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Journal of Geographical Sciences    2015, Vol. 25 Issue (7) : 836-850     DOI: 10.1007/s11442-015-1205-8
Orginal Article |
Simulating urban land use change by incorporating an autologistic regression model into a CLUE-S model
Weiguo JIANG1,2(),Zheng CHEN1,2,Xuan LEI3,*(),Kai JIA1,2,Yongfeng WU4
1. State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University, Beijing 100875, China
2. Key Laboratory of Environmental Change and Natural Disaster, Beijing Normal University, Beijing 100875, China
3. Tianjin University Research Institute of Urban Planning, Tianjin 300073, China
4. Institute of Environment and Sustainable Development in Agriculture, Chinese Academy of Agricultural Sciences, Beijing 100081, China
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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.

Keywords CLUE-S      Chang-Zhu-Tan      simulation and validation      urban land use change     
Fund: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
Corresponding Authors: Xuan LEI     E-mail: jiangweiguo@bnu.edu.cn;lx_aatu@yeah.com
Issue Date: 24 June 2015
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Weiguo JIANG
Zheng CHEN
Xuan LEI
Kai JIA
Yongfeng WU
Cite this article:   
Weiguo JIANG,Zheng CHEN,Xuan LEI, et al. Simulating urban land use change by incorporating an autologistic regression model into a CLUE-S model[J]. Journal of Geographical Sciences, 2015, 25(7): 836-850.
URL:  
http://www.geogsci.com/EN/10.1007/s11442-015-1205-8     OR     http://www.geogsci.com/EN/Y2015/V25/I7/836
Figure 1  Location of the study area
Figure 2  The observation map for 1995 (a); Spatial distribution of traffic system (b); DEM of Chang-Zhu-Tan megalopolis (c); Distribution of river network (d); Distribution of settlements (e)
Variables Built-up area Bare land Green land Wetland Cultivated land
B S.E. B S.E. B S.E. B S.E. B S.E.
DS (km) 0.086 0.021 0.04 0.01 -0.013 0.005 0.057 0.011 - -
DC (km) - - - - - - - - 0.011 0.004
DU (km) -0.039 0.011 - - - - 0.087 0.017 - -
DCS (km) 0.537 0.046 - - -0.184 0.049 - - -0.298 0.046
Elevation (m) -0.446 0.041 -0.062 0.029 1.303 0.042 -0.466 0.077 -1.075 0.042
DEW (km) -0.114 0.007 0.053 0.005 0.018 0.005 - - - -
Slope - - - - - - - - - -
Aspect - - 0.117 0.029 -0.134 0.039 - - - -
DRW (km) 0.058 0.013 - - 0.04 0.017 0.079 0.021 - -
DCT (km) -0.097 0.02 - - - - - - - -
DCR (km) 0.087 0.005 -0.01 0.004 - - 0.041 0.009 -0.042 0.005
DX (km) - - - - - - 0.122 0.035 - -
DBX (km) -0.127 0.011 - - - - -0.102 0.016 0.082 0.008
DVR (km) 0.117 0.034 -0.127 0.039 -0.079 0.036 0.107 0.055 - -
Soil type 0.212 0.057 - - -0.189 0.073 0.4 0.092 -0.255 0.066
Constant -0.108 0.02 - - - - - - -0.143 0.018
Table 1  Logistic regression results
Variables Built-up area Bare land Green land Wetland Cultivated land
B S.E. B S.E. B S.E. B S.E. B S.E.
DS (km) - - - - - - - - - -
DC (km) - - - - - - - - - -
DU (km) - - - - - - 0.07 0.018 - -
DCS (km) - - - - -0.167 0.054 - - -0.214 0.049
Elevation (m) -0.357 0.05 -0.062 0.053 0.702 0.047 -0.218 0.071 -0.715 0.044
DEW (km) -0.038 0.007 0.053 0.008 - - - - - -
Slope 0.111 0.038 0.133 0.043 -0.077 0.03 - - 0.13 0.036
Aspect 0.072 0.021 - - - - 0.109 0.023 -0.052 0.017
DRW (km) - - -0.054 0.012 - - - - - -
DCT (km) - - - - - - - - -0.032 0.005
DCR (km) - - - - - - 0.123 0.035 - -
DX(km) - - 0.052 0.013 - - - - 0.066 0.008
DBX(km) - - - - - - - - - -
DVR (km) - - - - - - - - -0.17 0.074
Soil type -0.082 0.024 - - - - -0.156 0.04 -0.066 0.019
Autocovariate 8.819 0.285 18.94 0.706 3.993 0.164 8.133 0.581 3.16 0.16
Constant 0.399 0.489 -1.995 0.182 -3.088 0.147 1.067 0.79 1.995 0.459
Table 2  Autologistic regression results
Models Built-up area Bare land Green land Wetland Cultivated land
ROC R2 ROC R2 ROC R2 ROC R2 ROC R2
Logistic 0.847 0.455 0.628 0.264 0.823 0.385 0.770 0.269 0.769 0.395
Autologistic 0.941 0.701 0.833 0.481 0.865 0.504 0.847 0459 0.813 0.481
Table 3  The ROC and pseudo R2 of the logistic regression and autologistic regression results
Figure 3  Moran’s I value for Pearson residuals of the logistic and autologistic regression models
Figure 4  Simulated maps of the OL-CLUE-S and the AL-CLUE-S models and the observed maps of Chang-Zhu-Tan megalopolis of 2000, 2005 and 2009
Model 2000 2005 2009
OL-CLUE-S 0.794 0.846 0.754
AL-CLUE-S 0.805 0.872 0.757
Table 4  The Kappa indexes of simulation results from 2000, 2005 and 2009
Figure 5  The simulated Fuzzy Kappa (FK) values of the OL-CLUE-S and the AL-CLUE-S models from 2000, 2005 and 2009
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