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  1. Syllabus
  2. Session 10
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On this page

  • In this session
  • Mapping humanities data
  • Case study: the geography of tragedy
    • The chain, end to end
    • What to look at
    • The awkward questions
  • Hands-on
    • Reflection
  • Reading for Session 11
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  1. Syllabus
  2. Session 10

Session 10

Data analysis: geovisualisation

Author
Affiliation

Tobias Hodel

Walter Benjamin Kolleg / Digital Humanities, University of Bern

Published

17 November 2026

Modified

2 September 2026

Hodel Taught by Tobias Hodel.

In this session

  • Space and place in humanities data
  • Hands-on with the DARIAH Geo-Browser and Palladio

Mapping humanities data

  • The gap between a place and a coordinate, and everything that falls into it
  • Geocoding and gazetteers: GeoNames, Wikidata, Pleiades
  • Historical geography: borders move, names change, places disappear
  • The base map is an argument. A modern map under early modern data is a claim.
  • Time plus space: how to show change without implying a smooth process

Case study: the geography of tragedy

We work through (Craig 2025) together, because it is a rare example of a geospatial humanities project that publishes every stage of its workflow — and so lets us see the decisions that a finished map normally hides.

The chain, end to end

Stage Where it lives
Source texts, TEI-encoded, with the printed editions they derive from Zenodo dataset
Place allusions extracted, georeferenced, mapped TLCMap layer 2246
The argument DSH article, open access

This is the course in miniature: encoding → modelling → analysis → publication. Open all three side by side.

What to look at

  • In the TEI: how is a place allusion marked up, and what does the encoding decide that the map then inherits? An allusion to “Rome” in a play is not a coordinate until somebody makes it one.
  • In TLCMap: the workbench holds the data and the visualisation. Switch between the map and the underlying table. Which points would you challenge?
  • In the article: how does Craig move from a distribution of dots to a claim about Shakespeare? Where is the inference, and is it carried by the map or by the reading?

The awkward questions

  • Every place in the sample is a place named in dialogue. What geography does that method make invisible?
  • The plays are early modern; the base map is not. What does that mismatch do to the argument?
  • Eleven Shakespeare plays against eleven randomly selected others: is that a control group?

Hands-on

Working from your Wikidata CSV (Session 5) or from any other place-bearing dataset:

  1. DARIAH Geo-Browser — georeferencing a table, and animating it over time
  2. Palladio — map, graph, gallery and table views over the same data; the point is the switching between them
  3. Deliberately break something: map an ambiguous place name and see where it lands

Reflection

  • Which of your points are wrong, and how would a reader ever know?
  • What does the visualisation make it impossible to say?
TipIn parallel in the Lab

The DH Lab does data cleaning and reconciliation with OpenRefine — the tool that fixes exactly the place-name problems this session exposes.

Reading for Session 11

An introduction to machine learning (to be announced).

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References

Craig, Hugh. 2025. ‘The Geography of Tragedy: Mapping Allusions to Places in a Set of Shakespearean Plays’. Digital Scholarship in the Humanities 40 (3): 747–61. https://doi.org/10.1093/llc/fqaf055.
Session 9
Session 11
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