Authors: Christoph Stich, University of Birmingham, Emmanouil Tranos*, University of Birmingham, Max Nathan, University of Birmingham, Zhaoya Gong, University of Birmingham
Topics: Economic Geography, Quantitative Methods, Urban Geography
Keywords: economic clusters, digital economy, digital archives, text mining
Session Type: Paper
Start / End Time: 9:55 AM / 11:35 AM
Room: Jefferson, Marriott, Mezzanine Level
Presentation File: No File Uploaded
This paper proposes a new methodological approach to identify economic clusters and their evolution over space and time, which builds upon recent developments in data science.
We employ an under-used open source of commercial, geolocated and archived webpages during the period 2000-2012. We interrogate these data using data science techniques to build postcode-level, time-varying classifications of economic activities using text data from individual webpages. We apply this method to take a fresh look at an iconic London neighbourhood – Shoreditch – that is rich in technology and creative industries, and has become a leading digital and creative cluster over the past two decades (Hutton 2008, Pratt 2009, Harris 2012, Foord 2013, Martins 2015).
Our approach tackles a number of problems inherent in empirical analysis of clusters: backward-looking industrial classifications, reliance on administrative geographies and low data frequency (Nathan and Rosso 2015, OECD 2013, Duranton and Overman 2005). As such, the paper aims to add to the limited empirical literature (Ter Wal and Boschma 2011; Balland, Boschma, and Frenken 2015; Delgado, Porter, and Stern 2015), which moves beyond a pre-determined understanding of economic clusters in spatial, temporal and technological terms (Catini et al. 2015). By allowing us to look flexibly at how co-located economic activities shift over time, our method also provides cleaner links back to theory, especially evolutionary frameworks, and questions of industry specialisation, diversity and branching.