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


Data Description:

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

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

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


Data Visualizations:

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

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

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

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

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

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

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


Process Documentation:

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

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

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

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

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

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



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

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

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

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

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

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

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

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

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

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


Further Research Questions:

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

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

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

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


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


Works Cited:

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


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


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