Tracking land cover change in mixed logit model: Recognizing temporal and spatial effects

Transportation Research Record

Volumn , Issue 1977, 2006, Pages 112-120

  Xiaokun, Wang ^a   Kockelman, Kara M ^a  

^a UNIVERSITY OF TEXAS AT AUSTIN   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTER SIMULATION; CORRELATION METHODS; MATHEMATICAL MODELS; PUBLIC POLICY; URBAN PLANNING;

CHOLESKY DECOMPOSITION; MODEL ESTIMATION; POLICY MAKERS;

LAND USE;

EID: 33947587956     PISSN: 03611981     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Conference Paper

References (39)

1. Fujita, M. Urban Economy Theory. Cambridge University Press, Cambridge, United Kingdom, 1989.
2. Nelson, G. C., and D. Hellerstein. Do Roads Cause Deforestation: Using Satellite Images in Econometric Analysis of Land Use. American Journal of Agricultural Economics, Vol. 79, 1997, pp. 80-88.
3. Clarke, K. C. Land Transition Modeling with Deltatrons. Presented at NCGIA Land Use Modeling Workshop, www.ncgia.ucsb.edu/conf/landuse97. Accessed July 10, 2005.
4. Munroe, D., J. Southworth, and C. M. Tucker. The Dynamics of Land-Cover Change in Western Honduras: Spatial Autocorrelation and Temporal Variation. agecon.lib.umn.edu/cgi-bin/pdf_view.pl?paperid=2611. Accessed July 10, 2004.
5. de la Barra, T. Integrated Land Use and Transport Modeling. Cambridge University Press, Cambridge, United Kingdom, 1989.
6. Waddell, P. UrbanSim: Modeling Urban Development for Land Use, Transportation, and Environmental Planning. Journal of the American Planning Association, Vol. 68, No. 3, 2002, pp. 297-314.
7. Klosterman, R. E. What If?: Collaborative Planning Support System. Environment and Planning B, Vol. 26, 1999, pp. 393-408.
8. Wooldrige, J. M. Econometric Analysis of Cross Section and Panel Data. MIT Press, Cambridge, Mass., 2002.
9. Greene, W. H. Econometric Analysis, 5th ed. Prentice Hall, Englewood Cliffs, N.J., 2002.
10. McFadden, D., and K. Train. Mixed MNL Models for Discrete Response. Journal of Applied Econometrics, Vol. 15, No. 5, 2000, pp. 447-470.
11. Parker, D. C., S. M. Manson, M. A. Janssen, M. J. Hoffmann, and P. Deadman. Multi-Agent Systems for the Simulation of Land-Use and Land-Cover Change: A Review. Annals of the Association of American Geographers, Vol. 93, No. 2, 2003, pp. 314-340.
12. Hunt, J. D., and J. E. Abraham. Design and Application of the PECAS Land Use Modeling System. Presented at 8th International Conference on Computers in Urban Planning and Urban Management, Sendai, Japan, 2003.
13. Candau, J., S. Rasmussen, and K. C. Clarke. A Coupled Cellular Automaton Model for Land Use/Land Cover Dynamics, www.geog.ucsb. edu/~kclarke/ucime/ banff2000/533-jc-paper.htm. Accessed July 10, 2005.
14. Clarke, K. C., S. Hoppen, and L. Gaydos. Methods and Techniques for Rigorous Calibration of a Cellular Automaton Model of Urban Growth. Presented at 3rd International Conference/Workshop on Integrating GIS and Environmental Modeling, Santa Fe, N. Mex., 1996.
15. Anselin, L. Spatial Econometrics: Methods and Models. Kluwer Academic Press, Dordrecht, Netherlands, 1988.
16. Anselin, L. Spatial Econometrics. www.csiss.org/ learning_resources/ content/papers/baltchap.pdf. Accessed July 10, 2005.
17. Besner, C. A Spatial Autoregressive Specification with a Comparable Sales Weighting Scheme. Journal of Real Estate Research, Vol. 24, 2002, pp. 193-211.
18. Miyamoto, K., V. Vichiensan, N. Shimomura, and A. Paez. Discrete Choice Model with Structuralized Spatial Effects for Location Analysis. In Transportation Research Record; Journal of the Transportation Research Board, No. 1898, Transportation Research Board of the National Academies, Washington, D.C., 2004, pp. 183-190.
