The dynamic spatial multinomial probit model: Analysis of land use change using parcel-level data

Journal of Transport Geography

Volumn 24, Issue , 2012, Pages 77-88

  Wang, Xiaokun Cara ^a   Kockelman, Kara M ^b   Lemp, Jason D ^c  

^a RENSSELAER POLYTECHNIC INSTITUTE   (United States)

^b The University of Texas at Austin   (United States)

^c Cambridge Systematics   (United States)

Author keywords

Discrete panel data; Land use change; Multinomial probit; Spatial autocorrelation

Indexed keywords

AUTOCORRELATION; BAYESIAN ANALYSIS; CROSS SECTION; DATA SET; LAND USE CHANGE; NEIGHBORHOOD; NUMERICAL MODEL; SPATIAL VARIATION; TEMPORAL VARIATION; TRANSPORTATION;

AUSTIN; TEXAS; UNITED STATES;

EID: 84864048103     PISSN: 09666923     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.jtrangeo.2012.06.011     Document Type: Article

References (53)

1. Anselin, L., 2001. Issues in spatial probit models. In: Workshop on Qualitative Dependent Variable Estimation and Spatial Effects, College of ACES, University of Illinois, April 20, 2001.
2. Anselin L., Gallo J.L., Jayet H. Spatial Panel Econometrics 2008, Springer, New York.
3. Beron K., Vijverberg W. Probit in a spatial context: a Monte Carlo analysis. Advances in Spatial Econometrics 2004, 169-192. Springer-Verlag, Heidelberg. L. Anselin, R. Florax, S. Rey (Eds.).
4. Bhat C.R., Guo J.Y. A mixed spatially correlated logit model: formulation and application to residential choice modeling. Transportation Research Part B: Methodological 2004, 38(2):147-168.
5. Brasington, D., Sarama, R.F., 2007. Deed Types, House Prices, and Mortgage Interest Rates. Working Paper. Department of Economics, Louisiana State University, 2007.
6. Chakir, R., Parent, O., 2009. Determinants of Land use Changes: a Spatial Multinomial Probit Approach. Papers in Regional Science 88.
7. Damien P., Kockelman K. Preface to special issue. Transportation Research Part B: Methodological 2010, 44(5):631-632.
8. Farber S., Páez A., Volz E. Topology and dependency tests in spatial and network autoregressive models. Geographical Analysis 2009, 41:158-180.
9. Geweke J., Keane M., Runkle D. Alternative computational approaches to inference in the multinomial probit model. The Review of Economics and Statistics 1994, 76:609-632.
10. Holloway G.J., Lapar L. How big is your neighbourhood? Spatial implications of market participation among Phillipino smallholders. Journal of Agricultural Economics 2007, 58(1):37-60.
11. Holloway G., Lacombe D., LeSage J.P. Spatial econometric issues for bio-economic and land-use modeling. Journal of Agricultural Economics 2007, 58(3):549-588.
12. Horrace W. Some results on the multivariate truncated normal distribution. Journal of Multivariate Analysis 2005, 94:209-221.
13. Imai K., van Dyk D.A. A Bayesian analysis of the multinomial probit model using marginal augmentation. Journal of Econometrics 2005, 124:311-334.
14. Kakamu, K., Wago, H., 2007. Bayesian spatial panel probit model with an application to business cycle in Japan. Working paper. Accessed May 10, 2007. <>. http://www.mssanz.org.au/modsim05/proceedings/papers/kakamu_2.pdf.
15. Kapoor M., Kelejian H., Prucha I. Panel data models with spatially correlated error components. Journal of Econometrics 2007, 140(1):97-130.
16. Klier T., McMillen D.P. Clustering of auto supplier plants in the US: GMM spatial logit for large samples. Journal of Business and Economic Statistics 2008, 26(4):460-471.
17. Knowles R.D. Transport shaping space: differential collapse in time-space. Journal of Transport Geography 2006, 14(6):407-425.
18. Koop G. Bayesian Econometrics 2003, John Wiley & Sons, West Sussex, England.
19. Kotecha J.H., Djuric P.M. Gibbs sampling approach for generation of truncated multivariate Gaussian random variables. IEEE Computer Society 1999, 3:1757-1760.
20. Landis J., Zhang M. Using GIS to improve urban activity and forecasting models: three examples. Spatial Models and GIS: New Potential and New Models 2000, 63-81. Taylor & Francis, London. A. Fotheringham, S. Wegener (Eds.).
21. Landis, J., Guhathakurta, S., Huang, W., Zhang, M., Fukuji, B., Sen, S., 1995. Rail Transit Investments, Real Estate Values, and Land Use Change: A Comparative Analysis of Five California Rail Transit Systems. Institute of Urban and Regional Development, University of California at Berkley.
22. Laud P.W., Damien P., Shively T.S. Sampling truncated distributions via rejection algorithm. Communications in Statistics-Simulation and Computation 2010, 39(6):1111-1121.
23. LeSage J., Pace R.K. An Introduction to Spatial Econometrics 2009, CRC Press, Boca Raton, FL.
24. LeSage J., Parent O. A space-time filter for panel data models containing random effects. Journal of Computational Statistics and Data Analysis 2011, 55(1):475-490.
25. LeSage J.P. Bayesian estimation of limited dependent variable spatial autoregressive models. Geographical Analysis 2000, 32(1):19-35.
