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2013 | OriginalPaper | Buchkapitel

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

verfasst von : Paul Hofmarcher, Bettina Grün, Kurt Hornik, Patrick Mair

Erschienen in: Algorithms from and for Nature and Life

Verlag: 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).
Fußnoten
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.
 
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Metadaten
Titel
Determining the Similarity Between US Cities Using a Gravity Model for Search Engine Query Data
verfasst von
Paul Hofmarcher
Bettina Grün
Kurt Hornik
Patrick Mair
Copyright-Jahr
2013
DOI
https://doi.org/10.1007/978-3-319-00035-0_24

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