Modeling clusters from the ground up: a web data approach

Christoph Stich, Emmanouil Tranos and Max Nathan

LSE Research Online Documents on Economics from London School of Economics and Political Science, LSE Library

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.

Keywords: cities; clusters; machine learning; technology industry (search for similar items in EconPapers)
JEL-codes: C50 L86 O31 R12 (search for similar items in EconPapers)
Pages: 24 pages
Date: 2023-01-01
New Economics Papers: this item is included in nep-big, nep-geo and nep-ure
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Published in Environment and Planning B: Urban Analytics and City Science, 1, January, 2023, 50(1), pp. 244 - 267. ISSN: 2399-8083

Downloads: (external link)
http://eprints.lse.ac.uk/115565/ Open access version. (application/pdf)

Related works:
Journal Article: Modeling clusters from the ground up: A web data approach (2023)
Working Paper: Modelling Clusters From The Ground Up: A Web Data Approach (2021)
Working Paper: Modelling Clusters From The Ground Up: A Web Data Approach (2021)
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Persistent link: https://EconPapers.repec.org/RePEc:ehl:lserod:115565

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