final project progress

This dataset consists of numeric, text and geographic data. It illustrates slave sales that took place during the years of 1775 to 1865 in different states. The slaves sales took place in 8 states. These states include Georgia, Louisiana, Virgina, North Carolina, South Carolina, Maryland, Tenessee, and Mississippi. The numeric data tells us the age of each slave in years and months. It provides the date of each sale along with the appraised value of each sale. The textual portion of the dataset provides the gender of each slave. It also provided the skills and defects of each slave. Some skills that were mentioned are a carpenter, driver, cook, house servant, laundry, midwife, nurse, painter, seamstress, cartmen, laborer, and blacksmith. Some defects that the slaves had were children, hernia, sickness, old age, blindness, loss of body parts, mental insanity, pregnancy, and deafness. Having defects caused the slaves appraised value to decrease tremendously. The were valued the least. Slaves who had skills were valued the most. If a slave had no skill and no defect it was still valued fairly. The age of the slaves ranged from 0 to 99 years old. A slave that was really young or really old and did not have the ability to do work had little to no value. For example, a male that was 1 year old had a value of 50 while a female of the same age was valued 150. A female that was 99 years old had little to no value while a male that same age had a value of 100. The years ranged from 1742 to 1865. The range of the appraised value has a minimum of -800 and a maximum 525000. The geographic locations start north and end in the south. It ranges from Maryland to Louisiana. The skills range from skinner to blacksmith. I’ve ranged this according to the appraised value. This helped to see which slave was worth more depending on what skill they had. The defects ranged from being deaf to being blind. This was also determined according to the appraised value. Each row of data is different. There are nine rows in total. Starting from the left, the first row is the geographic location where each sale occurred. The second row is the country code. The third row is the date of each sale. The fourth row is the gender of each slave. The fifth row is the age of each slave in years. The sixth row is the age of each slave in months. The seventh row is the appraised value of each slave sale. The eighth row is the skills of each slave. Finally, the ninth row is the defect of each slave.

After analyzing the slave sale dataset, it took several attempts to find relationships between the rows. My first failed attempt was thinking there was a correlation between the date of sale and the appraised value of a slave. I did not take into consideration the gender, age, location, skill, and defect of the slaves. A slave in the 1700’s was valued anywhere from 0 to 900. A slave in the 1800’s was valued anywhere from -800 to 525000. I came to the conclusion that there was no correlation between the two because there were many other factors that determined the appraised value. I later then discovered three other relationships. The first was the correlation between the gender of the slave and the appraised value. Male slaves have more value than females slaves especially if they had a skill. The second relationship was between the value of a slave if they had a defect and the value of a slave if they did not have a defect or a skill. A slave without a defect or skill was worth more than a slave with a defect. The third relationship I focused on was the value of a slave in its prime age (20-40) and the value of a slave that was either younger than 20 or older than 40 years of age. Slaves that were in their prime age were worth more.

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 8^th 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. If makes it harder to have a detailed account of what happened in the 1940s.

 

I have decided that for my final I have chosen option two, picking specifically the data in the 1756 census of Albany. I decided to choose this data set because of its still early time in Albany’s history, but also because of interesting time, being before the American Revolution and during the French and Indian War. The census data is divided into 11 categories: Household Number, Name, Trade, Gender, Conv for Officers (number of officers that can be housed in the household), Officers Upon a Pinch (number of men in the household that can be made officers), Conv for Men, Men Upon a Pinch, Number of Fireplaces, Number of Rooms without a Fireplace, and Rooms the Family Occupied. The data presented in this census is both textual and numerical. The categories 2 through 4 are textual, providing a worded answer while categories 1 as well as 5 through 11 are numerical. The numbers in the data range from 1 to 326 in category one and from 0 to 10 in categories 5 through 11.

For my three relationships in the data that I am going to explore, I am going to look at:
1.The end of the data of certain households has an extra row that designates if the household was “good” or “rich”. I want to explore who these people are that have these special designations and if I can see a pattern of how or what makes these specific people rich enough to have such designations.
2.I want to explore the relationship between male and female heads of households and see the similarities and differences between them. Specifically what occupations female heads of households have and what effect a female run household may have on the number of officers and men their households are designated to hold.
3.A comparison of the various occupations of Albany households during this time. What were the most popular or common occupations of the time, as well as the most unusual occupations and see if I can find either a trend in family size, household size and/or wealth based upon a given occupation.

Slave Sales:

Dataset Info:

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

Relationships:

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

The dataset that I decided to use was the 1883 pensioners set. The dataset includes numeric, text and geographic data. There is only one category that would have a minimum and a maximum and that is the amount that someone would receive in a monthly pension, with the minimum being $2 and the maximum being $25 (from the looks of it people receive pensions in increments of $4 or $8 based on reason). The other columns that have numbers in them are  the columns in which there are pensioners identification numbers shown (this column will probably not help for any research purposes) and the column that shows the date a person started getting a pension.This column opens up a couple questions that I could use to find relationships. All of the people in the dataset live within Albany County while another column shows which town each pensioner is from. Albany County does cover a variety of town types so the types of people do vary a good deal. Other columns describe the reason why a person receives their pension where reasons vary from axe injuries to being a single mother. There is also a column that shows people’s name so  that won’t really help for any research purposes.

