Modeling urban growth with geographically weighted multinomial logistic regression

Proceedings of SPIE - The International Society for Optical Engineering

Volumn 7144, Issue , 2008, Pages

  Luo, Jun ^a   Kanala, Nagaraj Kapi ^a  

^a MISSOURI STATE UNIVERSITY   (United States)

Author keywords

Geographically weighted multinomial logistic regression; GIS; Land use conversion; Urban growth

Indexed keywords

DRIVING FACTORS; GAUSSIAN FUNCTIONS; GEOGRAPHICALLY WEIGHTED MULTINOMIAL LOGISTIC REGRESSION; GIS; INDEPENDENT VARIABLES; LAND USE CHANGES; LOCAL COEFFICIENTS; METROPOLITAN AREAS; MULTINOMIAL LOGISTIC REGRESSION MODELS; SAMPLE LOCATIONS; SPATIAL HETEROGENEITIES; SPATIAL VARIATIONS; URBAN GROWTH;

DYNAMICS; GEOGRAPHIC INFORMATION SYSTEMS; LAND USE; LOCATION; LOGISTICS;

REGRESSION ANALYSIS;

EID: 62649093463     PISSN: 0277786X     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1117/12.812714     Document Type: Conference Paper

References (16)

1. K. C. Clarke, S. Hoppen and L. Gaydos, "A self-modifying cellular automaton model of historical urbanization in the San Francisco Bay area", Environ. Plan. B: Plan. Des. 24, 247-261 (1997).
2. Z. Hu and C. P. Lo, "Modeling urban growth in Atlanta using logistic regression ", Comput. Environ. Urban Syst. 31 (6), 667-688 (2007).
3. Q. Weng, "Land use change analysis in the Zhujiang Delta of China using satellite remote sensing, GIS and stochastic modelling", J. Environ. Manag. 64 (3), 273-284 (2002).
4. F. Wu and A. G. O. Yeh, "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 Stud. 34 (11), 1851-1879 (1997).
5. Y. Xie, M. Yu, G. Tian. and X. Xing., "Socio-economic driving forces of arable land conversion: A case study of Wuxian City, China", Glob. Environ. Chang. 15 (3), 238-252 (2005).
6. J. Cheng and I. Masser, "Urban growth pattern modeling: A case study of Wuhan City, PR China", Landsc. Urban Plan. 62 (4), 199-217 (2003).
7. R. I. McDonald and D. L. Urban, "Spatially varying rules of landscape change: lessons from a case study", Landsc. Urban Plan. 74 (1), 7-20 (2006).
8. A. S. Fotheringham, C. Brunsdon and M. Charlton., Geographically Weighted Regression : the Analysis of Spatially Varying Relationships John Wiley & Sons Ltd., Chichester, U.K., 2002.
9. D. A. Powers and Y. Xie, Statistical Methods for Categorical Data Analysis, Academic Press, San Diego, 2000.
10. D. P. McMillen, "One hundred fifty years of land values in Chicago: A nonparametric approach", J.Urban Econ. 40, 100-124 (1996).
11. J. P. LeSage, The theory and practice of spatial econometrics, http://www.spatial-econometrics.com/, 1999.
12. P. A. Longley, M. F. Goodchild, D. J. Maguire and D. W. Rhind, Geographyic Inofrmation Systems and Science, second ed, John Wiley & Sons Ltd., West Sussex, England, 2005.
13. J. Cheng and I. Masser, "Modelling urban growth patterns: A multiscale perspective", Environ. Plan. A 35 (4), 679-704 (2003).
14. K. C. Clarke and L. J. Gaydos, "Loose-coupling a cellular automaton model and GIS: long-term urban growth prediction for San Francisco and Washington/Baltimore", International Journal of Geographical Information Systems 12 (7), 699-714 (1998).
15. X. Li and A. Gar-on Yeh, "Modelling sustainable urban development by the integration of constrained cellular automata and GIS", Intel. J. Geog. Info. Sys. 14 (2), 131-152 (2000).
16. A. Páez, "Exploring contextual variations in land use and transportation analysis using a probit model with geographical weights", Journal of Transportation Geography 14, 167-176 (2006).