Advertisement

A comparison of Euclidean Distance, Travel Times, and Network Distances in Location Choice Mixture Models

  • Sabina BuczkowskaEmail author
  • Nicolas Coulombel
  • Matthieu de Lapparent
Article
  • 33 Downloads

Abstract

This article investigates the selection of a distance measure in location modeling. While in the empirical literature the choice usually boils down to picking one single measure, this research proposes a flexible approach in which several measures may be used in parallel to capture the surrounding economic landscape. This is intended to acknowledge that interactions between agents may take several forms, occurring through different channels and as such being based on different measures. The methodology is applied to the location choice of establishments in the Paris region, using a mixture of ”mono-distance” hurdle-Poisson models. Seven distance measures are considered: Euclidean distance, the travel times by car (for the peak and off-peak periods) and by public transit, and the corresponding network distances. For all the economic sectors considered, the mixture of hurdle-Poisson models performs significantly better than the “pure” mono-distance models. This corroborates that local spatial spillovers are indeed channeled by different means, hence best represented using several measures. The combination of peak and off-peak road travel times (slightly) outperforms other combinations including the Euclidean distance, supporting the choice of meaningful over more abstract measures in spatial econometric models. The distance measure most likely to capture local spatial spillovers varies depending on the economic sector examined, reflecting differences between sectors in operations and location choice criteria.

Keywords

Travel time Network distance Euclidean distance Mixture model Latent class Location choice model 

Notes

Acknowledgements

The authors would like to thank Professor Josep-Maria Arauzo-Carod and Professor James LeSage, all the anonymous reviewers, and the editor for their constructive suggestions and very helpful comments on the paper. We also thank the DRIEA-IF and Caliper for providing access to the MODUS model and the TransCAD software, respectively.

