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Quantifying Prestige

As with any scholarly project, in my dissertation on the development of small cities during the Gilded Age and Progressive Era I need to explain why it matters. I argue these cities are worth the time and effort of a dissertation because they provide a different narrative of urbanization and industrialization. Key to this alternative narrative is the dominant role of niche industries within each city. The cities built an urban identity around these industries, often claiming to be the “capitol of the world” in crafting a certain product. In addition to being catchy, these city slogans are actually quite central to my argument. As part of my work as a Digital Scholarship Incubator Fellow at UNL’s CDRH, I decided to use business and organizational records to attempt to see whether or not my case studies were in fact a leader in their respective industries. Rather quickly, I found calculating prestige would be more difficult than I had thought. While many sources frequently discuss these cities as industry leaders, quantifying this anecdotal evidence is a more complex project.

I began with Grand Rapids, in part because the sources I had were the easiest to copy into spreadsheets. My initial data set was the attendance records of the city’s furniture markets, during which buyers would travel to see new products. These furniture markets were a key in building up Grand Rapids as a leader in the industry. Though the markets dated back to 1878, my records began in 1923 (I have the numbers for many later years, but I decided to use 1933 as a cut off date because my dissertation’s focus ends around the Great Depression). Working with the numbers as spreadsheets I noticed a decline as the economy worsened, as I expected from prior research about the city and its industry.

I also noticed the most buyers attended from Michigan and nearby states: Illinois, Pennsylvania, New York, and Ohio. Although not terribly surprising, it did raise an important question: were these markets a sign of influence on the industry’s national stage, or were they simply large regional events? Could they be both? As I presented my early stage research to the UNL DH community and toyed with mapping the data, the question became more ambiguous. Cities like Grand Rapids did have a larger market share than their population size would indicate, but the city’s reputation was built on quality, not quantity. I had set out to measure abstract concepts like reputation, influence, and prestige while using very concrete numbers.

This dilemma became painfully clear as I created cholorpleth maps from the furniture market data. While thankfully R allowed me a quick and easy way to create these maps, how I colored them deeply affected the way in which the reader would perceive the data.

Continuous Scale Map

Using a sliding color scale, the dominance of Michigan and the surrounding states is clear. The vast majority of the nation remains close to zero while the hundreds of delegates from a few select states clearly dominated the markets. However, breaking the map into buckets shows a larger base from which the markets pulled buyers, suggesting it may be more than a purely regional event.

Map with Buckets

Even determining the size of the buckets was a difficult judgment call. How many buyers from each state does it take to make the event “national”? Obviously, the hundreds from Michigan are noteworthy, but what about the three dozen from Oklahoma? Are they insignificant? The next step seems to be weighting the attendance records by population, or furniture production/consumption, or some other metric. While I ponder how to proceed and this question of measuring reputation, for the time being, I’m moving on to working with spreadsheet data for other visualizations but I would greatly welcome any advice (in the comments, via email, twitter, etc)

Published in Digital Humanities Research


  1. Great post, Brian! I’d be curious to know if you could represent the data more granularly? In other words, rather than using state boundaries as a bounding box for the data, is your data granular enough that you could represent the data at the city level? I ask because it might give another view about what’s going on in your data. I’m thinking in particular of this example ( of using sized circles rather than the choropleth. Or, if not, what about at the county level? You could aggregate city data to the county boundaries — again, might be another, and more granular, look at what’s going on (e.g., There’s plenty of geo resources out there for working with historical counties (the best being

    Anyway, just some thoughts! I’m eager to see what else you’re working on.

    • Brian Sarnacki

      Thanks for the suggestions! Unfortunately, I only have the data at the state level. I found one later program with the names and cities of origin, which would have been great, but alas, no others in the archive. Playing with size instead of color is something to think about. It’s worth exploring if there is a visualization better than a color scale, though the same issues of proportion may arise.

  2. Brandon Locke

    You may want to look into spline interpolation. I don’t know much about it (I can barely even pronounce it), but I’ve used it with Gephi. There are some preferred functions for mapping different statistical distributions so that you can illustrate long tails and eliminate the skew that comes with an advanced power law distribution.

    This probably isn’t all that helpful, but there are some laws for skewed distributions:

  3. […] where I briefly discussed some initial maps and some of the issues I encountered working with quantitative data and qualitative concepts. What struck me from the audience’s very helpful comments and questions was that I needed […]

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