Simulating urban land use change by incorporating an autologistic regression model into a CLUE-S model

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 R^2 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.

Simulating multiple urban land use changes by integrating transportation accessibility and a vector-based cellular automata:a case study on city of Toronto

The accessibility provided by the transportation system plays an essential role in driving urban growth and urban functional land use changes.Conventional studies on land use simulation usually simplified the accessibility as proximities and adopted the grid-based simulation strategy,leading to the insufficiencies of characterizing spatial geometry of land parcels and simulating subtle land use changes among urban functional types.To overcome these limita-tions,an Accessibility-interacted Vector-based Cellular Automata(A-VCA)model was proposed for the better simulation of realistic land use change among different urban functional types.The accessibility at both local and zonal scales derived from actual travel time data was considered as a key driver of fine-scale urban land use changes and was integrated into the vector-based CA simulation process.The proposed A-VCA model was tested through the simulation of urban land use changes in the City of Toronto,Canada,during 2012-2016.A vector-based CA without considering the driving factor of accessibility(VCA)and a popular grid-based CA model(Future Land Use Simulation,FLUS)were also implemented for compar-isons.The simulation results reveal that the proposed A-VCA model is capable of simulating fine-scale urban land use changes with satisfactory accuracy and good morphological feature(kappa=0.907,figure of merit=0.283,and cumulative producer’s accuracy=72.83%±1.535%).The comparison also shows significant outperformance of the A-VCA model against the VCA and FLUS models,suggesting the effectiveness of the accessibility-interactive mechanism and vector-based simulation strategy.The proposed model provides new tools for a better simula-tion of fine-scale land use changes and can be used in assisting the formulation of urban and transportation planning.