Choice Set Formation in Microscopic Firm Location Models

  • M. de Bok
  • F. Pagliara
Part of the Advances in Spatial Science book series (ADVSPATIAL)


This chapter presents a spatial firm demographic model (SFM) that simulates changes in states of individual firms and their location choice behaviour. Firm location choice in such disaggregate models is characterised by large numbers of alternatives and complex spatial interdependencies among them. This chapter deals with a particular issue of firm location choice: the choice set composition in a disaggregate spatial choice context. A choice model is presented with probabilistic choice sets assuming that choice alternatives that are dominated by others are not taken into consideration in the location decision. The estimated models have significant parameters for dominance, and they are implemented in the SFM model, to test to what extent the simulation results are improved.


Location Choice Firm Location Business Trip Dominance Attribute Range Band 
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.


  1. Adler TJ, Ben-Akiva M (1976) Joint-choice model for frequency, destination and travel mode for shopping trips. Trans Res Rec 569:136–150Google Scholar
  2. Anderstig C, Mattsson LG (1991) An integrated model of residential and employment location in a metropolitan region. Pap Reg Sci 70:167–184CrossRefGoogle Scholar
  3. Bierlaire M, Bolduc D, McFadden D (2006) The estimation of generalized extreme value models from choice-based examples. Report TRANSP-OR 060810, Transport and Mobility. Laboratory, School of Architecture, Civil and Environmental Engineering, Ecole Polytechnique Federale de LausanneGoogle Scholar
  4. Birch D (1979) The job generating process. Cambridge University Press, CambridgeGoogle Scholar
  5. Carroll GR, Hannan MT (2000) The demography of corporations and industries. Princeton University Press, PrincetonGoogle Scholar
  6. Cascetta E, Papola A (2001) Random utility models with implicit availability/perception of choice alternatives for the simulation of travel demand. Transport Res C 9:249–263CrossRefGoogle Scholar
  7. Cascetta E, Papola A (2009) Dominance among alternatives in random utility models. Trans Res A Policy Pract 43:170–179CrossRefGoogle Scholar
  8. Cascetta E, Pagliara F, Axhausen KW (2007) The use of dominance variables in spatial choice set generation. In: Proceedings of the 11th World Conference on Transport Research, Berkeley, 24–28 June 2007Google Scholar
  9. De Bok M (2009) Estimation and validation of a microscopic model for spatial economic effects of transport infrastructure. Trans Res A Policy Pract 43:44–59CrossRefGoogle Scholar
  10. De Bok M, Pagliara F (2011) How to obtain representative spatial choice sets? Dominance and centrality analysed for firm location choices. Paper presented at the CUPUM conference, Lake Alberta, 5–8 July 2011Google Scholar
  11. De Bok M, van Oort F (2011) Agglomeration economies, accessibility and the spatial choice behaviour of relocating firms. J Trans Landuse 4(1):5–24Google Scholar
  12. Elgar I (2011) Modelling office mobility and location decisions in a microsimulation environment. PhD Dissertation, University of Toronto (Canada), Canada. Retrieved 21 Feb2011, from Dissertations & Theses @ University of Toronto (Publication No. AAT NR39401)Google Scholar
  13. Fortheringham AS (1983) A new set of spatial-interaction models: the theory of competing destinations. Environ Plann A 15:15–36CrossRefGoogle Scholar
  14. Gautschi DA (1981) Specification of patronage models for retail center choice. J Mark Res 18:162–174CrossRefGoogle Scholar
  15. Golledge RG, Timmermans H (1990) Application of behavioural research on spatial problems I: cognition. Prog Hum Geogr 14:57–99CrossRefGoogle Scholar
  16. Hägerstrand T (1970) What about people in regional science? Pap Reg Sci Assoc 24:7–21CrossRefGoogle Scholar
  17. Hague Consulting Group (2000) Het Landelijk Model Systeem versie 7.0 (in Dutch). Hague Consulting Group, The HagueGoogle Scholar
