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Visualizing geo-spatial data in science, technology and innovation

Author

Listed:
  • Kwakkel, Jan H.
  • Carley, Stephen
  • Chase, John
  • Cunningham, Scott W.

Abstract

This paper provides a range of alternatives for analysts when dealing with geo-spatial data addressing activities in science, technology and innovation. There are two sets of theory which frame the debate concerning science, technology and innovation, and which drive further methodological advancements. The power of a good visualization in providing insight to decision-makers is well known. Visualizations complete a full cycle of decision-making involving analysis, design, action and then further monitoring. We advance the paper by assessing available geographic information in science and technology databases. The paper then systematically outlines current best practices and alternatives for visualizing geographic data. Different geographic map options provide different possibilities for the display of data. We show some of these options in the paper. Future research is needed into both the available tools and techniques, as well as a more in depth specification of the kinds of decision support needs that exist and have a geo-spatial component.

Suggested Citation

  • Kwakkel, Jan H. & Carley, Stephen & Chase, John & Cunningham, Scott W., 2014. "Visualizing geo-spatial data in science, technology and innovation," Technological Forecasting and Social Change, Elsevier, vol. 81(C), pages 67-81.
    • Handle: RePEc:eee:tefoso:v:81:y:2014:i:c:p:67-81
    DOI: 10.1016/j.techfore.2012.09.007
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    Cited by:

    1. Yoon, Byungun & Magee, Christopher L., 2018. "Exploring technology opportunities by visualizing patent information based on generative topographic mapping and link prediction," Technological Forecasting and Social Change, Elsevier, vol. 132(C), pages 105-117.
    2. Proksch, Dorian & Haberstroh, Marcus Max & Pinkwart, Andreas, 2017. "Increasing the national innovative capacity: Identifying the pathways to success using a comparative method," Technological Forecasting and Social Change, Elsevier, vol. 116(C), pages 256-270.
    3. Sungchul Kim & Dongsik Jang & Sunghae Jun & Sangsung Park, 2015. "A Novel Forecasting Methodology for Sustainable Management of Defense Technology," Sustainability, MDPI, vol. 7(12), pages 1-17, December.

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