Abstract
The Conversion of Land Use and its Effects at Small regional extent (CLUE-S) model is a widely used method to simulate land use change. An ordinary logistic regression model was integrated into the CLUE-S model to identify explanatory variables without considering the spatial autocorrelation effect. Using image-derived maps of the Changsha-Zhuzhou-Xiangtan urban agglomeration, the CLUE-S model was integrated with the ordinary logistic regression and autologistic regression models in this paper to simulate land use change in 2000, 2005 and 2009 based on an observation map from 1995. Significant positive spatial autocorrelation was detected in residuals of ordinary logistic models. Some variables that were much more significant than they should be were selected. Autologistic regression models, which used autocovariate incorporation, were better able to identify driving factors. The Receiver Operating Characteristic Curve (ROC) values of autologistic regression models were larger than 0.8 and the pseudo R2 values were improved, compared with results of logistic regression model. By overlapping the observation maps, the Kappa values of the ordinary logistic regression model (OL)-CLUE-S and autologistic regression model (AL)-CLUE-S models were larger than 0.75. The results showed that the simulation results were indeed accurate. The Kappa fuzzy (Kfuzzy) values of the AL-CLUE-S models (0.780, 0.773, 0.606) were larger than the values of the OL-CLUE-S models (0.759, 0.760, 0.599) during the three periods. The AL-CLUE-S models performed better than the OL-CLUE-S models in the simulation of land use change. The results showed that it is reasonable to integrate autocovariates into CLUE-S models. However, the Kfuzzy values decreased with prolonged duration of simulation and the maximum range of time was not discussed in this paper.
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References
Bai Wanqi, Zhang Yongmin, Yan Jianzhong et al., 2005. Simulation of land use dynamics in the upper reaches of the Dadu river. Geographical Research, 24(2): 206–212. (in Chinese)
Batisani N, Yarnal B, 2009. Uncertainty awareness in urban sprawl simulations: Lessons from a small US metropolitan region. Land Use Policy, 26(2): 178–185.
Besag J, 1972. Nearest-neighbor systems and the auto-logistic model for binary data. Journal of the Royal Statistical Society B, 34(1): 75–83.
Cai Yumei, Liu Yansui, Yu Zhenrong et al., 2004. Progress in spatial simulation of land use change: CLUE-S model and its application. Progress in Geography, 23(4): 63–71. (in Chinese)
Dai Shengpei, Zhang Bo, 2013. Land use change scenarios simulation in the middle reaches of the Heihe river basin based on CLUE-S model: A case of Ganzhou district of Zhangye city. Journal of Natural Resources, 28(2): 336–348. (in Chinese)
De Koning G H J, Verburg P H, Veldkamp A et al., 1999. Multi-scale modelling of land use change dynamics in Ecuador. Agricultural Systems, 61(2): 77–93.
Gong Jianzhou, Chen Wenli, Liu Yansui et al., 2014. The intensity change of urban development land: Implications for the city master plan of Guangzhou, China. Land Use Policy, 40: 91–100.
Guo Yanfeng, Yu Xiubo, Jiang Luguang et al., 2012. Scenarios analysis of land use change based on CLUE-S model in Jiangxi Province by 2030. Geographical Research, 31(6): 1016–1028. (in Chinese)
Hagen A E, 2003. Fuzzy set approach to assessing similarity of categorical maps. International Journal of Geographic Information Systems, 17(3): 235–249.
He Chunyang, Shi Peijun, Chen Jin et al., 2005. Developing land use scenario dynamics model by the integration of system dynamics model and cellular automata model. Science in China Ser. D Earth Sciences, 48(11): 1979–1989.
Hubbell S P, Ahumada J A, Condit R et al., 2001. Local neighborhood effects on long-term survival of individual trees in a Neotropical forest. Ecological Research, 16(5): 859–875.
Koomen E, Rietveld P, De Nijs T, 2008. Modelling land-use change for spatial planning support. The Annals of Regional Science, 42(1): 1–10.
Kuang Wenhui, 2011. Simulation dynamic urban expansion at regional scale in Beijing-Tianjin-Tangshan metropolitan area. Journal of Geographical Sciences, 21(1): 317–330.
Lennon J J, 2000. Red-shifts and red herrings in geographical ecology. Ecography, 23(1): 101–113.
Liu Jiyuan, Kuang Wenhui, Zhang Zengxiang, et al., 2014. Spatiotemporal characteristics, patterns, and causes of land-use changes in China since the late 1980s. Journal of Geographical Sciences, 24(2): 195–210.
Liu Miao, Hu Manyuan, Chang Yu et al., 2009. Analysis of temporal predicting abilities for the CLUE-S land use model. Acta Ecologica Sinica, 29(11): 6110–6119. (in Chinese)
Liu Yan, Feng Yongjiu, Pontius R G, 2014. Spatially-explicit simulation of urban growth through self-adaptive genetic algorithm and cellular automata modelling. Land, 3(3): 719–738.
Manel S, Williams H C, Ormerod S J, 2001. Evaluating presence-absence models in ecology: The need to account for prevalence. Journal of Applied Ecology, 38(5): 921–931.
Moran P A P, 1950. Notes on continuous stochastic phenomena. Biometrika, 37(1/2): 17–23.
