, slave records continued to increase in number. Why, you might ask? The international slave trade wasn’t in use anymore, but slaves were still in high demand because of the lucrative cotton kingdom.  Northern states slowly illegalized slavery while southern states continued to collect capital from the institution.

In the 1800’s, more slaves states were admitted into the union, maintaining the balance between free and slave states which makes sense of the rise in the amount of slave records as legislations regarding slavery were passed.

Slave records also increased because slave labor became more profitable as a result of the cotton gin’s invention at the end of the 1700’s and it’s widespread use during the 1800’s. The cotton kingdom was the cash crop that emerged after tobacco crops started to dim in value. Cotton was not only valuable to southern states because it could be produced and in turn sold faster, but because European countries valued it too. Cotton was needed in countries such as Great Britain –since major countries depended on southern states they met the demand through buying more slaves (Quizlet).  This is another reason that the south was not hesitant to secede from the union –they had connections with countries on other continents because they had business affairs with them beforehand. Not only did the south have its own connection internationally apart from the northern part of the union, the cotton kingdom mad it so that the south was also economically viable on its own –southern states didn’t depend on northern action to make the bulk of their profit.

The main point behind this visualization is that slavery was common in southern states, but it grew drastically as the latter half of the 19th century started and progressed as circumstances nationwide changed, as did international demands on the south of the union.



“Quizlet QWait(‘dom’,function(){document.getElementById(‘PrintLogo’).setAttribute(‘src’,”https://quizlet.com/a/i/global/logo_print.du83.png”)});.” History Unit Two Flashcards. Accessed May 12, 2016. https://quizlet.com/14327446/history-unit-two-flash-cards/.


Process Documentation

Naturally, people are visual beings. Even if something tastes good, a person wouldn’t be likely to explore it if it’s not appealing to the eye. For this reason, chefs pride themselves on presentation –once our eyes see something good, we assume that it is good and vice versa. If people didn’t see what went on in concentration camps during the holocaust, they might not have believed its severity. All of the above examples and analogies provide a peek into the reasoning for the visual choices that I made regarding my first data visualization.

In class, we saw a visualization that used color and inversion to portray Iraq death tolls (if I’m not mistaken). These tools made the death tolls come alive without even having to look at the numbers. Such a visualization played a major part in this visualization of the slave sales data set. Though there are several visual interpretations of the same data set, the data being portrayed can vary extensively.

For this visualization, I saw the stacked timeline format as best for what I wanted to portray. I wanted to portray the growth of slavery in the states over time, and the stacking method shows the states more so as a unit. Meanwhile, the states having different colors still allows viewers to distinguish between the states in the dataset.

I chose to evaluate slave sales over time in this visualization because I wanted to make sure that even though I was using one data set to make two visualization, both visualizations were unique. In my eyes, slavery is such a broad topic that can be stretched to fit into almost any area of American history. Cotton production played a part in both of my arguments, but they revealed different things. I made this plain not only verbally, but visually. The stacked timeline literally appears as a growth at first sight and thus, the main point comes across. In addition, the visual appeal also manages to dramatize the 1800’s as a period during which slaves were in high demand. Throughout most of the graph, there is consistently a higher than normal amount of slave records that we see through spikes in the visualization.

Another part of my reasoning for choosing this type of visualization was that when I tried the bar graph form, neither my story nor my argument came across. If they didn’t come across to me clearly, viewers wouldn’t remotely grasp the idea behind the visualization. The collection of small bars representing each record that was entered showed how many records were entered in different time periods, but the stacked graph boldly illustrated the variance or lack thereof in slave records that were entered.

All in all, my final visualization came together seamlessly, especially with Professor Kane’s assistance. The bright colors draw viewers’ attention and the variations in color from state to state help readers to make sense of what each states records of slaves was from year to year within the time span of a century.



The institution of slavery in the United States was in place for almost 250 years. However, slavery seems more popular as the slave sales data set progresses in sequence (PBS, 2004). With peaks in slave records from 1770-1870, slavery in southern states seems to have grown as years went on, with its highest cumulative peak being in 1859. However, why were slave records so inconsistent? This is the question that will be answered through the evaluation of historical circumstances surrounding the 100 years between 1770 and 1870.

In the 1770’s there was a push for the liberation of slaves. In 1773, slaves in Massachusetts petitioned for their liberty and were not successful. By the end of 1774, the First Continental Congress decided to discontinue the slave trade and Virginia also took action against the importation of slaves. Georgia did the same in 1775, and the first abolition society was founded in Pennsylvania. However, by the next year the slave population in the colonies continued to grow. In 1820, Missouri was admitted as a slave state through the Missouri .Compromise (Educational Broadcasting Corporation, 2004). The growth of slavery in the 1800’s can also be attributed in part to the Louisiana Purchase that doubled the size of the United States territory (Rapid Growth of Slavery).

The cotton gin’s invention towards the end of the 1700’s also led to a burst in the demand for slaves in the south. Slaves were now able to produce cotton at a much higher rate which meant that masters could make more capital in a shorter span of time, and clearly they took advantage of such an opportunity (Rapid Growth of Slavery). As the 1800’s progressed, so did the causational relationship between the amount of cotton produced, and the number of slaves in the cotton producing United States. As the number of slaves grew, so did the amount of cotton in the U.S. Consequently, plantation income increased as well (8-1 Chains, 2010). The data visualization shows this relationship in its entirety. As the years go on, the amount of slave records increase first in smaller increments, and then they drastically increase by the late 1850’s.

Though Louisiana has one of the most presently large clusters of slave records, before 1840 Maryland had the highest amount of slaves in comparison to the other states. Before the cotton gin became a major factor, tobacco was a cash crop. Tobacco was lucrative in relation to European markets and Maryland was one of the epicenters of its production. However, as more northern states abolished slavery, tobacco production came to a low and the future of slavery was uncertain (Dodson, 2010).

The United States’ economy depended on slavery, and the economy shifted upwards or downwards depending on what and how much slaves produced. When the cotton industry was revolutionized by the cotton gin, the country had no choice but to shift in that direction because it was economically savvy. On another note, the economic benefits of slavery was a driving force behind how the confederate states could even be sustainable on their own.



Corporation, Educational Broadcasting. “Slavery and the Making of America -Time and Place.” PBS. 2004. Accessed May 12, 2016. http://www.pbs.org/wnet/slavery/timeline/1773.html.

Corporation, Educational Broadcasting. PBS. 2004. Accessed May 12, 2016. http://www.pbs.org/wnet/slavery/teachers/lesson1c.html.

Dodson, Howard. “How Slavery Helped Build a World Economy.” National Geographic. October 28, 2010. Accessed May 12, 2016. http://news.nationalgeographic.com/news/2003/01/0131_030203_jubilee2_2.html.

“The Growth of Slavery in the 1800’s.” 8-1Chains -. January 12, 2010. Accessed May 12, 2016. https://8-1chains.wikispaces.com/The Growth of Slavery in the 1800’s.

“Rapid Growth of Slavery in the 1800’s.” Frederick Douglass Heritage. Accessed May 12, 2016. http://www.frederick-douglass-heritage.org/slavery-1800s/





Final (Part One)

Dataset Description
The dataset that I’ve chosen to analyze and evaluate is entitled “Slave Sales 1775-1865”. It includes geographic, time range, as well as numeric data. The geographic data that the slave sales dataset includes is the location from which the slaves’ information was recorded –Chatham, Georgia. The better part of slave sales consists of numerical data, but there is some revealing textual data. The columns are labeled date entry (anywhere from 1790’s to about 1863), sex, age in years, age in months, appraised (the price they can be sold for), skills, and defects. One of the most revealing/astonishing columns is the defects column. The simple use of the word defect reveals how the person that recorded this data set feels about and views slaves. Defect is a word use in the context of things being made in a massive quantity in which one of them has a glitch that affects their function, for example. People are not things that are produced in mass quantities that are classified as normal or not, but the general mentality at this point (geographically and sequentially) in history is revealed simple by the title of this column. If a slave master of another white person had a hernia which was considered a defect of slaves, they’d be considered ill, or otherwise because they were classified as what they are –people and not objects, or property. The numbers describe the ages and prices of male and female slaves. Whereas, the text describes what skills some slaves had, and on the other hand, what “defects” slaves had. These rows directly describe slaves in the year range of 1775-1865. The ages of these slaves range from birth/a few months old to about 79 years old or so. Slave masters probably didn’t figure to place senior citizens in the market for slave trade after a certain age, because input into keeping the person alive most likely is more or equal to the output they’d receive from them.
The data presented in Slave Sales 1175-1865 are all related. For example, males are priced higher than females. Males usually have more years in which they can work before their bodies start to decline, and they ate not restricted to just one type of work. In addition, males are best at things that bring in the most revenue –such as field work, for example. A woman’s body declines quicker than a man’s body. In addition, there may be a few days in which a woman can’t work because of child bearing. Women may not work as long hours as men because they’re the ones that cook for their families. In another sense, both women and men that are “in their prime” so to speak are also worth more. For example, a female slave age sixty is worth $50, while a female that is sixteen years old is worth $500, as is a 30 year old woman. A woman that is 60 years old is post-menopausal most likely, can’t breastfeed, and has fragile bones, among other things. Surprisingly, the 16 year old and 30 year old are worth exactly the same and neither has any skills or defects listed. Both of these women, and women in their age range in general are of age to be child-bearers, which slave masters may see as a skill. In terms of slavery, child bearing brings forth more slaves and in some instances, children from the slave master. Slave masters can also add these women to their list of mistresses. Women that are of age to have children age are most likely expected to breastfeed the slave masters children as well. These abilities are exclusive to women of age to bear children. Therefore, these women are worth more monetarily.
A man that is 50 years old with no defects or skills is also priced highly (generally speaking) at around $550 –more than a woman that is in her prime. Men are probably more valuable to slave owners because they can produce the higher amounts of product for longer periods of time because of their stamina. A 50 year old slave in 1848 is probably a lot or active than your average 50 year old today. These men can still have children with younger women (increasing the slave population) and do field work for most of their lives. They serve a dual purpose for a longer period of time. Historically, at that point in time the 50 year old could have very well been born into slavery and as a result is accustomed to slave labor, its excruciating pain, extended hours, and mental and physical abuse.

Data Visualization/Story

The visual data that I chose to use to describe the slave sales data set is a graph. Graphs with entities separated by color are more appealing to a person’s eye in general, and their mind automatically notices the difference in volume of each color, or lack thereof. For example, if a person sees a pie chart that is 75 percent red and the remainder is green, they’ll automatically wonder what the red area represents and why it’s so plentiful. On the other hand, colors in bar graphs create distinctions, but the length of the bars is what tells all. Where the z-axis is placed (on the bottom, side, or top of the graph) also has an impact on what viewers’ perception. An x-axis that’s on top as oppose to on the bottom typically has an adverse effect at the first glance compared to if it was on the bottom because it looks as if numbers are decreasing as the bars decrease in length.
I chose the bar graph lay out because it makes it seem as if certain states were forging ahead of others. Essentially, leaving them in the dust of the money they spent on slaves. This scale isn’t the typical graph, but I do think that it gets the point across visually without having to see the prior spread sheet to analyze the data. I chose the deep burgundy color because it wasn’t alarmingly red, but the burgundy resembles blood and this tugs on views heart strings –especially in the context of slave sales.
The context surrounding the slave sales data set is the rise of the cotton kingdom. The spike in Louisiana slave purchases may be due to the expansion of slavery and cotton production, which makes sense. The raw data set itself shows that men in their prime are bought for higher prices (keep in mind that man’s prime is longer than a woman’s). Women, on the other hand, are of more value when they are of age to bear children and their value probably depreciates so in a time when the goal is to increase production, men are probably the more ideal choice. Though child bearing and reproduction is important, this timeline probably seems longer to a person that wants to capitalize off of cotton production high while it’s hot –wait nine to ten months for a mother to give birth and a few more years for that baby to be mature enough to pick cotton themselves. Women were still being bought at an increasing rate, while men, as we see in Louisiana, were in higher demand.
In terms of sequence, the range of the slave sales data set covers the rise of the cotton kingdom which was vaguely 1830-1861. Therefore, the increase in millions spent by the states is associated with the rise in cotton demand. Aside from natural reasons, the cotton revolution is the main reason that states in that time period spent hundred off dollars to buy quality slaves because they’d prove vital in capitalizing off of the cotton kingdom.

First Visualization Process Documentation
The first visualization that I created to represent the slave sales data set described the amount of females versus males that southern states purchased in the approximately 100 year time frame that the data covered. My choices in color and design reflected what I aimed for the visualization to portray.
In terms of my color choices, I used a deep red color because slavery in the U.S. wasn’t a cheerful time for African Americans, and its main purpose was to highlight how states valued specifically male and female slaves. In class, I saw another graph that used inversion to portray the creator’s point of view on the data –death tools in Iraq. This tactic caught my eye because the creator didn’t change the data, they changed the way it was presented. Though I didn’t use inversion, I rotated the bar graph so that the bars stemmed from the left side as oppose to growing from the bottom as in conventional bar graphs. With this format, it looks as if the bars are racing each other in a sense, and Louisiana is surpassing them all.
I chose to compare how the states valued males versus females to expound upon a broader idea –male slaves often had higher value from a slaver master’s perspective. Through my research that is expounded upon in my argument, gender wasn’t the only thing that effected a slave’s appraised value –age played a part as well. Young women that of age to bear children were valued as high as men in their physical prime.
A slave’s value wasn’t only what they were worth at the time, but what services they could provide for the slave master in the long run. For example, a 19 year old man would be valued more than a 19 year old woman because after the young woman’s child bearing years are over, her value decreases. On the other hand, a man’s body had more longevity in terms of field work and things of that nature that were of value to slave owners. This was displayed through the bar graph because all of bars representing the sum each state spent on either gender were higher for males. Slave masters made an investment in both male and female slaves because of obvious reasons such a reproduction, but females also has value outside of child bearing. Women often worked inside of the slave masters homes tending to his children in addition to her own children, cooking, cleaning, and things of the like. Slave masters also took female slaves as their concubines to satisfy their sexual desires.
All in all, this visualization for the slave sales data set provides a stepping stone for all that my argument encompasses. The position of the bars gives readers insight into my argument because males are clearly valued more than females, but women don’t fall too far behind men for most states except for Louisiana. Viewers see that Louisiana doesn’t follow the common trend and it leaves them wondering why that is so.

