• Adrijana Car – Thomas Jekel – Josef Strobl – Gerald Griesebner (Eds.)

GI_Forum 2018, Volume 6, Issue 1

Journal for Geographic Information Science

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Verlag der Österreichischen Akademie der Wissenschaften
Austrian Academy of Sciences Press
A-1011 Wien, Dr. Ignaz Seipel-Platz 2
Tel. +43-1-515 81/DW 3420, Fax +43-1-515 81/DW 3400
https://verlag.oeaw.ac.at, e-mail: verlag@oeaw.ac.at

GI_Forum publishes high quality original research across the transdisciplinary field of Geographic Information Science (GIScience). The journal provides a platform for dialogue among GI-Scientists and educators, technologists and critical thinkers in an ongoing effort to advance the field and ultimately contribute to the creation of an informed GISociety. Submissions concentrate on innovation in education, science, methodology and technologies in the spatial domain and their role towards a more just, ethical and sustainable science and society. GI_Forum implements the policy of open access publication after a double-blind peer review process through a highly international team of seasoned scientists for quality assurance. Special emphasis is put on actively supporting young scientists through formative reviews of their submissions. Only English language contributions are published.

Starting 2016, GI_Forum publishes two issues a Year.
Joumal Information is available at: GI-Forum
GI_Forum is listed on the Directory of Open Access Journals (DOAJ)

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GI_Forum 2018, Volume 6, Issue 1

ISSN 2308-1708
Online Edition

ISBN 978-3-7001-8359-4
Online Edition



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Verlag der Österreichischen Akademie der Wissenschaften
Austrian Academy of Sciences Press
A-1011 Wien, Dr. Ignaz Seipel-Platz 2,
Tel. +43-1-515 81/DW 3420, Fax +43-1-515 81/DW 3400
https://verlag.oeaw.ac.at, e-mail: bestellung.verlag@oeaw.ac.at
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Generating Big Spatial Data on Firm Innovation Activity from Text- Mined Firm Websites

    Jan Kinne, Bernd Resch

GI_Forum 2018, Volume 6, Issue 1, pp. 82-89, 2018/06/22

Journal for Geographic Information Science

doi: 10.1553/giscience2018_01_s82

doi: 10.1553/giscience2018_01_s82


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doi:10.1553/giscience2018_01_s82



doi:10.1553/giscience2018_01_s82

Abstract

Innovation is one of the major drivers of economic growth, where spatial processes of knowledge spillover play a vital role. Current practices in assessing firms’ innovation activity, including patent analysis and questionnaires, suffer from severe limitations. In this paper, we propose a novel approach to estimate firms’ innovation activity based on the texts on their websites. We use an automated web-scraper to harvest text from the websites, then extract semantic topics in a self-learning, generative topic-modelling approach, and finally analyse these topics using an Artificial Neural Networks (ANN) method to assess each firm’s level of innovation. This procedure results in a large-scale dataset that will be used for further spatial economic analysis of the distribution of innovative firms and the processes that drive the development of innovation in firms.

Keywords: firm location, microgeography, innovation, web scraping, Big Spatial Data, text mining, topic modelling, neural networks

Published Online: 2018/06/22 09:52:00

Object Identifier: 0xc1aa5572 0x00390cc6

Document viewed: Calculating...

Rights: https://creativecommons.org/licenses/by-nd/4.0/