skip to main content
10.1145/1463434.1463514acmconferencesArticle/Chapter ViewAbstractPublication PagesgisConference Proceedingsconference-collections
poster

Clustering of German municipalities based on mobility characteristics: an overview of results

Published: 05 November 2008 Publication History

Abstract

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.

References

[1]
T. Calinski and J. Harabasz. A dendrite method for cluster analysis. Communications in Statistics, 3:1--27, 1974.
[2]
W. Christaller. Die zentralen Orte in Süddeutschland. Fischer, 1933. translated by C. Baskin. Central Places in Southern Germany. Prentice Hall, 1966.
[3]
M. Ester, J. Sander, H.-P. Kriegel, and X. Xu. A density-based algorithm for discovering clusters in large spatial databases with noise. In Proceedings of the 2nd International Conference on Knowledge Discovery and Data Mining (KDD'96), pages 226--231. AAAI Press, 1996.
[4]
J. Han, M. Kamber, and A. K. H. Tung. Spatial clustering methods in data mining. In H. J. Miller and J. Han, editors, Geographic Data Mining and Knowledge Discovery, chapter 8. Taylor & Francis, 2001.
[5]
R. M. Haralick and L. G. Shapiro. Computer and Robot Vision, volume 2, pages 379--492. Addison-Wesley, 1993.
[6]
A. G. Hevesi. Outdated Municipal Structures. Office of the New York State Comptroller, 2006.
[7]
B. Hofmeister. The study of urban form in Germany. Urban Morphology, 1(8):3--12, 2004.
[8]
B. Korte and J. Vygen. Combinatorial Optimization. Springer, 2 edition, 2000.
[9]
L. Makra and Z. Zümeghy. Objective analysis and ranking of Hungarian cities, with different classification techniques. In Acta Climatologica et Chorologica, volume 40--41, pages 79--100. University of Szeged, 2007.
[10]
D. Malerba, A. Appice, A. Varlaro, and A. Lanza. Spatial clustering of structured objects. In S. Kramer and B. Pfahringer, editors, Proc. of the 15th International Conference on Inductive Logic Programming (ILP), pages 227--245. Springer, 2005.
[11]
P. Taylor, G. Catalano, and D. Walker. Exploratory analysis of the world city network. Urban Studies, 39(13):2377--2394, 2002.
[12]
I. H. Witten and E. Frank. Data Mining: Practical Machine Learning Tools and Techniques, pages 418--419. Morgan Kaufmann, second edition, 2005.

Cited By

View all
  • (2021)Manifestations of Subnational Fiscal Federalism in Lithuanian Local Self-governmentsEurasian Business and Economics Perspectives10.1007/978-3-030-71869-5_6(87-106)Online publication date: 1-Jun-2021

Recommendations

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences
GIS '08: Proceedings of the 16th ACM SIGSPATIAL international conference on Advances in geographic information systems
November 2008
559 pages
ISBN:9781605583235
DOI:10.1145/1463434
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Sponsors

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 05 November 2008

Permissions

Request permissions for this article.

Check for updates

Author Tags

  1. clustering
  2. mobility
  3. spatial data mining

Qualifiers

  • Poster

Conference

GIS '08
Sponsor:

Acceptance Rates

Overall Acceptance Rate 257 of 1,238 submissions, 21%

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • Downloads (Last 12 months)2
  • Downloads (Last 6 weeks)0
Reflects downloads up to 08 Mar 2025

Other Metrics

Citations

Cited By

View all
  • (2021)Manifestations of Subnational Fiscal Federalism in Lithuanian Local Self-governmentsEurasian Business and Economics Perspectives10.1007/978-3-030-71869-5_6(87-106)Online publication date: 1-Jun-2021

View Options

Login options

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Figures

Tables

Media

Share

Share

Share this Publication link

Share on social media