“Quizlet QWait(‘dom’,function(){document.getElementById(‘PrintLogo’).setAttribute(‘src’,”https://quizlet.com/a/i/global/logo_print.du83.png”)});.” History Unit Two Flashcards. Accessed May 12, 2016. https://quizlet.com/14327446/history-unit-two-flash-cards/.

Slavery –the practice or system of owning slaves (Random House Inc., 2016). Such a system served as a pillar of the U.S. economy and social structure. By 1850, slaves in the U.S. were worth 1.3 billion dollars. Or in other words, American slaves were worth one fifth of the entire nation’s wealth (Goyette, 2014). Such information makes sense of the data that is displayed in the slave sales data set. It’s easy for people to think about how rich America’s history is, but how often do these people think about the hands that made it great? From the Caribbean to the mainland slaves hands were goldmines. Cotton wouldn’t have boomed without people to grow, harvest, and pick it. Tobacco would be a delicacy if lives weren’t stolen and then bought in order to harvest it. These statements ring true for many of the goods produced by slaves. This may be contrary to popular belief, but the American Economy must have depended on slavery for the better part of its history before the start of the 20th century. As a result, slaves were in high demand. But the question is –which slaves were in high demand and why?
According to measuring worth, a slave’s value was truly the value of the how much they’re expected to produce (Williamson and Cain, 2011). In other words the value of a slave was not really the slave’s value per say, but the value of the service that they could provide. For example, an elderly woman wouldn’t be expected to produce much, especially if she has any outstanding physical condition (or “defects”) such a missing finger or cataracts. As we see in this data visualization, males were clearly expected to produce more because generally, more money was spent on males. In Louisiana, males that were between the ages of 15 and 44 had the highest values and men within the 24-35 age-range held the peak values. As for females, those in the approximate age range of 14-33 years old held the highest value. This is no surprise, since these are typically a female’s peak child-bearing years (Williamson and Cain, 2011). Slaves weren’t only valued for what they could produce in the fields, but for their skills as well. Premiums were paid for slaves that had artisan skills such as cooking, carpentry, and blacksmithing, among other domestic skills. On the other hand, a slave’s value was depleted if they had characteristics or deformities that would inhibit their production such as drinking, being crippled, or being a frequent runaway (Williamson and Cain, 2011).
The spreadsheet itself uses appraised values that are generally under one thousand dollars. However, if we were to convert these prices to what they’d be today, the average range for which a slave would be sold would be 12 thousand to 176 thousand dollars. In other words, a slave was worth anywhere between the price of buying a used car and a mortgage. For example, a slave that would be sold for $400 in 1850 would be worth about $82,000 today. (Williamson and Cain, 2011). For slave owners, perhaps foregoing purchasing a home or another luxury item was worth investing in a few decades worth of slave services that would have a major return in the long run.
Though all states in the slave sales data set purchased slaves to some degree, the massive amount of capital spent on both female and male slaves by Louisiana is strikingly higher than the other states. Louisiana was most likely subject to the other factors such as the cotton boom that justified the desire across the country for slaves in their prime. If this is the case, why was Louisiana so much more passionate (according the data visualization) in the buying of slaves? At the top of the 18th century, Louisiana was the resting ground for only ten people of color. However, the French imported about six thousand slaves in Louisiana (Whitney Plantation). After the Seven Years War that concluded in 1763, Louisiana was occupied partly by Britain and partly by Spain. Subsequently the territory was reopened to large scale imports of slaves. By 1795, about thirty years later, the amount of slaves ballooned to almost 20,000. A few years later in 1807, the Atlantic slave trade was prohibited. However, this didn’t stop those that were persistent about sustaining slavery. Thousands of slaves were smuggled into the territory from Africa and the Caribbean illegally in addition to the domestic slave trade in the upper southern part of the U.S. If we fast-forward towards the end of the data visualization in 1860, there were over three hundred thousand slaves in Louisiana and nearly 20,000 free people of color.
In the time period that the slave sales data set spanned, Louisiana had avid reasoning for demanding so much slave labor. While the territory was under French rule, the services that slaves provided varied and the territory was highly dependent on slave labor. Such tasks included cooking, hulling rice with mortars and pestles, carpentry, and raising cattle (oxen, sheep, cows, and poultry among other animals). Female slaves also took care of their master’s personal task of caring for their children. Though aiding in raising their children mad a masters life easier, the mass importation of slaves gave masters a new lease on life. Wealth was easily in a master’s reach with the slave trade (Whitney Plantation).
Coupled with indigo production, the mass importation of slaves gave masters a more prestigious standard of living. Another reason for Louisiana’s higher dispensed capital for slaves is indigo production under Spanish rule. Females were a main part in raising indigo crops and males extracted them –which makes sense of why the territory spend large amounts of capital on both females and males (Whitney Plantation).
Slaves were a part of American culture for centuries, and part of that time is covered in the slave sales data set. The U.S. depended on slaves for their free services in order to make capital. So much so, that they were willing to shell out what would now be thousands upon thousands of dollars on slave labor because of its returns. Where would the U.S. be on a global scale without slave labor? –A question that can answer itself.


Slavery. Dictionary.com. Dictionary.com Unabridged. Random House, Inc. http://www.dictionary.com/browse/slavery (accessed: April 25, 2016).

Goyette, Braden. “5 Things About Slavery You Probably Didn’t Learn In Social Studies: A Short Guide To ‘The Half Has Never Been Told'” The Huffington Post. October 23, 2014. Accessed April 26, 2016. http://www.huffingtonpost.com/2014/10/23/the-half-has-never-been-told_n_6036840.html.

Whitney Plantation. “Slavery In Louisiana.” Slavery In Louisiana. Accessed April 26, 2016. http://www.whitneyplantation.com/slavery-in-louisiana.html.

Williamson, Samuel H., and Louis P. Cain. “Measuring Worth – Measuring the Value of a Slave.” Measuring Worth – Measuring the Value of a Slave. 2011. Accessed April 26, 2016. https://www.measuringworth.com/slavery.php.

1800 Census of Albany Visualization

1800 Census of Albany Visualization

1800 Census of Albany Visualization

by Ryan Scarpelli

I. Data Description

For the intents and purposes of the final assignment in this course, I have chosen the 1800 Census of Albany, New York as my dataset. The reason I chose this specific set of data is three-fold. First, I was particularly interested by the age of the census. The 1800 Census was only the second of its kind taken in Albany, the first was taken 10 years prior in 1790 (Grondahl). Second, because this data is so historical it contains records of slave-ownership and the race/gender of the head of household. This will make it rather rewarding to work with because there is not much currently known about slavery in Albany, NY during this period. Lastly, I am a current resident in the City of Albany so the data is of particular personal interest.

The raw data for the 1800 Census can be found on the course resources page and is downloadable as a Comma-Separated Values (.CSV) file. The file contains a spreadsheet which contains 946 total records. Each record is a household that was surveyed during the census. For each record we are given 16 different columns of quantitative and qualitative data used to describe the homeowner, the home, and the occupants. The 16 columns are as follows:

  1. Full Name – A string value containing the given name and surname of the homeowner.
  2. Head of Household Race – A string value, either “White” or “Black”, which describes the race of the homeowner.
  3. Head of Household Gender – A string value, either “Male” or “Female”, which describes the gender of the homeowner.
  4. Ward Number – A number value (1, 2, or 3) which corresponds to the area within Albany the homeowner lives.
  5. Free White Men < 10 Years – A numerical headcount for Free White Men within the household. (Applies to all Free White Men columns)
  6. Free White Men 10-16 Years
  7. Free White Men 16-26 Years
  8. Free White Men 26-45 Years
  9. Free White Men > 45 Years
  10. Free White Women < 10 Years – A numerical headcount for Free White Women within the household. (Applies to all Free White Women columns)
  11. Free White Women 10-16 Years
  12. Free White Women 16-26 Years
  13. Free White Women 26-45 Years
  14. Free White Women > 45 Years
  15. Other Free Persons – A numerical headcount for persons described as “other…excepting Indians not taxed” within the household.
  16. Slaves – A numerical headcount for enslaved persons within the household.

For the sake of cleaning up our data for the uses of this assignment, I made a few alteration to the spreadsheet. First, I created a new column simply called “Free White Men” which aggregated the total Free White Men values for all ages into one column. This essentially condenses the records on Free White Men for each individual household. Next, I followed the same exact steps to create an aggregate column called Free White Women. Third, once both of these columns were created I deleted the columns I had just aggregated; I no longer need them as I now have their data in the respective totals column. The results are that we now have 8 columns, half of how me we had at the start.

Custom Ward Map Overlay (Click to Enlarge)

After alterations were made, I performed my own independent research on the Wards column. Our dataset contains a Wards column, with the only possible values being the numbers 1, 2, and 3. I found that these numbers correspond to political subdivision of Albany which were implemented in 1790 (Bielinski). The issue with these values is that ward lines for the City of Albany have drastically changed over time. Today, Albany is currently divided into 16 of these wards and none match the wards referenced in the census. Thankfully, I was able to find a description of the ward lines including very dated maps on the New York State Museum website (Bielinski). The next issue I found was that the historical map was very dated and illegible, I could not meaningfully draw any conclusions about the wards. Stuck with a currently-useless map, I turned to Adobe Photoshop CC to see if I could manipulate the image for my needs. First, I captured a screenshot of a map of Albany, NY from Google Maps and imported it. Next, I imported the image of the historical map on top of the Google Map. From there, I reduced the opacity of the historical so that I could see the current map underneath. Using the transform tool on the historical map, I was able to rotate it to align the two maps perfectly. The new hybrid map made it much easier to identify physical features, street names, and ward lines. The final step was to find which geographic coordinates and zip codes correspond to the wards I had just identified. After a quick internet search, I added these values to 3 new columns: Ward Latitude, Ward Longitude, and Ward Zip.

My intention for this dataset is to manipulate the data and make comparisons between values. In doing so, I hope to be able to identify discernible trends between the Homeowner Race, Homeowner Gender, and the types of occupants within the household.

II. Data Visualizations


Visualization 1 – House Demographics

For the first visualization, I started by introducing my data and findings at the most general level. Being that this dataset is from the 1800 Census, I began with comparisons about the households themselves before diving into the occupants/population. There were 946 total houses surveyed during this census. I was highly interested in seeing how that number breaks down by race and gender independently before comparing them simultaneously. Therefore, I created three independent graphics: a breakdown of houses by head race, a breakdown of houses by head gender, and a simultaneous breakdown of houses by both head race and gender.

  • The first pie chart breaks down the totality of houses by the race of their head, either White (light green) or Black (dark green). Out of the total 946 houses, 914 were run by a White head and only a miniscule 32 were run by a Black head of household. This breakdown is staggering as houses with a White head of household account for over 96.6% of all homes recorded.
  • The second pie chart is very similar to the first; however, the totality of houses is now broken down from 946 by gender (instead of race) of the head. As you can see, there were 853 Male-head houses (blue) and 93 Female-head houses (purple). This breakdown shows a marginally smaller differential than the race comparison, however Male-led houses still represented 90.1% of the population.
  • The final graphic for this visualization is a packed bubbles chart which divides the households in four head-types: White Male, White Female, Black Male, and Black Female. Basically, this chart combines the findings of the two previous charts to see a more accurate depiction of how many homes were run by each of the types of head. The cumulative breakdown of the 946 homes shows that of the four head-types, the predominant leader is White Males with a count of 833. Inversely, Black Females were the smallest group with a total count of only 12 houses.


Visualization 2 – Race vs. Gender (Totals)

Now that we have a solid understanding of the types of houses we are working with at general level, we are going to look into meaningful trends about the occupants of these houses. As stated above, our dataset broke down occupants into four main categories: Free White Males (blue), Free White Females (purple), Other Free Persons (orange), and Slaves (green). To better understand what effect the head-type had on these person counts, I divided homes once again by head race and head gender to compare. This visualization contains two independent graphs.

  • The first graph compares the Race of the Head of Household to the Breakdown of Occupants. Basically, I am comparing the race of the head, white or black, to the number of occupants in each category. We found out in the previous visualization that the number of White Households (914) heavily outweighs the number of Black Households (32). This fact has carried through and is evident in this graph as well.The results of this comparison are that White Households lead Black Households in every category except Other Free Persons. In the case of Other Free Persons, White Households contain 54 people while Black Households contain 95 people. Additionally, the 95 Other Free Persons in Black Households account for more than double the other counts combined. Other interesting aspects include the fact that Black Households in total only had 2 Free White Men and 2 Free White Women. The historical context of this data correlates with the findings.
  • The second bar graph in the visualization compares the Gender of the Head of Household to the Breakdown of Occupants. As you can see, the comparison between White-Black and Male-Female Households is very similar. The Male Households beat out the Female Households across the board in each category.

