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Size and Power of Multivariate Outlier Detection Rules Read chapter Read first chapter

2013 | OriginalPaper | Chapter

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

Authors : Paul Hofmarcher, Bettina Grün, Kurt Hornik, Patrick Mair

Published in: Algorithms from and for Nature and Life

Publisher: Springer International Publishing

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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).

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Footnotes
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
We also performed our estimation for the Euclidean norm which gave roughly the same results.
 
3
In order to get the figures readable, identical projections are slightly shifted.
 
Literature
Askitas, N., & Zimmermann, K. F. (2009). Google econometrics and unemployment forecasting. Applied Economics Quarterly, 55(2), 107–120.CrossRef
Borg, I., & Groenen, P. (2005). Modern multidimensional scaling (2nd ed.). New York: Springer.MATH
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.CrossRef
de Leeuw, J., & Mair, P. (2009) Multidimensional scaling using majorization: SMACOF in R. Journal of Statistical Software, 31(3), 1–30.
Ginsberg, J., et al. (2009). Detecting influenza epidemics using search engine query data. Nature, 457(7232), 1012–1014.CrossRef
Haynes, K. E., & Fotheringham, A. S. (1988). Gravity and spatial interaction models. Beverly Hills: Sage.
Michel, J. B., et al. (2011). Quantitative analysis of culture using millions of digitized books. Science, 331(6014), 176–182.CrossRef
Stone, P. J., Dunphy, D. C., & Smith, M. S. (1966) The general inquirer: A computer approach to content analysis. Cambridge: MIT.
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).
Metadata
Title
Determining the Similarity Between US Cities Using a Gravity Model for Search Engine Query Data
Authors
Paul Hofmarcher
Bettina Grün
Kurt Hornik
Patrick Mair
Copyright Year
2013
Book
Algorithms from and for Nature and Life

Print ISBN: 978-3-319-00034-3

Electronic ISBN: 978-3-319-00035-0

Copyright Year: 2013

DOI
https://doi.org/10.1007/978-3-319-00035-0_24

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