Outline– My final project will trace the development of the labor force of the American whaling industry over the nineteenth century. Using the New Bedford whaling crew list database as a data set and the use of network analysis and mapping this project reveals the racial and geographic makeup of the whaling crews. There has been previous research revealing the cosmopolitan makeup of the New Bedford crews, by mapping residency, skin color, rank and other metrics this anecdotal evidence will become visually apparent. Further, running a network analysis upon a select number of ships and filtering by race and residency, patterns of the crew makeups become apparent. Mapping residency onto a globe will display the specific hubs form which American whale men came from. While a network analysis of a collection of select voyages will trace the unique stories of specific crews. The whalers who sailed from New Bedford are incredibly diverse, and many crews display specific makeups. This project will attempt to verify previously known crew patterns as well as reveal others.
Visualization- I plan on creating an interactive map that centers on New Bedford. Either a point to point map to connect places of residency to New Bedford or a heat map to color the density of crew members from a specific region will make up the base of the project. From there I hope to run a map that changes over the years of the 19th century to show change over time. The map can be filtered by race which will be divided into colored, light and unknown. While not particularly fine, the filter will reveal the racial makeup up of whalers from specific regions. This should raise questions about why certain geographic regions vary drastically and how this changes over time. I would also like to use network analysis to trace the geographic make up of specific crew to see to what extent and how often ships crews are geographically similar. For example, over 60% of the crew of the whaleship Admiral Blake from the years 1860-1880 come from the neighboring towns of Marion, Rochester, and Wareham MA.
Data Cleaning- Data cleaning will be an important process for this project. The most cumbersome will be cleaning the residency column of the database. A decision will have to be made as to clean by town, or by state/region and how much of a difference does that make for final analysis. Currently I am leaning towards a state/regional filter. Racially, I have chosen to filter by colored or light for the reason that most of categories in the database are minimally different Dark/Black/Brown/Colored. By cleaning to a category of colored vs light it appears that the labor pool is split fairly evenly. There is also the problem of doubled record containing differing information that will need to be addressed. For example, there are two records of one person on one ship, but one record contains, race, residency, and height. The other contains lay, and rank. Those will need to be merged or deleted. A second aspect of the data cleaning will be to transform the text dates to numeric form in order to analyze change over time.
Data Critique- The Whaling Crew List Database is a collection of over 125,000 identification records of those who sailed from the port of New Bedford on one of the thousands of whaling voyages during the golden age of American whaling. Started by the New Bedford Free Public library through the work of volunteers and currently maintained by the New Bedford Whaling Museum the Whaling Crew List database is one of the largest collections of whalemen records in America
Spanning from 1809 to 1927, the database record vital record of theses sailors as interpreted by customs officials in New Bedford. The Whaling Crew List includes the name, age, vessel boarded, aprox. date of embarkment, and place of residence among its records. These more traditional records are easily sort-able and useful in establishing the commercial and migratory patterns of New Bedford in the 19th and early 20th centuries. Included with the more traditional who, when, where statistical marks are more detailed records of height, hair, skin, and eye color, as well as rank, lay, and other notable remarks. It is in these detailed remarks that the Whaling Crew List becomes a powerful tool for researchers of the American whaling industry and more broadly, the cosmopolitan make-up of American port cities in the 19th century.
Each voyage and vessel are assigned a coded number which can be used to separate individual voyages and uncover the crew of a particular voyage of interest. The data set can be sorted into a variety of different sets and the addition of sailor details like height, skin and hair color, and rank allows for the examination of more narrow questions particularly involving the racial make-up of whaling voyages. Additional comments, noted by either Customs official or archivist also add to the data sets uses, particularly notable are notes of sicknesses, violent deaths, and unique characteristic of employment.
The Whaling Crew List is limited only to those who sailed from New Bedford and does not often report those who either disembarked before return or were picked up along the whaling voyage. As both and desertion and recrewing were prominent during resupplying stops in this era, the Whaling Crew List is only useful in assessing whalemen who were physically in New Bedford at some point. While the Whaling Crew List is comprised of an enormous amount of data, the project is by no means a complete or perfect account of the men who sailed from New Bedford. Due to the nature of customs records, the information collected varies widely within the collection. Even within the same year, recorded information varies, indicating that there likely were multiple customs officials recording data. Collection differences also vary over the decades, with later years more likely to include detailed information like lay, rank, and color.
3/25- Race Category filtered
3/27- Dates Transformed to numeric values
3/30- Conference at Stony Brook (focus on that)
4/1- Make final choice between town or region differentiation
4/2-4/5- Residency Data Cleaning
4/6-4/9- Create Networks using Gephi for at least the years 1850-1855
4/9- Have Filterable Wireframe for Dates
4/10-4/14- Residency Cleaning/Merging Repetitive Information
4/15-4/18-Imput Data into Tableau
4/19-4/20- See How Data Looks on the Map
4/20.1- Panic and Send Dr. Kane Email
4/21-4/24- Play Around with Visualization and Find traceable Narratives of Particular Ships/Crews
4/25-4/29- Clean and Filter Outliers and Abnormal Data Remaining After Cleaning
5/16- Due Date
5/20- Disney World
Dolin, Eric Jay. Leviathan: The History of Whaling in America. Reprint edition. W. W. Norton & Company, 2008.
Lund, Judith Navas. Whaling Masters and Whaling Voyages Sailing from American Ports: A Compilation of Sources. New Bedford Whaling Museum, 2001.
Pease, Zeph W. and Lewis Historical Publishing Co. History of New Bedford. New York : The Lewis historical publishing company, 1918.
Rotch, William 1734-1828. Memorandum Written by William Rotch in the Eightieth Year of His Age. Boston and New York, Houghton Mifflin company, 1916.
Stackpole, Edouard A. The Sea-Hunters: The New England Whalemen during Two Centuries, 1635-1835. 1st ed. Philadelphia: Lippincott, 1953.
———. Whales and Destiny: The Rivalry Between America, France, and Britain for Control of the Southern Whale Fishery, 1785-1825. First edition. University of Massachusetts Press, 1972.
Starbuck, Alexander. History of the American Whale Fishery from Its Earliest Inception to the Year L876. Washington, 1878. http://archive.org/details/historyofamerica00star.
Taylor, George Rogers. “Nantucket Oil Merchants & the American Revolution.” The Massachusetts Review 18, no. 3 (1977): 581–606.
“Whaling History – Connecting All Things Whaling.”. https://whalinghistory.org/.
Re: the networks, you should not plan on pre-selecting your ships. This will skew your results by pre shaping your data. Do a very bare bones network with the whole dataset first, and then see what you get. You should also not pre-filter your dates in the dataset you give Gephi, since it’s easy to use Gephi’s own filters to narrow your network and you only make more work for yourself later. This will be especially important in answering your crew make up question–plan on doing a network that displays edge weight, which makes the line between a ship and a location larger if there are many people from that location. You can then use this to find ships that do/don’t fit your pattern and discuss them in more detail like you do here.
How many double records do you have?
Your overall argument may not be apparent now, but you should plan on going beyond confirming the argument of others. This may mean a more focused approach, like: does rank make a difference to geographic origin? Eg, are men from near New Bedford more likely to be higher ranked than men from elsewhere?
Forgot to mention: for the US specifically, it may be worth comparing sailor numbers vs census population to see if there’s disproportionate numbers from an area as the US expands west. Obviously MA is going to be over represented, but other areas may be as well. We would do this by pulling a sheet of population totals for the years covered by your dataset and comparing your total number of sailors per state.