GIS clearly has changed the way we document and analyze historical data. Although, as stated multiple times in the reading, the foundation of GIS is obviously quantitative, which presents its own set of biases and pitfalls. While it can very easily create rich data visualizations and map spatial, cartesian-based data effectively, it also implies a sort of “ground truth” with the way location and geographic data should be mapped. Especially since historians often converse about events relationally, the locations of events might not always be the most important thing to visualize.

Further, so much of our history is imprecise, inconsistent, and written in natural language. And because GIS inherently requires precise and consistent quantitative data, using it thus requires additional interpretation to try to make an “objective” geographic sense of history. While these methods can be successful, there is clearly a large variety of interpretations and favors “official representations of the world, a result that was highly problematic because this view reflected the influence of money and power.”

Also, not everyone knows how to code, but being able to visualize really complex and high dimensional data — especially language — is currently reserved for engineers. However, the people who can analyze these complicated, qualitative questions the best are usually not engineers, so there’s a lot of work to be done in terms of making spatial analysis accessible and work with qualitative data rather than around it.