26. LeSage, J.P., 2003. Matlab code to construct a row-stochastic nearest neighbor spatial weight matrix. Accessed July 2011. <>. http://www.spatial-econometrics.com.
27. McCulloch R.E., Polson N.G., Rossi P.E. A Bayesian analysis of the multinomial probit model with fully identified parameters. Journal of Econometrics 2000, 99:173-193.
28. McMillen D.P. Spatial effects in probit models: a Monte Carlo investigation. New Directions in Spatial Econometrics 1995, 189-228. Springer-Verlag, Heidelberg. L. Anselin, R. Florax (Eds.).
29. McMillen D.P. Probit with spatial autocorrelation. Journal of Regional Science 1992, 32:335-348.
30. Miller H.J. Potential contributions of spatial analysis to geographic information systems for transportation (GIS-T). Geographical Analysis 1999, 31:373-399.
31. Nobile A. Comment: Bayesian multinomial probit models with a normalization constraint. Journal of Econometrics 2000, 99:335-345.
32. Pace, K.R., LeSage, J.P., 2011. 'Fast Simulated Maximum Likelihood Estimation of the Spatial Probit Model Capable of Handling Large Samples. Available at SSRN. <> or <>. http://dx.doi.org/10.2139/ssrn.1966039.
33. Páez A. Exploring contextual variations in land use and transport analysis using a probit model with geographical weights. Journal of Transport Geography 2006, 14(3):167-176.
34. Páez A. Spatial perspectives on urban systems: developments and directions. Journal of Geographical Systems 2007, 9:1-6.
35. Páez A. Spatial analysis of economic systems and land use change. Papers in Regional Science 2009, 88:251-258.
36. Páez A., Scott D.M. Spatial statistics for urban analysis: a review of techniques with examples. Geo Journal 2004, 61:53-67.
37. Páez A., Scott D.M. Social influence on travel behavior: a simulation example of the decision to telecommute. Environment and Planning A 2007, 39:647-665.
38. Páez A., Suzuki J. Transportation impacts on land use change: an assessment considering neighborhood effects. Journal of the Eastern Asia Society for Transportation Studies 2001, 4:47-59.
39. Páez A., Scott D.M., Volz E. A discrete-choice approach to modeling social influence on individual decision making. Environment and Planning B: Planning and Design 2008, 35:1055-1069.
40. Paleti, R., Bhat, C., 2011. The composite marginal likelihood (CML) estimation of panel ordered-response models. In: TRB 90th Annual Meeting Compendium of Papers DVD, Washington DC, January 2011.
41. Pinkse J., Slade M.E. Contracting in space. An application of spatial statistics to discrete-choice models. Journal of Econometrics 1998, 85:125-154.
42. Pinkse J., Slade M.E. The future of spatial econometrics. Journal of Regional Science 2010, 50(1):103-117.
43. Pinkse J., Slade M.E., Shen L. Dynamic spatial discrete choice using one step GMM: an application to mine operating decisions. Spatial Economic Analysis 2005, 1(1):53-99.
44. Potoglou D., Kanaroglou P.S. Modelling car ownership in urban areas: a case study of Hamilton, Canada. Journal of Transport Geography 2008, 16(1):42-54.
45. Sener I., Pendyala R., Bhat C. Accommodating spatial correlation across choice alternatives in discrete choice models: an application to modeling residential location choice behavior. Journal of Transport Geography 2011, 19(2):293-303.
46. Sidharthan, R., Bhat, C., 2011. Incorporating Spatial Dynamics and Temporal Dependency in Land Use Change Models. Technical paper. Accessed July 2011. <>. http://www.caee.utexas.edu/prof/bhat/ABSTRACTS/LandUse_unabridged.pdf.
47. Stefan, W., Manjunath, B.G., 2010. Package 'tmvtnorm'. User Handbook. Accessed May 30, 2010. <>. http://cran.r-project.org/web/packages/tmvtnorm/tmvtnorm.pdf.
48. Travisu, C.M., Camagni, R., Nijkamp, P., 2006. Analysis of Environmental Costs of Mobility Due to Urban Sprawl - A Modelling Study on Italian Cities. Tinbergen Institute Discussion Paper, Rotterdam. Accessed May 20, 2010. <>. http://www.gtkp.com/assets/uploads/20091117-172906-6305-SSRN-id901180%5B1%5D.pdf.
49. Vandenbulcke G., Steenberghen T., Thomas I. Mapping accessibility in Belgium: a tool for land-use and transport planning?. Journal of Transport Geography 2008, 17(1):39-53.
50. Wang X., Kockelman K. Tracking land cover change in a mixed logit model: recognizing temporal and spatial effects. Transportation Research Record 2006, 1977:112-120.
51. Wang X., Kockelman K. Specification and estimation of a spatially and temporally autocorrelated seemingly unrelated regression model: application to crash rates in China. Transportation 2007, 34(3):281-300.
52. Wang X., Kockelman K. Application of the dynamic spatial ordered probit model: Patterns of land development change in Austin, Texas. Papers in Regional Science 2009, 88(2):345-365.
53. Wang Y., Kockelman K., Wang X. Anticipation of Land use change through use of geographically weighted regression models for discrete response. Transportation Research Record 2011, 2245:111-123.