Abstract
This paper proposes a new methodological framework to identify economic clusters over space and time. We employ a unique open source dataset of geolocated and archived business webpages and interrogate them using Natural Language Processing to build bottom-up classifications of economic activities. We validate our method on an iconic UK tech cluster – Shoreditch, East London. We benchmark our results against existing case studies and administrative data, replicating the main features of the cluster and providing fresh insights. As well as overcoming limitations in conventional industrial classification, our method addresses some of the spatial and temporal limitations of the clustering literature.
| Original language | English |
|---|---|
| Pages (from-to) | 1-24 |
| Number of pages | 24 |
| Journal | Environment and Planning B: Urban Analytics and City Science |
| Early online date | 17 Jun 2022 |
| DOIs | |
| Publication status | E-pub ahead of print - 17 Jun 2022 |
Bibliographical note
Funding Information:The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: The authors acknowledge funding from the Consumer Data Research Centre (CDRC) and Engineering and Physical Sciences Research Council (ESRC). This paper represents the views of the authors, not the funders or data providers.
Publisher Copyright:
© The Author(s) 2022.