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Clustering of German municipalities based on mobility characteristics

: Zanda, A.; Körner, C.; Giannotti, F.; Schulz, D.; May, M.

Postprint urn:nbn:de:0011-n-875523 (895 KByte PDF)
MD5 Fingerprint: 051ae6b5cf1de45bb62489962e5eb9b0
Copyright Association for Computing Machinery (ACM)
Created on: 27.1.2009

Aref, W.G. ; Association for Computing Machinery -ACM-, Special Interest Group on Spatial Information:
16th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM GIS 2008. Proceedings : November 5-7, 2008, Irvine, California, USA
New York: ACM, 2008
ISBN: 978-1-60558-323-5
International Conference on Advances in Geographic Information Systems (GIS) <16, 2008, Irvine/Calif.>
Conference Paper, Electronic Publication
Fraunhofer IAIS ()
spatial data mining; clustering; mobility

This paper presents a clustering approach which groups German municipalities according to mobility characteristics. As the number of measurements for nationwide mobility studies is usually restricted, this clustering provides a means to infer mobility information for locations without measurements based on values of their respective cluster representatives. Our approach considers local and global information, i.e. characteristics of municipalities as well as relationships between municipalities. We realize previous findings in urban geography by using techniques from graph theory and computer vision. Our clustering consists of a two-step model, which first extracts and condenses single mobility characteristics and subsequently combines the various features. We apply our model to all German municipalities between 10,000 and 50,000 inhabitants. The clustering has been successfully applied in practice for the inference of traffic frequencies.