I’ve just finished reading about half of Placing History: How Maps, Spatial Data, and GIS are Changing Historical Scholarship, edited by Anne Kelly Knowles. I’ll briefly go through each one, and focus on the ones that particularly interested me.
“Creating a GIS for the History of China,” by Peter K. Bol. Bol, chair of Harvard’s Department of East Asian Languages and Civilizations, discusses his China Historical GIS project. The project attempts to create a basic framework and data source (both spatial and temporal) for geospatial analysis of Chinese history. On a theoretical note, Bol argues that in the case of China, historical GIS should utilize a greater reliance on point data in place of polygons for marking boundaries and territory, in order to better replicate the top-down administrative system of traditional Chinese cartography.
“Teaching With GIS,” by the late Robert Churchill and Amy Hillier. Churchill gives a good overview of the value of GIS in a liberal arts education. I liked his point that one of the benefits of using historical GIS is that any in-depth use of the technology requires an equally in-depth understanding of the problem you’re looking to address. Great point. Because so much of GIS is front-loaded, in that you spend a huge amount of effort in obtaining and managing the data, it requires you to really get your hands dirty in the sources themselves. Hillier gives a lot of great examples of students’ work using historical GIS, mostly Philadelphia-based data. Some of them also included a great 1896 map by W.E.B. Du Bois detailing social class in the city. She also gives some useful tips for educators who want to incorporate GIS.
“Scaling the Dust Bowl,” by Geoff Cunfer. I loved this chapter. Cunfer follows up his previous research in Knowles first book, Past Time, Past Place, by additional analysis of the Dust Bowl. In this chapter, he takes on the common perception of the dust bowl as championed by Donald Worster’s Dust Bowl: The Southern Plains in the 1930’s. While some of Cunfer’s analysis supports Worster, he takes issue with Worster’s commonly-held assertion that the capitalistic over-development of lands for farming the major factor in the fabled 1930’s dust storms. Cunfer first demonstrates through spatial analysis that, although plow-up during the 1920’s did contribute to the Dust Bowl, it was in fact instances of drought that had a much more direct correlation.
He goes on to further his critique of the notion that the Dust Bowl was an extraordinary phenomena caused by human activity. By examining and mapping newspaper accounts of dust storms from the 19th century, along with storms after the 1930’s, he finds that “dust storms are a normal part of southern plains ecology, occurring whenever there are extended dry periods.” Although extensive plowing can enhance the problem, it was not “the sole and simple cause of the Dust Bowl.” Cunfer’s analysis succeeds on many different levels. First, I like the accessibility of it. There’s always a temptation to include too much in the final products, to show off the fruits of your hours and hours of labor. Instead, his maps are clear, uncluttered, and persuasive. Second, I like the way he blended traditionally quantitative analysis tools (GIS) with qualitative historical research (newspaper accounts). He does a good job of highlighting this tension, and aptly warns of its danger, while explaining simply how he accomplished it. Third, his work is a great example of the “right way” to use new technology to both challenge and supplement traditional historical arguments, and in doing so, present an original and different narrative.
“‘A Map is Just a Bad Graph’: Why Spatial Statistics are Important in Historical GIS,” by Ian Gregory. This chapter was much more technical, and included scary words like “regression coefficients” and “heteroscedasticity.” Although statistics in particular, and math in general, is low down on my list of skills, I got a fair amount out of the chapter. I liked his critique of the traditional thematic map, which usually displays one type of data, and with usually one variable involved. Statistical analysis can go beyond simple thematic maps and really open up the powerful underbelly of GIS.
There are several more chapters that I am looking forward to reading and reviewing in a later post.