19. Frazier, C., and K.M. Kockelman. Spatial Econometric Models for Panel Data: Incorporating Spatial and Temporal Data. In Transportation Research Record: Journal of the Transportation Research Board, No. 1902, Transportation Research Board of the National Academies, Washington, D.C., 2005, pp. 80-40.
20. Beron, K., and W. Vijverberg. Probit in a Spatial Context: A Monte Carlo Analysis. In Advances in Spatial Econometrics (L. Anselin, R. Florax, and S. Rey, eds.), Springer-Verlag, Heidelberg, Germany, 2004.
21. Wear, D. N., and P. Bolstad. Land-Use Changes in Southern Appalachian Landscapes: Spatial Analysis and Forecast Evaluation. Ecosystems, Vol. 1, 1998, pp. 575-594.
22. Cliff, A. D., and J. K. Ord. Spatial Processes: Models and Applications. Pion, London, 1981.
23. Mohammadian, A. K., M. Haider, and P. S. Kanaroglou. Incorporating Spatial Dependencies in Random Parameter Discrete Choice Models. Presented at 84th Annual Meeting of the Transportation Research Board. http://www.milute. mcgill.ca/Research/Senior/Spatial-Mixed-TRB_rev.pdf. Accessed July 10, 2005.
24. Jensen, R. J. Introductory Digital Image Processing. Prentice Hall, Englewood Cliffs, N.J., 1996.
25. TransCAD GIS Software, Version 4.7. Caliper Corporation, Newton, Mass., 2004.
26. Bhat, C. R. Quasi-Random Maximum Simulated Likelihood Estimation of the Mixed Multinomial Logit Model. Transportation Research Part B: Methodological, Vol. 35, 2001, pp. 677-693.
27. Train, K. Discrete Choice Methods with Simulation. Cambridge University Press, New York, 2003.
28. Gill, J., and G. King. What to Do When Your Hessian Is Not Invertible: Alternatives to Model Respecification in Nonlinear Estimation. Socio-logical Methods and Research, Vol. 32, No. 1, 2004, pp. 54-87.
29. Gill, J., and G. King. Generalized Cholesky Decomposition. GAUSS code. psblade.ucdavis.edu/papers/gmchol.g. Accessed July 10, 2005.
30. Train, K., D. Revelt, and P. Ruud. Mixed Logit Estimation Routine for Panel Data. GAUSS code. elsa.berkeley.edu/Software/abstracts/train0296.html. Accessed July 10, 2005.
31. Faure, H. Good Permutations for Extreme Discrepancy. Journal of Number Theory, Vol. 42, 1992, pp. 47-56.
32. Tuffin, B. On the Use of Low-Discrepancy Sequences in Monte Carlo Methods. Monte Carlo Methods and Applications, Vol. 2, 1996, pp. 295-320.
33. Braaten, E., and G. Weller. An Improved Low-Discrepancy Sequence for Multidimensional Quasi-Monte Carlo Integration. Journal of Computational Physics, Vol. 33, 1979, pp. 249-258.
34. Atanassov, E. I. On the Discrepancy of the Halton Sequences. Mathematica Balkanize, Vol. 18, No. 1-2, 2004, pp. 15-32.
35. Mascagni, M., and H. Chi. On the Scrambled Halton Sequence. Monte Carlo Methods and Applications, Vol. 10, No. 3, 2004, pp. 435-442.
36. Hess, S., J. W. Polak, and A. J. Daly. On the Performance of Shuffled Halton Sequences in the Estimation of Discrete Choice Models. Presented at European Transport Conference, Strasbourg, France, 2003. www.cts.cv.imperial.ac. uk/StaffPages/StephaneHess/papers/Hess_Polak_Daly_ETC_oct_16.pdf. Accessed July 10, 2005.
37. Hensher, D. A., and L. W. Johnson. Applied Discrete-Choice Modelling. John Wiley & Sons, New York, 1981.
38. McKay, M. D. Evaluating Prediction Uncertainty. Technical Report NUREG/CR-6311. U.S. Nuclear Regulatory Commission and Los Alamos National Laboratory, 1995.
39. Kotz, S., and N. Johnson. Encyclopedia of Statistical Sciences. John Wiley & Sons, New York, 1982.