Visualization 3 – Average Occupancy by Race/Gender

The third visualization displays the average number of Free White Men, Free White Women, Other Free Persons, and Slaves when filtered by Race, Gender, and both. The intention is to compare the averages to the head-type to identify any discernable trends. The first graph will show the average occupancies when divided by Race. The second will show the same comparison but by Gender. The third graph will compound the findings of the previous graphs into one seamless table.

  • The first graph displays the average counts for each person-type by the race of the head of household. The key finding in this bar chart is that Black Households contain on average 2.969 Other Free Persons while White Household only contain 0.059. This means that on average there are nearly 3 (2.91) additional Other Free Persons in Black Households which represents an increase of a whopping 4932.20%. A shocking value to look at is that Black Households led in Average Slaves with 0.781 to White’s 0.562. This may be counterintuitive to some to see that Black Households had more slaves on average.
  • The second graph displays the average counts for each person-type by the gender of the head of household. The results from this graph do not indicate any logical upsets; the findings are exactly what you would expect from our previous data. Male Households beat Female Households in average count for each person-type category.
  • The third graph shows the data from the previous graphs combined. Therefore, it displays the average counts for each person-type by race and gender. This is by far my favorite chart from the entire assignment as I believe it is the most insightful. Our whole population is broken this time by Race AND Gender to display the average counts for each person-type.

There are a number of important and interesting results from the final graph. First, Black-Female Households contained the highest average number of slaves (1.083) while White-Male contained the lowest (0.551). Second, Black-Male Households contained the most average Other Free Persons (3.450) while White-Male households had the lowest (0.059)

Visualization 4 – Slave Distribution

The fourth visualization consists of three packed bubbles graphs which portray homeowners with the most amount of slaves.

The first chart shows the homeowners with the highest number of slaves. I chose a packed bubbles graph because it is easy to view large amounts of data and also because the attributes of size and color allow me further make points. Chart 4A was constructed by setting the Name dimension as a column and the sum of the Slaves measure as a row. I was then able to filter the results to only show homeowners with at least once slave. Next, I used the sum of the Slaves measure to indicate both color and size. This allows us to nicely differentiate and find the houses with the highest slave population.

The second and third charts (4B – Highest Slaves by Gender and 4C – Highest Slaves by Race) are very similar to one another. Both of these charts are packed bubble graphs which are used to represent the same data as 4A. The difference is that for 4B, Head of Household Gender is used as the color indicator instead of Slaves. This allows us to easily see which homes are male-run or female-run, and where the highest concentration of slaves lies (hint: Males). On the other hand, Chart 4C does the same exact thing however this time we are differentiating by race instead of gender.


Visualization 5 – Wards

The fifth and final visualization is the results of my pain-staking work with Albany’s wards. Using the reference from the NYSM website, I was able to recreate the ward lines from 1800 on a current map of Albany. From there, I found their geographic coordinates online. Lastly, I researched which current Albany zip codes most closely match up to the historical ward lines. Though the match is not perfect, it is close enough to be a decently accurate representation. The visualization consists of two filled-map graphs which display the number of homes and people in each ward, respectively.

The first graph, 5A, uses a filled-map chart to show the number of homes in each of the three wards. It was created by importing the custom values for zip codes I found. Once the zip codes were part of our data, I assigned the geographic role “Zip Code” to the measure Ward Zip before assigning the measure as a column. This nicely produced a rough version of the map you are looking at. I assigned the sum of the Number of Records as the color differentiator; higher populations are darker blue, lower populations are lighter blue. Finally, I edited the label to include the Ward Number and the Number of People in each ward.

The second graph, 5B, is almost identical to 5A. It only differs in that the colors represent the number of individual people in each ward instead of households. Please see above for further detail.





III. Process Documentation

The first visualization is intended to introduce the audience to the number of households by Race and Gender.

  1. The comparison I chose was White Households vs. Black Households in order to show the large discrepancy in number between the two. I created this visualization by assigning the dimension Head of Household Race to the column and Number of Records to the rows. Due to the fact that we are comparing sections of a population to the whole, it was an easy choice to select a bar graph as my graph-type. When working with visuals representing race, I find it the least-awkward to use shades of the same color rather than randomly assigning colors. This helps avoid assigning specific colors to each race which some may find offensive.
  2. The comparison being made is the number of Male-Head Households vs. Female-Head Households. This graph was constructed by placing Head of Household Gender in the column and Number of Records in the row. Along the same vein as Chart A, I chose the pie chart style to represent chunks of a whole. I selected traditional colors which correspond to gender: Blue for Males, Purple for Females. My choices for this graph clearly highlight the drastic difference in number of Male Household over Female Households.
  3. Chart C takes a unique-but-similar approach to Chart’s A & B. It compares the number of households when simultaneously divided by race and gender into four categories: White Male, White Female, Black Male, and Black Female. To do this, I placed Head of Household Race and Head of Household Gender into the columns, and Number of Records into rows. I used a packed bubbles chart so that I could manipulate the appearance of each category by attributes of my choosing. I chose to have the Number of Records indicate Size, Color indicate Head of Household Gender, and I formatted the label to appear cleaner. I believe it makes it easier to visualize the data and its underlying meaning with these attributes.

The visualization itself consists of these three graphs organized into one dashboard. This provides a detailed view of the houses by their criteria.

The second visualization is intended to show the audience a deeper look into the census. In this visualization, we will be breaking down the total number of each person-type by the race and gender of the Head of Household. The reason I chose to compares these values is because it allows us to identify if the type of head affects the occupants of the household.

  1. The first graph for the second visualization compares the Head of Household Race to the Number of Occupants by Type. The intention in making this comparison is to see whether White/Black Households are more/less likely to contain a certain type of person. I chose a side-by-side bar graph to display these totals so that their values can be easily compares. I assigned Head of Household Race to the column and the sum of measures Free White Men, Free White Women, Other Free Persons, and Slaves to the row. Again, I chose Blue for Men, Purple for Women, and this time I assigned Orange to Other Free Persons and Green to Slaves.
  2. The second graph displays the effect of the Head of Household Gender of the total occupants in each person-type. To construct this graph, I dragged the dimension Head of Household Gender to columns and the sum of measures Free White Men, Free White Women, Other Free Persons, and Slaves to the rows. The colors match those in Graph A.

The visualization itself was designed by placing the graphs on top of each other within a custom dashboard. The key/legend for both graphs appears in between them.

The third and final visualization focuses on the average counts of each person-type by race and gender. The reason I chose to use averages is to get a clearer understanding of the data when dealing with big differences in sample size (Male-Female, White-Black).

  1. The first graph of the third visualization compares Household Race and Person-Type Averages. The process to create this graph included dragging the dimension Head of Household Race to the columns and the average of measures Free White Men, Free White Women, Other Free Persons, and Slaves to the rows.
  2. The second graph represents the comparison between Household Gender and Person-Type Averages. I took the following steps to make this graph: assign Head of Household Gender to columns, assign Free White Men, Free White Women, Other Free Persons, and Slaves to rows.
  3. The third graph combines the data from the previous two; it accurately depicts the relationship between Household Race, Household Gender, and Person-Type Averages. To combine the two previous graphs I assigned Head of Household Race and Head of Household Gender to columns. Next, I assigned the average of measures Free White Men, Free White Women, Other Free Persons, and Slaves to the rows.

The visualization was constructed by incorporating the three graphs into a custom dashboard. The first two graphs appear side-by-side with the combined graph appearing below. The key appears in the middle of the graph.

IV. Argumentation

Argument A

When dealing with data from the past, especially from a census as far back as 1800, it is absolutely necessary to understand the historical context behind the information. The societal norms and expectations of this time over two centuries ago are a far cry from those familiar in 2016. Namely, the institution of slavery was still very much alive and profitable during this time. This census was only the second to ever be taken for the City of Albany; the first census occurred in 1790. The 1790 census revealed a staggering number of slaves within Albany – 3,722 to be exact out of 3,498 residents. This number ranked Albany first in the entire state for slave population in 1790 (Grondahl).

Therefore, there is no surprise that the very structure of the census data reflects slave ownership. During this time in Albany’s history, slaves were unfortunately extremely common. They were owned by residents ranging from minimal income-earners to prominent, posh families such as the Schuyler family of the Schuyler Mansion (Grondahl). Thankfully, our census only has records of 539 enslaved persons within Albany. While this number is a positive trend of decreasing slave numbers, it is also drastic enough to question the cause. Through online research I was able to find a possible cause of this drastic decrease, from our own Times Union (Grondahl):

The annuity program was put in place to compensate slave owners after the gradual manumission act was passed. The payments were for infants born to slaves after 1799 and they were placed on a permanent state poor list. Masters of each slave child received between $12 to $18 a year until the slave reached the age of 25. On average, the state paid each owner $400 to $500 per slave in annuities.

“The fear on the part of the government was that if you freed all the slaves immediately, they’d become a huge burden on the state,” Barbagallo said. “That just wasn’t the case. Those were misconceptions based on racism.”

Essentially, once manumission was passed within Albany it started a trend of large-scale offloading of slaves. The government feared a huge influx of recently-freed albeit penniless slaves so they began offering owners compensation for kin of slaves born after 1799 (Grondahl). This is one of the many factors that led to the dispersion of slaves from Albany. While this fact, which largely signaled the ‘beginning-of-the-end’ of slavery, heavily contributed to decreasing numbers it wasn’t the sole reason.

The other key determinants which led to a decreasing slave population had little to do with law and everything to do with location. Albany has historically been a travel and trade hotspot for the upper New York region, especially during this time period. When the 1800 census was taken, Albany had just completed construction on a turnpike which vastly opened up channels of travel to the capital city (Wiki). Moreover, by the year 1815 Albany had already become the turnpike center of New York State.  The high accessibility of the city combined with high slave population, increasing slave laws, and emerging contacts with the outside world may all explain the decrease.

Mini-Argument B

With respect to key findings in my analysis, I would like to focus on average slave occupancy by homeowner type. I have provided a comprehensive breakdown of how homeowner race/gender effects the types of occupants in their household. The results of my analysis were largely expected and/or predictable, however there were anomalies and findings regarding which I was surprised.

In Chart 3C “Average Occupants by Demographic”, we can clearly see that houses with Black Female homeowners contain the largest average number of slaves. For reference, the relevant data is below:

  • White Male – 0.551 Average Slaves
  • White Female – 0.679 Average Slaves
  • Black Male – 0.600 Average Slaves
  • Black Female – 1.083 Average Slaves

The chart states that by average slave ownership, from most to least is: Black Female, White Female, Black Male, White Male. This result is counterintuitive as most people would expect White Males to have the highest number, not the lowest. There are a number of possible causes for this ranking order.

For example, all counts for White Male Households may be skewed as they make up the overwhelming majority of all households. As a result, all averages are diluted when compared to Homeowners with miniscule numbers such as Black Females. I personally believe this is the most likely cause, however alternative explanations exist. Out of all households, only 94/946 were owned by Females and just 32/946 had a Black head of household. When combined, the number of Black AND Female homeowner is microscopic.

V. Further Research Questions

The primary question I would like to pose looking forward relates to the outward dispersion of both enslaved and recently-freed persons from Albany. In the decade from 1790 when the first census was taken until 1800, over 3,183 persons previously listed as slaves had left the city. The most basic question I have regarding this finding is “Where did they go?” There are a number of approaches I could take to find my answer.

First, I could perform traditional topic-area research. There are undoubtedly countless articles and journals about the migration of slaves in New York during this period. Particularly, I believe that the journal “Slavery in Albany, New York, 1624-1827” by Oscar Williams may cover this exact practice in detail (Williams). The reason I did not for this assignment is due to the journal being pay-to-access.

The second research method is more advanced. To truly find out where these people have gone after leaving Albany, I need to compare more data. For this assignment I examined a single spreadsheet containing data from a single year in a single county. In order to accurately track the missing persons, I would need to build a custom MySQL database. First, I would need to important all records from the 1790 Albany census as a baseline. I would only keep records on the persons listed as slaves, as they are only of interest. Second, I would import records for all Albany censuses until around 1915. This is done to track the slaves that never actually left the city. Finally, I would have to import records from all other counties within New York State beginning with 1790. Ultimately, using advanced database queries and perhaps some custom programming and scripting I would hopefully be able to track these persons and their location across time.


VI. References

Bielinski, Stefan. “Albany Wards.” Albany Wards. Accessed May 12, 2016. http://exhibitions.nysm.nysed.gov//albany/wards.html.

Grondahl, Paul. “Ultimate Payoff for Slaves’ Freedom.” Times Union. Accessed May 12, 2016. http://www.timesunion.com/local/article/Ultimate-payoff-for-slaves-freedom-3684612.php.

Grondahl, Paul. “Albany’s Long, Neglected History of Slavery.” Times Union. Accessed May 12, 2016. http://www.timesunion.com/tuplus-local/article/Albany-s-long-neglected-history-of-slavery-6808527.php.

Oscar, William. “Slavery in Albany, New York, 1624-1827.” Slavery in Albany, New York by Williams, Oscar. Accessed May 12, 2016. https://www.questia.com/library/journal/1G1-236731633/slavery-in-albany-new-york-1624-1827.

“History of Albany, New York.” Wikipedia. Accessed May 12, 2016. https://en.wikipedia.org/wiki/History_of_Albany,_New_York.

