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.

CustomWardMapOverlay
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.

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.

1940 Census FINAL

DATA DESCRIPTION
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.

STORIES-VISUALIZATION

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.

PROCESS

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.

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.

RESEARCH QUESTIONS

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.

SOURCES

https://en.wikipedia.org/wiki/Central_Avenue_(Albany,_New_York)#Chinatown

http://zipatlas.com/us/ny/albany/zip-code-comparison/percentage-filipino-population.htm

1883 Pensioners Final Project

Data Description

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

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

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

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

Data Visualization 1

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

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

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

Process Documentation 1:

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

Argument 1:

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

Data Visualization 2:

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

Process Documentation 2:

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

Argumentation 2:

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

Further research Questions:

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

 

Bibliography

“620,000 Soldiers Died during the Civil War, Two-thirds Died of Disease, Not Wounds: WHY?” Civil War Trust. Accessed May 11, 2016. http://www.civilwar.org/education/pdfs/civil-was-curriculum-medicine.pdf.

Council on Foreign Relations. Accessed May 12, 2016. http://www.civilwar.org/education/history/biographies/jennie-hodgers.html.

United States Pension Bureau. “List of Pensioners on the Roll January 1, 1883.” Google Books. January 1, 1883. Accessed May 11, 2016. https://books.google.com/books?id=aLkqAAAAMAAJ.

United States Pension Bureau. “List of Pensioners on the Roll January 1, 1883.” Google Books. January 1, 1883. Accessed May 12, 2016. https://books.google.com/books?id=t7oqAAAAMAAJ.

Smith, Sam. Council on Foreign Relations. Accessed May 12, 2016. http://www.civilwar.org/education/history/untold-stories/female-soldiers-in-the-civil.html.

Final

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

Data Description

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

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

 

Story

Education and the type of occupation one holds are often correlated with one another. From the time a child is put in school until the time they finish, it is drilled in the head of the individual that a good education is needed in order to obtain a well-paying job. It is taught that hard work breeds success and young men especially are taught to be providers for their families, which comes into play with the types of jobs they seek and level of education they want to achieve. Although, in most cases, hard work does breed success, this what not necessarily the case for some people that are a part of the 1940 census. The census provides a wide range of information including the types of jobs that were held, the race of those that held these jobs, their level of education and the difference between men and women when it comes to the work force. It is evident that many women did not work and often stayed at home to care for their children and other household duties. Although many of the women did not work, some did and they held quite prestigious jobs. In addition to more men being in the work force than women, the men began to work at an earlier age than the women. There are numerous jobs held by the men and women in the 1940 census; some of the jobs include accountants, barbers, bartenders, book keepers, lawyers, carpenters, cooks and much more. The different jobs show the level of education that those in field held and how many people the criteria applied to. For example, one of the jobs held is a file clerk. Some of the people that were file clerks had different backgrounds in education. The census shows that twenty-four people completed high school and eleven completed elementary school-this shows that being a file clerk is not a job that may qualify as being of high standing or one that a person needed to have much experience in. In comparison to those that are lawyers, three people completed college and nine people went beyond a college degree. This shows that being a lawyer was a great accomplishment, one that not many could achieve for one reason or another.

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

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

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

Argumentation

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

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

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

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

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

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

Process Documentation

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

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

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

Further Research Questions

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

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

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

 

 

 

 

 

Bibliography

“Demographics of New York.” Wikipedia, the Free Encyclopedia, April 30, 2016. https://en.wikipedia.org/w/index.php?title=Demographics_of_New_York&oldid=717934581.

 

Final

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

 

Data Description:

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

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

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

 

Data Visualizations:

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

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

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

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

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

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

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

 

Process Documentation:

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

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

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

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

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

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

 

Argumentation:

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

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

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

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

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

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

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

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

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

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

 

Further Research Questions:

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

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

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

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

 

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

 

Works Cited:

Banaszak, Shannon. “Women in the Workforce: Before 1900.” Oswego. December 6, 2012. Accessed May 3, 2016. http://www.oswego.edu/Documents/wac/Dens’ Awards, 2013/Banaszak, Shannon.pdf.

 

“Irish and German Immigration.” US History. Accessed May 3, 2016. http://www.ushistory.org/us/25f.asp.

 

“Great Famine (Ireland).” Wikipedia. May 14, 2001. Accessed May 3, 2016. https://en.wikipedia.org/wiki/Great_Famine_(Ireland).

 

 

Final

Slave Sales 1775-1865.

Data Description

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

Visualization one

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

Process documentation

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

Visualization two

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

Process documentation

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

Argumentation

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

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

Further Questions:

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

1940 Census Argument Draft

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 within this argument are that men make more money than women, the higher education obtained, the better the occupation is, and the presence of white privilege/lack of representation for people of color in any data.
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 places 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. Today, that same issue applies, where people attend college and do not always immediately find work in their desired career. 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. Three other races were present in the census: Chinese, Filipino, and Black. Based on their demographic information, they all lived on Fleetwood Avenue or Vanschoick Avenue. Their education levels, occupations, and income varied. The data alone does not show if one minority was more established financially than others. It is however a small sample of information 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.

Argument for Visualization Number One

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.

Bibliography

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.

Argumentation Draft

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 as of the year nine thirty-five, which is only five years prior to nine forty. This visualization is a graph of circles that vary in size based upon the number of residents that resided in a specific state or foreign country five year prior to the census being taken. These circles also vary in color based on whether the entity is a state within the United States or if the circles represents a foreign country. Foreign countries are colored some shake of red while states are colored some shade of blue. This visualization depicts that a lot more of new residents to Albany came from neighboring states such as New England, New Jersey and Pennsylvania. The foreign countries take back seat to states that are further away from New York, meaning that foreign countries had the least amount of people migrating to the United States. I feel that this is a very likely trend due to the 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.