Matching office firms types and location characteristics: An exploratory analysis using Bayesian classifier networks

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.

Research highlights
► We model the match between firm types and the set of location characteristics.
► We use Bayesian classifier networks compared to commonly used decision trees.
► Bayesian classifiers perform better by considering relationships among variables.
► Firm types respond differently to diverse sets of urban and accessibility characteristics.
► This is reflected by the spatial distribution of office firms.