Final Project

Data Description:

The data set that I chose to work with is Slave Sales 1775-1865. This data set is very large, with a lot of information. There are three different types of data represented within the data set, including geographic, textual, and numerical data. The first two columns in the set are “state-code” and “county-code”. “State code” represents what state that the slave being sold was sold in. While there are large ranges of state that are listed, they are all located in the Mid-to-South East. All of the states that are listed include Georgia, Louisiana, Maryland, Mississippi, North Carolina, South Carolina, Tennessee, and Virginia. “County-code” represents the counties within those states that slaves were sold in. Each state listed has a different amount of counties listed as well. Georgia has 8 different counties where slaves were sold while Louisiana has 16, Maryland has 3, Mississippi has 4, North Carolina has 9, South Carolina has 2, Tennessee has 4, and Virginia has 8.

As for numerical data, there are 4 different latitudinal rows. The first numerical column is the third column in the data set, which is “date-entry”. This row contains the years in which each slave was sold. The years range from the earliest being 1742 to the latest being in 1865. The second numerical row (5th column in the overall set) in the data set is “age-yrs”. What this row represents is how old, in years, each slave was at the time that they were sold. While the data set begins with the age of 0, I didn’t include it in my visualizations because most of the zeros represent a null. The data set goes from 0 to the age of 99. The third numerical row in the data set (6th column in whole set) is “age-months”, which represents the age in months that the salve being sold was in months after years. For example, a slave could be 1 year and 5 months old. This row ranges from the lowest of 0 to the highest of 11 months old. This column in used predominantly for children under 1 year old, or very young children. The fourth numerical column (7th column in whole set) is “appraised-value”. This column represents how much each slave is appraised for when they are sold. Like the years column, there are many zeros in this column. Many of those zeros represent a null as well. The appraised value ranges from $0 all the way up to $525,00. I believe that the appraised value of $525,00 is possibly a mistake. If that were the case, the highest appraised value would be $6,000.

The last type of data that is listed in the dataset is textual data. The remaining three columns in the set are “sex”, “skills”, and “defects”. “Sex”, which is the 4th column in the set, represents what sex each slave being sold identifies with being. The two different options listed are male or female. “Skills”, the 8th column in the set, represents skills that each slave has. For this column, each slave isn’t listed as having a particular skill. The slaves who do have skills tend to be valued a little bit higher. Skills vary greatly, and many are listed. Some examples of skills listed are axman, blacksmith, carpenter, cook, driver, gardener, house servant, laborer, etc. Lastly, “defects” represent the defects that each slave being sold had. Just like the skills, not every slave had a defect. Slaves that had defects were more likely to sell for cheaper. Defects listed range from hernia, asthmatic, complaining, blind, cripple, drunk, idiot, lame, etc.

Visualization 1 Story:

My first visualization shows the comparison between different ages and the appraised values for those ages. The ages range from one-year old to sixty-five years old. As you can see, 1 year olds are valued particularly low. Due to the very young age, they are at a much higher risk of becoming ill. They are also a lot more responsibility to care for than the older ages are. Also, the low number accounts for the low number of 1 year olds being sold. From the ages of 1 to 22 there is a steady increase of value. Instead of the sum of appraised value growing with every year of age evenly, there are drastic peaks that get larger every two years, starting with 8 years of age. The first peak begins at 8 years old, then 10 years old, 14 years old, 16 years old, 18 years old, continuing every other year up until 22 years old. At the age of 22 a slow, gradual decrease in appraised value begins. Although there is a slow decline, there are very high peaks every 5 years, beginning at 25 years old, then 30 years, 35 years, etc., all the way up to the age of 65 years old. While there are very high peaks, after the peak at 30 years of age, the very high peaks represent a gradual lowering of value every 5 years as well. What I mean by this is that, the sum of appraised value for 25 year olds is $1,129,221, then reaches it’s highest for 30 year olds with the sum of appraised value being $1,337,584. From there, the sum of appraised value for 35 year olds shows a drastic decline with an average of $1,049,747. The sum of appraised value stays low, while slowly getting lower every year up until 35 where we see the extremely high peak again. This occurs over and over again until the peaks reach their lowest at 65 years old. In between the peaks from the ages of 30 to 65, every 5 years, the numbers are very low. After the highest peak at 30 years old, the sum of appraised value of 31 year olds drops down to $177,149. After 31 years old, at 32 the average sum of appraised value goes back up to the normal negative slope amount at $380,478 where the graph continues its gradual slow drop. At 33 years old, the drop continues with a sum of appraised value being %323,920, continuing to the sum being $278,147 at 34 years old. This continuous gradual downward slope, with high peaks every 5 years continues up until the age cut off at 65 years old. Throughout the entire visualization the highest sum of appraised value was at the age of 35 years old with the sum equaling $1,337,584. The lowest sum of appraised value is when the slaves were 64 years old, equaling $3547 all together. Over all, the visualization increases from the age of 1 up until the mid twenties. From there, there is a gradual decrease in appraised value up until the age of 65.


Visualization 1 Process Documentation:

When I first began to look at all of the data presented in the slave trade data set I had no idea what to work with. There is a lot of information presented, therefor I wasn’t quite sure where to go with my visualizations, stories, or even what historical data could be worked in with the data that was shown in the original set. I began to compare the appraised values with the different ages. When I realized that there was a pattern in the data that’s when I decided to make my first visualization the way that I did. I noticed that children were just as big of a part of the slave trade as adults. Therefor, this graph compares the sum of the appraised value that slaves were sold on in comparison to their ages. In this visualization I used the ages going from 1 year old up until 65 years old. I decided to start with the age of 1 year of age because at 0, most of the data was null. From there, I decided to end with the age of 65 years of age because once the graph got to 65 years old, the data kind of dwindled down to about nothing. I felt that this age group had the best representation for the entire data set that was presented to me. Once I had my age group to work with, I decided on what kind of graph to use. This was tricky for me because I wasn’t sure whether to stick with using a bar graph or a line graph to better represent my data. For bar type graphs, I had the option to use either a horizontal bars graph or a histogram. For line type graphs I had the option to use either a normal line graph, or an area chart. After trying all four options, I chose the area chart. I did so because I liked how the color I chose filled the entire bottom half of the data points. This graph gave my information a more dramatic feel, showing how significant the differences in appraised value actually are. As for the color, I tried a few different options. I started off with the color red because it is a very dramatic, and attention grabbing color. Once I had the graph in red, I didn’t like it because I didn’t feel like it actually represented the feeling that I was trying to give off from the data I was presenting. From there, I chose green. I chose green because it’s the color of money. When I looked at the color after I tried it out I really liked the way that it looked, and felt that it’s easier to grasp that its comparing different values pertaining to money. The way that I chose to make my visualization is representative of my argument as well. In my argument I discuss the difference in values of different age groups, and why the values may be different for different ages.


Visualization 1 Argument:

The data that is represented in this visualization shows how the appraised value of slaves rises from the age of 1 to the age of 20, but then slowly decreases with peaks every 5 years. The reason why the values increase and decrease is because of what that slave can do for their owner. First, I believe that the very high peaks are due to the fact that that most of these slaved didn’t have birth certificates. Furthermore, slave traders that would sell these slaves would estimate the slaves’ ages. While many people would believe that the slaves themselves would keep track of their age, it was very hard to do so when all of the days seem to just melt together. From birth to around 7 years of age the appraised value is particularly low. The reason for this is because the amount of children being sold at those very young ages is a lot less than the amount of 20 year olds being sold. Another reason for the low sum of appraised value could be because if a mother, who was a slave, had a baby then they would usually be able to care for their baby until it’s a little older. It was common that if a mother was a slave, the baby then became property of that same owner as well. This would mean that many young children weren’t being sold. While staying in the same plantation as the mother was the norm, sometimes they were sold to near by plantations, or even worse, to “speculators”. What “speculators” did was go around and buy slaves, and then sell them at a higher cost to make a profit. When this happened, the child was usually taken far away from the mother. Those would be the young children that are accounted for in the visualization. As children grew old enough to work they became more valuable to plantation owners. This can account for the gradual increase in appraised value from the ages of 10 to about 20 years old. The data set represents slave sales from the years 1775 to 1865. Children weren’t big in slave sales until around the 1830’s when the abolitionist movement started to begin. The movement threatened slave supply to those living in the south, so more and more slave owners began buying the slaves at much younger ages. They believed that if they got the slaves while they were younger, they would live a lot longer and therefor be able to work for them a lot longer (Vasconcellos.2016). As you can tell, adults were worth the most to slave traders. This was because unless they had a disability, they were the strongest and able to do the most work. Adult ages ranges from early twenties to around early to mid thirties. The extreme decline after the mid thirties was due to the fact that life expectancy of U.S. slaves was about 36 years old in 1850 (Wallace.2012). Also, elders became to be considered burdensome and unsalable for their owners. So while there probably were a lot of elders in slavery, the average appraisal value declines because they weren’t really being sold, but made to do tasks that they were capable of doing. The elders that were sold were sold for very cheap because they weren’t really a use to their owners anymore. The data set actually represents some elders that were sold for negative amounts. This means that they would pay people to take them.


Visualization 2 Story:

My second visualization is a map of the United States. On this map are different states that were listed in the data set that were responsible of slave sales from 1775 o 1865. Starting in the most northern states listed on this map that were responsible for slave sales are Maryland, Virginia, North Carolina, South Carolina, Tennessee, Georgia, Mississippi, and Louisiana. Within each state there are counties listed. The counties in Maryland are Baltimore County, Queen Anne County and Anna Arundel County. The counties listed in Virginia are Essex County, Henrico County, Albemarie County, Lynchburg County, Prince George County, Sussex County, Greensville County, and Southampton County. The countries that were responsible for slave sales in North Carolina on the map are Halifax County, Franklin County, Nash County, Edgecombe County, Johnstone County, Greene County, Duplin County, Anson County, and Mecklenburg County. As for South Carolina, the state that sold slaves during this time period represented on the map is Charleston county and Edgefield county. Georgia has listed 8 counties, including Oglethorpe County, Gwinnett County, DeKalb County, Troupe County, Taliaferro County, Richmond County, Jefferson County, and Chatham County. Tennessee’s counties that sold slaves were Ruth County, Madison County, and Williamson County. Mississippi only had three counties with sales at the time, which were Wilkinson County, Adams County, Hinds County, and Rankins County. The last state, Louisiana, had the most listed counties responsible for slave sales during that time period. These counties include, Union County, East Carroll County, Ouchita County, De Soto County, Natchitoches County, Tensas County, Concordia County, Avoyelles County, West Feliciana County, St. Helena County, Iberville County, St. Mary County, St. Charles County, Jefferson County, Orleans County, and Plaquemines County. Each different county is outlines on the map and range from a shade of very light res, to a prominent red. The more slaves that were sold in that county, the darker the shade of red is. Furthermore, the less slaves that were sold in that county, the lighter the shade of red is. As you can see by the map, the darkest shades of red are shown in Anne Arundel County, Maryland and Queen Anna County, Maryland. From there, Charleston County, South Carolina is the third brightest shade of red. North Carolina as well as Louisiana have some counties with brighter shades of red as well. As for Tennessee and Georgia, the shades of red in the counties tend to be very dull. This represents that not as many slave sales were made in those states. South Carolina has the least amount of counties in which sell slaves, with only two. Meanwhile, Louisiana has the most amounts of counties that were responsible of slave sales, with a total of 18 counties. On the map, there are no sales listed in some of the in-between states. These states include Alabama, Florida, Delaware, West Virginia and Arkansas. Also, the states that have slave sales, which have states that are not selling slaves, have fewer counties that were responsible for selling slaves. For example, Georgia and Mississippi have fewer counties that sold slaves, and Alabama, a state that sold no slaves, sits right in-between them.


Visualization 2 Process Documentation:

Just like the first visualization that I had created, I got stuck on what I should do while starting this one as well. For the whole beginning of this project I was convinced that I wanted to compare the differences in value between children that were sold during slave sales and the difference in value between adults sold during slave sales. I was going to compare them in a few different aspects, including value, defects, skills and value. I did research on the differences and couldn’t really find much. One I realized that there wasn’t too much that I could have worked off ofI lined up all of the states and counties in which slaves were being sold in, I realized that there were not too many states being listed. I found this interesting because slavery was such a huge deal all over the entire country during that time. I was curious as to why the slaves in this data set were mostly representing counties in the East Coast and South Coast. I tried to represent the data in a couple different ways. I started with a stacked bar graph, which included each state, with the counties stacked on top of one another. I didn’t like how that looked because it was a lot to take in. I thought of how I could make my information easy to visualize. I wanted the person looking at my visualization to easily be able to tell what information was being represented just within ten seconds of looking at it. From there, I chose a map. As soon as you look at the map you realize that the information that you need is within a handful of states. From there you see the outlined counties, which are really easily distinguishable from each other. After I decided that a map was the best way to go, I needed to decide how the person looking at my visualization would be able to tell which county had the most slave sales, comparable to the counties which had the least slave sales. First I tried out using filled circles to determine the size of sales. The circles went from very small to pretty large. I didn’t like that because it didn’t show the outline of the counties. I then decided to go with the county being highlighted by one color, and to have that color fade to almost transparent when there were low amount of sales, to bright when there were a lot of sales made in that county. After I tried that idea, I liked it because it made it so much easier to realize what was going on, fairly quick. When it came down to choosing color I wanted to use a color that would really stand out. I chose red for this one because it is a very dramatic color. It is also easy to distinguish between the nearly transparent counties that have fewer slave sales, and the bright counties, which have a lot of slave sales. This visualization relates to my argument because I’m going over what areas had the most concentrated slave sales, and why those regions may have had such a large number of sales.


