Modelling firm (re-)location choice in UrbanSim

Over the last decade, low economic growth rates resulted in intensified competition between nations, regions, and towns in trying to attract new firms and inhabitants. In particular, the establishment of new firms has become one of the most vital objectives of governments and public authorities all over Europe. To raise the attractiveness of a region, different instruments have been used: tax reductions, incentives for new establishments, as business destination promotion activities, supply of outstanding infrastructure and public services. On the one hand, this paper investigates effects of different possible options for cantonal and municipal authorities’ intent to attract firms: improvements in transport infrastructure, designation of new building zones, and last but not least tax reductions. These actions have been tested by simulating the decisions of existing firms. The parameters for these simulations have been estimated with a discrete choice model using data of the cantons St.Gallen and both Appenzell as well as Zurich. On the other hand, the paper aims to provide an approach to implement these models in UrbanSim. UrbanSim is a software-based simulation system for supporting planning and analysis of urban development, incorporating the interactions between land use, transportation, the economy, and the environment. At the moment, UrbanSim is adapted to an European context (see the according research project SustainCity, www.sustaincity.eu).

The Zurich case study of UrbanSim

UrbanSim is an open-source software being developed by Waddell and colleagues(Waddell and Ulfarsson, 2004), simulating land use-development in cities based on the choices of households, businesses, land owners and developers, interacting in urban Real Estate markets and with the option to be connected to a transportation simulation. SustainCity is an EU-funded project with twelve European research-institutions1, coordinated by the IVT of the Swiss Federal Institute of Technology Zurich (ETHZ). Within the project of SustainCity2, UrbanSim is being adapted to European conditions by creation of a European version (UrbanSimE) with new calibration of choice-models and additional models for households, demographics and firmographics. Focus will be on the data-structure in Europe as well as the different behaviour of companies, residents and developers. For this UrbanSim will be used in three case studies: Brussels, Paris and Zurich. Although previous studies have been implemented in all of those region, the previous study in Zurich can be considered as a new set up as it uses another version of UrbanSim. This paper will report on the implementation of this parcel-based version of UrbanSim within the Zurich case study of SustainCity. It will refer to the data acquired and necessary as basis for the simulation, discuss the approach of data preparation through PostGIS and report on the new structure of the data-models defined within UrbanSim. Finally the first results of the UrbanSim runs of the Zurich case study will be presented and compared to the runs of previous versions

Econometric guidance for developing UrbanSim models. First lessons from the SustainCity project.

In the context of the SustainCity project (www.sustaincity.eu), three European cities (Brussels, Paris and Zurich) will be modelled using the land use microsimulation platform UrbanSim. This platform relies on various models interacting with each other, to predict long-term urban development. The aim of this paper is to provide some econometric insight into this process. A common set of notation and assumptions are first defined, and the more common model structures (linear regression, multinomial logit, nested logit, mixed MNL and latent variable models) are described in a consistent way. Special treatments and approaches that are required due to the specific nature of the data in this type of applications (i.e. involving very large number of alternatives, and often exhibiting endogeneity, correlation, and (pseudo-)panel data properties) will also be discussed. For example, importance sampling, spatial econometrics, Geographically Weighted Regression (GWR) and endogeneity issues will be covered. Applications and specific options of the following models: (i) household location choice model, (ii) jobs location/firmography, (iii) real estate price model, and (iv) land development model, will be demonstrated using examples from the on-going case studies in Brussels, Paris and Zurich. Finally, lessons learnt in relation to the econometric models from these on-going case studies will be summarized.

A marketing elszámoltathatóságának problémája

A tanulmány célja, hogy szakirodalmi összefoglalást adjon a marketing elszámoltathatóságának kérdéséről, problematikájáról. Kiindulópontját az a tény adja, miszerint nagyon változó a marketing vállalaton belüli megítélése, stratégiai szerepe sok esetben jelentősen leértékelődik a versenyképességre gyakorolt hatása ellenére. A marketing alulértékeltségének egyik oka, ha a vállalaton belül nem látják az általa „hozott számokat”, csupán azokat, amelyeket „elvisz”. Ugyanakkor azt is látni kell, hogy ma egy erősen fogyasztóorientált piaci korszakot élünk, amikor is a fogyasztói (és társadalmi) igények határozottabban vannak jelen a gazdálkodók gondolkodásában, mint valaha. Világosan adódik tehát az igény a marketing teljesítményének mérhetővé tételére. A szerző arra keresi a választ, milyen okai és következményei vannak az elszámoltathatóság hiányának, illetve milyen módszertant alkalmazhatnak a döntéshozók annak érdekében, hogy számszerűsíthetővé váljon a marketingteljesítmény

