Modeling clusters from the ground up: a web data approach

Stich, Christoph, Tranos, Emmanouil and Nathan, Max (2023) Modeling clusters from the ground up: a web data approach. Environment and Planning B: Urban Analytics and City Science, 50 (1). 244 - 267. ISSN 2399-8083

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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.

Item Type:	Article
Official URL:	https://journals.sagepub.com/home/epb
Additional Information:	© 2022 The Authors
Divisions:	Centre for Economic Performance
Subjects:	H Social Sciences > HB Economic Theory H Social Sciences > HC Economic History and Conditions
JEL classification:	L - Industrial Organization > L8 - Industry Studies: Services > L86 - Information and Internet Services; Computer Software C - Mathematical and Quantitative Methods > C5 - Econometric Modeling > C50 - General O - Economic Development, Technological Change, and Growth > O3 - Technological Change; Research and Development > O31 - Innovation and Invention: Processes and Incentives R - Urban, Rural, and Regional Economics > R1 - General Regional Economics > R12 - Size and Spatial Distributions of Regional Economic Activity; Interregional Trade
Date Deposited:	14 Jul 2022 16:42
Last Modified:	25 Apr 2024 16:24
URI:	http://eprints.lse.ac.uk/id/eprint/115565

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