Skip to main content
SpringerLink
Log in
Book cover

Algorithms from and for Nature and Life pp 243–250Cite as

Determining the Similarity Between US Cities Using a Gravity Model for Search Engine Query Data

  • Conference paper
  • First Online:

  • 2802 Accesses

Abstract

In this paper we use the gravity model to estimate the similarity of US cities based on data provided by Google Trends (GT). GT allows to look up search terms and to obtain ranked lists of US cities according to the relative frequencies of requests for each term. The occurences of the US cities on these ranked lists are used to determine the similarities with the gravity model. As search terms for GT serve dictionaries derived from the General Inquirer (GI), containing the categories Economy and Politics/Legal. The estimated similarity scores are visualized with multidimensional scaling (MDS).

This is a preview of subscription content, log in via an institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

Notes

  1. 1.

    Note that the gravity distance is not a distance measure in the strict mathematical sense, since it neither fulfills d(x, x) = 0 nor the triangle inequality.

  2. 2.

    We also performed our estimation for the Euclidean norm which gave roughly the same results.

  3. 3.

    In order to get the figures readable, identical projections are slightly shifted.

References

  • Askitas, N., & Zimmermann, K. F. (2009). Google econometrics and unemployment forecasting. Applied Economics Quarterly, 55(2), 107–120.

    Article  Google Scholar 

  • Borg, I., & Groenen, P. (2005). Modern multidimensional scaling (2nd ed.). New York: Springer.

    MATH  Google Scholar 

  • Choi, C., & Yi, M. H. (2009). The effect of the internet on economic growth: Evidence from cross-country panel data. Economics Letters, 105(1), 39–41.

    Article  Google Scholar 

  • de Leeuw, J., & Mair, P. (2009) Multidimensional scaling using majorization: SMACOF in R. Journal of Statistical Software, 31(3), 1–30.

    Google Scholar 

  • Ginsberg, J., et al. (2009). Detecting influenza epidemics using search engine query data. Nature, 457(7232), 1012–1014.

    Article  Google Scholar 

  • Google (2010). About Google trends. URL http://www.google.com/intl/en/trends/about.html

  • Haynes, K. E., & Fotheringham, A. S. (1988). Gravity and spatial interaction models. Beverly Hills: Sage.

    Google Scholar 

  • Michel, J. B., et al. (2011). Quantitative analysis of culture using millions of digitized books. Science, 331(6014), 176–182.

    Article  Google Scholar 

  • Stone, P. J., Dunphy, D. C., & Smith, M. S. (1966) The general inquirer: A computer approach to content analysis. Cambridge: MIT.

    Google Scholar 

  • Wilson, T., Wiebe, J., & Hoffmann, P. (2005). Recognizing contextual polarity in phrase-level sentiment analysis. In Proceedings of Human Language Technology Conference and Conference on Empirical Methods in Natural Language Processing, Vancouver (pp. 347–354).

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Institute for Statistics and Mathematics, WU (Vienna University of Economics and Business), Augasse 2–6, 1090, Wien, Austria

    Paul Hofmarcher, Kurt Hornik & Patrick Mair

  2. Department of Applied Statistics, Johannes Kepler University Linz, Altenbergerstraße 69, 4040, Linz, Austria

    Bettina Grün

Authors
  1. Paul Hofmarcher
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Bettina Grün
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Kurt Hornik
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Patrick Mair
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Paul Hofmarcher .

Editor information

Editors and Affiliations

  1. University of Essex Department of Mathematical Sciences, Colchester, United Kingdom

    Berthold Lausen

  2. Ghent University Department of Marketing, Ghent, Belgium

    Dirk Van den Poel

  3. University of Marburg Databionics, FB 12, Marburg, Germany

    Alfred Ultsch

Rights and permissions

Reprints and permissions

Copyright information

© 2013 Springer International Publishing Switzerland

About this paper

Cite this paper

Hofmarcher, P., Grün, B., Hornik, K., Mair, P. (2013). Determining the Similarity Between US Cities Using a Gravity Model for Search Engine Query Data. In: Lausen, B., Van den Poel, D., Ultsch, A. (eds) Algorithms from and for Nature and Life. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Cham. https://doi.org/10.1007/978-3-319-00035-0_24

Download citation

Publish with us

Policies and ethics

Search

Navigation