Monthly Archives: November 2019

Portfolio Activity 9: Received Data and Derived Data (Critique)

Tidy data refers to a method of a set of data that is in form of a data matrix. In class, we built a table of data based on George Washington’s List of Enslaved People 1799. We had names of people in the rows and different variables in columns, such as age, sex, marriage status: yes or no, spouse, children, number of children, trade, is able to labor: yes or no, enslaver: Dower or GW, “employed” location.

From my experience with building a tidy data, I think tidy data makes data easier to read for historian because it provides a table of data with clean sorted information rather pages of reading, so that increase efficiency of answering historical questions.

In addition, when I was looking at the primacy source of George Washington’s List of Enslaved People 1799, some handwriting was very hard for me to read because the document was old and I was not familiar with reading this type of handwriting. Luckily, there was a transcript version of the original data, it was way easier to read and helped me a lot to build the data table. If I only use the original source, I might get different results from other people. Therefore, I think the form of data received by others is very important because it changes the final result.

From the tidy data we built in class, I can ask questions like what is the average age of enslaved in 1799, or what is the percentage of male and female of enslaved people in 1799. As a result, I think tidy data enables me to ask question in a border perspective. Moreover, I think tidy data is very useful and helpful to study history, I will definitely use tidy data to extract information when I interact with contemporary data in the future.

Portfolio Activity 8: Data Maps (Construction)

The map I created represents the population of the United States Nonwhite female slavery in 1850. With this map, it allows me to see the population of different parts of the states easily base the shade of blue. For instance, there is higher number of nonwhite female slavery population with a darker blue. on the contrary, there is a lower number of Nonwhite female slavery population with a shallower blue.

Moreover, I can also click on a county on the map, there is more data about the population based on different ages. Taking Amherst as an example, there were even 26 nonwhite female slaves under 1 year old, 488 at the age of 1 to 4, 474 at the age of 5 to 9, which really surprised me.

In addition, I also found that there were population of Nonwhite female slavery in the northern parts of the country.

To conclude, a map enables data appears visually with a geographic space and helps the learning process of history.

Here is a link to my map:

Portfolio Activity 7: Narrative Maps (Construction)

I built a map of James Sterling’s life and the journey he took in order to accomplish fur trade in the U.S. during 1700-1800. First, I created a timeline of important events of James Sterling while I was reading the book. For example, in early 1700s, James Sterling’s birth Ireland; In 1750s, he served in Pennsylvania during French and Indian War. Then, I listed couple events after 1761, when he started to do fur trade business.

After I have listed the timeline, I chose StoryMapJS as a tool to build a map because I found easy to use. However, I could not search location by using the search bar in the website so I have to locate Detroit and other location by myself. In addition, I also uploaded pictures to make the map more interesting to read.

here is link of my map:

Portfolio Activity 6: Geospatial work

In class, we explored the project The Spread of Slavery in the US by Lincoln Mullen, which is basically a map that shows the spread of U.S. Slavery during 1790-1860. I found that answering historical question by using geospatial visualization is really convenient, geospatial visualization and analysis is an easier way to deliver the message in a geographic space.

Taking an example of The Spread of Slavery in the US by Lincoln Mullen, this multi-dimensional map shows the population, location, and time of slavery in the U.S., it also used ten colors from shallow to dark to represent enslaved population. In addition, I can also click on the map to see a certain area with more details, such as the total enslaved population and free population of Knox, Kentucky. Therefore, I think geospatial visualization and analysis helps readers understand the process of slavery spread in different time and different places more easily than just reading text content.

Considering the pitfalls of geospatial visualization and analysis, using false maps and inaccurate navigation are two of them. Because the map is changing over the time period, so it is important to use the correct one in certain time period.

Portfolio Activity 5: Text Analysis (Construction)

I got 40 topics after I uploaded the American South: North American Slave Narratives to Topic Modeling Tool. Below is a picture of topics from 0 to 26. Among those 40 topics, I chose NO.12 as my topic: bond, water, scott, carver, dr, cotton, sugar, put, add, 1, peanut, make, cut, sweet, gin, seed, plant, corn, potatoes, and milk. Since most of the items are related to food, so I made a hypothesis that this topic is about trading goods. In addition, I also found that “cotton” and “sugar” were products of slavery and slave labor, so I also assume that the topic could be related to slavery goods.

After making the hypothesis, I clicked on the topic and I found there are 204 documents in this topic. I decided to select top 10 documents to put into Voyant because these documents have higher number of word that assigned to this topic. And then here is the data generated from Voyant.

            From the cloud, I selected Top 85 words that appears most frequently in the documents and I was surprised that most words has nothing to do with food, such as ‘time’ (appears 1578 times), ‘man’ (appears 1373 times), ‘mr’ (appears 1548 times), ‘said’ (appears 1467).However, these words seemed meaningless because it had nothing to do with my hypothesis, so I looked into words that has relatively less appearance but might relate to my hypothesis, such as ‘work’, ‘negro’, ‘white’, ‘school’, ‘money’, ‘life’ . Then, I selected some of these words and Voyant generated into a chart below.

From the chart, I didn’t find anything interesting except that the line ‘white’ and ‘negro’ rise and drop together. Then, I also looked into other parts of Voyant.

To conclude, by analyzing data from these documents, I realized that my topic is not about trading goods neither slave produced goods. My topic is about the life of slavery, for example, what they went through as a slave? What was the social environment at that times?