This week we learned about and practiced using quantitative data in a historical study. We began by learning about Geographic Information Systems (GIS) and quantitative data that can be manipulated and interpreted. Originally a tool for scientists and architects to map numerical environmental and geographical data, it surprisingly found itself in the digital historian’s toolkit. The embrace of these tools is described by David Bodenhamer, in History in a Digital Age, as a scholarly shift that interprets how people groups interact with space. As these programs help scholars visualize this space, they can more accurately see how certain historical narratives not only flow through time but through space (David Bodenhamer, “The Spacial Humanities”). Many digital historians are using GIS to present information as well as geographers and archaeologists. These systems do not perfectly fit the mold for a humanist scholar. The tools do not always provide the scope and depth to cover societal and historical information thoroughly. For example, though an archaeologist may know the general area of an ancient city, they may not know the exact longitude and latitude points for it. Plotting these points on a GIS without exact information can be difficult. A historian may plot the location of different engagements on a battlefield, but specifying why the engagement happened and who was involved in the engagement is information that GIS cannot convey as easily. A textual document, in contrast, can provide space for subjective information, or qualitative data, to be explored, with footnotes for tangential thoughts or a quick analysis of details. Despite these contrasting differences, quantitative data is essential for a humanist scholar. It can be channeled into a GIS program to produce maps to visualize change over time such as changes in urban development, the movement of religious beliefs, or variations in populations.
It is then the job of people using a GIS to interpret it. If there is a change or pattern in the visual map or simply in the data set, it is the scholar’s job to connect the factors that could potentially be the origin of this pattern. They can then construct a thesis as to why or how something happens. Many things may influence data such as an environmental factor or a political action. One is sure to find unseen factors in a historical narrative as well, which complicates the relationship between GIS and the humanities.
This week we used ArcGIS, Microsoft Excel, and Microsoft Access to explore the 1900-1930 Harrisburg census data. Focusing on a street, neighborhood, or ward, we collected data from our selected location and looked for patterns. My research focused on the 10th ward of Harrisburg and the Welsh population who lived there. In 1900, the population of Harrisburg rested at 50,167, and grew to 80,394 by 1930. Ward 10 grew exponentially according to records from 1900-1920 changing from 473 to 8,768. The population then dropped down to 1,442 in 1930. The reason for this may be the redrawing and the reorganization of the ward as the city grew.
In 1900, individuals that were either born Welsh or from a Welsh father only consisted of .34% of the population. The Welsh were quite numerous in the 10th ward accounting for 12% of the population. One of the potential reasons for the concentration of the Welsh in this area may be due to a tin mill located originally in the 10th Ward. Of the 39 workers that labored at the tin mill, 7 of them were Welsh. If a significant Welsh community existed prior to 1900, then a Welsh community and employment opportunities centered around the factory would have attracted Welsh immigrants. Using GIS, I identified the mill as well as a Welsh church that is nearby. Welsh who were either born in Wales or children of Welsh parents peaked in 1910 and 1920 accounting for 227 and 223 people in the city overall. However, the population decreased to 180 people in 1930. In addition, individuals from Wales or the children of a parent from Wales decreased in population in the 10th ward a little each decade. There are two possible reasons for the population decrease that occurred. The first is that the Welsh stopped immigrating to the United States. The second is that the Welsh population assimilated into the general Pennsylvania population. Even though they may identify as Welsh, they were not born there and neither were their parents. The average age of a person born in Wales continued to grow, rising to 38 by 1900, staying level in 1910-1920 at 45.7 and 43.58 respectively, but by 1930 resting at 53.4.
This small project that I undertook for class revealed both the strengths and the weaknesses of this type of research. The information that I accessed, organized, and interpreted revealed to me the patterns of a specific ethnic group. Nevertheless, I perceived the subjectivity that came from interpreting the data. For example, why the Welsh came to Harrisburg in the first place cannot be pulled from the data collected. Despite the awkwardness of incorporating GIS and quantitative research into the humanities, it allows students, amateur historians, and professional historians to add to or create new historical narratives.