References

  1. Acs ZJ, Audretsch D, Feldman MP (1994) R&D spillovers and recipient firm size. Rev Econ Stat 100(1):336–367CrossRefGoogle Scholar
  2. Anselin L (2003) Spatial externalities, spatial multipliers, and spatial econometrics. Int Reg Sci Rev 26(2):153–166CrossRefGoogle Scholar
  3. Arauzo-Carod JM, Liviano-Solís D, Manjón-Antolín M (2010) Empirical studies in industrial location choice: An assessment of their methods and results. J Reg Sci 50(3):685–711CrossRefGoogle Scholar
  4. Aten B (1997) Does Space Matter? International Comparisons of the Prices of Tradables and Nontradables. Int Reg Sci Rev 20:35–52CrossRefGoogle Scholar
  5. Axhausen KW (2003) Definitions and measurement problems. In: Axhausen KW, Madre J-L, Polak JW, Toint P (eds) Capturing long distance travel. Research Science Press, BaldockGoogle Scholar
  6. Benezech V, Coulombel N (2013) The value of service reliability. Transp Res B Methodol 58:1–15CrossRefGoogle Scholar
  7. Bhat ChR, Paleti R, Singh P (2014) A spatial multivariate count model for firm location decisions. J Reg Sci 54(3):462–502Google Scholar
  8. Billé AG, Benedetti R, Postiglione P (2017) A two-step approach to account for unobserved spatial heterogeneity Spatial Economic Analysis, in pressGoogle Scholar
  9. Bodson P, Peeters D (1975) Estimations of the coefficients in a linear regression in the presence of spatial autocorrelation: an application to a Belgian labour-demand function. Environ Plan A 7:455–72CrossRefGoogle Scholar
  10. Boscoe FP, Henry KA, Zdeb MS (2012) A nationwide comparison of driving distance versus straight-line distance to hospitals. Prof Geogr 64(2):188–196CrossRefGoogle Scholar
  11. Buczkowska S, de Lapparent M (2014) Location choices of newly-created establishments: Spatial patterns at the aggregate levels. Reg Sci Urban Econ 48:68–81CrossRefGoogle Scholar
  12. Buczkowska S, de Lapparent M (2017) Location choices under strategic interactions: Interdependence of establishment types. Working paperGoogle Scholar
  13. Buczkowska S, Coulombel N, de Lapparent M (2018) The Paris region travel times and distances dataset.  https://doi.org/10.7910/DVN/E85DBD, Harvard Dataverse, V1
  14. Chalasani VS, Denstadli JM, Engebretsen Ø, Axhausen KW (2005) Precision of Geocoded locations and network distance estimates. J Transp Stat 8(2):1–15Google Scholar
  15. Chatman DG, Noland RB, Klein NJ (2016) Firm births, access to transit, and agglomeration in Portland, Oregon, and Dallas, Texas. Transp Res Rec: J Transp Res Board 2598:1–10CrossRefGoogle Scholar
  16. Chen Z, Haynes K (2014) Spatial impact of transportation infrastructure: A spatial econometric CGE approach. In: Rose A, Nijkamp P (eds) Regional science matters? studies dedicated to walter Isard. Springer, BerlinGoogle Scholar
  17. Cole JP, King CAM (1968) Quantitative geography. Wiley, LondonGoogle Scholar
  18. Combes PP, Lafourcade M (2003) Core-periphery patterns of generalized transport costs: France, 1978-98 C.E.P.R. Discussion papers 3958Google Scholar
  19. Combes PP, Lafourcade M (2005) Transport costs: measures, determinants, and regional policy implications for France . J Econ Geogr 5(3):319–349. Oxford University PressCrossRefGoogle Scholar
  20. Condeço-Melhorado A, Reggiani A, Gutiérrez J (2018) New data and methods in accessibility analysis. Netw Spatial Econ 18(2):237–240CrossRefGoogle Scholar
  21. Conley TG, Ligon E (2002) Economic distance and cross-country spillovers. J Econ Growth 7(2):157–187CrossRefGoogle Scholar
  22. Conn AR, Gould NIM, Toint PL (2000) Trust region methods. MPS-SIAM series on optimization, society for industrial and applied mathematicsGoogle Scholar
  23. Corrado L, Fingleton B (2012) Where is the economics in spatial econometrics? J Reg Sci 52(2):210–239CrossRefGoogle Scholar
  24. Coulombel N, Leurent F (2013) Les ménages arbitrent-ils entre coût du logement et coût du transport: une réponse dans le cas francilien. Economie & Statistique 457-458:57–75Google Scholar
  25. de Dios Ortuzar J, Willumsen LG (2011) Modeling transport, 4th edn. WileyGoogle Scholar
  26. Dempster AP, Laird NM, Rubin DB (1977) Maximum likelihood from incomplete data via the EM algorithm. J R Stat Soc Ser B 39(1):1–38Google Scholar
  27. Denain JC, Langlois P (1998) Cartographie en anamorphose. Mappemonde 49(1):16–19Google Scholar
  28. DRIEA Île-de-France (2008) MODUS v21 Documentation détaillée du modéle de déplacements de la DREIF Technical reportGoogle Scholar
  29. Dubé J, Brunelle C, Legros D (2016) The review of regional studies location theories and business location decision: A micro-spatial investigation of a nonmetropolitan area in Canada. Rev Reg Stud 46:143–170Google Scholar
  30. Duran-Fernandez R, Santos G (2014) Gravity, distance, and traffic flows in Mexico. Res Transp Econ 46:30–35CrossRefGoogle Scholar
  31. Duranton G, Overman HG (2005) Testing for localization using micro-geographic data. Rev Econ Stud 72(4):1077–1106CrossRefGoogle Scholar
  32. Eaton BC, Lipsey RG (1980) The block metric and the law of markets. J Urban Econ 7:337–347CrossRefGoogle Scholar
  33. Eliste P, Federiksson PG (2004) Does trade liberalization cause a race to the bottom in environment policies? A spatial econometric analysis. In: Anselin L, Florax R (eds) Advances in spatial econometrics. Springer, New YorkGoogle Scholar
  34. Faber B (2014) Trade integration, market size, and industrialization: Evidence from China’s national trunk highway system. Rev Econ Stud 81(3):1046–1070CrossRefGoogle Scholar
  35. Fetter FA (1924) The economic law of market areas. Q J Econ 39:520–529CrossRefGoogle Scholar
  36. Fingleton B (2008) A generalized method of moments estimator for a spatial model with moving average errors, with application to real estate prices. Empir Econ 34:35–57CrossRefGoogle Scholar
