Advertisement

Estimating the Number of Buildings in Germany

  • M. BehnischEmail author
  • A. Ultsch
Conference paper
Part of the Studies in Classification, Data Analysis, and Knowledge Organization book series (STUDIES CLASS)

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.

Keywords

Building stock Data mining Knowledge discovery Spatial planning 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Behnisch, M. (2008). Urban data mining. Doctoral thesis, Universitätsverlag, Karlsruhe.Google Scholar
  2. Hassler, U., & Kohler, N. (2004). Das Verschwinden der Bauten des Industriezeitalters (pp. 116–123). Tübingen: Wasmuth.Google Scholar
  3. Hassler, U., Kohler, N., & Paschen, H. (Eds.) (1999). Stoffströme und Kosten in den. Bereichen Bauen und Wohnen. Berlin: Springer.Google Scholar
  4. Hofman, F. (Aachener Institut für Bauschadensforschung und angewandte Bauphysik) (Ed.) (2001). Urban heritage – Building maintenance. Final report. COST Action C5, European Commission (p. 13).Google Scholar
  5. Kohler, N., & Hassler, U. (2002). The building stock as a research object. Building Research and Information, 30, 226–236.CrossRefGoogle Scholar
  6. 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.Google Scholar
  7. Streich, B. (2005). Stadtplanung in der Wissensgesellschaft. Wiesbaden: VS Verlag für Sozialwissenschaften.Google Scholar
  8. 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.Google Scholar
  9. 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.Google Scholar
  10. 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.Google Scholar
  11. 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/.

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  1. 1.Institute of Historic Building Research and ConservationETH HoenggerbergZurichSwitzerland

Personalised recommendations