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. These relationships are valuable for the estimation. The quality of the estimation is analyzed by training and test data sets. Information optimization leads to the conclusion that 20% of the communes hold 80% of all buildings. For an improvement of the estimation it is essential to refine the amount and quality of data in the larger communes.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Behnisch, M. (2008). Urban data mining. Doctoral thesis, Universitätsverlag, Karlsruhe.
Hassler, U., & Kohler, N. (2004). Das Verschwinden der Bauten des Industriezeitalters (pp. 116–123). Tübingen: Wasmuth.
Hassler, U., Kohler, N., & Paschen, H. (Eds.) (1999). Stoffströme und Kosten in den. Bereichen Bauen und Wohnen. Berlin: Springer.
Hofman, F. (Aachener Institut für Bauschadensforschung und angewandte Bauphysik) (Ed.) (2001). Urban heritage – Building maintenance. Final report. COST Action C5, European Commission (p. 13).
Kohler, N., & Hassler, U. (2002). The building stock as a research object. Building Research and Information, 30, 226–236.
Spillner, A., Russig, V., Dullinger, P., von Roncador, T., & Schunk, E. (Eds.) (1999). EUROPARC – Der Gebäudebestand in Europa: Deutschland, Frankreich, Grossbritannien, Italien und Spanien. Munich: ifo Institut.
Streich, B. (2005). Stadtplanung in der Wissensgesellschaft. Wiesbaden: VS Verlag für Sozialwissenschaften.
Ultsch, A. (2001). Eine Begründung der Pareto 80/20 Regel und Grenzwerte für die ABC Analyse (Technical Report No. 30). Department of Mathematics and Computer Science, University of Marburg.
Ultsch, A. (2003). Pareto density estimation: A density estimation for knowledge discovery. In D. Baier, & K. D. Wernecke (Eds.), Innovations in classification, data science, and information systems (pp. 91–100). Berlin: Springer.
Ultsch, A. (2006). Analysis and practical results of U*C clustering, In Proceedings 30th Annual Conference of the German Classification Society (GfKl 2006), Berlin: Germany.
United Nations (2008). World population prospects. New York: Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat. Reteieved May 20, 2008 from http://esa.un.org/unup/.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Behnisch, M., Ultsch, A. (2009). Estimating the Number of Buildings in Germany. In: Fink, A., Lausen, B., Seidel, W., Ultsch, A. (eds) Advances in Data Analysis, Data Handling and Business Intelligence. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-01044-6_54
Download citation
DOI: https://doi.org/10.1007/978-3-642-01044-6_54
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-01043-9
Online ISBN: 978-3-642-01044-6
eBook Packages: Mathematics and StatisticsMathematics and Statistics (R0)