  37. Fingleton B, Le Gallo J (2008) Estimating spatial models with endogenous variables, a spatial lag and spatially dependent disturbances: finite sample properties. Pap Reg Sci 87(3):319–339CrossRefGoogle Scholar
  38. Fransen K, Neutens T, De Mayer P, Deruyter G (2015) A commuter-based two-step floating catchment area method for measuring spatial accessibility of daycare centers. Health Place 32:65–73CrossRefGoogle Scholar
  39. Graham DJ (2007) Variable returns to urbanization and the effect of road traffic congestion. J Urban Econ 62(1):103–120CrossRefGoogle Scholar
  40. Gutiérrez J (2001) Location, economic potential and daily accessibility: an analysis of the accessibility impact of the high-speed line Madrid-Barcelona-French border. J Transp Geogr 9(4):229–242CrossRefGoogle Scholar
  41. Guy CM (1983) The assessment of access to local shopping opportunities: A comparison of accessibility measures. Environ Plann B 10:219–238CrossRefGoogle Scholar
  42. Haggett P (1967) Network models in geography. In: Chorley RJ, Haggett P (eds) Integrated models in geography. Methuen, London, pp 609–668Google Scholar
  43. Kang D, Dall’erba S (2016) An examination of the role of local and distant knowledge spillovers on the US regional knowledge creation. Int Reg Sci Rev 39(4):355–385CrossRefGoogle Scholar
  44. Klier T, McMillen D (2008) Clustering of auto supplier plants in the United States. Bus Econ Stat Am Stat Assoc 26(4):460–471CrossRefGoogle Scholar
  45. Kwon Y (2002) Rent-commuting cost function versus rent-distance function. J Reg Sci 42(2):773–791CrossRefGoogle Scholar
  46. Lambert DM, Brown JP, Florax RJGM (2010) A two-step estimator for a spatial lag model of counts: theory, small sample performance and an application. Reg Sci Urban Econ 40(4):241–252CrossRefGoogle Scholar
  47. de Lapparent M, Koning M (2015) Travel discomfort-time tradeoffs in Paris subway: an empirical analysis using interval regression models EPFL-REPORT-204605Google Scholar
  48. Le Gallo J, Dall’erba S (2008) Spatial and sectoral productivity convergence between European regions, 86, 1975-2000. Pap Reg Sci 87(4):505–525CrossRefGoogle Scholar
  49. LeSage JP (2014) What regional scientists need to know about spatial econometrics. Rev Reg Stud 44(1):13–32Google Scholar
  50. LeSage JP, Pace RK (2008) Spatial econometric modeling of origin-destination flows. J Reg Sci 48(5):941–967CrossRefGoogle Scholar
  51. Liesenfeld R, Richard JF, Vogler J (2015) Likelihood evaluation of high-dimensional spatial latent gaussian models with non-gaussian response variables. Available at SSRN: SSRN-id2196041 2Google Scholar
  52. Liviano-Solís D, Arauzo-Carod JM (2013) Industrial location and interpretation of zero counts. Ann Reg Sci 50:515–534CrossRefGoogle Scholar
  53. Márquez MA, Ramajo J, Ewings G (2010) Measuring the spillover effects of public capital: a bi-regional structural vector autoregressive analysis. Lett Spat Resour Sci 3:111–125CrossRefGoogle Scholar
  54. Matisziw TC, Demir E (2016) Measuring spatial correspondence among network paths. Geogr Anal 48:3–17CrossRefGoogle Scholar
  55. Miller HJ (2004) Tobler’s first law and spatial analysis. Ann Assoc Am Geogr 94(2):284–289CrossRefGoogle Scholar
  56. Miller HJ, Wentz EA (2003) Representation and spatial analysis in geographic information systems. Ann Assoc Am Geogr 93(3):574–594CrossRefGoogle Scholar
  57. Nguyen CY, Sano K, Tran TV, Doan TT (2012) Firm relocation patterns incorporating spatial interactions. Ann Reg Sci 50(3):685–703CrossRefGoogle Scholar
  58. Parent O, LeSage JP (2008) Using the variance structure of the conditional autoregressive spatial specification to model knowledge spillovers. J Appl Econ 23(2):235–256. John Wiley & Sons LtdCrossRefGoogle Scholar
  59. Partridge MD, Rickman DS, Ali K, Olfert MR (2008) Lost in space: population growth in the American hinterlands and small cities. J Econ Geogr 8(6):727–757CrossRefGoogle Scholar
  60. Perreur J, Thisse J (1974) Central metrics and optimal location. J Reg Sci 14:411–421CrossRefGoogle Scholar
  61. Rietveld P, Zwart B, van Wee B, van den Hoorn T (1999) On the relationship between travel time and travel distance of commuters. Ann Reg Sci, Springer 33(3):269–287CrossRefGoogle Scholar
  62. Rincke J (2010) A commuting-based refinement of the contiguity matrix for spatial models, and an application to local police expenditures. Reg Sci Urban Econ 40:324–330CrossRefGoogle Scholar
  63. Rushton G, Thill JC (1989) The effect of distance metric on the degree of spatial competition between firms. Environ Plan A 21(4):499–507CrossRefGoogle Scholar
  64. Slade ME (2005) The role of economic space in decision making, ADRES Lecture. Annales D’Economie et de Statistique 77:1–20Google Scholar
  65. Talen E, Anselin L (1998) Assessing spatial equity: an evaluation of measures of accessibility to public playgrounds. Environ Plan A 30:595–613CrossRefGoogle Scholar
  66. Tobler W (1970) A computer movie stimulating urban growth in the Detroit region. Econ Geogr 46:234–240CrossRefGoogle Scholar
  67. Vega SH, Elhorst JP (2013) On spatial econometrics models, spillovers effects, and W ERSA conference papers ersa13p222, European Regional Science AssociationGoogle Scholar
  68. Veneri P (2010) Urban polycentricity and the costs of commuting: evidence from Italian metropolitan areas. Growth and Change 41(3):403–429CrossRefGoogle Scholar
  69. Weisbrod G (2008) Models to predict the economic development impact of transportation projects: historical experience and new applications. Ann Reg Sci 42(3):519–543CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.LokadParisFrance
  2. 2.LVMT, UMR-T 9403Ecole des Ponts, IFSTTAR, UPEMChamps-sur-MarneFrance
  3. 3.School of Business and Engineering Vaud (HEIG-VD)HES-SO University of Applied Sciences and Arts Western SwitzerlandYverdon-les-BainsSwitzerland

Personalised recommendations