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Mapping without a map: Exploring the UK business landscape using unsupervised learning

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  • Stathoulopoulos, Kostas
  • Mateos-Garcia, Juan

Abstract

Policy interventions have to be timely and tailored to specific sectors of the economic ecosystem to maximise their potential impact. We propose a system based on open data that offers policy makers two capabilities. First, it enables them to explore the digital and tech company space with high granularity through keywords, specific technologies or company names, and identify relevant organisations and those most similar to them. Second, it provides an overview of the ecosystem by creating thematic topics that characterise the activities of these companies. We demonstrate the effectiveness of this system in three activity areas not currently captured in the SIC codes.

Suggested Citation

  • Stathoulopoulos, Kostas & Mateos-Garcia, Juan, 2017. "Mapping without a map: Exploring the UK business landscape using unsupervised learning," SocArXiv ryxdk, Center for Open Science.
    • Handle: RePEc:osf:socarx:ryxdk
    DOI: 10.31219/osf.io/ryxdk
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    References listed on IDEAS

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    1. Timothy Besley & Miguel Coelho & John Van Reenen, 2013. "Investing for Prosperity: Skills, Infrastructure and Innovation," National Institute Economic Review, National Institute of Economic and Social Research, vol. 224(1), pages 1-13, May.
    2. Max Nathan & Anna Rosso, 2014. "Mapping Information Economy Businesses with Big Data: Findings for the UK," CEP Occasional Papers 44, Centre for Economic Performance, LSE.
    3. Borrás, Susana & Edquist, Charles, 2013. "The choice of innovation policy instruments," Technological Forecasting and Social Change, Elsevier, vol. 80(8), pages 1513-1522.
    4. Abdullah Gök & Alec Waterworth & Philip Shapira, 2015. "Use of web mining in studying innovation," Scientometrics, Springer;Akadémiai Kiadó, vol. 102(1), pages 653-671, January.
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    Cited by:

    1. David Lenz & Peter Winker, 2020. "Measuring the diffusion of innovations with paragraph vector topic models," PLOS ONE, Public Library of Science, vol. 15(1), pages 1-18, January.

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