Details
| Original language | English |
|---|---|
| Pages (from-to) | 400-416 |
| Number of pages | 17 |
| Journal | Environment and Planning B: Urban Analytics and City Science |
| Volume | 47 |
| Issue number | 3 |
| Publication status | Published - Mar 2020 |
| Externally published | Yes |
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.
Keywords
- data mining, knowledge elicitation, random forest, Residential choice, tacit knowledge
ASJC Scopus subject areas
- Engineering(all)
- Architecture
- Social Sciences(all)
- Geography, Planning and Development
- Social Sciences(all)
- Urban Studies
- Environmental Science(all)
- Nature and Landscape Conservation
- Environmental Science(all)
- Management, Monitoring, Policy and Law
Sustainable Development Goals
Cite this
- Standard
- Harvard
- Apa
- Vancouver
- BibTeX
- RIS
In: Environment and Planning B: Urban Analytics and City Science, Vol. 47, No. 3, 03.2020, p. 400-416.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - Combining tacit knowledge elicitation with the SilverKnETs tool and random forests – The example of residential housing choices in Leipzig
AU - Scheuer, Sebastian
AU - Haase, Dagmar
AU - Haase, Annegret
AU - Kabisch, Nadja
AU - Wolff, Manuel
AU - Schwarz, Nina
AU - Großmann, Katrin
N1 - Publisher Copyright: © The Author(s) 2018.
PY - 2020/3
Y1 - 2020/3
N2 - 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.
AB - 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.
KW - data mining
KW - knowledge elicitation
KW - random forest
KW - Residential choice
KW - tacit knowledge
UR - http://www.scopus.com/inward/record.url?scp=85047903750&partnerID=8YFLogxK
U2 - 10.1177/2399808318777500
DO - 10.1177/2399808318777500
M3 - Article
AN - SCOPUS:85047903750
VL - 47
SP - 400
EP - 416
JO - Environment and Planning B: Urban Analytics and City Science
JF - Environment and Planning B: Urban Analytics and City Science
SN - 2399-8083
IS - 3
ER -