Heavy-tailed distributions for building stock data. (September 2019)

• Record Type: Journal Article
• Authors: Bradley, Patrick Erik; Behnisch, Martin
• Other Names: Behnisch Martin guest-editor.; Hecht Robert guest-editor.; Herold Hendrik guest-editor.; Jiang Bin guest-editor.
• Is Part Of: Environment & planning. Volume 46:Number 7(2019)
• Page Start: 1281
• Page End: 1296
• Publication Date: 2019-09
• Subjects: Heavy-tailed distribution -- Zipf's law -- log-normal distribution -- event histories -- building stock; City planning -- Periodicals; Urban ecology (Sociology) -- Periodicals; 307.11605
• DOI: 10.1177/2399808318794499
• Languages: English
• ISSNs: 2399-8083

Abstract

The question of inferring the owner of a set of building stocks (e.g. from which country the buildings are taken) from building-related quantities like number of buildings or types of building event histories necessitates the knowledge of their distributions in order to compare them. If the distribution function is a power law, then a version of the 80/20 rule can be applied to describe the variable. This distribution is an example of a heavy-tailed distribution; another example is the log-normal distribution. Heavy-tailed distributions have the property that studying the effects of the few large values already yields most of the overall effect of the whole quantity. For example, if reducing the CO2 emissions of the buildings of a country is the issue, then in case of a heavy-tailed distribution, only the effects of the relatively few large cities need to be considered. It is shown that the number of buildings in German municipalities or counties or the number of building-related event histories of a certain vanished building stock follow a heavy-tailed distribution and give evidence for the type of underlying distribution. The methodology used is a recent statistical framework for discerning power law and other heavy-tailed distributions in empirical data.