Visualization 2 Argumentation:

As you look at my second visualization, you notice that there are an abundance of slave sales in some areas, while there are nearly half in other regions. First, Maryland has two different counties that have the most sales on the entire map. After Maryland, Charleston County, South Carolina had the most sales. After doing research on Maryland during the time period of 1775 to 1865, I found that the second most important port was located in Baltimore County. This was very important because at this time, ports were the fastest means of transportation of goods, as well as slaves. The port was founded in 1706 as a port to transport tobacco to England. By 1729, the port was incorporated into Baltimore Town. Once slave trade was established, slaved were shipped in to this port. Many of the slaves stayed in Maryland from this port in Baltimore due to the vast amount of tobacco plantations in that region (History of Slavery in Maryland). This accounts for the large number of slave trade in Queen Anne County, Anne Arundel County, as well as Baltimore County. One of the largest plantations in Maryland, Roedown Plantation, was located in located in Anne Arundel County. A man who was once a soldier in the American Revolution owner this plantation. By the time he dies in 1824, the plantation had over 80 slaves. The plantation produced cotton, poultry, corn, and cattle. As the plantations grew more popular in Maryland, the number of slaves increased dramatically. Between 1619 and 1697, there were less than 1,000 slaves in the state, while in 1755 there were over 100,000 slaves in the state, nearly 1/3 of the entire population (Maryland State Archives).

While Baltimore County had the 2nd most important port in the United States the most important port was located in Charleston, South Carolina. Charleston County was the slave trade capital in the U.S. due to the fact that because of this port, a majority of slaves coming into the U.S were brought to this port first. By 1860, there were 4 million slaves in the U.S., and 400,000 of them lived in South Carolina. That’s about 10% of all of the slaves in the entire country. Enslaved and free slaves accounted for 57% of the South Carolina population. It’s said that most of the city was predominately built by slaves alone (Hicks.2011). Not only did the slaves in South Carolina were forced to work in the city, but in the country as well. The greatest amount of slaves worked on plantations throughout South Carolina, and because their most predominant cash drop was rice. Rice required ten times the labor as other crops, such as cotton, so the amount of slaves per plantation was also a lot higher than other states as well. While slavery was all over the state, sales were mostly all done in Charleston. This accounts for Charleston County being bright red on the map visualization.

The third state that ill be talking about is Louisiana due the large numbers of counties representing slave sales on the map. In Louisiana cotton was a huge cash crop with more than 2 million acres producing cotton. Due to the fact that so much cotton was being produced in Louisiana, they imported very large numbers of slaves across the state. By the end of the Civil War, Louisiana had over 1,600 plantations that were large enough to have 50, or more, slaves per plantation (LOUISIANA SLAVERY: An Introduction). On the map, the county in Louisiana that is the brightest red is Natchitoches County. In that county alone, there are three very large plantations that are listed online, which are still available to go visit as historical landmarks. The most important historical plantation, Oakland Plantation, started in 1789, and by the time he died, he owned 104 slaves, being the one of the largest plantations in the state (National Park Service).


Further Research Questions:

I found this data set to be very interesting. There was a lot of data available to do research on, from value of slaves being sold compared to their sex, defects they had, skills that they had, where they were from, etc. I’m glad that I chose this data set because I learned a lot from the research that I had done on the information that was given to me. While there was ample information given in the data set, I still have some questions that could be further researched. My first question would be in regard to there not being any slave sales listed in the surrounding states.   Slavery was a huge deal in all of the U.S, especially the South. I did research on states that didn’t have any listed in the set, such as Alabama, and found that there were sales in those states. Also, in previous classes, I have learned that NYC had a lot to do with the slave trade. Further research that could be done on my question would be why slave sales were only listed from the states of Maryland, Virginia, North Carolina, South Carolina, Georgia, Tennessee, Mississippi, and Louisiana, and states like Alabama, Arkansas, and Florida were not. I could go about answering this question by comparing the amount of salves being sold during this time period between the states that are listed, and the ones that are not listed. Perhaps, they were listed because they had a lot more sales compared to the other states. Another further research question that I have that was prompted by my first visualization, would be what was the reasoning behind the prices that were chosen for each slave. Slaves had very different appraised values, from $1 to thousands of dollars. I would like to see how they calculated the amount that they wanted for each individual slave. I could research this further by comparing skills and defects to age and appraised value. While skills and defects have a little bit to do with how much they were sold for, there were still some slaves with out skills that were sold for a lot of money. There were also slaves that had defects that were sold for a lot of money, and slaves that had skills that were sold for only a little bit of money. I also would like to know how they determine which skills are worth more than other skills. I could research this question by looking up the area that each slave that had a skill was from and comparing it to other areas. Maybe from there I could research what type of work was being done in each area. From there I could see which disabilities were more affluent in each area as well and why they paid more, or less, for each skill or defect. My last research question that I have would be why the average life span for slaves was only around 36 years old. This makes me wonder about living conditions and if they slept in homes, or outside, etc. I could further research this by looking up illnesses that were popular in different areas, as well s living conditions.




Colleen A. Vasconcellos, “Children in the Slave Trade,” in Children and Youth in History, Item #141, http://chnm.gmu.edu/cyh/case-studies/141 (accessed May 11, 2016)

Wallace, Hunter. “Slavery Myths: Life Expectancy.” October 15, 2102. Accessed May 9, 2016. http://www.occidentaldissent.com/2012/10/15/slavery-myths-life-expectancy/.

“History of Slavery in Maryland.” Wikipedia. Accessed May 12, 2016. https://en.wikipedia.org/wiki/History_of_slavery_in_Maryland.

Maryland State Archives. “A Guide to the History of Slavery in Maryland.” Accessed May 9, 2016. http://msa.maryland.gov/msa/intromsa/pdf/slavery_pamphlet.pdf.

Hicks, Brian. “Slavery in Charleston: A Chronicle of Human Bondage in the Holy City.” Post and Courier. 2011. Accessed May 12, 2016. http://www.postandcourier.com/article/20110410/PC1602/304109945.

“LOUISIANA SLAVERY: An Introduction.” Times Union. Accessed May 12, 2016. http://www.timesunion.com/living/article/Building-a-new-kind-of-Motown-5765204.php.

United States. National Park Service. “Oakland Plantation–Cane River National Heritage Area: A National Register of Historic Places Travel Itinerary.” National Parks Service. Accessed May 12, 2016. https://www.nps.gov/nr/travel/caneriver/oak.HTM.

Nineteen Forty Albany Census Final

Data Description:
The nineteen forty Albany census provides us with resident’s basic personal information, but it contains a much deeper story in between the lines. When simplifying the information and the numbers, many judgments can be made about the circumstances of this time period. The data given by the nineteen forty Albany census has a lot of hidden information that one needs to read between the lines to understand. The data consists of all different types of data such as numerical or geographical, based upon how you go about the visualization process. Longitude, latitude and place of birth can help us depict how many persons emigrated from other countries and what countries those people were immigrating from. It offers many different categories of information such as age, occupation, place of birth and value of home. These pieces of information can be put together to create stories of the time period and it’s people. Certain occupations made a lot of money, while other occupations saw little income. If you incorporate age and income together, it can be seen if younger, more energetic workers made more money or if the older, more experienced workers made a higher salary. Instead of age, one could use race or gender to specify which demographic group saw higher incomes. The geographical data offers a perspective on the immigration rates from specific foreign countries, which speaks a lot to the time period because immigration was a huge issue. There is an endless amount of data combinations that one can make with the nineteen forty Albany census and I feel my visualizations can represent that well.

Story 1:
The data in this visual provides us with how many people who were residents of Albany in nineteen forty were born outside of the United States and what countries they were born in, as well as how many people were born in each country. It also includes how many people were born domestically, or within the United States. During this time period they were several laws in place regarding immigration to the United States. Asians had it the worst because of the Chinese Exclusion Act, which prohibited any Chinese laborers from entering the United States. They could barely make their way into the United States legally, which is why the number of people born in Asian countries are very low in Albany, some having no residents and some with one or two, and these residents probably had to go through a long, grueling process just to make it to the United States. Once they got here, it was almost impossible for them to find a job because of the law in place. It was meant to completely stop the flow of workers from china due to the overwhelming amount that were coming to the United States seeking work so that natives could find a job before those foreigners do. European countries also were limited in the amount of people that could migrate to the United States, as they were only allowed to send two percent of the nations’ population each year. All of these laws were passed to ensure that American-born citizens are able to find a job before any foreigners do. Although most these laws were passed prior to the Great Depression, the limited amount of immigrants allowed did play a significant role in aiding the economy after the depression. The anti-immigration laws weren’t the only things that stopped immigrants from moving to the United States. The economic conditions of the nineteen thirties were not a secret to foreigners. Many of the immigrants who immigrated there do so in order to find a better job. Due to the fact that there weren’t enough jobs for native-born citizens, the potential immigrants made the decision to stay where they were, rather than risk having no job in a foreign country.
Foreign countries that consisted of the most persons born there that were living in Albany in nineteen forty were Germany, United Kingdom, Soviet Union/Russia, Greece, Turkey and Italy. All of these nations were heavily populated during the time. This led to the most persons immigrating to Albany because the Immigration Act of nineteen twenty-four, which allowed European countries to limit the amount of citizens it allowed to immigrate to the United States to two percent of the given population of that specific country. So, the countries with the highest populations were allowed to send the most people over to the United States because two percent of their population was larger than those countries with smaller populations.

Argument 1:
The nineteen forty Albany census provides us predecessors to get a more statistical point of view of what society was like back then. Although it does not give us individual accounts of those who lived during this time period, it does tell us what age they were in nineteen forty, what state or foreign country they lived in five years prior to nineteen forty and even what level of education they completed. These numbers and data allows us to understand a lot of the city’s trends based off of what jobs people had, how much money they made at these jobs and what level of education was required to acquire those jobs and make a desirable salary within those jobs. It is tough to read all of these random words and letters, therefore we visual the data through program like Tableau to make the data easier to read and also not as boring or unappealing to read.
One of the data visualizations I have put together involve the resident’s home state or country that they were born in. This visualization is a map of every foreign country that an Albany resident in nineteen forty was born in, and how many fellow Albany denizens were also born there. These countries vary in color, but the color scheme represents nothing, meaning that each color has no significance in my visual other than to look nice. Foreign countries had a very small amount of people migrating to the United States. I feel that this is a very likely trend due to the time period and the conditions of the United States during this time period. The United States had just been recovering from the worst economic situation this country has ever seen; The Great Depression. Foreigners obviously heard about the news of the poor economic conditions of the United States and most of them decided to stay where they were. Immigration to the United States was wildly popular prior to the Great Depression because they wanted to be free and believe in their own religions without getting punished by the governing system. Also, immigrants were attracted to moving to the United States because they felt it was the best economic decision they could make. It is still a cliché phrase; “The American Dream”. Immigrants wanted to get a well-paying job, buy a house with a white fence all while raising a family. Once the idea of a well-paying job feel through the cracks, the large sum of potential immigrants did not see the appeal that was once there. With that being said, Albany might not be the most attractive spot for immigrants to relocate to in the first place, but the capital city of the state that most immigrants were arriving in might attract more of said immigrants once they arrived at Ellis Island. If such a small amount of immigrants were immigrating to the capital of the empire state, then it leads me to believe that the national trend stays consistent with the trend of Albany.
The census is a good source to investigate into what type of social and economic trends occurred during a specific time period. Based upon the information given within the nineteen forty census I was able to make the information given a little easier to read and understand through using a visualization and through this visualization it is much easier to make an assumption about the conditions of Albany during the year nineteen forty and the surrounding years as well.

Story 2:
            The nineteen forty Albany census provides us with resident’s basic personal information. When simplifying the information and the numbers, many judgments can be made about the circumstances of this time period. Society is ever-changing, socially and politically, and with the aid of the data from the nineteen forty Albany census I was able to create a visualization that represented the views of society in regards to gender and sexism.
              My data visualization displays the average age and average income of employed for pay or not employed for pay persons based on gender. On the female side, the average age of women not employed for pay was over forty years old, just about forty-two, and the average age of employed women was just over thirty-five years old. This tells me that the average women in the nineteen forty’s worked for pay for a shorter span of their lives than males did. With that being said, Men in the nineteen forty’s saw an average age of unemployment of just under forty years old, only a few years younger than their female counterparts. The average age of males who were employed for pay is greater than the average of those who are unemployed, being just over forty. This represents that males may have been struggling to find jobs when they were younger, but as they aged and gained experience then they were able to find a job and work there until they retire. This matches the national trend in the United States at the time. Men were likely faced with working at lower-waged jobs or low killed jobs until they were able to find a better paying job. The experience in the work force led to them eventually getting a job. Due to the economic conditions given to the country by the Great Depression a lot of people found themselves unemployed or working a low-wage job just to support their families or themselves. As the economic conditions were healed overtime, more and more individuals were lucky enough to find jobs, but because of this they were much older and that has an impact on why older men were more successful in their job searches. A person whose gender is nullified on the census demonstrates a higher average age of unemployed persons than employed persons. This relationship is similar to that of females so this leads me to believe that most of the persons whose gender is nullified on the census are women. For those whose employment status was nullified in the census they saw a very low average age for both females and males, about 15 and 18 respectively. These persons were most likely nullified when it comes to employment status because they are so young and may not have been expected to work just yet because of academic reasons
         There is a clear discrepancy between the incomes of each gender. Males received a much higher average alary than women, and it is a very significant difference. This speaks volumes to the sexism that was still alive during this time period. Women were still seen as “stay-at-home” parents so even when they did get employed for pay, they did not receive close to the same salary as males. Whether or not women were working in similar positions as men, they did not make as much money as their male counterparts, which stills holds true today but not as extreme.

