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Matching office firms types and location characteristics : an exploratory analysis using Bayesian classifier networks
- Source :
- Expert Systems with Applications, 38(8), 9665. Elsevier, 89th Annual Meeting of the Transportation Research Board, 1-17, STARTPAGE=1;ENDPAGE=17;TITLE=89th Annual Meeting of the Transportation Research Board, Expert Systems with Applications, 38(8), 9665-9673. Elsevier
- Publication Year :
- 2011
-
Abstract
- While most models of location decisions of firms are based on the principle of utility maximizing behavior, the present study assumes that location decisions are just part of business cycle models, in which location is considered along other business decisions. The business model results in a series of location requirements and these are matched against location characteristics. Given this theoretical perspective, the modeling challenge then becomes how to find the match between firm types and the set of location characteristics using observations of the spatial distribution of firms. In this paper, several Bayesian classifier networks are compared in terms of their performance, using a large data set collected for the Netherlands. Results demonstrate that by taking relationships between predictor variables into account the Bayesian classifiers can improve prediction accuracy compared to commonly used decision tree. From a substantive point of view, our results indicate that different sets of urban characteristics and accessibility requirements are relevant to different office types as reflected in the spatial distribution of these office firms.
- Subjects :
- Matching (statistics)
Computer science
Decision trees
Bayesian probability
Decision tree
Business model
computer.software_genre
Machine learning
Office location
Set (abstract data type)
Naive Bayes classifier
models
Artificial Intelligence
Bayesian classifier networks
Business cycle
Sociale Geografie & Planologie
Point (typography)
business.industry
General Engineering
Computer Science Applications
Data set
LUTI
Land Use-Transport Interaction
Data mining
Artificial intelligence
business
computer
Subjects
Details
- Language :
- English
- ISSN :
- 09574174
- Volume :
- 38
- Issue :
- 8
- Database :
- OpenAIRE
- Journal :
- Expert Systems with Applications
- Accession number :
- edsair.doi.dedup.....a24550b8a66cc49e86e48f1ea598d562