Forecasting jobs location choices by Discrete Choice Models: A sensitivity analysis to scale and implications for LUTI models

  • Jonathan Jones Center for Operations Research and Econometrics, Université Catholique de Louvain
  • Isabelle Thomas Center for Operations Research and Econometrics, Université Catholique de Louvain
  • Dominique Peeters Center for Operations Research and Econometrics, Université Catholique de Louvain
Keywords: Discrete Choice Models, Scale, Brussels

Abstract

This paper proposes an empirical analysis of the sensitivity of Discrete Choice Model (DCM) to the size of the spatial units used as choice set (which relates to the well-known Modifiable Areal Unit Problem). Job's location choices in Brussels (Belgium) are used as the case study. DCMs are implemented within different Land Use and Transport Interactions (LUTI) models (UrbanSim, ILUTE) to forecast jobs or household location choices. Nevertheless, no studies have assessed their sensitivity to the size of the Basic Spatial Units (BSU) in an urban context. The results show significant differences in parameter estimates between BSUs. Assuming that new jobs are distributed among the study area proportionally to the utility level predicted by the DCM for each BSU (as in a LUTI model), it is also demonstrated that the spatial distribution of these new jobs varies with the size of the BSUs. These findings mean that the scale of the BSU used in the model can influence the output of a LUTI model relying on DCM to forecast location choices of agents and, therefore, have important operational implications for land-use planning.

References

Alamà-Sabater, L., Artur-Tur, A. & Navaro-Azorin, J. M. (2011) “Industrial location, spatial discrete choice models and the need to account for neighbourhood effects”, The Annals of Regional Science 47(2), 393-418

Amrhein, C. G. (1995) “The search for the elusive aggegation effect: evidence from statistical simulations”, Environment and planning A 27, 105-119

Anselin, L. (1995) “Local indicators of spatial association – lisa”, Geographical Analysis 27(2), 93-115

Arauzo-Carod, J. M. & Antolin-Manjon, M. (2004) “Firm size and geographical aggregation: an empirical appraisal in industrial location”, Small Business Economics 22(3-4), 299-312

Arauzo, J. M. (2008) “Industrial location at a local level: comments on the territorial level of the analysis”, Tijdschrift voor economische en sociale geografie 99(2), 193-208

Arauzo-Carod, J. M., Liviaon-Solis, D. & Manjon, M. (2010) “Emprical studies in industrial location: an assessment of their methods and results”, Journal of regional sciences 50(3), 685-711

Arbia, G. (1989), Statistical effects of spatial data transformations: a proposed general framework, Taylor and Francis, London

Archer, W. & Smith, M. (1993) “Why do suburban office cluster?”, Geographical Analysis 25(1), 53-64

Baudewyns, D. (1999) “La localisation intra urbaine des firmes: une estimation logit multinomiale”, Revue d’économie régionale et urbaine 5, 915-930

Baudewyns, D. Sekkat, K. & Ben-Ayad, M. (2000) Infrastructure publique et localisation des enterprises a Bruxelles et en Wallonie”, in M. Beine & F. Docquier, eds, “Convergence des regions: cas des regions belges”, De Boeck, pp. 280-303

Ben Akiva M. E. & Lerman, S. R. (1985) Discrete choice analysis: theory and application to travel demand, MIT Press.

Briant, A., Combes, P-P. & Lafourcade, M. (2010) “Dots to boxes: do the size and shape of spatial units jeopardize economic geography estimations?”, Journal of Urban Economics 67(3), 287-302

Calinski, T. & Harabasz, J. (1974) “A dendrite method for cluster analysis”, Communications in statistics – theory and methods 3(1), 1-27

Cheshire, P. (2010) Why Brussels needs a city-region for the city, MIMEO

Croissant, Y. (2012) mlogit: multinomial logit model. R package version 0.2-3 URL: http://CRAN.R-project.org/package=mlogit

Duda, R. O. & Hart, P. E. (1973) Pattern classification and scene analysis, John Wiley and Sons