Argument 2:
             The Great Depression made such a significant impact on the history of the United States and it is quite evident in the nineteen forty census. Even through such broad information it is easy to tell certain stories through the census. Jobs and occupations were necessary in order to acquire currency legally back in nineteen forty and that still holds true today. Albany residents of nineteen forty who were male faced lower employment rates when they were younger versus when they were older, and therefore more experienced. Employers during the nineteen thirty’s would look for workers with more experience. Since the aggregate demand was so low, economics tells us that unemployment rises. What I mean by this is fewer goods and services were being bought by consumers because of the economic conditions, and therefore employers were looking for the best possible workers, or workers with the most experience, in order to make their products as best as possible so that consumers would buy the product. They felt if they had high quality products or services than those goods and services would be highly sought after by consumers. The experience in the work force led to them eventually getting a job. Most males were faced with a tough challenge supporting their families in such a tough economic situation, so this led to them working lower-wage jobs, which are also lower-skill jobs just to feed their families and themselves. As the conditions began to get better, the job market expanded and more workers were able to get hired, literally meaning that the workers are now older than they were before the Great Depression. So, if the people who were hired after the depression were hired before hand, or if the depression didn’t happen, those people would have been working at a younger age and the average age for persons who are employed for pay the nineteen forty would have been a lower age. But due to the occurrence of The Great Depression, the average age of persons employed for pay was higher and it forced workers to be unemployed, work low wage jobs or no-wage jobs before they were able to get a well-paying job.
          Persons employed for pay saw a different relationship between employment and age than males. The data says that the average age of unemployed women was over forty. As for women who were employed for pay, the average age was thirty-five. This number represents that as women aged, they were less likely to be working, which is the opposite of males rates. This can be because they had children to look after while her husband worked, or because employers did not want elder women working. This time period was not a “civilized” as society is today. Political Segregation between blacks and whites was still intact within the United States and so was sexism.

Visualization Process:
Through the likes of Tableau Public I was able to construct my visualizations efficiently and in a manner that is easy to understand. There was a long period of time where trial-and-error was my strategy. I would mix up categories and try to make a useful graph or map out of it. Some of the combinations I was able to create I was also able to ruin by throwing in too many or too little filters or even too many other dimensions. Sometimes the filters were so specific that it was tough to remember what I was filtering, so if I had to start a fresh visual over again it was difficult to get back to where I was. This led to a lot of decent ideas being thrown away involuntarily. I feel I was able to be most interested in my final two visuals and come up with the best stories and arguments. One has to do with immigration and I have a strong interest here. I always find myself reading deeply into articles or anything about immigration, especially immigration to my homeland, the United States and more specifically New York. I was able to make a good argument as to why the immigration numbers were the way they were in nineteen forty. I had to filter a lot of the data out and filter it together. Spelling errors of countries names and other similar kinds of errors from the census forced me to filter some groups or individuals together. The country England or Britain was not a choice on my map as it was named United Kingdom. Any persons who said their birthplace was England did not originally appear on the data map. I used the filter to conjoin the United Kingdom, England and Britain for the most accurate results.
My other visualization of data offers a more in-depth look into the average age and average income of employment for males and females. The stacked bar graph gives the viewer a visual comparison of what average age each gender and whether they were employed for pay or not employed for pay, or even in an institution which represents a institutional home such as prison, hospitals and orphanages. In some cases these adults were not expected to work due to long stays at these institutions. The stacked bars serve well for my argument because it gives viewers an easy-to-look-at comparison of averages ages or males or females. Males who were employed for pay were on average five years older than females who were also employed for pay, and that number five might not be distinguishable from just looking at the graph, but you are able to understand that females were slightly younger when it comes to those who were employed for pay, on average. Also, males saw a much larger average income than females in all categories; employed for pay, not employed for pay or institution. I grouped together two different categories that are both labeled “institution” but one is capitalized and the other is not, so the program did not automatically group them together. I also filtered out any persons under the age of fifteen because they were probably not expected to work, and if they were to work at a younger age it was most likely not for profit but to simply assist their parents or guardians at their place of work such as helping out on the farm or working within the family’s store. This is important because I do not want to have biased data.

Further Research:
After completing my research and visualizations, I feel that I can further research some of the data and patterns. My geographical visualization offers a lot of information about the immigration rates. Many anti-immigration laws were passed several years prior to this census being distributed and I felt that is why the numbers were the way they were in nineteen forty Albany. As for further researching this topic, I should inquire into the nineteen thirty and nineteen twenty censuses and even the ones before that. I feel this is something I should further research so I can get a better idea of when these laws really started to have an impact on the number of immigrants. I did do some research upon the anti-immigrations laws that were passed and they were meant to increase the employment rate of native-born citizens to the United States. Some laws prohibited Asian laborers, while other laws prohibited too many Europeans from entering the United States. These laws were passed in different decades so I am curious as to how the initial anti-immigration laws influenced the following laws. Maybe too many persons of one demographic had desirable jobs and political leaders felt his was not the best-suited situation for the country. Once they saw the results of the initial laws, did it influence the political leaders of the United States to pass even more laws on even more demographic groups? Did these results from the initial laws influence any type of change in the policies behind the laws? My data cannot give a definite answer to this, but it is somewhat clear that this could have very well likely have been the case that the results from the initial laws had an impact on how the following laws were structured and carried out. Also in regards to the geographic visual, I am curious as to why there are only six native-born Canadians living in Albany. Canada is very close to the capital city and I feel six native-Canadians is too little of a number.
Another topic I want to further research in regards to my data is why there were over twice as many females than males living in some kind of institutional housing. It could be because the average age of a male in this type of situation was over fifty-eight years old. This is an old age for the time period and it could be said that most of these males were retired and living in some type of group home. Females’ average age was just over thirty-seven. It is tough to make an assumption off of this number but it could lead me to believe that most of the females within this category were in mental health institutions or possibly even prison. As I mentioned earlier, this time period saw a lot of sexism compared to today’s society. This could potentially led to women being arrested easier for more petty crimes or being placed into an institution easier. I want research how active sexism was in Albany during this time period. It was only twenty years prior to the census that women earned the political right to vote. Although they had some political equality, society potentially was still very sexist.
There is a lot of educated assumptions that can be made from using the data given by the nineteen forty Albany census when you simplify the data, but these assumptions do remain assumptions. Researching furthering into the ideas I had in regards to this data will definitely give me a better understanding of what the data is saying and how it is saying it.


Slave Sales 1775-1865.

Data Description

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

Visualization one

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

Process documentation

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

Visualization two

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

Process documentation

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


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

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

Further Questions:

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

1940 Census FINAL

I chose to analyze the 1940 census data set, which categorizes data for over 10,000 people. The demographics include numerical, text, and geographic data. The categories for the information include: addresses, value of purchased homes, head of households, gender, race, age, marital status, birth year, highest education level, birthplace, residence city, employment, income, parental birth place, and native language. If we were to look at the range of data from the first hundred people, we would see that there are large ranges of numerical and descriptive data. For numerical data, the value of homes ranged from 55 to 10,000 in New York. The age range varied from 1 to 81 years old. Income of people in the 1940s ranged from 0 to 5,000. For geographic and descriptive data, the household ownership had two categories (rented or owned). Marital status ranged from widowed, single and the most common status, married. The relation to the head of household varied from wife and husband to sister-in-law and grandson. Occupations ranged from lawyers, librarians, electricians, maids, in paid family workers, and salary workers in government or private work. School attendance ranged from no educational background to attending college for over 5 years. Over 90% of the dataset consisted of white men and women, with very few black people living in these areas. There was a limited range of street names, for example the first 200 people lived on three blocks: Fleetwood Avenue, Vanschoik Avenue, and Cardinal Avenue. Majority of the residence were born in New York and continued in the same residence.
If we were to compare categories within the 1940 data set, analyzing two columns can raise questions and conclusion about the type of living situations. The first comparison is between the highest level of education and occupation. Those that have only completed some elementary or high school education up until the second year are unemployed. Several people from Vanschoik Avenue that completed the 8th grade were not employed for pay or salary workers in private or government work. Those that have a college education show occupations of Feler Booker and Dental Doctors. Those with no education showed the clear distinction of no occupation. A second comparison is between income and house ownership. It is clear to see those that rented their homes are forced to work harder since they’re making payments more frequently. The income of renters was nearly twice as the income of a home owner ($4,800 vs. $2,200). The census is skewed in showing more people owning homes in the 40s than renting. Another comparison in the data set is between gender and attendance in school or college. The census is unclear in confirming the difference between “attended school or college” and “highest graded completed”. Most of the responses in “attended school or college” said no, but had some sort of educational level in “highest grade completed”. Surprisingly, there was an even amount of males and females that have yes in the category for completing school. Majority of the ones that say “yes” only have elementary or high school education, while very few have attended college. Overall, the census does uncover some patterns in the demographics within certain areas in New York. The downfall to gathering information is the areas that are left black in the data set. It makes it harder to have a detailed account of what happened in the 1940s.


The 1940s was a time where the U.S slowly recovered from the Great Depression. New York specifically since then has maintained the number one spot for highest population since the early 1900s. Looking at demographic information of residents in Albany, New York in 1940 can tell various stories. The addresses, marital status, race, and education are the few parts of the census that raise questions regarding the lifestyles of people in this particular year. Was the residential area rural or urban? Were people financially stable? How did levels of education effect future endeavors? This visualization focuses on how education levels effect occupation decisions.
Initially looking at the data, it is unclear in figuring out who was enrolled in school for that year, and the kinds of occupations at the time. You must take into consideration age, and filter which jobs have different spelling, but are the same title. After making those changes, the visualization shows the variations in occupations and the education background one possessed in that field. The information is represented through a bar graph with a colored key to indicate whether the person attended college, high school, or elementary school. For each occupation there are numerical distinctions for how many people in 1940 worked in the same occupation.
Before looking at the occupations, the census does provide addresses of people in Albany. With some research, I found that Fleetwood Avenue and Cardinal Avenue were in the Whitehall area of Albany. This shows that these residents lived in close proximity of each other, yet obtained various jobs. For example, two people that live on Fleetwood Avenue both in their late 30s/ early 40s white, male, and highest education level is high school. One has a career in sales, while the other is an electrician. You can then compare those two people to a woman in her early 40s, married, with the highest education received in elementary school. Her occupation is not listed in the census.
The comparisons stated show us that creating one story can then lead to others. Were women still suggested to stay in the home in 1940? If she obtained higher education, would she be working?
Looking back at the visualization, something interesting within the story is the placement for those with no educational background. Most work in the same field as those that have went to high school and/or college (housekeeping, inspector, etc…). The highest number of jobs with varying educations obtained were wage/salary workers in government and private businesses, proprietors, owners, laborers, and inspectors. These occupations are closely related to either working for the government or working for themselves. We can build the assumption that this area of Albany is more suburban with many small businesses. Albany today is assumed to be very government orientated because it is the capital, yet many parts in the downtown region do support this assumption created from the data. A final observation following the census is the wide range of jobs that were surprisingly held at the time, especially following the economic downtown a few years before.
Drawing upon the trends shown between race, gender, and income, the census uncovers distinct disparities. There are 128 residents listed in the census that are non-white citizens in Albany, New York. There were Chinese, Filipino, one Japanese, and Negro population. The reference to “negro” alone, and the year 1940 can suggest that racial tensions still existed in the Albany region.

The context of the second visualization connects with the first based on the education levels of races and genders. The group of Chinese residents have a range of education from none to one that attended high school. Filipino residents all attended college except one, whose highest level of education was high school. Only one Japanese resident was listed who attended high school. Majority of the non-white population consisted of Negro residents, with majority of their education levels stopped at elementary school. When comparing these findings to income, the highest income listed between all non-white races was $2,250. The white residents’ education levels varied, however the incomes were at substantially higher levels. If we look at the correlation between gender and education the differences between men and women are surprisingly different than the differences in income. For Chinese men and women, more women were in school or completed higher education levels than men. All Filipino residents listed in the census went to college, with the exception of one woman. However, there were only two males listed in the census, whereas there were eight females. For the Negro population, there was an approximately even distribution of educational levels and listings of men and women. When examining the relationship between gender and income, there are two immediate trends found. Within the Negro population, you see that males makes more money than females.

Other factors such as head of household contribute to this finding. Providing for a family will have affects on income levels for anyone despite gender. In regards to race, the Negro population were systematically disadvantaged. Being a woman and Negro creates a larger disadvantage to make money, especially in the 1940s. The only women that were head of the household in Albany were widowed. If they were married, the chances of receiving any form on income were smaller than single Negro women. The occupations held by women also play into the stereotypes of how society depicts women. They held positions of housework or waitresses, despite their education levels. When looking at white men and women, the incomes of women rarely exceeded $1000. The occupations listed for women who didn’t obtain income were either unpaid family worker or housework. For women that did work, their occupations varied as opposed to non-white women in 1940s Albany. They held positions in medical fields, clerical work, sales, and restaurants. The trend found not only in the census, but in our lives today is the fact that white men hold the highest income. The census itself has less gaps in demographic information than any other gender and race. The variety of incomes, occupations, and education levels can conclude the fact that white men are more privileged that anyone else. It is clear that the stories made from each visualization intersect to show trends that are similar to ones seen today.


