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Modeling clusters from the ground up: A web data approach

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  • Christoph Stich
  • Emmanouil Tranos
  • Max Nathan

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.

Suggested Citation

  • Christoph Stich & Emmanouil Tranos & Max Nathan, 2023. "Modeling clusters from the ground up: A web data approach," Environment and Planning B, , vol. 50(1), pages 244-267, January.
    • Handle: RePEc:sae:envirb:v:50:y:2023:i:1:p:244-267
    DOI: 10.1177/23998083221108185
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    More about this item

    Keywords

    clusters; cities; technology industry; machine learning;
    All these keywords.

    JEL classification:

    • L86 - Industrial Organization - - Industry Studies: Services - - - Information and Internet Services; Computer Software
    • C50 - Mathematical and Quantitative Methods - - Econometric Modeling - - - General
    • O31 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Innovation and Invention: Processes and Incentives
    • R12 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General Regional Economics - - - Size and Spatial Distributions of Regional Economic Activity; Interregional Trade (economic geography)

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