Visualizing Associations of Place Names in Texts

In my last post, I discussed some early analysis, based on the HESTIA project, on place-names in the Histories of Herodotus. After lots of grief with memory and other server configuration issues, I finally managed to deploy a preliminary interface and visualization to these analyses. One thing to note.

The more red the place-marks and lines, the stronger the association of between place terms. The GoogleMaps rendering loads pretty big / complicated KML files in the background. It’s sometimes not too smooth and may take a page refresh or playing with the zoom controls before you can see something.

  1. Here’s an example for Byzantium (direct link to a large map)
  2. Here’s another example for a more distant place term (from Herodotus’ perspective), Palestine (direct link to a large map)

Notice, in the case of Palestine, the strongest associations with other place terms go to Egypt and Syria. These are two other distant regions from Herodotus’ perspective.

The ‘Strength of Association’ metric we’re playing with is based on the following method. Each token in the Histories has an index number, counting from 1 at the beginning of the text to 241,950 at the end of the text. We use these index numbers to calculate distances between toponymns.

Right now, we’re using an inverse square relationship to calculate strength of association. We chose this for no particular reason, except that it seems to nicely weigh very close co-occurance of toponymns much more than more distant co-occurances (gravity, light, and other physical phenomena relate nicely to inverse square laws). The closer two toponymns occur in the text, the stronger the association.

Next up: Referencing Pleiades place entities!

Please note! These results are preliminary and exploratory. We’re playing with methods and make no claims that this approach has any particular analytic value.

About erickansa

Working to do the right thing with open data, on Open Context (
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