Nagelkerke N J D, 1991. A note on general definition of the coefficient of determination. Biometrika, 78(3): 691–692.
Overmars K P, De Koning G H J, Veldkamp A, 2003. Spatial autocorrelation in multi-scale land use models. Ecological Modelling, 164(2/3): 257–270.
Pontius R G, 2000. Quantification error versus location error in comparison of categorical map. Photogrammetric Engineering and Remote Sensing, 66(8): 1011–1016.
Pontius R G, 2002. Statistical methods to partition effects of quantity and location during comparison of categorical maps at multiple resolutions. Photogrammetric Engineering and Remote Sensing, 68(10): 1041–1049.
Pontius R G, Schneider L C, 2001. Land-cover change model validation by a ROC method for the Ipswich watershed, Massachusetts, USA. Agriculture, Ecosystems & Environment, 85(1–3): 239–248.
Triantakonstantis D P, Kalivas D P, Kollias V J, 2013. Autologistic regression and multicriteria evaluation models for the prediction of forest expansion. New Forests, 44(2): 163–181.
Veldkamp A, Fresco L O, 1996. CLUE: A conceptual model to study the conversion of land use and its effects. Ecological Modelling, 85(2/3): 253–270.
Veldkamp A, Fresco L O, 1997. Exploring land use scenarios: An alternative approach based on actual land use. Agricultural Systems, 55(1): 1–17.
Verburg P H, Chen Y Q, Veldkamp A, 2000. Spatial explorations of land use change and grain production in China. Agriculture, Ecosystems & Environment, 82(1–3): 333–354.
Verburg P H, Eickhout B, Van Meijl H, 2008. A multi-scale, multi-model approach for analyzing the future dynamics of European land use. The Annals of Regional Science, 42(1): 57–77.
Verburg P H, Schot P P, Dijst M J et al., 2004. Land use change modelling: Current practice and research priori ties. GeoJournal, 61(4): 309–324.
Verburg P H, Soepboer W, Veldkamp A et al., 2002. Modeling the spatial dynamics of regional land use: The CLUE-S Model. Environmental Management, 30(3): 391–405.
Wang Liyan, Zhang Xueru, Zhang Hua et al., 2010. Principle and structure of CLUE-S model and its progress. Geography and Geo-information Science, 26(3): 73–77. (in Chinese)
Wu Daqian, Liu Jian, Zhang Gaosheng et al., 2009. Incorporating spatial autocorrelation into cellular automata model: An application to the dynamics of Chinese tamarisk. Ecological Modelling, 220(24): 3490–3498.
Wu Guiping, Zeng Yongnian, Xiao Pengfeng et al., 2010. Using autologistic spatial models to simulate the distribution of land-use patterns in Zhangjiajie, Hunan Province. Journal of Geographical Sciences, 20(2), 310–320.
Wu Hulin, Huffer F W, 1997. Modelling the distribution of plant species using the autologistic regression model. Environmental and Ecological Statistics, 4(1): 49–64.
Wu Jiansheng, Feng Zhe, Gao Yang et al., 2012. Recent progresses on the application and improvement of CLUE-S model. Progress in Geography, 31(1): 3–10. (in Chinese)
Xie Hualin, Li Bo, 2008. Driving forces analysis of land-use pattern changes based on Logistic regression model in the farming-pastoral zone: A case study of Ongiud Banner, Inner Mongolia. Geographical Research, 27(2): 294–304. (in Chinese)
Zeng Yongnian, He Lili, Jin Wenping et al., 2012. Quantitative analysis of the urban expansion models in Changsha-Zhuzhou-Xiangtan Metroplan Areas. Scientia Geographica Sinica, 32(5): 544–549. (in Chinese)
Zheng Wei Helen, Shen Qiping Geoffrey, Wang Hao et al., 2014. Simulating land use change in urban renewal areas: A case study in Hong Kong. Habitat International, 46: 23–34.
Zhou Guohua, He Yanhua, 2007. The influencing factors of urban land expansion in Changsha. Journal of Geographical Sciences, 17(4): 487–499.
Zhou Rui, Su Hailong, Wang Xinjun et al., 2011. Simulation of land use change in Xinzhuang town under different scenarios based on the CLUE-S model and Markov model. Resources Science, 33(12): 2262–2270. (in Chinese)
Zhu Zhanqiang, Liu Liming, Chen Zhantao et al., 2010. Land-use change simulation and assessment of driving factors in the loess hilly region: A case study as Pengyang County. Environmental Monitoring and Assessment, 164(1–4): 133–142.
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Foundation: National Natural Science Foundation of China, No.41171318; National Key Technology Support Program, No.2012BAH32B03; No.2012BAH33B05; Special Fund for Forest Scientific Research in the Public Welfare, No.201204201
Author: Jiang Weiguo (1976–), Associate Professor, specialized in ecological remote sensing and natural hazard and risk analysis.
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Jiang, W., Chen, Z., Lei, X. et al. Simulating urban land use change by incorporating an autologistic regression model into a CLUE-S model. J. Geogr. Sci. 25, 836–850 (2015). https://doi.org/10.1007/s11442-015-1205-8
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DOI: https://doi.org/10.1007/s11442-015-1205-8