The demographic information displayed in the census, can provide a clearer demonstration of trends if visualized. Before creating these visualizations, it is important to know what information you plan on comparing in order to show significant changes in the data. Tableau was recommended as the software to help us further understand our census.
For my first story, I needed to asses whether education levels or occupations would be in rows or columns. There about three graphs that could be used to visualize the data: circle, line, or scatter plot. A scatter plot shows the clearest distinction in the number of people that completed a particular education level and its relation to their occupation. When finalizing that step, I realized there were an exceeding amount of items in each section. Without any categorization, you cannot see any trends or patterns. The range of data for educational levels were specific starting from no educational background to college 5th year or higher. I knew I didn’t want each grade in its own category so I grouped education by none, elementary school, middle school, high school, and college. I decided to filter out null information because it would then complicate the filtering process for occupations. The range of occupations were extremely long, therefore required multiple groupings. I initially based the groupings by related job descriptions, but realized I had to continue to condense and place similar occupations together. For example, I had restaurant related occupations as on group. Bartending was placed in its own group, but can be categorized with restaurant as well. I finalized the visualization with 32 categories of occupations that ranged from accounting to yard work. Factoring age into the visualization helped understand who held these positions at what point in time. There were people that were not of working age, therefore their education levels would not have relevance to the correlation between education and occupations. Once adding this category, my graph changed into a combination of scatter and stacked graph.
My second visualization showing the correlation between race, gender, and income was a simpler process. I made gender and race the columns and income placed in the row section. There were complications in representing the five race categories mentioned earlier. When creating the visualization, the Filipino average income would not show on the graph. The Chinese and Japanese population did not have any numerical income displayed in the census, therefore was removed from the visualization. I chose to use a bar graph so that you can see differences in income based on race and gender. Income was the only dimension that needed to be filtered to show a range in numbers, versus counting each persons’ income.
For both visualizations, it was best to choose colors that were appealing to the eye and made it easy to see the trends that were described in my argument.


When looking at any historical information, you may see a pattern regarding disparities based on societal factors. The 1940s census provides information on a select number of people living on neighboring streets, their marital status, income, gender, education level, occupation, age, and more. Demographic data is a good way to track populations, but requires good observation to find correlations within the data. The patterns we see today are presented in the census once we create visualizations. There is a distinct trend in the societal differences based on class, gender, and race. Three main points will be made within this argument:
1. The higher education obtained by people in society, the better their occupation is.
2. Men make more than women in nearly every profession.
3. There is a presence of white privilege and lack of representation for people of color in any given data set.

The first trend within the argument is education levels influence occupations. The census data lists the number of people who have either attended elementary school to college. Although several names have no information regarding the highest level of education received, a pattern is still present. There is a subset of data that shows people who have only completed elementary school with an occupation as an unpaid family worker, wage or salary worker, janitor, or laborer. Those with only high school education had similar jobs, or some more advanced such as beautician, attendant, and typist. College education residents in Albany were lawyers, civil engineers, stenographers, and accountants. Those job titles/careers advance as the education level increases, showing the value society has always placed on receiving a higher education. The 1940s census does however, lack a large amount of occupations for residents. People in Albany overall did not attend college. You also see that many people that have an elementary school education hold the same positions of those who attended college. After categorizing jobs into the category “Business/City/Police”, there are more people that have an elementary school education than college. I noticed a potential reason for this is my placement of business or private owners into the category. Entrepreneurs don’t necessarily need higher education to be successful. There were also information in the data that assumes college attendees do not always immediately find work in their desired career. There were twelve residents listed in the “housekeeping/cleaning” field who more than likely did not intent to work there. Also ten years prior to 1940, the United States went through economic turmoil. This can have an effect on people who worked in state, local, and financial offices that may have lost their jobs during that time.

The second correlation found within the census is the difference in income based on gender. It is already a known fact that men make more than women today. History has shown us that society has trained women to be comfortable in the household. They are created to be wives, mothers, and attend to duties in the home. All of this work fulfilled and even when doing so, that work is still not measurable to work for pay. When visualized in a bar chart, you can see men made more than women. The census does lack information on occupations for women. The marital status also plays a role, as you can see who is married versus who is the head of the household. Typically, the relation to head of household for women is wife, mother, or daughter. The men would be the head of the household or the son. Both the head and the son still would have occupations listed in the census more frequently than women during this time.

The last pattern within the argument is racial disparities that continue to exist. With a simple glance at the census, it is clear that over ninety percent of the population in Albany was white. It is uncertain if people of color were undocumented purposely, or they simply did not reside in Albany. Four other races were present in the census: Chinese, Filipino, Japanese, and Black. Based on their demographic information, they all lived on Fleetwood Avenue or Vanschoick Avenue. Their education levels, occupations, and incomes varied. The data alone does not show if one minority was more established financially than others. In comparison to white residents, the financial difference is drastically skewed. The sample of information on the minority population is somewhat too small to make any major conclusions. By assessing the information, you can question whether the area in which people of color lived was worse financially, safe or unsafe, encouraged or lacked opportunity for growth in comparison to the neighborhoods where white people resided. Those factors are a part of the present struggle of equal opportunities for all races today.
There are other correlations that can be made with the census data. You can compare martial status to head of household, immigrant status and occupation, home ownership and income, gender and employed for pay/non-pay. The census is simply surface material, yet can unveil many trends about populations in specific areas. These patterns are not new findings to how society is run today. They give additional information for how these patterns came to life.


The following questions presented for research were considered before and during the visualization of the census data.
1. What was the population like 10-20 years prior to 1940?
• There were nearly 15,000 people listed in the census. It would be good to learn how many people lives in Albany in the years prior to compare changes in residency. Was there anything happening in the city that increased or decreased population rates? Albany is the capital of New York, was there something economically attracting people to the city.
2. What brought Chinese, Filipino, and Japanese people to Albany?
• Between the late 1800s and 1920s, Chinese people migrated to Albany, NY and started business. The Chinese Exclusion Act, however, kept their population to a small amount. As late as 2010, the Chinese only makes up nearly 2% of Albany’s population. The percentage of Filipinos and Japanese in Albany has been less than 1% of the population.
3. Why are more women listed in the census than men?
• Initial thoughts behind this question were based on whether men were active in World War II at the time. What did these women do collectively in the city? Where there any places or programs where they came together?
4. Where areas in Albany segregated by race?
• The census provides streets of residence, but further research or visualization of where the streets were in relation to who lived there could show how Albany was form a social standpoint. What jobs were present on the listed streets? How was transportation? Were the areas safe or unsafe?

Overall, the questions and visualizations presented show the need for further research to fully understand life in Albany, NY. Some trends were shown immediately, while others were found through creation of comparisons with the data presented.




Final- Religion NY

Data Description:

The dataset I selected was the census of NY religion by County. It consisted a lot of fundamental information as well as more detailed pieces of information. Looking at the row of data collects; it shows that the years 1850-1890, state (New York), counties, denomination and the number of churches that had been collected over the course of four decades. The geographic data refers to the data that looks at the number of church within a demonization and which county these churches are located in. The geographic data is the set of counties, which this dataset focus on state of New York. The numeric data is the year the data was collected, in addition to the number of churches. All three descriptions are vital in breaking down the number and words that were given in the census.

As I explore the various columns and rows of the dataset its hard not to notice the different ranges of trends, with these trends forming it natural develops a story. The first column is a section of number (years). Its numeric column ranges from the year 1850 and goes to 1890. It resets the year every time it switches to a new demonization it is calculating. This column provides us with the information needed to know which years the data was collected. The next column is the state in which this data was collected. It is consistently focused on the state of New York. The next column is the county in New York. The counties are arranged in alphabetical order and restart as well, every year that the data was collected. This keeps the data extra organized. This column is geographical, because it depicts where all the churches from each religion was local a crossed the state of New York. The counties were being counted starts with Albany and ends with the county of Yates. When this data was being collected there was sixty-two counties that contributed to this census. The next column looks at the demonization that was being established in New York during the census. This is a textual column because it is starting with the Baptist and Congregation denomination; it also looks various denominations such as Judaism, African Methodist Episcopal Church (AEM) etc. The final column the census looks at is the number of churches in a particular denominations within the previous counties discussed. This is a numerical column; the number of churches ranges from zero churches established in a county up to eighty counted in one denomination and in one particular county. With this sample of the New York Religion by county we can gather a great deal of information. It allows the viewer to piece together a story just by examining the finer details within the rows and column of the census.


Data Visualizations: (Map of New York)

With the dataset collects a story develops out of the number and text that is provided. The story of how throughout the four decade that data was collected the United States, more specifically New York became more and more of a melting pot of religions. People believed that migrating here whole heartily hope that this was a land of opportunity. When this data was being gathered. The nation was transitioning into a working class society. Where wage labor and having a boss was becoming the norm. Rather than years prior when individual shaped this country and farming was a major dynamic of this nation. Immigration will play a big role in the years to come. In these next decades urbanization will also be playing a role.

The dataset it includes information about religions that we being established in the state of New York. Every ten years apart from the year of 1880, it was kept track the amount of churches and what denomination it belonged too. This contains valuable insight into the rapid spread of religion in the New York. It provides insights as to how many churches in each demonization were being used and in which county were these churches being worshiped. This visualization allows the viewers to compare the different types of denomination, which ones were popular, when and where they were located in New York. With the visualization of the map you can see which religions were promenade throughout the curse of the dataset. It maps out all of the different counties giving the viewers a better visual to see why certain place may have higher church counts and why other may have lows counts. The visualization enables us to show the progression or digression of every denomination that was collected in the census. The map of New York shows the progression of most of the demonization and the down fail of others. It shows religions that were previous not existent such as AME. Its shows the immigration patterned of each religion. Most of them start in the cities because that is where the immigrant ships first dropped them off. As times goes on and the cities get filled with corruption and filth. People started to move to the outer counties, taking with them their religious beliefs. Immigrant could have started creating their homes in the Queens, Kings, Suffolk and Richmond County. Later in the 1890 the viewer can see the progression of churches being established in counties such as Erie and Genesee. By looking at the map you can also see the out of ordinary the number of churches will jump. For example in the county of Oneida, the denomination of Presbyterian was consistently around twenty churches up until the 1870s but my 1890 the number of churches shoot up to fifty-four in that county. Same goes for the Erie county the number of churches on the Lutherans, Moravian and German Reform church prior to the 1870 was about twenty and it too by the end of the last data collected in 1890 the amount of churches double with fifty-four in Erie county as well.

Process Documentation:

I started my quest to better understand the census by using Tableau. Incorporating different combination of rows and columns to see what kind of results I would get make things interesting but also complicating because the design on the computer looked like it would make a complying point but in reality I would make the viewer extremely confused or not understand the story I was trying to make them understand. Before creating my graphs the census was just number and words to me, they had no significant. But once I started to play around with Tableau and familiarize myself with it. I could see how vital it could be.

Once I started putting the pieces together I started to notice trends. I made my data into a geographical graph, I made this decision because I believed that it would show the ample difference between the denomination and which counties they were located in. This aloud me to sort out the number of denomination in each county by color to make show the differences of each county but year I used complementary colors like orange and blue. A dark orange symbolizing the largest amount of churches in one denomination and a dark navy blue to repent the lowest or zero churches in said county. Once I had my graph made trends started to appear on the computer screen. I was looking to keep the graph easy to navigate through. It became too much information over load even for myself, to look at all at once so I decided to use filters in my graph. With a click of the mouse the viewer could now just look at one denomination and its raise or fall over the four decades that the data was collected in each county of New York.

For my second graph I decide to go the route of making a bar graph it was actually the first graph I made using Tableau. By using the bar graph it enabled the viewer to see the total amount of churches in each denomination for the state of New York. I believe with the bar graph you could make out a better story than with the geography map. With the bar chart each denomination was give a different pastel colors. A part from the hideous brown color bestowed to Quakers. Within the large bar chart each year that the data was collected was given their own dataset. With the bar chart it help show the progression over time of when religions some doubling in the number so churches. It also brought to my attention some outliers or missed data. For example it data claims that in 1870 there were not Jewish, Quaker or Other denomination accounted for in that year. Then when by 1890 results appear for all of those denominations. I decide that this bar chart should also have a quick filter just so the viewer want to look at one denomination is one county they could have that option. Even though if you hoover the mouse over one of the bars it tells you in alphabetical order which county it is and how many churches were there.


Argumentation (Map of New York):

When looking at the visualization there are something’s that drew my attention. The color contrast of the orange and navy blue caught my eye while I was clicking around with my quick filters. While clicking from one denomination to the next, started to see a elucidation as to why the dataset may look this way. The first thing that grabbed my attention was the increase number of churches.

When doing research for my data I was intrigued to find many different connections for all aspects of the data. The New York State religion census is categorized in a variety of number classification such as the location, the county of the church within New York State and what denominations the church is affiliated with. The information that is given to the viewers allows them to piece together the lives of the citizens who lived during this time and to get a better sense of what religion theses citizens practiced. In the data shown, 1850-1890, the viewer can see the steady growth of most all of the various religious denominations that were established in New York State. Additionally, using my first bar chart, featuring the different dimensions over time, it can be concluded that the most dominant religion sect establishing itself throughout New York State were the Methodist, Baptist and Congregational. Presbyterians however, shown by the data, did not see dramatic increases its church presence throughout New York State. Through the time period of 1850-70, the number of churches remained constant at about seven hundred every time the data was collected. The data also concluded that the Quaker churches in New York were the only denomination experiencing a decrease in numbers. By 1870 the data that was collect suggests that no Quaker churches were present in any New York county. Finally by 1890 they reappear, with only ninety-two churches.  I believe the influx of churches correlates what was going on in American history, the first and second wave of immigrants and the urbanization of America. The first waves of immigration to the United States happen in 1840-1860. At this time immigrants that were coming to the land of opportunity were mostly Irish and German. As the second wave approached in 1880-1940, is where the data set stopped in 1890. The immigrants that were arriving to the United States were mostly Eastern and Southern Europeans. During the first wave, on average, about 2.4 million came to the land of opportunity. For the second wave on average about 5.2 million came to America, more than double the previous wave, this shows the sudden increase in Judaism at the end of the 1890. Judaism is a prominent religion in the Eastern and Southern parts of Europe. With America’s rise of immigration grew a concept know as Urbanization, a process by which towns and cities are formed and become larger due to increase people living and working in those areas. Once the immigrants got off the boat many of them had very little money to travel out of the city where their ship had ported. These people stayed and correlation with the immigration increase, lead to Industrial growth in those pockets of newly arrived immigrants seeking work. In my map data set of New York, the number of churches and the variety of religions found in the county of Queens, Kings, New York, Richmond and even Suffolk doubled in size. During these times America develops into working class, where wage labor replace an agrarian work concept that was prior, a nation of farms. Going from someone that controls your own wages and the amount of food you can provide for family to one that was dictated for you is a huge shift from farming and being independent, to someone (Boss) controlling your wages.

