Stich, Christoph;
Tranos, Emmanouil;
Nathan, Max;
(2022)
Modeling Clusters From The Ground Up: A Web Data Approach.
Environment and Planning B: Urban Analytics and City Science
(In press).
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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.
| Type: | Article |
|---|---|
| Title: | Modeling Clusters From The Ground Up: A Web Data Approach |
| Open access status: | An open access version is available from UCL Discovery |
| Publisher version: | https://journals.sagepub.com/home/epb |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher's terms and conditions. |
| Keywords: | clusters, cities, technology industry, machine learning |
| UCL classification: | UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of the Built Environment UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of the Built Environment > Centre for Advanced Spatial Analysis UCL > Provost and Vice Provost Offices > UCL BEAMS UCL |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10149515 |
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