Dujardin, C., Thomas, I. and Tulkens, H. (2007) “Quelles frontiers pour Bruxelles? Une mise à jour”, Reflets et perspectives de la vie économique 46(2-3), 155-176

Fotheringham, A., Brundson, C. & Charlton, M. (2000) Quantitative geography: perpectives on spatial data analysis, Sage publications ltd

Fotheringham, A. M. & Wong, D. W. (19991) “The modifiable areal unit problem in multivariate statistical analysis”, Environment and planning A 23(7), 1025-1044

Giuliano, G. & Small, K. (1991) “Subcenters in the Los Angeles region”, Regional sciences and urban economics 21, 163-182

Guo, J. & Bhat, C. (2004), Modifiable areal unit: a problem or a matter of perception in the context of residential location choice modelling? In Transportation research board conference

Guo, J. & Bhat, C. (2007) “Operationalizing the concept of neghbourhood: application to residential location choice analysis”, Journal of transport geography 15, 31-45

Hunt, J. D., Miller, E. J. & Kriger, D. S. (2005) “Current operational urban land-use transport modelling frameworks”, Transport reviews 25(3), 329-376

Ladd, H. & Wheaton, W. (1991) “Cause and consequences of the changing urban form”, Regional sciences and urban economics 21, 157-162

Landis, J. & Zhang, M (1998a) “The second generation of the California urban futures model. Part 1: model logic and theory”, Environment and planning B: planning and design 25(5), 657-666

Landis, J. & Zhang, M. (1998b) “The second generation of the California urban futures model. Part 2: specification and calibration results of the land-use change sub model”, Environment and planning B: planning and design 25(6), 795-824

Marissal, P., Medina Lockhart, P., Vandermotten, C & van Hamme, G. (2006) Les structures socio-économiques de l’espace belge, Monographie de l’enquête socio économique générale 2001, Bruxelles, 133 p. URL: http://statbel.fgov.be/fr/modules/publications/statistiques/enquetes_et_methodologie/monographies_de_l_enquete_socio-economique_2001.jsp

McFadden, D. (1978) Modelling the choice of residential location, in A. Karlqvist, L Lundqvist, F. Snickars & J. Weibull, eds, “Spatial interactions theory and planning models”, North Holland, pp. 75-96

McMillen, D. P. (2001) “Nonparametric employment subcenters identification”, Journal of urban economics 50(3), 448-473

McMillen, D. P. & Smith, S. (2003) “The number of subcenters in large urban areas”, Journal of urban economics 53, 321-338

Moniteur Belge (2004) Ordonnance portant assentiment à la convention du 4 avril 2003 entre l’état fédéral, la région flamande, la région wallone et la région de bruxelles-capitale visant à mettre en oeuvre le programme du réseau express régional de, vers, dans et autour de Bruxelles. URL : http://www.ejustice.just.fgov.be/cgi/welcome.pl

Nicolas, J-P., Bouvard, A., Million, F., Homocianu, M. Toillier, F. & Zucarello, P. (2008) La localisation des activités économiques au sein de l’aire urbaine de Lyon, Laboratoire d’économie des transports, Lyon, 124 p. URL: http://simbad.let.fr/localisations/

Noth, M., Borning, A. & Waddell, P (2003) “An extensible modular architecture for simulating urban development, transportation and environmental impact”, Computer, Environment and Urban Systems 27, 181-203

Openshaw, S.& Taylor, P. J. (1979) A million or so correlation coefficients: three experiments on the modifiable areal unit problem, in N. Wrigley, ed., Statistical applications in the social sciences, Plon, London

Pagliara, F. & Wilson, A. (2010) “The state-of-the-art in building residential location models”, Residential location choice 1, 1-20

Redfearn, C. (2007) “The topography of metropolitan employment: identifying centers of employment in a polycentric urban area”, Journal of urban economics 61, 519-541

Riguelle, F., Thomas, I. & Verhetsel, A. (2007) “Urban Polycentrims: a measurable reality. The case of Brussels”, Journal of economic geography 7, 193-215