Further Research Question:

When I was examining my dataset and as I was creating my visualizations, different question popped up that with further research might lead to something. While look at my geographical map I notice that the county of Schuyler was not counted for in the first year of the dataset, 1850? Was it not considered to be a county, and during the ten years of waiting to make the next set of data did it become a county. Looking at the same visual during the years of 1850 and 1860 Suffolk county Methodist church was counting to raise but then by the next year they collected the data 1870, the number of churches in that drop dramatically to nine churches. What cause the sudden down fall? Was it an error on the collector of data? I claimed in my argumentation that immigrant had to deal with the influx of churches and the more increase variety of denomination with further research that question could be answered. A trend you see with the visualization is the Quaker religion is one of the few denominations that decrease throughout the dataset. What caused the pushing away people from the Quaker denomination?

With my bar chart visualization it aloud me to see different flaws or kumquats that dataset had collect. Only with further research could these questions be answered. The First to note is by 1870; the denominations of African Methodist Episcopal Church (AEM), Jewish, Quaker and other were not calculated in that year’s census. I have mention before that the year 1880 census was not collected. There is not data found on that particular year. What happened in 1880 that people made the decision to not collect the sense? Look at the visualization something that surprised we was that the Dutch Reform denomination have one of the lowest number of churches established in New York. Now living in Albany I know that the Dutch were one of the first people to settle in the state of New York and more predominantly Albany County. Whoever this dataset shows that was only had the second highest amount of churches established in There County. What took the led was Ulster County. In addition by the 1890 is fell to third falling behind Kings County. Why is that a county know for being settled by the Dutch had so few churches established? The Methodist religion was the predominate religion is the state of New York, I would be interested to see why and how did it become so popular is the span of four decades.


Data Description:

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

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

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


Data Visualizations:

The first of my data visualizations is actually meant to be a pair. The two charts being used are titled “Box and Whisker” and “Changes”. With these two visualizations you are able to see the differences in pay for the same or similar injury or other reason for pension. The question that I thought that these graphs could answer were why were there differences in pay grade. In the box and whisker chart the idea was to a more statistic approach to the data. You look at the raw data and notice some differences right from the start. In this chart you see various statistical points such as median, quartiles, the whiskers. For this the larger the box is the larger the disparity between people with the same reason for being pensioned. From a quick glance you can see that it is about fifty-fifty on reasons that have no difference and reason that have some difference. And then when you look only at the reasons that a difference, and you once again realize a fifty-fifty split with small difference and a large difference. The differences were what were driving me to dig deeper and deeper. Then I decided to create an accompanying chart. This chart shows the differences again, however the data is grouped. I grouped the data based on similar reason for pensioning. Some examples include by right arm or leg, or being a dependent mother. This further shows the differences in pay for a certain reason. In creating these graphs, I set out to find why there were these differences. One would assume that if you have the same reason for receiving a pension as someone else you would get the same amount as them. And even without the visualizations you can see this is not the case. And then you think it must be because it must be one person has a worse injury or other reason than another person, wrong again.  After some light digging I found out that it wasn’t that simple. It turns out it was completely out of the hands of the pensioners. In those days you got what you got, forever. Then, as time went on and pension laws evolved, the government adjusted for inflation and also changed who could get a pension in the first place. The second part makes sense, the first part is where it gets tricky. As I said before pay rates were adjusted, and you got what forever, so in essence on one day you could not be eligible for a pension, and the next you could receive a pension and also at a higher rate than a person who came before you with the same injury. This is something you can see in the visualization, with the later dates generally being the higher of the pays where there is a difference. Overall, for this set it took some digging to find out why the data had such weird breakdowns. (ElderWeb)

For my second visualization I decided to use a packed bubble chart. The story for this chart is literally nothing like what the first was. With this chart I set out to find demographic data about Albany pensioners. The process for this visualization was a little more tedious, because with this you can only do so much. When I first chose the packed bubble I knew by the way that I formatted that the largest bubble that I was going to have was Albany, that was a given. I knew that by sheer volume Albany would vastly out number the smaller neighboring towns. I then grouped the towns by type. If you look at the chart, the green bubbles are urban, the suburban red, and the rural blue. You see that while larger, the green circles are far outnumbered by the blue circles. You understand that in cities there is significantly more people so there’s going to be a good number of pensioners. My question came from the lack of pensioners in the smaller towns. Small towns are generally older people so how is there often times one or two people receiving pensions in a town. In this time shortly after the Civil War everybody that was able bodied would’ve fought in the war and by the law of averages people are going to get hurt. From that fact alone you’d guess that there would be at least two people per town to receive a pension. Add that to your everyday ailments and you’d suspect at least a handful of people per town regardless of size. Some of the reasons were not even bad reasons to get some money to live on either.Looking at the amount of people in Albany get pensions I wouldn’t say that it was particularly difficult to obtain a pension. Perhaps it was just the proximity to an office or something that was stopping the outer towns from receiving a pension. This is the interesting part of this chart because these questions can never really be answered. You can think of a million different reasons for why this would happen and you may never be able to guess right. So while I was not able to find any concrete results for this visualization, it opens up some of the fun parts of history that are unwritten, where you can speculate and think about what might have happened and maybe if you find a good enough theory or dig deep enough you may some day find out.

Process Documentation:

Doing this part of the assignment is completely new to me so I don’t really know how to start. I’ve never worked with Tableau either, so in all aspect this process is completely new. I’ll start at the beginning I guess. When I first looked at all the choices for possible data this was the only one that really stood out to me. I always like to work with things that I know how to use so when I saw that there was a choice for monetary data to be used and the fact that I’m a business admin. major the choice was almost automatic. So after the choosing process, I started to look at the data and look for any type of difference in similar data and went from there, this first reaction eventually became the main ideas for visualization. Like I said before, I really had no idea how to use any of the programs so I again with what I know how to use and worked on graphs that I knew could show the story I was trying to tell. First, I went with the box and whisker. I knew this would show exactly wanted to show so I went with it. This type of visualization draws the eye to the differences in value, similar to what happens when you use different color or size, so this worked. I also thought that this wouldn’t make the visualization too simple. Meaning that it takes a little investigating to fully understand what was being said. This visualization is meant to be paired with the graph titled “Changes” where I grouped similar ailments based on body part or otherwise. On that graph it is easy to see the varied pay rates, the main story of this project. By doing this it was easier to see the correlation of the data then trying to remember what the rates of similar injuries were. Another visualization that I created had a little more of real process to it. I chose the packed bubble chart to show the distribution of payments in two different ways. Showing the differences in payments per town and where the money was all going. I’d be lying if I had a reason about the color choice, but I do know why I wanted them to be grouped. The green bubbles are urban areas, and the red and blue are suburban and rural respectively. By grouping them like this I was able to show that most of the money was being given to those in the City of Albany but rural areas there are more suburban and rural towns receiving money. Overall I found this to be an interesting task. The fact that I really had no idea what I was doing probably added to that but I look forward to using this program in the future.


When doing research for my data I was intrigued to find many different connections for all aspects of the data. When looking I found that many of my question were answered to a certain extent. Within minutes of searching for how pensions were paid out I found a few fast facts that really applied across the board for this data set. The main idea of the first article that I came across was specifically how Civil War pensions were paid. In this data set many of the pensioners were likely receiving such pensions from injuries suffered from the Civil War so I thought this would be a good place to start. The main question that I had from the first visualization was how there were so many differences in pensions paid and this article was very helpful. Almost instantly my question was answered, it turns out that those who applied for pensions earlier were likely to receive lower pensions. Using today’s logic that seems like it would be really unfair and it was, and it only got worse. Due to pension laws evolving over time a person would receive more for the same injuries on different types of technicalities. So, if someone who was certain about their ailments significantly effecting their lives to receive a pension would in fact be paid lower than someone who would try to work or survive without a pension. And the pay wouldn’t change over time for those earlier applicants either, someone would just get paid more based on whichever technicality was passed before they applied. I found this to be particularly disturbing because as I said before, people who were certain that they would need a pension to keep going who benefit less by applying than someone who thought that they could at least make ends meet for a while without a pension. Similarly, in regards to different pieces of pension qualifications being passed your ability to be pensioned changed as well. So if you weren’t eligible to be pensioned one day the next day you could be eligible. So overall, one day you would not be eligible to receive a pension and then the next day, you could not only receive a pension but have a higher monthly payout that someone who has been receiving one for years. From these two statements alone my data started to make so much sense. Perhaps it was that people knew that they could receive more if they waited, but for those who really needed the pensions, who couldn’t wait, were not to benefit. This is kind of disappointing, how most of these people, veterans, or widows from the war, had a portion of there income governed by dates on a page possibly missing a potential meal by a day or two. (ElderWeb)

I’ve also learned that the whole pensions system was kind of weird. Just for the sake of taking a look I Googled the 1886 census and looked at some data from a county in Illinois, and shockingly the data was almost exactly the same. Many of the pensions were in the same area and some were exactly the same for a certain field. In the mother category most people received $8, this was the same in this county in Illinois as we’ll as Albany. Also many of the abbreviations for the certain injuries. So somewhere along the way they made some sort of rigid system that was adopted by every census taker in the country. If this kind of communication was out there why couldn’t as system be to create some sort of uniformity for the payments? It was like they made all these new rules, let new people in, adjusted a little for inflation but forgot about the people who filed for pension earlier on. The weirdest part of all of this was that there was no federal government action at this time. Civil War veterans did not start receiving pensions from the federal government until 1930. After all this I look at the relative similarities as nothing short of a miracle. Especially in those times where there was no form of really efficient communication, this would’ve been ridiculously hard. So since there was no federal structure the whole payment of pensions was based on state or county rules. It would’ve been up to the counties that the people lived in to keep track and maintain the records for the pensioners, another tall task, so I can see where the temptation to cut corners comes in. One thing I thought that was interesting about how states and counties paid pensions was that some counties would have a competitive rate compared to another county that could have a greater ability to pay a higher pension. I would think a county like Albany who at that time was fairly prosperous would presumably have higher pensions then say a smaller county in a lesser state or especially the South. The fact that almost across the board pension rates are similar is simply astounding.

Overall I find that the this dataset’s true story is lost in history somewhere. It’s not one of the more glorious parts of history but it’s in there. For this set the main factor that drove the pay scale was when you applied to be pensioned. This is something that you wouldn’t guess from first glance. And that is something that I experienced first hand. At first I looked at this data and thought why would there be a difference at all? Even though injuries ranged from challenging to life altering it was basically all left up to a date. That is something in today’s times could never happen. To think of something that could change the ability to feed your family left up to a chance date, especially set by the government would not fly. By today’s standard that is borderline unfathomable.Though most logic would go against the actual truth of how the pension was paid it’s still interesting to find it.

Further Questions:

For further research questions I will be basing them off of some of the questions that I had coming into the assignment and then had answered throughout the project. Many of the questions that I had coming in were due to the idea of being fair in pensions paid. When I went into further investigation with the project I found that the pensions were originally based on need, severity of injury, etc. but as time went on it became that pensions were paid based on time, and were adjusted for rate of inflation or other unknown reasons. After doing all this research and still considering the fairness of all of this, this issue particularly struck me. I always thought that everything would just work because everything would be considered. Then to have all this unravel basically because of carelessness or laziness, that bothered me. So from this fact my first question is how this kind of carelessness just be allowed to happen. I understand that certain things just kind of get forgotten about. But those are little laws that don’t really matter anymore or something to that effect. After all, these laws were constantly being adjusted, and frequently at that. In a time immediately following the Civil War new pensioners were being added every day. So in this case it wasn’t a matter of neglect. The government just didn’t update the records of the people that were already enrolled in the pension program. I guess that this wouldn’t happen today thanks to advancements in technology. Also, this isn’t something that could really be checked either. Unless you knew someone with all the same ailments you would have no idea that you would be shorted. For my second question, I looked at the where the pensions were actually being allocated. For the most part it was people in the areas further away from Albany that were being paid. Though most of the money was going to the people living in Albany, there were far more small towns receiving pensions. This was the visualization that yielded more questions than answers. What was intriguing was the fact that in these towns or postal areas that there would only be one or two people that would get pensions in this specific town. That of all the crazy ailment that people had in this data set there would only be one person that could possibly receive a pension. There are some ailments that just affect the average person like arthritis or back problems and only one person qualified? Also, many of the people that live in those periphery towns are older, I just know that from living in the country growing up. So it would be a good guess that a few people in each community would have general ailments. Overall, I found that the start of this project had questions and that the answers gave me more questions to answer at the end of the project. I don’t know if I’ll ever figure them out but it’ll be interesting to see if I do.

“1890: Civil War Veterans Pensions.” ElderWeb. Accessed May 12, 2016. http://www.elderweb.com/book/appendix/1890-civil-war-veterans-pensions.