As my last post demonstrated, historical population statistics can offer a rich source for historical analysis. The outbreaks of yellow fever south of Fulton Street forced drastic migration patterns and arguably aided in the development of Greenwich Village from a pastoral paradise to an urban hub. If the information were available, I think it would be fascinating to track and aggregate data on contractions of yellow fever, deaths, and migration patterns from the late eighteenth and early nineteenth century.
Fortunately, geographical information systems (GIS) help us to collect, analyze, and then interpret this sort of data. It has been described as the merging of cartography, statistical analysis, and computer science, and GIS allows users to search and analyze spatial information, edit data, and present the results of all these operations on maps.
The Center for Population Economics at the University of Chicago Booth School of Business has created a historical GIS data set for analyzing the urban health statistics of seven major US cities from 1830 through 1930. These cities include: Baltimore, Philadelphia, Chicago, Cleveland, Brooklyn and Manhattan, Cincinnati, and Boston. The Historical Urban Ecological (HUE) data set includes ward boundary changes, street networks, and ward-level data on disease, mortality, and crime. As their website describes, “These materials constitute a framework on which users can build additional spatial data and conduct a wide range of historical inquiries.”
Unfortunately, HUE data set does not span back to the times of the early yellow fever outbreaks that I wrote about previously (circa 1790-1830). It does, however, have statistics on deaths from yellow fever starting in the 1860s. For a researcher (like me) interested in Greenwich Village history, he or she could choose “Manhattan” as their city, and select a number of different data categories; for example, “crime” or “disease,” along with another search value. I chose “yellow fever” These search criteria only bring up 13 results.
I then selected to look at all the deaths from yellow fever in the year 1868:
When I downloaded the results, the folder contained a text file with the sources used to collect this information and an excel sheet with the number of deaths per ward in 1868.
Astoundingly, it appears that only one death from yellow fever is reported from that year. Considering the havoc that yellow fever caused only forty years ago, I can use this information to conclude that the main culprits of the disease (the poor water supply) had been mostly rectified by this time. Behold, the power of geographical information!
While the HUE data set gives you the aggregate data to create informative maps for free, to get the full experience users will need to use software such as ArcGIS (which comes at a cost) to display the data on maps. However, people can also use free, open-source platforms like Quantum GIS to map and analyze the data. To a more expert researcher, the results can be visually powerful. Here is an example that the Center for Population Economics Provides:
The website claims that their HUE data set has already been used in the following ways:
“Researchers have mapped early public transportation networks, the construction of modern sanitation systems block by block, characteristics of the built environment from fire insurance maps, and locations of business, industry, schooling, and social services from city directories. Researchers have also employed census aggregations and historical weather reports at both the city and county levels.”
Historical application of GIS is a fairly new, but extremely powerful tool for analysis. I’m excited (albeit — as a GIS novice — a bit confused at times) at the research possibilities of this new tool. The Center for Population Economics has a PDF tutorial for the HUE data set, and I highly encourage researchers at all levels to see what they can uncover.