Big Data Mapping Projects

A couple of interesting resources have been launched recently which attempt to use maps to read cities’ literary histories.

LitLong, launched at the end of last month, still has several of its major features in development, but it currently allows the user to see the locations mentioned in a large corpus of texts onto a map of Edinburgh.  It’s ‘the visual, interactive output of the Palimpsest project, a collaboration between the University of Edinburgh’s School of Literatures, Languages and Cultures; the School of Informatics; the University of St Andrews’ SACHI research group; and EDINA.’  This is an example of what can be done by bringing together a number of big, fairly messy data sets – I’m looking forward to having a bit of a play around with it and seeing what it can do, and also to hearing how the project team plan to develop it further.

Mapping Emotions in Victorian London is a Stanford Literary Lab project supported by the Mellon Foundation and hosted on Historypin.  In the words of its ‘About’ page: ‘The project has invited anonymous participants to annotate whether passages drawn from novels, published mainly in the Victorian era, represented London places in a fearful, happy, or unemotional manner.  This data from the crowd allowed us to generate the maps you find here, revealing a previously unseen emotional geography of Victorian London.’  Unlike many crowdsourced projects, this one used a payment model, hiring labour from Amazon’s Mechanical Turk.  This has a really nice map overlay, built using Ordinance Survey maps from the National Library of Scotland, and some interesting masks for examining different aspects of the city.  At the moment, though, it seems quite difficult to get an kind of overall sense of what texts are used on the site, how these were selected and what the rationale for including or excluding things was.  Hopefully the nature of the emotional geography the project examines will become clearer when the team publish more of their findings.

Matthew Sangster

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