final project progress

For my final project I have chosen option 2, the Data Analysis. The dataset that I have selected to use for my final project is the dataset titled “slave-sales-1775-1865”. The dataset that I have chosen includes both numeric and text data. The numeric data includes information such as the year of slave sales, the age of the slave that was being sold in years, the age of the slave being sold in months, and the appraised value of the slave being sold. The textual data that is inluded describes things such as the state where the sale was taking place, the county in the state, the gender of the slave being sold, any skills that the slave may possess, and any defects that the slave may have. The minimum and maximum ranges for the numeric data in the columns are as follows:

Date of the sale: 1742 –  1865

Age of the slave being sold (in years): 0 – 99

Age of the slave being sold (in months): 0 – 11

Appraised value of the slave: -800 – 525000

The ranges for the descriptive data are as follows:

State of sale (sorted alphabetically): Georgia – Virginia

County of sale (sorted alphabetically): Adams – Williamson

Gender of the slave: Male or Female

Skills the slave may possess (sorted alphabetically): Apprentice – Woodcutter

Defects the slave may possess (sorted alphabetically): Asthmatic – Without Fingers

Each row that the chart contains describes the characteristics of a slave that was sold between the years 1775 and 1865. The amount of information that is given about each slave appears to vary depending on whether or not the slave in questions possessed any skills that would make them more desirable or any defects that could make them less valuable to a potential buyer.

 

There are a couple of comparisons that can be drawn from this data after analyzing the information in the dataset. The first relationship that I noticed almost immediately is how the appraised value of the slaves changed depending on whether or not they were male or female. It does not appear to be true in all situations, but it seems as though in most cases, slaves that were female were valued at a much lower value than slaves that were listed as males. The next relationship that I noticed between the different columns was that slaves that were listed as having a defect were valued much lower than ones that did not. Conversely, slaves that were listed as possessing some type of skill were valued much higher than slaves that did not. Further, any slave that possessed a skill in tasks that were considered skilled labor, were valued at a higher rate than slaves that possessed skills in domestic labor. Another comparison that I noticed is that the value of slaves in terms of age would resemble a bell curve when put on a graph. This means that newborn slaves and slaves of a young age are valued very little. As slaves get older into there 20’s and 30’s they are valued the most. As they age past this age their value begins to decline again as they enter into old age. One more comparison that I was able to draw from analyzing the data is that the slaves that excelled in skilled labor tasks tended to be male, while the slaves that tended to have more domesticated labor skills tended to be more female.

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

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

For my final project, I chose to do option 2 with the data set being 1756. The data that this dataset includes is the quartering information of soldiers during the year of 1756. There are 12 columns across and 110 longitudinal columns. What each of the 12 latitudinal column describes goes as follows:

1. Number: The first column is the number of home that is being described in the column across in the list of homes that quarter soldiers. This column ranges from 1 to  327
2. Name: The second column is the name of the head of the household that is quartering soldiers in their home.
3. Trade: The third column is the job that the head of the household has.
4. Gender: The fourth column is the sex of the head of the household, being either male of female.
5. Conv for officers: The fifth column is how many officers can fit in the house being described in the column across.  This column only ranges from 1 officer to 2 officers.
6. Officers upon a pinch: The sixth column represents how many officers can fit in the household, in a pinch. This number, for the most part, is higher than the conv for officers column, but not by much (usually just 1 higher).
7. Conv for men: The seventh column is how many men can fit into the quartering home being described. The numbers in this column range from 2 men to 6 men.
8. Men upon a pinch: This column is how many men can fit into the home being described, if absolutely necessary. This number ranges from 4 men to 10 men. This number, in every case, is higher than the previous column, conv for men,
9. Number of fireplaces: This column is how many fire places that there are per household. The numbers range from 0 to 6.
10. Rooms without fire: This column tells us how many rooms that there are in the home that don’t include fireplaces. The numbers range from 0 to 4.
11. Rooms the family occupied: This column represents how many rooms in the house that are being occupied by family members.
12. (no label): The last column is what the condition of the house is like. The options are none, good house, a very good house and rich, and rich.

The first relationship that I have found in the dataset that I have chosen is that most of the heads of the households that are willing to quarter soldiers are men, and when women did quarter soldiers, it was only men. Only 1 woman in the entire dataset was willing to quarter officers. Because of this, I’m going to research if there are and risks of women quartering soldiers vs. risks of women quartering officers. I’m also interested in fining the percent of households that had female heads of the household, versus the percent of households that were headed by men. I also want to find if there were any benefits for quartering soldiers during this time period.

