Abstract
Cities relate to other cities in many ways, and much scholarly effort goes into uncovering those relationships. Building on the principle that strongly related cities will co-occur frequently in texts, we propose a novel method to classify those toponym co-occurrences using a lexicon-based text-mining method. Millions of webpages are analysed to retrieve how 293 Chinese cities are related in terms of six types: industry, information technology, finance, research, culture and government. Each class displays different network patterns, and this multiplexity is mapped and analysed. Further refinement of this lexicon-based approach can revolutionize the study of inter-urban relationships.
| Original language | English |
|---|---|
| Pages (from-to) | 1592-1604 |
| Number of pages | 13 |
| Journal | Regional Studies |
| Volume | 57 |
| Issue number | 8 |
| Early online date | 13 Oct 2022 |
| DOIs | |
| Publication status | Published - 2023 |
Bibliographical note
Funding Information:The authors thank Zhao Yin and Ziyu Bao at Delft University of Technology for computer programming support. The paper also benefitted from feedback received at various international conferences and research seminars, as well as constructive feedback from external reviewers.
Publisher Copyright:
© 2023 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.
Keywords
- city networks
- multiplexity
- text-mining
- toponym co-occurrence
- urban systems