Skip to main content

Google Trends for Data Mining. Study of Czech Towns

  • Conference paper
  • 1907 Accesses

Part of the Lecture Notes in Computer Science book series (LNAI,volume 8083)


Selected web search engines provide statistics of user activities according to the topics, time and locations. The utilization requires well prepared phrases and searching range. The system of etalons for calibration searching frequencies provided by Google Trends is proposed. It was applied for evaluation of searching names of Czech towns. The regression analysis proved high correlation with population. Highlighted anomalies were explored. K-means cluster analysis enabled a categorization of selected towns. The geographical network analysis of relationships among towns suffers from low quality of locations provided by Google. The discussion includes an overview of main pros and cons of Google Trends and provides recommendations.


  • Google
  • web search engine
  • data mining
  • Czech towns

This is a preview of subscription content, access via your institution.

Buying options

USD   29.95
Price excludes VAT (USA)
  • DOI: 10.1007/978-3-642-40495-5_11
  • Chapter length: 10 pages
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
USD   99.00
Price excludes VAT (USA)
  • ISBN: 978-3-642-40495-5
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Softcover Book
USD   131.00
Price excludes VAT (USA)


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. ITU: The World in 2011: ICT Facts and Figures (2011),

  2. Mana, M.: Internetová populace. Statistika & My 3/2012, pp. 30–33 (2012),$File/1804120330_33.pdf

  3. CSU: Využívání informačních a komunikačních technologií v domácnostech a mezi jednotlivci (2010),$File/970110.pdf

  4. Nielsen Media: Search engines most popular method of surfing the web. Commerce Net (1997),

  5. Breyer, B.N., Sen, S., Aaronson, D.S., Stoller, M.L., Erickson, B.A., Eisenberg, M.L.: Use of Google Insights for Search to Track Seasonal and Geographic Kidney Stone Incidence, the United States. Urology 8(78(2)), 267–271 (2011)

    Google Scholar 

  6. Brownstein, J.S., Clark, C.F., Madoff, L.C.: Digital Disease Detection—Harnessing the Web for Public Health Surveillance. N. England. J. of Medicine 360, 2153–2157 (2009)

    CrossRef  Google Scholar 

  7. Ibuka, Y., Chapman, G.B., Meyers, L.A., Li, M., Galvani, A.P.: The dynamics of risk perceptions and precautionary behavior in response to 2009 (H1N1) pandemic influenza. BMC Infectious Diseases 10(1) (2010)

    Google Scholar 

  8. Ginsberg, J., Mohebbi, M., Patel, R., Brammer, M., Brilliant, L., Letter, L.: Detecting influenza epidemics using search query data. Nature (457), 1012–1014 (2009)

    Google Scholar 

  9. Clipp, C.: An Exploration of Multimedia Multitasking: How Television Advertising Impacts Google Search, 39 p. Duke University, North Carolina (2011)

    Google Scholar 

  10. Webb, G.K.: Internet search statistics as a source of business intelligence: Searches on foreclosure as an estimate of actual home foreclosures. Issues in Information Systems X(2), 82–87 (2009)

    Google Scholar 

  11. StatOwl (2013),

  12. Boulton, A., Devriendt, L., Brunn, S.D., Derudder, B., Witlox, F.: City Networks in Cyberspace and Time: Using Google Hyperlinks to Measure Global Economic and Environmental Crises. In: Firmino, R.J., Duarte, F., Ultramari, C. (eds.) ICTs for Mobile and Ubiquitous Urban Infrastructures: Surveillance, Locative Media and Global Networks, pp. 67–87. IGI Global, Hershey (2011)

    Google Scholar 

  13. Google Trends (2013),

  14. Scheitle, C.P.: Google’s Insights for Search: A Note Evaluating the Use of Search Engine Data in Social Research. Social Science Quarterly (92), 285–295 (2011)

    Google Scholar 

  15. Al-Eroud, A.F., Al-Ramahi, M.A., Al-Kabi, M.N., Alsmadi, I.M., Al-Shawakfa, E.M.: Evaluating Google queries based on language preferences. Journal of Information Science 37(3), 282–292 (2011)

    CrossRef  Google Scholar 

  16. Krejcar, O., Janckulik, D., Motalova, L.: Dataflow Optimization Using of WiFi, GSM, UMTS, BT and GPS positioning in Mobile Information Systems on Mobile Devices. In: 2nd International Conference on Computer Engineering and Applicatons (ICCEA 2010), vol. 2, pp. 127–131. IEEE Comp Soc., Bali Island (2010)

    Google Scholar 

Download references

Author information

Authors and Affiliations


Editor information

Editors and Affiliations

Rights and permissions

Reprints and Permissions

Copyright information

© 2013 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Horák, J., Ivan, I., Kukuliač, P., Inspektor, T., Devečka, B., Návratová, M. (2013). Google Trends for Data Mining. Study of Czech Towns. In: Bǎdicǎ, C., Nguyen, N.T., Brezovan, M. (eds) Computational Collective Intelligence. Technologies and Applications. ICCCI 2013. Lecture Notes in Computer Science(), vol 8083. Springer, Berlin, Heidelberg.

Download citation

  • DOI:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-40494-8

  • Online ISBN: 978-3-642-40495-5

  • eBook Packages: Computer ScienceComputer Science (R0)