Employment Location Models: An Overview

Chapter
Part of the Advances in Spatial Science book series (ADVSPATIAL)

Abstract

In this chapter the state-of-the-art of the modelling of the spatial distribution of employment in cities and regions is presented. Different economic approaches are described such as input–output analysis; Computable General Equilibrium models and utility maximising behaviour, translated into logit functions as probabilities. The models presented here are rich in content and build on decades of evolution.

Keywords

Location Choice Travel Demand Computable General Equilibrium Computable General Equilibrium Model Microsimulation Model 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. Ben-Akiva M, Lerman S (1985) Discrete choice discrete choice analysis: theory and application to travel demand. MIT Press, CambridgeGoogle Scholar
  2. Boarnet MC (1994) An empirical model of intrametropolitan population and employment growth. Pap in Reg Sci 73:135–152CrossRefGoogle Scholar
  3. Carlino GA, Mills ES (1987) The determinants of country growth. J Reg Sci 27:39–54CrossRefGoogle Scholar
  4. Cascetta E (2009) Transportation systems analysis: models and applications. Springer, New YorkCrossRefGoogle Scholar
  5. de Bok M (2009) Estimation and validation of a microscopic model for spatial economic effects of transport infrastructure. Transp Res Part A: Policy Pract 43:44–59CrossRefGoogle Scholar
  6. de la Barra T, de la Barra T (1989) Integrated land use and transport modelling: decision chains and hierarchies, vol 12, Cambridge urban and architectural studies. Cambridge University Press, CambridgeCrossRefGoogle Scholar
  7. Domencich TA, McFadden D (1975) Urban travel demand: a behavioural analysis, vol 93, Contributions to economic analysis. North-Holland, Amsterdam/OxfordGoogle Scholar
  8. Echenique MH (2004) Cambridge futures 2: what transport for Cambridge. The Martin Centre, University of Cambridge, CambridgeGoogle Scholar
  9. Echenique MH, Crowther D, Lindsay W (1969) A spatial model for urban stock and activity. Reg Stud 3:281–312CrossRefGoogle Scholar
  10. Hunt JD, Abraham JE (2003) Design and application of the PECAS land use modelling system. Paper presented at the 8th international conference on computers in urban planning and urban management, Sendai, Japan, 27–29 May 2003Google Scholar
  11. Khan AS, Abraham JE, Hunt JD (2002) Agent-based micro-simulation of business establishments. In: Congress of the European Regional Science Association ERSA, DortmundGoogle Scholar
  12. Landis J, Zhang M (1998) The second generation of the California urban futures model. Part 1: model logic and theory. Environ Plann B: Plann Des 25:657–666CrossRefGoogle Scholar
  13. Leontief W (1986) Input–output economics, 2nd edn. Oxford University Press, New YorkGoogle Scholar
  14. Lowry I (1964) Model of metropolis, Memorandum RM-4035-RC. Rand Corporation, Santa MonicaGoogle Scholar
  15. Maoh H, Kanaroglou P (2009) Intrametropolitan location of business establishments. Microanalytical model for Hamilton, Ontario, Canada. Transp Res Rec: J Transp Res Board 2133:33–45CrossRefGoogle Scholar
  16. Martínez FJ (1996) MUSSA: land use model for Santiago city. Transp Res Rec 1552:126–134CrossRefGoogle Scholar
  17. McFadden D (1974) Conditional logit analysis of qualitative choice behavior. In: Zarembka P (ed) Frontiers in econometrics. Academic, New York, pp 105–142Google Scholar
  18. Mills ES (1967) An aggregative model of resource allocation in a metropolitan area. Am Econ Rev 57:197–210Google Scholar
  19. Muth RF (1969) Cities and housing, the University of Chicago press, Chicago. RAND Europe and Bureau Louter: 2006, System documentatie tigris xl 1.0. prepared for the Transport Research Centre, LeidenGoogle Scholar
  20. Simmonds DC (2001) The objectives and design of a new land-use modelling package: DELTA. In: Clarke G, Madden M (eds) Regional science in business. Advances in spatial sciences. Springer, Berlin, pp 159–188Google Scholar
  21. Steinnes DN (1977) Do people follow jobs’ or’do jobs follow people’? A causality issue in urban economics. J Urban Econ 4:69–79CrossRefGoogle Scholar
  22. van Wissen LJG (2000) A micro-simulation model of firms: applications of concepts of the demography of the firm. Pap Reg Sci 79:111–134CrossRefGoogle Scholar
  23. Waddell P (2002) UrbanSim: modeling urban development for land use, transportation, and environmental planning. J Am Plann Assoc 68(3):297–314CrossRefGoogle Scholar
  24. Waddell P, Borning A, Noth M, Freier N, Becke M, Ulfarsson GF (2003) Microsimulation of urban development and location choice: design and implementation of UrbanSim. Netw Spat Econ 3:43–67CrossRefGoogle Scholar
  25. Wegener M (1982) Modeling urban decline: a multilevel economic-demographic model for the Dortmund region. Int Reg Sci Rev 7(2):217–241CrossRefGoogle Scholar
  26. Wilson (1970) Entropy in urban and regional modelling, Pion, London; re-issued by Routledge, London, 2011Google Scholar
  27. Zondag B, Schoemakers A, Pieters M (2005) Structuring impacts of transport on the spatial distribution of residents and jobs. In: 8th Nectar conference, Las Palmas, Gran Canaria, Spain, 2–4 June 2005Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.Centre for Advanced Spatial AnalysisUCLLondonUK
  2. 2.Department of Transportation EngineeringUniversity of Naples Federico IINaplesItaly

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