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Estimating the number of buildings in Germany

Behnisch, Martin; Ultsch, Alfred

Abstract:
The debate on sustainable development has lead to the view of buildings as flows (mass, energy, money and information) or capitals. In this context buildings are considered as the largest physical, economical, social and cultural capital of a society. In Germany many institutions record different kind of data about buildings. Unfortunately there are just a few basic statistics about the amount of buildings. Collection of data is very complicated, often expensive and the handling of missing data is one of the biggest handicaps. With the exception of data about residential buildings and particularly monuments, it is an unsolved problem to determine the total number of buildings. Thus the main issue of this article is the description of an appropriate estimation procedure. This procedure relies on 12,430 communes and refers to data from the Cadaster of Real Estates and the Federal Office for Building and Regional Planning (BBR). The estimation is based on statistical data from well-known and easily accessible institutions. The number of buildings is estimated for communes with missing data. Using methods from the, so called, Urban Data Mining approach, unsuspected relationships are found in the urban data. ... mehr



Originalveröffentlichung
DOI: 10.1007/978-3-642-01044-6_54
Scopus
Zitationen: 4
Zugehörige Institution(en) am KIT Institut für Industrielle Bauproduktion (ifib)
Publikationstyp Buchaufsatz
Jahr 2008
Sprache Englisch
Identifikator ISBN: 978-3-642-01043-9
ISSN: 1431-8814
KITopen-ID: 1000094509
Erschienen in Advances in data analysis, data handling and business intelligence. Ed.: A. Fink
Verlag Springer, Berlin
Seiten 585-593
Serie Studies in Classification, Data Analysis, and Knowledge Organization
Bemerkung zur Veröffentlichung Proceedings of the 32nd Annual Conference of the Gesellschaft für Klassifikation e.V., Joint Conference with the British Classification Society (BCS) and the Dutch/Flemish Classification Society (VOC), Helmut-Schmidt-University, Hamburg, July 16–18, 2008
Schlagworte Building stock; Data mining; Knowledge discovery; Spatial planning
Nachgewiesen in Scopus
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