Comparative performance of logistic regression and survival analysis for detecting spatial predictors of land-use change

International Journal of Geographical Information Science

Volumn 27, Issue 10, 2013, Pages 1960-1982

  Wang, Ninghua ^a   Brown, Daniel G ^b   An, Li ^a   Yang, Shuang ^a   Ligmann Zielinska, Arika ^c  

^a SAN DIEGO STATE UNIVERSITY   (United States)

^b UNIVERSITY OF MICHIGAN   (United States)

^c MICHIGAN STATE UNIVERSITY   (United States)

Author keywords

agent based model; land change science; logistic regression; pseudo data set; space time analysis; survival analysis

Indexed keywords

DATA SET; GIS; LAND USE CHANGE; NUMERICAL MODEL; PREDICTION; REGRESSION ANALYSIS; RESIDENTIAL LOCATION; SPATIAL ANALYSIS;

EID: 84885949349     PISSN: 13658816     EISSN: 13623087     Source Type: Journal    
DOI: 10.1080/13658816.2013.779377     Document Type: Article

References (69)

1. Agarwal, C., et al., 2002. A review and assessment of land-use change models: dynamics of space, time, and human choice. USA: Forest Service. Northeastern Research Station.
2. Allison, P., 2001. Survival analysis using the SAS system: a practical guide. Cary, NC: SAS Publishing.
3. An, L. and Brown, D., 2008. Survival analysis in land change science: integrating with giscience to address temporal complexities. Annals of the Association of American Geographers, 98, 323-344.
4. An, L., et al., 2005. What statistical models help to detect land-change mechanisms? GeoComputation 2005, Ann Arbor, Michigan.
5. An, L., et al., 2011. Variations in development of exurban residential landscapes: timing, location, and driving forces. Journal of Land Use Science, 6, 13-32.
6. Andersen, P.K. and Keiding, N., 2002. Multi-state models for event history analysis. Statistical Methods in Medical Research, 11, 91-115.
7. Angel, S., et al., 2005. The dynamics of global urban expansion. Transport and Urban Development Department, The World Bank, Washington D.C., 1, 1-11.
8. Angelsen, A. and Kaimowitz, D., 1999. Rethinking the causes of deforestation: lessons from economic models. The World Bank Research Observer, 14, 73-98.
9. Aspinall, R., 2004. Modelling land use change with generalized linear models-a multi-model analysis of change between 1860 and 2000 in Gallatin Valley, Montana. Journal of Environmental Management, 72, 91-103.
10. Briassoulis, H., 2000. Analysis of land use change: theoretical and modeling approaches. Available from: http://www.rri.wvu.edu/WebBook/Briassoulis/contents.htm [21 January 2012].
11. Brown, D., et al., 2005. Path dependence and the validation of agent-based spatial models of land use. International Journal of Geographical Information Science, 19, 153-174.
12. Burton, A., et al., 2006. The design of simulation studies in medical statistics. Statistics in Medicine, 25, 4279-4292.
13. Cheng, J. and Masser, I., 2003. Urban growth pattern modeling: a case study of Wuhan city, PR China. Landscape and Urban Planning, 62, 199-217.
14. Clarke, K.C., et al., 2007. A decade of SLEUTHing: lessons learned from applications of a cellular automaton land use change model. In: P. Fisher, ed. Classics in IJGIS: Twenty Years of the International Journal of Geographical Information Science and Systems, Boca Raton, FL: Taylor & Francis, 413-427.
15. Coomes, O., Grimard, F., and Burt, G., 2000. Tropical forests and shifting cultivation: secondary forest fallow dynamics among traditional farmers of the Peruvian Amazon. Ecological Economics, 32, 109-124.
16. Cox, D.R., 1972. Regression models and life-tables. Journal of the Royal Statistical Society. Series B, Methodological, 34, 187-220.
17. Cox, D.R. and Oakes, D., 1984. Analysis of survival data. Boca Raton, FL: Chapman & Hall/CRC.
18. Cramer, J.S., 1999. Predictive performance of the binary logit model in unbalanced samples. Journal of the Royal Statistical Society: Series D (The Statistician), 48, 85-94.
19. Epperson, B., et al., 2010. Utility of computer simulations in landscape genetics. Molecular Ecology, 19, 3549-3564.
