dc.rights.license | CC-BY-NC-ND | |
dc.contributor.advisor | Meijers, Evert | |
dc.contributor.author | Nijman, Brecht | |
dc.date.accessioned | 2022-09-09T02:02:48Z | |
dc.date.available | 2022-09-09T02:02:48Z | |
dc.date.issued | 2022 | |
dc.identifier.uri | https://studenttheses.uu.nl/handle/20.500.12932/42548 | |
dc.description.abstract | Toponym co-occurrence analysis on unstructured sources has been suggested as a possible method for obtaining data for the study of urban networks. This method is particularly beneficial for mod- elling international relations and the relations between smaller places. However, it also suffers from potentially introducing space-language bias into the networks. This paper creates a network of 151 European cities derived from English and French versions of Wikipedia using toponym co-occurrence analysis to be used as a case study. City-pairs in the English and French language sphere are found to have a tendency to be over-represented in the respective data sources compared to the patterns expected from gravity modelling. Nevertheless both of the resulting networks fit expected patterns, showing the applicability of toponym co-occurrence analysis. | |
dc.description.sponsorship | Utrecht University | |
dc.language.iso | EN | |
dc.subject | Toponym co-occurrence analysis on unstructured sources has been suggested as a possible method for obtaining data for the study of urban networks. This method is particularly beneficial for modelling international relations and the relations between smaller places. However, it also suffers from potentially introducing space-language bias into the networks. This paper uses a network of European cities derived from English and French wikipedia as a case study to identify and quantify this bias. | |
dc.title | The Effect of Space-Language Bias on Toponym Co-occurence Derived Networks | |
dc.type.content | Master Thesis | |
dc.rights.accessrights | Open Access | |
dc.subject.keywords | urban networks; space-language bias; wikidata; toponym co-occurrence; network analysis | |
dc.subject.courseuu | Applied Data Science | |
dc.thesis.id | 9605 | |