Simulation of Firms as a Planning Support System to Limit Urban Sprawl of Jobs

Besides the urban sprawl of housing, many regions suffer from an employment shift from city centres to the suburbs. A simulation model has been developed that analyses alternative strategies for limiting the suburbanisation of employment. It applies microsimulation by representing single businesses individually. The establishment, growth, decline, closure, and relocation of firms are simulated. The model was implemented in the urban region of Dortmund in Germany. Zoning, transport, and tax-related policies were tested and compared to estimate their likely success in fostering a more sustainable land-use development. The analyses reveal that a regionally coordinated approach would be the most successful policy option for reducing the urban sprawl of employment. Benefits and drawbacks of using microsimulation for business modeling are discussed.

[1]  W. G. Hansen.  How Accessibility Shapes Land Use , 1959 .

[2]  M. Storper,et al.  The theory of labour and the theory of location , 1983 .

[3]  L. Wissen.  A spatial interpretation of the density dependence model in industrial demography , 2004 .

[4]  Michael Wegener,et al.  The Dortmund Housing Market Model: A Monte Carlo Simulation of a Regional Housing Market , 1985 .

[5]  G. Peterson,et al.  The response of city economies to national business cycles , 1988 .

[6]  M. D. McKay,et al.  Creating synthetic baseline populations , 1996 .

[7]  D. Manson,et al.  The Effect of Business Cycles on Metropolitan Suburbanization , 1984 .

[8]  V. Wissen.  A micro-simulation model of firms: applications of the concepts of the demography of firms , 2000 .

[9]  John E Abraham,et al.  Agent-based micro-simulation of business establishments , 2002 .

[10]  Alan Wilson,et al.  A statistical theory of spatial distribution models , 1967 .

[11]  M. D. Bok,et al.  Infrastructure and firm dynamics: A micro simulation approach , 2006 .

[12]  Everett M. Rogers,et al.  Innovation Diffusion As a Spatial Process , 1967 .

[13]  E. Ravenstein.  The Laws of Migration , 1885, Encyclopedia of Gerontology and Population Aging.

[14]  Paul Waddell,et al.  Accessibility and Agglomeration: Discrete-Choice Models of Employment Location by Industry Sector , 2003 .

[15]  Martin Clarke,et al.  MICROSIMULATION METHODS IN SPATIAL ANALYSIS AND PLANNING , 1987 .

[16]  J. Landis,et al.  The Second Generation of the California Urban Futures Model. Part 1: Model Logic and Theory , 1998 .

[17]  Franz-Josef Bade.  Locational Behaviour and the Mobility of Firms in West Germany , 1983 .

[18]  M. Porter.  Clusters and the new economics of competition. , 1998, Harvard business review.

[19]  M. Hesse.  Handbook of Transport Geography and Spatial Systems , 2005 .

[20]  Alan Borning,et al.  Microsimulation of Urban Development and Location Choices: Design and Implementation of UrbanSim , 2003 .

[21]  M. Wegener.  Overview of Land Use Transport Models , 2004 .

[22]  Ming Zhang,et al.  The Second Generation of the California Urban Futures Model. Part 2: Specification and Calibration Results of the Land-Use Change Submodel , 1998 .

[23]  A. G. Wilson,et al.  A new representation of the urban system for modelling and for the study of micro-level interdependence , 1976 .

[24]  George I. Kassinis.  Location, Networks and Firm Environmental Management Practices , 2001 .