  18. Hansen ER (1987) Industrial location choice in São Paulo, Brazil: a nested logit model. Reg Sci Urban Econ 17:89–108CrossRefGoogle Scholar
  19. Kim H, Waddell P, Shankar VN, Ulfarsson GF (2008) Modelling micro-spatial employment location patterns: a comparison of count and choice approaches. Geogr Anal 40:123–151CrossRefGoogle Scholar
  20. Landau U, Prashker JN, Alpern B (1982) Evaluation of activity constrained choice sets to shopping destination choice modelling. Trans Res A Policy Pract 16(3):199–207Google Scholar
  21. Law AM, Kelton WD (1991) Simulation modelling and analysis. McGraw-Hill, New YorkGoogle Scholar
  22. Manski C (1977) The structure of random utility models. Theory Decis 8:229–254CrossRefGoogle Scholar
  23. Maoh M, Kanaroglou P (2007) Business establishment mobility behaviour in urban areas: a microanalytical model for the City of Hamilton in Ontario, Canada. J Geogr Syst 9(3):229–252CrossRefGoogle Scholar
  24. Maoh H and Kanaroglou P (2012) Modelling firm failure: towards building a firmo-graphic microsimulation model. In: Pagliara F, de Bok M, Simmonds D and Wilson A (eds) Employment location in cities and regions – Models and applications. Advances in spatial sciences. Springer, Berlin, pp 243–262Google Scholar
  25. Miller EJ, O’Kelly ME (1983) Estimating shopping destination models from travel diary data. Prof Geogr 35:440–449CrossRefGoogle Scholar
  26. Moeckel R (2007) Business location decisions and urban sprawl: a microsimulation of business relocation and firmography, vol 126, Blaue Reihe. Institut fur Raumplanung, DortmundGoogle Scholar
  27. Moekel R (2012) Firm location choice vs. job location choice in microscopic simulation models. In: Pagliara F, de Bok M, Simmonds D and Wilson A (eds) Employment location in cities and regions – Models and applications. Advances in spatial sciences. Springer, Berlin, pp 223–242Google Scholar
  28. Paci R, Usai S (1999) Externalities, knowledge spillovers and the spatial distribution of innovation. GeoJournal 49:381–390CrossRefGoogle Scholar
  29. Pagliara F, Timmermans H (2009) Choice set generation in spatial contexts: a review. Trans Lett Int J Trans Res 1:181–196CrossRefGoogle Scholar
  30. Pelligrini PA, Fortheringham AS (2002) Modeling spatial choice: a review and synthesis in a migration context. Prog Hum Geogr 26:487–510CrossRefGoogle Scholar
  31. Rietveld P (1994) Spatial economic impacts of transport infrastructure supply. Transp Res Part A: Policy Pract 28:329–341CrossRefGoogle Scholar
  32. Scott DM (2006) Constrained destination choice set generation: a comparison of GIS-based approaches. In: Proceedings of the 85th annual meeting on Transportation Research Board, Washington, DCGoogle Scholar
  33. Shukla V, Waddell P (1991) Firm location and land use in discrete urban space: a study of the spatial structure of Dallas-Fort worth. Reg Sci Urban Econ 21:225–253CrossRefGoogle Scholar
  34. Southworth F (1981) Calibration of multinomial logit models of mode and destination choice. Transport Res A 15:315–325CrossRefGoogle Scholar
  35. Thill JC (1992) Choice set formation for destination choice modeling. Prog Hum Geogr 16:361–382CrossRefGoogle Scholar
  36. Thill JC, Horowitz JL (1997) Travel time constraints on destination choice sets. Geogr Anal 29(2):108–123CrossRefGoogle Scholar
  37. Van Oort FG (2004) Urban growth and innovation; spatially bounded externalities in the Netherlands. Ashgate, AldershotGoogle Scholar
  38. 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
  39. Waddell P, Ulfarsson GF (2003) Accessibility and agglomeration: discrete-choice models of employment location by industry sector. In: Conference proceedings of the 82nd annual meeting of the Transportation Research Board, Washington, DCGoogle Scholar
  40. Weisbrod G, Parcells RJ, Kern C (1984) A disaggregate model for predicting shopping area market attraction. J Retail 60(1):65–83Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.SignificanceThe HagueThe Netherlands
  2. 2.Department of Transportation EngineeringUniversity of Naples Federico IINaplesItaly

Personalised recommendations