G Gustavo Garcia Manzato

Location and accessibility mediated influences on office firm closure rates : a proportional hazard model

This paper addresses an exploratory study about the mediated influence of a set of location and accessibility attributes along with firm types and the effect on office firm closures. We use a proportional hazard modeling approach, examining the probability that an office firm will go out of business in any given year, conditional on the fact that it did not go out of business in the years prior. Variables include urbanization levels and regional effects; accessibility to airports, train stations, shopping centers and the roadway network; availability of parking facilities and schools; demographic and economic aspects; effects of agglomeration economies; rent price; and the type of economic activity of the office firm. Most of them presented significant information to assist in the explanation of the firm closure. Also, interaction effects between covariates and firm types brought a better understanding about the firm closure patterns in relation to urban characteristics. Assessing firm closures is an element of a firm demographic approach and overall dynamics. In turn, this is part of a multi-agent system to simulate the co-evolution of firm dynamics and changes in activity-travel patterns.

A support system that delineates location-choice sets for firms seeking office space

Factors influencing the location decisions of offices include traffic, accessibility, employment conditions, economic prospects and land-use policies. Hence tools for supporting real-estate managers and urban planners in such multidimensional decisions may be useful. Accordingly, the objective of this study is to develop a GIS-based tool to support firms who seek office accommodation within a given regional or national study area. The tool relies on a matching approach, in which a firms characteristics (demand) on the one hand, and environmental conditions and available office spaces (supply) on the other, are analyzed separately in a first step, after which a match is sought. That is, a suitability score is obtained for every firm and for every available office space by applying some value judgments (satisfaction, utility etc.). The latter are powered by a focus on location aspects and expert knowledge about the location decisions of firms/organizations with respect to office accommodation as acquired from a group of real-estate advisers; it is stored in decision tables, and they constitute the core of the model. Apart from the delineation of choice sets for any firm seeking a location, the tool supports two additional types of queries. Firstly, it supports the more generic problem of optimally allocating firms to a set of vacant locations. Secondly, the tool allows users to find firms which meet the characteristics of any given location. Moreover, as a GIS-based tool, its results can be visualized using GIS features which, in turn, facilitate several types of analyses.

Exploration of Location Influences on Firm Survival Rates with Parametric Duration Models

This study explored the influences of location on business firm survival rates with the use of parametric duration models applied to a data set. Of the variables included, those found to be the most significant were accessibility to infrastructure supply, regional effects, demographic and economic aspects, and rent price. Analyses also were depicted by firm type (economic activity sector) along with interactions between them and some location attributes. The study led to a better understanding of firm survival patterns with respect to urban characteristics. The results presented in this paper are part of a demographic approach, which in turn is part of a multiagent system to simulate the coevolution of firm dynamics and changes in activity-travel patterns.

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.