Arid areas with low precipitation and sparse vegetation typically yield compact urban pattern,and drought directly impacts urban site selection,growth processes,and future scenarios.Spatial simulation and projection b...Arid areas with low precipitation and sparse vegetation typically yield compact urban pattern,and drought directly impacts urban site selection,growth processes,and future scenarios.Spatial simulation and projection based on cellular automata(CA)models is important to achieve sustainable urban development in arid areas.We developed a new CA model using bat algorithm(BA)named bat algorithm-probability-of-occurrence-cellular automata(BA-POO-CA)model by considering drought constraint to accurately delineate urban growth patterns and project future scenarios of Urumqi City and its surrounding areas,located in Xinjiang Uygur Autonomous Region,China.We calibrated the BA-POO-CA model for the drought-prone study area with 2000 and 2010 data and validated the model with 2010 and 2020 data,and finally projected its urban scenarios in 2030.The results showed that BA-POO-CA model yielded overall accuracy of 97.70%and figure-of-merits(FOMs)of 35.50%in 2010,and 97.70%and 26.70%in 2020,respectively.The inclusion of drought intensity factor improved the performance of BA-POO-CA model in terms of FOMs,with increases of 5.50%in 2010 and 7.90%in 2020 than the model excluding drought intensity factor.This suggested that the urban growth of Urumqi City was affected by drought,and therefore taking drought intensity factor into account would contribute to simulation accuracy.The BA-POO-CA model including drought intensity factor was used to project two possible scenarios(i.e.,business-as-usual(BAU)scenario and ecological scenario)in 2030.In the BAU scenario,the urban growth dominated mainly in urban fringe areas,especially in the northern part of Toutunhe District,Xinshi District,and Midong District.Using exceptional and extreme drought areas as a spatial constraint,the urban growth was mainly concentrated in the"main urban areas-Changji-Hutubi"corridor urban pattern in the ecological scenario.The results of this research can help to adjust urban planning and development policies.Our model is readily applicable to simulating urban growth and future scenarios in global arid areas such as Northwest China and Africa.展开更多
The production and selection of driving factors are essential to building a strong Cellular Automata(CA)model of dynamic urban growth simulation.A critical issue that should be addressed is how the spatial representat...The production and selection of driving factors are essential to building a strong Cellular Automata(CA)model of dynamic urban growth simulation.A critical issue that should be addressed is how the spatial representation and the generalization scale of driving factors affect the CA modeling and the simulation results.It is challenging to evaluate the effectiveness of the selected driving factors because they have no true values.To explore the impacts of the generalization scales,we produced nine sets of driving factors at nine scales to calibrate the CA models based on the Particle Swarm Optimization(CAPSO)and applied them to simulate urban growth of Suzhou during 2000-2020.Our results show that the driving factors at a smaller scale have much better performance in explaining urban growth simulations as inferred by the Explained Residual Deviance(ERD)of the Generalized Additive Models(GAMs).Specifically,the ERD declined from 51.9%to 45.9%as the factor scale became larger during 2000-2020,but there was a peak value(52.2%)at Scale-2.For all simulations during 2000-2020,the CAPSO models with larger-scale factors have slightly lower overall accuracy and Figure-of-Merit(FOM),which respectively decreased by 3.1%and 4.4%as compared to the CA models with scale-free factors.We concluded that the driving factors at a smaller scale(200~400 m for point-like facilities and 7~14 m for line-like facilities)can build more accurate CA models to simulate urban growth patterns,and the optimal scale for factors can be identified using the ERD.This study contributes to the methods of evaluating the effectiveness of driving factor production and reveals the impacts of spatial representation of factors on the CA modeling and simulation considering the factor generalization scales.展开更多
基金supported the National Natural Science Foundation of China(42071371)the National Key R&D Program of China(2018YFB0505400).
文摘Arid areas with low precipitation and sparse vegetation typically yield compact urban pattern,and drought directly impacts urban site selection,growth processes,and future scenarios.Spatial simulation and projection based on cellular automata(CA)models is important to achieve sustainable urban development in arid areas.We developed a new CA model using bat algorithm(BA)named bat algorithm-probability-of-occurrence-cellular automata(BA-POO-CA)model by considering drought constraint to accurately delineate urban growth patterns and project future scenarios of Urumqi City and its surrounding areas,located in Xinjiang Uygur Autonomous Region,China.We calibrated the BA-POO-CA model for the drought-prone study area with 2000 and 2010 data and validated the model with 2010 and 2020 data,and finally projected its urban scenarios in 2030.The results showed that BA-POO-CA model yielded overall accuracy of 97.70%and figure-of-merits(FOMs)of 35.50%in 2010,and 97.70%and 26.70%in 2020,respectively.The inclusion of drought intensity factor improved the performance of BA-POO-CA model in terms of FOMs,with increases of 5.50%in 2010 and 7.90%in 2020 than the model excluding drought intensity factor.This suggested that the urban growth of Urumqi City was affected by drought,and therefore taking drought intensity factor into account would contribute to simulation accuracy.The BA-POO-CA model including drought intensity factor was used to project two possible scenarios(i.e.,business-as-usual(BAU)scenario and ecological scenario)in 2030.In the BAU scenario,the urban growth dominated mainly in urban fringe areas,especially in the northern part of Toutunhe District,Xinshi District,and Midong District.Using exceptional and extreme drought areas as a spatial constraint,the urban growth was mainly concentrated in the"main urban areas-Changji-Hutubi"corridor urban pattern in the ecological scenario.The results of this research can help to adjust urban planning and development policies.Our model is readily applicable to simulating urban growth and future scenarios in global arid areas such as Northwest China and Africa.
基金partially supported by the National Key Research and Development Program of China(2016YFA0600104)supported by donations made by Delos Living LLC,and the Cyrus Tang Foundation+2 种基金supported by the National Natural Science Foundation of China(41471419)Beijing Institute of Urban Planningsupported by the Fundamental Research Funds for the Central Universities(CCNU19TD002).
基金the National Natural Science Foundation of China[Grant No.42071371]the National Key R&D Program of China[Grant Nos.2018YFB0505000 and 2018YFB0505400].
文摘The production and selection of driving factors are essential to building a strong Cellular Automata(CA)model of dynamic urban growth simulation.A critical issue that should be addressed is how the spatial representation and the generalization scale of driving factors affect the CA modeling and the simulation results.It is challenging to evaluate the effectiveness of the selected driving factors because they have no true values.To explore the impacts of the generalization scales,we produced nine sets of driving factors at nine scales to calibrate the CA models based on the Particle Swarm Optimization(CAPSO)and applied them to simulate urban growth of Suzhou during 2000-2020.Our results show that the driving factors at a smaller scale have much better performance in explaining urban growth simulations as inferred by the Explained Residual Deviance(ERD)of the Generalized Additive Models(GAMs).Specifically,the ERD declined from 51.9%to 45.9%as the factor scale became larger during 2000-2020,but there was a peak value(52.2%)at Scale-2.For all simulations during 2000-2020,the CAPSO models with larger-scale factors have slightly lower overall accuracy and Figure-of-Merit(FOM),which respectively decreased by 3.1%and 4.4%as compared to the CA models with scale-free factors.We concluded that the driving factors at a smaller scale(200~400 m for point-like facilities and 7~14 m for line-like facilities)can build more accurate CA models to simulate urban growth patterns,and the optimal scale for factors can be identified using the ERD.This study contributes to the methods of evaluating the effectiveness of driving factor production and reveals the impacts of spatial representation of factors on the CA modeling and simulation considering the factor generalization scales.