Rodrigue, J-P., Comtois, C. & Slack, B. (2009) The geography of transport systems, Routledge

Salvini, P. A. & Miller, E. J. (2005) “ILUTE: an operational prototype of comprehensive micro simulation model of urban systems”, Networks and spatial economics 5, 217-234

Sarle, W. S. (1983) SAS Technical report a-108, cubic clustering criterion, SAS Institute Inc. URL: https://support.sas.com/documentation/onlinedoc/v82/techreport_a108.pdf

Sener, I., Pendyala, R. & Bhat, C. (2011) “Accomodating spatial correlation across choice alternatices in discrete choice models: an application to modelling residential location choice behavior”, Journal of transport geography 19, 294-303

Shukla, V. & Waddell, P. (1991) “Firm location and land use in discrete urban space: a study of the spatial structure of Dallas-Fort Worth”, Regional sciences and urban economics 21, 225-253

Thill, J. C. (1992) “Choice set formation for destination choice modelling”, Progress in human geography 16(3), 361-382

Thisse, J-F. & Thomas, I. (2007) “Bruxelles et Wallonie: une lecture en terme de géo économie urbaine”, Reflets et perspectives de la vie économique 46(1), 75-93

Thisse, J-F. & Thomas, I. (2010) “Bruxelles au sein de l’économie belge: un bilan”, Reflets et perpectives de la vie économique 80, 1-18

Train, K. (2003), Discrete choice methods with simulation, Cambridge university press

Van Hecke, E., Halleux, J-M., Decrolyn J-M. & Merenne-Schoumaker, B. (2001) noyaux d’habitats et regions urbaines dans une Belgique urbanisée, Monographie de l’enquête socio-économique générale 2001, Bruxelles, 205 p. URL: http://statbel.fgov.be/fr/modules/publications/statistiques/enquetes_et_methodologie/monographies_de_l_enquete_socio-economique_2001.jsp

Van Malderen, L., Jourquin, B., Vanoutrive, T., Verhetsel, A. & Witlox, F. (2012) “On the mobility policies of companies: what are the good practices? The Belgian case”, Transport policy 21, 10-19

Vandenbulcke, G., Steenberghen, T. & Thomas, I. (2007) Accessibility indicators to place and transport, Politique scientifique fédérale (BELSPO), Bruxelles, 353 p. URL: http://www.belspo.be/belspo/organisation/publ/pub_ostc/AP/rAP02_en.pdf

Waddell, P. (2000) “A behavioural simulation model for metropolitan policy analysis and planning: residential location and housing market components of UrbanSim”, Environment and planning B: planning and design 27, 247-263

Waddell, P. (2002) “Urbansim: modelling urban development for land use, transportation and environmental planning”, Journal of american planning association 68(3), 297-314

Waddell, P., Borning, A., Noth, M., Freier, N;, Becke, M. & Ulfarsson, G. (2003) “Microsimulation of urban development and location choices: design and implementation of urbansim”, Networks and spatial economics 3(1), 43-67

Waddel, P., Ulfarsson, G., Franklin, J. & Lobb, J. (2007) “Incorporating land use in metropolitan transportation planning”, Transportation research part A 41, 382-410

Wardman, M. (1988) “A comparison of revealed preference and stated preference model of travel behaviour”, Journal of transport economics and policy 22(1), 71-91

Witlow, F., Jourquin, B., Thomas, I., Verhetsel, A., Vande Vijver, E., Van Malderen, L. & Vanoutrive, T. (2011) Assessing and developing initatives of companies to control and reduce commuter traffic, Politique scientifique fédérale (BELSPO), Bruxelles, 104 p. URL: http://www.belspo.be/belspo/ssd/science/Reports/ADICCT_FinRep_AD.pdf

Published
2015-06-15
How to Cite
Jones, J., Thomas, I. and Peeters, D. (2015) “Forecasting jobs location choices by Discrete Choice Models: A sensitivity analysis to scale and implications for LUTI models”, REGION, 2(1), pp. 67-93. doi: 10.18335/region.v2i1.63.
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Articles
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