Digital History: Data Visualization

Digital History


For my data visualization, I made the chart I wish I'd had last semester while studying the 1918 Spanish Flu Epidemic in Philadelphia. This chart visualizes mortality rates in different wards of the city of the leading causes of death in the year 1918. This makes it easy to see that although pneumonia, for example, was a major cause of death, particularly in poorer wards, its impact hardly compares with deaths from epidemic influenza.

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A screenshot of the final, interactive HTML file

I chose to create the graph by creating an HTML file that calls up the Google Charts API for formatting. I did this for several reasons. First, my data is preserved in an easily accessible code-based source (anyone can click “view source” and see what numbers I’ve entered), in a stable format that works with all web browsers. I recently had a conversation with someone who remarked that visualization tools are great, but they need to be reliable in order to be really useful; if the tool breaks or the web changes and the tool no longer has development support, it’s useless. Standard CSS and HTML are reliable old standbys for at least skeletal web development, because they are still a standard markup language after more than 20 years in existence and are likely to remain so for a while longer. This translates into project longevity.

Unfortunately, it also means that I wasn’t able to embed the chart into this WordPress blog; I can’t place iframes or even upload the HTML source file for blog readers to download and open through their web browser. Even though it’s interactive (in a very basic way), I had to post a screenshot to make it visible- certainly a downside that I did not anticipate.

I got the source material from a primary document, The Annual Report of the Bureau of Health of the City of Philadelphia for 1918, which I first found as an excerpt (that the University of Michigan rather disingenuously didn’t reveal wasn’t a complete document), but then found and was able to download in its entirety on HathiTrust, courtesy of Princeton University. I “curated” the data (read: limited the amount of superfluous data entry) by making the conscious choice to only include the top four causes of death in my data. Although I recognize that that decision to do this is not ideal in terms of transparency about the mortality rates, I also thought that to include all 99 causes of death in the chart would be too visually confusing to the viewer.