Spatial Information Research

, Volume 27, Issue 1, pp 109–120 | Cite as

Text mining geo-visualization of patent documents on geo-spatial big-data industry

  • Wonwook Choi
  • Jongwook AhnEmail author
  • Dongbin Shin
Part of the following topical collections:
  1. Academia and Industry collaboration on the Spatial Information


This study attempts to establish prototype-leveled patent fusion data based on collecting structured and unstructured geo-spatial big data (GSBD) patent information, to distinguish GSBD technical ecosystems into their spatial and non-spatial aspects, and to propose a method to analyze visualizations in a multi-dimensional way. Spatially, we visualize the patent citation data among applicants for a patent at local and national levels, and implement a visualization analysis of the competitive relations for the locational traits of applicants for patent and technology innovation by comparing technology dependence and technology impacts in GSBD technology. Non-spatially, we analyzed the trend of time series of GSBD technology innovation activities based on Industry Classification and technology keywords. We establish the related networks among industry classification, IPC patent classification and technology keywords and implement a visualization analysis of convergence structure in element technologies through graph network analysis and Venn diagram analysis. We extracted issues related with the establishment of patent fusion data and interpretation of visualization analysis through the examination of research methodology and analysis results and discussed future research tasks to solve these problems.


Geo-spatial big data Social network analysis Patent citation analysis Technology convergence analysis Technical impact analysis 



This research, ‘Geospatial Big Data Management, Analysis and Service Platform Technology Development’, was supported by the MOLIT (The Ministry of Land, Infrastructure and Transport), Korea, under the national spatial information research program supervised by the KAIA (Korea Agency for Infrastructure Technology Advancement) (18NSIP-B081011-05).


  1. 1.
    Li, S., Dragicevic, S., Castro, F. A., Sester, M., Winter, S., Coltekin, A., et al. (2016). Geospatial big data handling theory and methods: A review and research challenges. ISPRS Journal of Photogrammetry and Remote Sensing, 115, 119–133.CrossRefGoogle Scholar
  2. 2.
    Yu, S. C., Choi, W. W., Shin, D. B., & Ahn, J. W. (2014). A study on concept and services framework of geo-spatial big data. Spatial Information Research, 22(6), 13–21. Scholar
  3. 3.
    Yu, S. C., Shin, D. B., & Ahn, J. W. (2016). A study on concepts and utilization of Geo-Spatial Big Data in South Korea. KSCE Journal of Civil Engineering, 20(7), 2893–2901.CrossRefGoogle Scholar
  4. 4.
    Trippe, A. (2015). Guidelines for preparing patent landscape reports. Geneva: World Intellectual Property Organization.Google Scholar
  5. 5.
    Research Councils UK. (2016). The UK knowledge and research landscape: A report on available resources. London: Council for Science and Technology.Google Scholar
  6. 6.
    Érdi, P., Makovi, K., Somogyvári, Z., Strandburg, K., Tobochnik, J., Volf, P., et al. (2013). Prediction of emerging technologies based on analysis of the US patent citation network. Scientometrics, 95(1), 225–242.CrossRefGoogle Scholar
  7. 7.
    Leydesdorff, L., Alkemade, F., Heimeriks, G., & Hoekstra, R. (2015). Patents as instruments for exploring innovation dynamics: Geographic and technological perspectives on photovoltaic cells. Scientometrics, 102(1), 629–651.CrossRefGoogle Scholar
  8. 8.
    Leydesdorff, L., & Bornmann, L. (2012). Mapping (USPTO) patent data using overlays to Google Maps. Journal of the American Society for Information Science and Technology, 63(7), 1442–1458.CrossRefGoogle Scholar
  9. 9.
    Choi, W. W., Hong, S. K., & Ahn, J. W. (2015). A study on analysis and development of geo-spatial collaboration platform. Spatial Information Research, 23(4), 33–46. Scholar
  10. 10.
    Kim, Y. G., Suh, J. H., & Park, S. C. (2008). Visualization of patent analysis for emerging technology. Expert Systems with Applications, 34(3), 1804–1812.CrossRefGoogle Scholar
  11. 11.
    Tseng, Y. H., Lin, C. J., & Lin, Y. I. (2007). Text mining techniques for patent analysis. Information Processing and Management, 43(5), 1216–1247.CrossRefGoogle Scholar
  12. 12.
    Karvonen, M., & Kässi, T. (2013). Patent citations as a tool for analysing the early stages of convergence. Technological Forecasting and Social Change, 80(6), 1094–1107.CrossRefGoogle Scholar
  13. 13.
    Looy, B. V., Vereyen, C., Schmoch, U. (2015). Patent statistics: Concordance IPC V8—NACE REV.2 (version 2.0), Eurostat working papers, No. 2015/10, Eurostat Publishing, Luxembourg.Google Scholar
  14. 14.
    Johnson, D. K. (2002). The OECD technology concordance (OTC): Patents by industry of manufacture and sector of use, OECD Science, Technology and Industry Working Papers, No. 2002/05, OECD Publishing, Paris.Google Scholar
  15. 15.
    Korea Intellectual Property Office. (2013). KIPO concordance table. Daejeon: Korea Intellectual Property Office.Google Scholar
  16. 16.
    Korea Intellectual Property Office. (2016). KIPRIS plus patent information utilizing service. Daejeon: Korea Institute of Patent Information.Google Scholar
  17. 17.
    Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent dirichlet allocation. Journal of Machine Learning Research, 3(Jan), 993–1022.Google Scholar

Copyright information

© Korean Spatial Information Society 2018

Authors and Affiliations

  1. 1.Smart Urban Space InstituteAnyang UniversityAnyang-siSouth Korea
  2. 2.Department of Urban Information EngineeringAnyang UniversityAnyang-siSouth Korea

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