The second relationship that I found in the dataset that I chose is that every single house that is listed as a good house, a very good house and rich, and rich are all headed by males. Due to this, I wonder what income for women was in that year, versus the average income for men for that same year. The only jobs that women are listed for having in this data set are Indian trader, muntua maker, mead house worker, and merchant. The list of jobs that are held by men during this time period include weapon maker, tavern keeper, taylor, silversmith, selling liquor, shop keeper, inn keeper,Indian trader, dram shop, brewer, britches maker, and black smith. Due to the short list of jobs that women had, I also am interested in finding out the types of jobs that men and women both had during that time period.

The third relationship in the dataset that I found is  that many men were willing to quarter men, but not willing to quarter officers. Again, I’m interesting in finding out why this is.

1940 Census

Identify Dataset

The 1940 data set 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 text include 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 birth place, 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 2o 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 and all of these subheadings either describe a person (the individual’s age, race, marital status etc.), a place (address) or a thing.

Comparisons

When the data set is looked at, there is information that does that need to be searched for because it is already given; some information however is not given and conclusions must be made based on the information that is. One comparison that can be made from the information provided is the relationship between gender and employment; upon initial assumption it may appear as though most of the women in the data set did not work and that proves to be true. Although most did not work, some did; some of the women in the data set were servants which would be considered employment and there were other women that received a high level of education which provided them with the knowledge and skills for an occupation. Another comparison that can be drawn from this data is whether the level of education that the person received plays a role in their occupation. Based on the data, those that received less than a high school education are mostly unemployed, those that have a high school education have jobs such as traveling salesman and electricians and the few that received a college education have an occupation as a lawyer or a librarian. A comparison can also be made between whether a home was owned and the gender of the person who owned it. Based on the data, most, if not all of the people that owned homes were male and were the head of their household; their wives were often unemployed and so were most of the daughters. There is a comparison that cannot be determined simply off of the information provided but the relationship between whether the area a person lived in affected their ability to own their home is one to look into; if there is a correlation between the area and home ownership that may help to explain why many people lived in similar areas even those that were not related to one another.

The Title:
The Albany Muster Rolls of the 8th Militia.

The Data Set:
The data set of the Albany Muster Roll of the 8th Militia includes the name of each enlisting Soldier, when the soldier enlisted, the soldiers age, where the soldier was born, the soldier’s trade prior to enlisting, the company the soldier belonged to, the soldier’s rank, the soldiers stature, and the soldiers descriptive qualities that included: complexion, eye color, and hair color, and lastly the volume/page the soldier’s name was found on. The Albany Muster Roll of the 8th Militia has both descriptive,and numeric data within its data set. The data within the data set that can be considered descriptive includes the following:the occupation or the trade of the soldier enlisting in the Militia, where the enlisting soldier originally came from (that ranges from places such as Germany, to Connecticut), the soldiers ranking in the Militia (Lieutenant, Captain, Private, etc), as well as the soldiers physical attributes such as eye color ( blue, brown, etc), complexion (brown, fair, swarthy), and hair color (brown, black, fair). The Albany Muster Rolls of the 8th Militia can also be considered numerical data. The data sets includes the following: the age of every soldier entering the Militia, as well as the date that each soldier enlisted. The range of the numerical data for the ages is the minimum age of 16, and foreseeably the maximum age of 58. The range of enlistment date is from the beginning of april 1760, to the end of june 1762, three years prior to the breakout of the American Revolution.

Three Relationships or Comparisons:
The first comparison in the data set of the Albany Muster Rolls of the 8th Militia is if the data set can show a connect between the trades of the enlisting soldiers to the actual rank they received in the Militia. From my brief inspection of the data sets it seems plausible that soldiers who before they enlisted worked as labourers, carpenters, or maintained a blue collar type of trade became Captains, but there are outliers, where for example, a baker also became a Captain.
The second relationship in the data set of the Albany Muster Rolls of the 8th Militia is which homeland or state received the most male enlistments in Albany during the two years between 1760 through 1762. More specifically does the majority of men from certain homelands outweigh others because of migration patterns prior to 1760 through 1762.
The third relationship in the data set of the Albany Muster Rolls of the 8th Militia is whether race, ethnicity or the enlisted complexion (dark, swarthy, fair, negro, pale, ruddy, etc.) of the soldier also affected the rank he was able to achieve. I would also like to look at how the age of the enlisting soldiers to their rank in the 8th Militia.