20. Fletcher, D., MacKenzie, D., and Villouta, E., 2005. Modelling skewed data with many zeros: a simple approach combining ordinary and logistic regression. Environmental and Ecological Statistics, 12, 45-54.
21. Foss, T., et al., 2003. A simulation study of the model evaluation criterion MMRE. Software Engineering, IEEE Transactions on, 29, 985-995.
22. Geist, H. and Lambin, E., 2002. Proximate causes and underlying driving forces of tropical deforestation. Bioscience, 52, 143-150.
23. Geist, H. and Lambin, E., 2004. Dynamic causal patterns of desertification. Bioscience, 54, 817-829.
24. Green, K., Kempka, D., and Lackey, L., 1994. Using remote sensing to detect and monitor land-cover and land-use change. Photogrammetric Engineering and Remote Sensing, 60, 331-337.
25. Green, M.S. and Symons, M.J., 1983. A comparison of the logistic risk function and the proportional hazards model in prospective epidemiologic studies. Journal of Chronic Diseases, 36, 715-723.
26. Harrell, F., 2001. Regression modeling strategies: with applications to linear models, logistic regression, and survival analysis. New York: Springer Verlag.
27. Heinze, G. and Schemper, M., 2002. A solution to the problem of separation in logistic regression. Statistics in Medicine, 21, 2409-2419.
28. Hersperger, A.M. and Bürgi, M., 2009. Going beyond landscape change description: quantifying the importance of driving forces of landscape change in a Central Europe case study. Land Use Policy, 26, 640-648.
29. Hirzel, A., Helfer, V., and Metral, F., 2001. Assessing habitat-suitability models with a virtual species. Ecological Modelling, 145, 111-121.
30. Holland, P.W., 1986. Statistics and causal inference. Journal of the American Statistical Association, 81, 945-960.
31. Huang, Q., Cai, Y., and Peng, J., 2007. Modeling the spatial pattern of farmland using GIS and multiple logistic regression: a case study of Maotiao River Basin, Guizhou Province, China. Environmental Modeling and Assessment, 12, 55-61.
32. Huang, B., Zhang, L., and Wu, B., 2009. Spatiotemporal analysis of rural-urban land conversion. International Journal of Geographical Information Science, 23, 379-398.
33. Iovanna, R. and Vance, C., 2007. Modeling of continuous-time land cover change using satellite imagery: an application from North Carolina. Journal of Land Use Science, 2, 147-166.
34. Irwin, E. and Bockstael, N., 2004. Land use externalities, open space preservation, and urban sprawl. Regional Science and Urban Economics, 34, 705-725.
35. Irwin, E.G. and Geoghegan, J., 2001. Theory, data, methods: developing spatially explicit economic models of land use change. Agriculture, Ecosystems & Environment, 85, 7-24.
36. King, G. and Zeng, L., 2001. Logistic regression in rare events data. Political Analysis, 9, 137.
37. Klein, J. and Moeschberger, M., 1997. Survival analysis: techniques for censored and truncated data. New York: Springer-Verlag.
38. Lambin, E., 1997. Modelling and monitoring land-cover change processes in tropical regions. Progress in Physical Geography, 21, 375.
39. Lambin, E.F. and Geist, H., 2006. Land-use and land-cover change: local processes and global impacts. New York: Springer Verlag.
40. Lee, E.T. and Wang, J.W., 2003. Statistical methods for survival data analysis. Hoboken, NJ: Wiley & Sons, Inc.
41. Ligmann-Zielinska, A., 2009. The impact of risk-taking attitudes on a land use pattern: an agent-based model of residential development. Journal of Land Use Science, 4,215-232.
42. Ligmann-Zielinska, A. and Sun, L., 2010. Applying time dependent variance-based global sensitivity analysis to represent the dynamics of an agent-based model of land use change. International Journal of Geographical Information Science, 24, 1829-1850.
43. Liverman, D.M., 1998. People and pixels: linking remote sensing and social science. Washington, D.C.: National Academies Press.
44. López, S. and Sierra, R., 2010. Agricultural change in the Pastaza River Basin: a spatially explicit model of native Amazonian cultivation. Applied Geography, 30, 355-369.
45. Lunetta, R.S., et al., 2006. Land-cover change detection using multi-temporal MODIS NDVI data. Remote Sensing of Environment, 105, 142-154.