Relationships:

The relationship between the reason for pension and amount the person receives:

This seems pretty obvious but I feel like in those days there would be weird reasons everyone would do things. Like being a single mother or a widow looks to be one of the lower paying pensions. The way we look at things now we would assume that a single mother with presumably with a few kids would receive a higher pension than someone with a minor injury.

The relationship between pension start date and the amount received:

The hypothesis that I have about this area is that people who received pensions for the same reason might have different amounts that they would receive. I feel like there could be a variety of reasons that someone might get a different amount possibly based on gender, race, age etc. I also feel that possibly people with an earlier start date could have a higher pension than a newer person or vice versa because the might have been giving people too much or too little.

The relationship of where people live and what injuries they have:

This kind of falls under the category of “could be something, could be nothing”, based on my knowledge of the area, I know that some of the towns listed are city and some are rural. I think that there might be a correlation between where people live and the injuries that they have. Like, the people in the rural areas would presumably have the axe injuries and city people would not. If I can find that correlation I could get into the real stats of the distribution of the pensions in Albany County. For example I could find where the money for particular injuries are distributed and so on.

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

For my final, I chose second option and to do it on Albanys 8th Militias Muster Roster. The data includes the soldiers full first and last name, their age, and enlistment date. It also includes where they were born, their current occupation, who their leaders are. On top of that is also gives details about their physical appearance. For example their, stature, complexity, and finally their eye and hair color. The only numeric data in this table are their birth dates, age and height. All other pieces of information are text. The maximum age given in this document is 58 years old while the smallest being only 16. The smallest height on this chart was recorded as 4 foot 11 while the tallest is 6 1/4 feet. Each row describes an individual very descriptively in both a physical sense and a administrative sense. By reading this piece of data, one could get a very good feel about the current condition of the Militia during that time. You could tell that a there were soldiers as young as 16. You can also find out that there was no one larger that 6 1/4 feet. Giving the sense that maybe they weren’t the largest, most intimidating group of guys. However they were brave enough to sign up to fight and defend the people that they care about.

One thing that you would notice by looking at this is that there is a very decent amount of people who have signed up that for this are from foreign countries. This is interesting because this is around the time the Revolutionary war is about to kick off. It shows that people from all walks of life were willing to lay down their lives in order to gain their Independence. A second comparison I made form this was that a majority of the men who enlisted come form rather blue color jobs. Weather it be just the title of laborer or a saddler, there really aren’t many people that work as business owners that enlisted during this time.A third comparison being the complexion that is listed. With the assumption that “dark” meaning that they are African American, one would have to wonder if the color of their skin had an impact on their rank in the Militia.

 

 

Title: Slave Sales

Description: The information that my data set includes are what state each slave resides in. There are a number of different states that are included in the census that tells the person viewing the census which particular state each slave is from. In addition to the states that the slaves live in, the county that they live in is also included. There are several different counties per state that are located in the census. Each slaves date of entry is part of the information provided to us. The sex of each slave is included since it is deemed important for obvious reasons. How many years old is each slave as well as how many months they are as far as the infants are concerned. The price of each slave is also part of the census. Each slave has an appraisal number attached to them stating how much they are essentially “worth”. Any skill or skills that the slaves have assuming they have any at all are included as well as any possible defects that any slave may have. The data involves a combination of numerical values as well as text and geographic. On the numerical side, the age, value, as well as the entry date are included. This numeric values have a wider range than its geographic counterpart. As far as the geographic side each slaves state, as well as county is included in the census. The text that’s included in the census is the names of each slaves state as well as their sex and defects and skills. Each defect that the slaves have is listed in the census and any skills that they posses. The maximum range for age is 80 while the minimum range is 1.  The maximum range for the infants months is 11 while the minimum is 0. The geographic range is from the state of Georgia, Louisiana, Maryland, Mississippi, North Carolina, South Carolina, Tennessee, and Virginia.

Relationship: There are several relationships between some of the columns and rows in the census. One relationship that is in the census is the states relationship with the some of the counties that reside in the states. Each state may have a different relationship with each county located in the state.  Another relationship between the data is the relationship between the sex of each slave and the appraisal number. It looks like the males are appraised at a higher value than the price of a woman slave. Looking at this someone can infer that males are worth more than the females because of the amount of work they can do as well as the type of work they can do. Another relationship from the census is the relationship between age and appraisal. It appears that male and female slaves between the ages of 22-30 are more valuable than the males and females that are over that age range. This is appears to be the ideal age range that slave owners would like to have their slaves. The men are more productive and the women are more likely to have healthy children within this age range. As for women, their appraisals are less than the males even if their ages are closely related. If the slaves have any defects it may lower their value as well, depending on the severity of the defect. This is due to the fact that if a slave has a serious defect they won’t be able to be as productive as another slave that is perfectly fine. A relationship that is in the census is the skills that some of these slaves posses adds to their value. They are able to do tasks that the average slave can’t do, therefore makes them more diverse than the other slaves.