Analysing urban policy discourses using textometry: An application to French urban transport plans (2000-2015)

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TitreAnalysing urban policy discourses using textometry: An application to French urban transport plans (2000-2015)
Type de publicationJournal Article
Year of Publication2020
AuteursBuhler T, Lethier V
JournalURBAN STUDIES
Volume57
Pagination0042098019873824
Date PublishedAUG
Type of ArticleArticle
ISSN0042-0980
Mots-clésDiscourse, textometry, transport planning, urban planning, urban transport plans
Résumé

This article supplements the array of methods in urban policy discourse analysis by applying textometry to a corpus of planning documents. Textometry is a systematic computer-assisted analysis of textual data. When applied to large corpora, the method can reveal contrasts that cannot readily be detected by non-instrumented human reading. It seems promising therefore to use textometry to analyse urban planning documents which, in France as in other countries, are known for being at first sight pretty indistinguishable from each other. By analysing dozens of documents at a time, this method increases the scale of analysis, enabling us to identify massive transitions in discourse over time or major contrasts among discourses emanating from specific groups of actors. When combined with classical methods (i.e. interviews, qualitative archival research), textometry seems to be effective at identifying new ways of understanding urban policy discourses. To exemplify the potential of such a method, the analysis developed here draws on a corpus of 36 French urban transport plans (plans de deplacements urbains) for the period 2000-2015. Our results display marked contrasts in discourses, mainly as a result of changes over time. Paradoxically, for most cities between 2000 and 2010, the discourses become increasingly abstract in their content while highlighting a proactive attitude and providing specific information on the document implementation process and a precise list of institutional partners.

DOI10.1177/0042098019873824, Early Access Date = {OCT 2019