46. Luo, J. and Wei, Y.H., 2009. Modeling spatial variations of urban growth patterns in Chinese cities: the case of Nanjing. Landscape and Urban Planning, 91 (2), 51-64.
47. Malanson, J., 2005. Comparison of the structure and accuracy of two land change models. International Journal of Geographical Information Science, 19, 745-748.
48. Manson, S.M., 2006. Bounded rationality in agent-based models: experiments with evolutionary programs. International Journal of Geographical Information Science, 20, 991-1012.
49. Mertens, B. and Lambin, E., 2000. Land-cover-change trajectories in southern Cameroon. Annals of the Association of American Geographers, 90, 467-494.
50. Mudholkar, G.S., Srivastava, D.K., and Freimer, M., 1995. The exponentiated Weibull family: a reanalysis of the bus-motor-failure data. Technometrics, 37, 436-445.
51. Neel, M.C., McGarigal, K., and Cushman, S.A., 2004. Behavior of class-level landscape metrics across gradients of class aggregation and area. Landscape Ecology, 19, 435-455.
52. Overmars, K. and Verburg, P., 2005. Analysis of land use drivers at the watershed and household level: linking two paradigms at the Philippine forest fringe. International Journal of Geographical Information Science, 19, 125-152.
53. Parker, D., et al., 2003. Multi-agent systems for the simulation of land-use and land-cover change: a review. Annals of the Association of American Geographers, 93, 314-337.
54. Pontius, R., et al., 2008. Comparing the input, output, and validation maps for several models of land change. The Annals of Regional Science, 42, 11-37.
55. Rindfuss, R.R., et al., 2007. Frontier land use change: synthesis, challenges, and next steps. Annals of the Association of American Geographers, 97, 739-754.
56. Rindfuss, R.R., et al., 2008. Land use change: complexity and comparisons. Journal of Land Use Science, 3, 1-10.
57. Serneels, S. and Lambin, E.F., 2001. Proximate causes of land-use change in Narok District, Kenya: a spatial statistical model. Agriculture, Ecosystems & Environment, 85, 65-81.
58. Shuster, J.J., 1992. Practical handbook of sample size guidelines for clinical trials. Boca Raton, FL: CRC Publishing.
59. Singer, J.D. and Willett, J.B., 2003. Applied longitudinal data analysis: modeling change and event occurrence. USA: Oxford University Press.
60. Smith, K.R. and Zick, C.D., 1994. Linked lives, dependent demise? Survival analysis of husbands and wives. Demography, 31, 81-93.
61. Turner, M.G., Gardner, R.H., and O'neill, R.V., 2001. Landscape ecology in theory and practice: pattern and process. New York: Springer Verlag.
62. Turner, B.L., Lambin, E.F., and Reenberg, A., 2007. The emergence of land change science for global environmental change and sustainability. Proceedings of the National Academy of Sciences, 104, 20666.
63. Turner, M.G., Wear, D.N., and Flamm, R.O., 1996. Land ownership and land-cover change in the Southern Appalachian Highlands and the Olympic Peninsula. Ecological applications, 6, 1150-1172.
64. Vagen, T.G., 2006. Remote sensing of complex land use change trajectories-a case study from the highlands of Madagascar. Agriculture, Ecosystems & Environment, 115, 219-228.
65. Walker, R., 2004. Theorizing land-cover and land-use change: the case of tropical deforestation. International Regional Science Review, 27, 247.
66. Wu, F. and Yeh, A.G.O., 1997. Changing spatial distribution and determinants of land development in Chinese cities in the transition from a centrally planned economy to a socialist market economy: a case study of Guangzhou. Urban Studies, 34, 1851-1879.
67. Wyman, M. and Stein, T., 2010. Modeling social and land-use/land-cover change data to assess drivers of smallholder deforestation in Belize. Applied Geography, 30, 329-342.
68. Yashin, A.I., Vaupel, J.W., and Iachine, I.A., 1995. Correlated individual frailty: an advantageous approach to survival analysis of bivariate data. Mathematical Population Studies, 5, 145-159.
69. Zhou, Q., Li, B., and Kurban, A., 2008. Trajectory analysis of land cover change in arid environment of China. International Journal of Remote Sensing, 29, 1093-1107.