Combining tacit knowledge elicitation with the SilverKnETs tool and random forests – The example of residential housing choices in Leipzig

verfasst von

Sebastian Scheuer, Dagmar Haase, Annegret Haase, Nadja Kabisch, Manuel Wolff, Nina Schwarz, Katrin Großmann

Abstract

Residential choice behaviour is a complex process underpinned by both housing market restrictions and individual preferences, which are partly conscious and partly tacit knowledge. Due to several limitations, common survey methods cannot sufficiently tap into such tacit knowledge. Thus, this paper introduces an advanced knowledge elicitation process called SilverKnETs and combines it with data mining using random forests to elicit and operationalize this type of knowledge. For the application case of the city of Leipzig, Germany, our findings indicate that rent, location and type of housing form the three predictors strongly influencing the decision making in residential choices. Other explanatory variables appear to have a much lower influence. Random forests have proven to be a promising tool for the prediction of residential choices, although the design and scope of the study govern the explanatory power of these models.

Externe Organisation(en)

Humboldt-Universität zu Berlin (HU Berlin)
Helmholtz Zentrum München - Deutsches Forschungszentrum für Gesundheit und Umwelt
Helmholtz-Zentrum für Umweltforschung (UFZ)
Fachhochschule Erfurt (FHE)

Typ

Artikel

Journal

Environment and Planning B: Urban Analytics and City Science

Band

47

Seiten

400-416

Anzahl der Seiten

17

ISSN

2399-8083

Publikationsdatum

03.2020

Publikationsstatus

Veröffentlicht

Peer-reviewed

Ja

ASJC Scopus Sachgebiete

Architektur, Geografie, Planung und Entwicklung, Urban studies, Natur- und Landschaftsschutz, Management, Monitoring, Politik und Recht

Ziele für nachhaltige Entwicklung

SDG 11 – Nachhaltige Städte und Gemeinschaften

Elektronische Version(en)

https://doi.org/10.1177/2399808318777500 (Zugang: Geschlossen)
https://research.utwente.nl/en/publications/cc3190e7-ce71-44ca-807e-b94f3bbb22b9 (Zugang: Offen)