Modeling clusters from the ground up: A web data approach

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.

Determinants of rural creative microclustering: Evidence from web‐scraped data for England

“…Our approach to identifying these microclusters draws upon recent studies that have explored clustering at a fine geographical level using data scraped from company websites (e.g., Papagiannidis et al, 2018; Rammer et al, 2020; Siepel et al, 2020; Stich et al, 2023). These studies generally use the addresses provided on company websites to identify and inductively map clusters of activity, often in a way that differs or is more insightful than standard SIC codes (as in Papagiannidis et al, 2018).…”

“…These studies generally use the addresses provided on company websites to identify and inductively map clusters of activity, often in a way that differs or is more insightful than standard SIC codes (as in Papagiannidis et al, 2018). Our approach differs from previous studies in that, whereas other studies looking at clustering using web data have looked at clustering in an individual neighbourhood or city (Rammer et al, 2020; Stich et al, 2023) or one region (Papagiannidis et al, 2018), we attempt to map across the whole of England, and we try to use this data to identify clusters in rural areas. To our knowledge, neither of these has been done previously.…”