Nathan, M;
Rosso, A;
(2017)
Exploring digital technology industry clusters using administrative and frontier data.
In: Schintler, Laurie A and Chen, Zhenhua, (eds.)
Big Data for Regional Science.
(pp. 143-152).
Routledge: Taylor & Francis Group: London, UK.
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Abstract
Industrial clusters help firms and workers become more productive, so are of great interest to researchers and policymakers. However, exploring and analyzing such clusters is challenging. Big data and data-driven approaches can help. We study the emerging digital sector using big data obtained from administrative datasets but also from data science routines to develop modelled firm variables and firms’ activities. These matched datasets allow us to have new insights on the importance of digital technology sectors in the United Kingdom, their structure and their co-location patterns.
| Type: | Book chapter |
|---|---|
| Title: | Exploring digital technology industry clusters using administrative and frontier data |
| ISBN-13: | 9781138282186 |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 9781315270838 |
| 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. |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS 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 |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10175157 |
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