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Urban data mining using emergent SOM

Behnisch, Martin; Ultsch, Alfred

Abstract:
The term of Urban Data-Mining is defined to describe a methodological approach that discovers logical or mathematical and partly complex descriptions of urban patterns and regularities inside the data. The concept of data mining in connection with knowledge discovery techniques plays an important role for the empirical examination of high dimensional data in the field of urban research. The procedures on the basis of knowledge discovery systems are currently not exactly scrutinised for a meaningful integration into the regional and urban planning and development process. In this study ESOM is used to examine communities in Germany. The data deals with the question of dynamic processes (e.g. shrinking and growing of cities). In the future it might be possible to establish an instrument that defines objective criteria for the benchmark process about urban phenomena. The use of GIS supplements the process of knowledge conversion and communication.



Scopus
Zitationen: 6
Zugehörige Institution(en) am KIT Institut für Industrielle Bauproduktion (ifib)
Publikationstyp Buchaufsatz
Jahr 2008
Sprache Englisch
Identifikator ISBN: 978-3-540-78239-1
ISSN: 1431-8814
KITopen-ID: 1000094508
Erschienen in Data Analysis, Machine Learning and Applications. Ed.: C. Preisach
Verlag Springer, Berlin
Seiten 311-318
Serie Studies in classification, data analysis, and knowledge organization
Bemerkung zur Veröffentlichung Proceedings of the 31st Annual Conference of the Gesellschaft für Klassifikation e.V.,Albert-Ludwigs-Universität Freiburg, March 7–9, 2007
Schlagworte High Dimensional Data; Decision Boundary; Urban Pattern; Urban Research; Benchmark Process
Nachgewiesen in Scopus
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