The Zurich case study of UrbanSim

Abstract-- UrbanSim is an open-source software being developed by Waddell and colleagues(Waddell and Ulfarsson, 2004), simulating land use-development in cities based on the choices of households, businesses, land owners and developers, interacting in urban Real Estate markets and with the option to be connected to a transportation simulation. SustainCity is an EU-funded project with twelve European research-institutions1, coordinated by the IVT of the Swiss Federal Institute of Technology Zurich (ETHZ). Within the project of SustainCity2, UrbanSim is being adapted to European conditions by creation of a European version (UrbanSimE) with new calibration of choice-models and additional models for households, demographics and firmographics. Focus will be on the data-structure in Europe as well as the different behaviour of companies, residents and developers. For this UrbanSim will be used in three case studies: Brussels, Paris and Zurich. Although previous studies have been implemented in all of those region, the previous study in Zurich can be considered as a new set up as it uses another version of UrbanSim. This paper will report on the implementation of this parcel-based version of UrbanSim within the Zurich case study of SustainCity. It will refer to the data acquired and necessary as basis for the simulation, discuss the approach of data preparation through PostGIS and report on the new structure of the data-models defined within UrbanSim. Finally the first results of the UrbanSim runs of the Zurich case study will be presented and compared to the runs of previous versions. Keywords: UrbanSim; Urban Simulation; SustainCity; Zurich case study 02.03.2011

An agent-based model as a tool of planning at a sub-regional scale

This paper describes an agent-based model developed to simulate the impact that different planning policies may have in enhancing the attractiveness of the industrial estates located in a network of four municipalities located in the North of Portugal. The policies were simulated using three scenarios that can be distinguished by the municipal level of coordination they are implemented and by the type of action performed. In the model, enterprises are agents looking for a suitable location and the estates attractiveness is based on their level of facilities, amenities, accessibility and in the cost of soil. The coordinated qualification of the industrial estates is the most effective policy to strengthen their attractiveness. It was in this scenario that more industrial estates become attractive and more enterprises relocated. Results also indicate that the promotion of diffused and unqualified industrial estates is an inefficient policy to attract enterprises.Portuguese Foundation for Science and Technology (SFRH/BD/48567/2008 grant

Influence of the Event Rate on Discrimination Abilities of Bankruptcy Prediction Models

In bankruptcy prediction, the proportion of events is very low, which is often oversampled to eliminate this bias. In this paper, we study the influence of the event rate on discrimination abilities of bankruptcy prediction models. First the statistical association and significance of public records and firmographics indicators with the bankruptcy were explored. Then the event rate was oversampled from 0.12% to 10%, 20%, 30%, 40%, and 50%, respectively. Seven models were developed, including Logistic Regression, Decision Tree, Random Forest, Gradient Boosting, Support Vector Machine, Bayesian Network, and Neural Network. Under different event rates, models were comprehensively evaluated and compared based on Kolmogorov-Smirnov Statistic, accuracy, F1 score, Type I error, Type II error, and ROC curve on the hold-out dataset with their best probability cut-offs. Results show that Bayesian Network is the most insensitive to the event rate, while Support Vector Machine is the most sensitive

COMPARISON OF BANKRUPTCY PREDICTION MODELS WITH PUBLIC RECORDS AND FIRMOGRAPHICS

Many business operations and strategies rely on bankruptcy prediction. In this paper, we aim to study the impacts of public records and firmographics and predict the bankruptcy in a 12-month-ahead period with using different classification models and adding values to traditionally used financial ratios. Univariate analysis shows the statistical association and significance of public records and firmographics indicators with the bankruptcy. Further, seven statistical models and machine learning methods were developed, including Logistic Regression, Decision Tree, Random Forest, Gradient Boosting, Support Vector Machine, Bayesian Network, and Neural Network. The performance of models were evaluated and compared based on classification accuracy, Type I error, Type II error, and ROC curves on the hold-out dataset. Moreover, an experiment was set up to show the importance of oversampling for rare event prediction. The result also shows that Bayesian Network is comparatively more robust than other models without oversampling