Interoperable scenario simulation of land-use policy for Beijing–Tianjin–Hebei region, China

In land-use change studies, scenario simulations cannot be effectively realized because of Geographic Information System (GIS) temporal-spatial interoperability bottlenecks. Based on a previous temporal-spatial dynamics method (TSDM) established by the author, this study extended the previous model and proposed an extended TSDM (ETSDM): (1) The neighborhood of cellular automata (CA) model was extended to a “Square + Circle” neighborhood, making the neighborhood more realistic and improving the simulation accuracy to a certain extent. (2) To achieve dynamic data exchange between the CA model and GIS, the scenario simulation of temporal and spatial visual interoperability from a national planning scheme or spatial location delineation to planning implementation effects can be implemented. Based on land-use data for 1995, 2005, and 2013, the simulation accuracy of the ETSDM was verified and development patterns were predicted under the following scenarios. Scenario 1 used the independent Beijing, Tianjin, and Hebei Province, and was designed as a blank control. Scenario 2 used the coordinated Beijing–Tianjin–Hebei (BTH) development area. This area was projected in order to study the probable land-use patterns in temporal and spatial dimensions under the effects of national policy data. Scenario 3 added the Xiongan New Area on the basis of Scenario 2, which was used to explore the influences of sudden land-use policies on regional land-use patterns. The results indicate that: (1) A “Square + Circle” neighborhood details the type of neighborhood cells and has an approximately 1% accuracy improvement relative to the general neighborhood rules; (2) According to the interactive operation in the model, land-use graph-number changes in the specific target region under different land-use policies can be monitored; and (3) Under different development policies, the built-up land gross of Beijing will be conserved approximately 600 km2, along with the coordinated development of the BTH region and the establishment of the Xiongan New Area in 2030. At the same time, cropland conditions will be improved. A reason for the results may be that some of the non-capital functions will be transferred to Tianjin and Hebei Province under the national policies.

[1]  Lin Xiao,et al.  Simulation of urban expansion and encroachment using cellular automata and multi-agent system model—A case study of Tianjin metropolitan region, China , 2016 .

[2]  Lu Dai,et al.  Simulation of urban expansion patterns by integrating auto-logistic regression, Markov chain and cellular automata models , 2015 .

[3]  Wenzhong Shi,et al.  Development of Voronoi-based cellular automata -an integrated dynamic model for Geographical Information Systems , 2000, Int. J. Geogr. Inf. Sci..

[4]  Youjia Liang,et al.  Modeling urban growth in the middle basin of the Heihe River, northwest China , 2014, Landscape Ecology.

[5]  Norio Okada,et al.  Modeling urban expansion scenarios by coupling cellular automata model and system dynamic model in Beijing, China , 2006 .

[6]  P. Kyle,et al.  Land‐use change trajectories up to 2050: insights from a global agro‐economic model comparison , 2014 .

[7]  R. White,et al.  High-resolution integrated modelling of the spatial dynamics of urban and regional systems , 2000 .

[8]  Jiyuan Liu,et al.  Spatiotemporal characteristics, patterns, and causes of land-use changes in China since the late 1980s , 2014, Journal of Geographical Sciences.

[9]  F. Wu,et al.  Simulation of Land Development through the Integration of Cellular Automata and Multicriteria Evaluation , 1998 .

[10]  Y. Hayashi,et al.  Application of an integrated system dynamics and cellular automata model for urban growth assessment: A case study of Shanghai, China , 2009 .

[11]  Jamal Jokar Arsanjani,et al.  ntegration of logistic regression , Markov chain and cellular automata odels to simulate urban expansion amal , 2012 .

[12]  B. Amiri,et al.  Simulating urban expansion and scenario prediction using a cellular automata urban growth model, SLEUTH, through a case study of Karaj City, Iran , 2015 .

[13]  Xiaoping Liu,et al.  An extended cellular automaton using case‐based reasoning for simulating urban development in a large complex region , 2006 .

[14]  Rinku Roy Chowdhury,et al.  Does zoning matter? A comparative analysis of landscape change in Redland, Florida using cellular automata , 2014 .

[15]  Hongbin Wang,et al.  A new temporal–spatial dynamics method of simulating land-use change , 2017 .

[16]  J. Bouma,et al.  A spatial explicit allocation procedure for modelling the pattern of land use change based upon actual land use , 1999 .

[17]  Hualin Xie,et al.  Exploring the factors influencing ecological land change for China's Beijing–Tianjin–Hebei Region using big data , 2017 .

[18]  Xiubing Li,et al.  Urban land expansion and arable land loss in China - a case study of Beijing-Tianjin-Hebei region , 2005 .

[19]  Russell G. Congalton,et al.  A review of assessing the accuracy of classifications of remotely sensed data , 1991 .

[20]  Patrick Hostert,et al.  Uncovering land-use dynamics driven by human decision-making - A combined model approach using cellular automata and system dynamics , 2012, Environ. Model. Softw..

[21]  中華人民共和国国家統計局 China statistical yearbook , 1988 .

[22]  Hualin Xie,et al.  Spatial disparities of regional forest land change based on ESDA and GIS at the county level in Beijing-Tianjin-Hebei area , 2012, Frontiers of Earth Science.

[23]  H. Mooney,et al.  Human Domination of Earth’s Ecosystems , 1997, Renewable Energy.

[24]  Tong Xu,et al.  Incorporation of extended neighborhood mechanisms and its impact on urban land-use cellular automata simulations , 2016, Environ. Model. Softw..

[25]  Hai-long Ma,et al.  Exploring the relationship between urbanization and the eco-environment-A case study of Beijing-Tianjin-Hebei region , 2014 .

[26]  B. Pijanowski,et al.  Urban expansion and its consumption of high-quality farmland in Beijing, China , 2015 .

[27]  Roger White,et al.  Cellular Automata and Fractal Urban Form: A Cellular Modelling Approach to the Evolution of Urban Land-Use Patterns , 1993 .

[28]  Xingjian Liu,et al.  Simulating urban expansion using a cloud-based cellular automata model: A case study of Jiangxia, Wuhan, China , 2013 .

[29]  H. Tian,et al.  Spatial and temporal patterns of China's cropland during 1990¿2000: An analysis based on Landsat TM data , 2005 .

[30]  Xiaoping Liu,et al.  Modeling urban land-use dynamics in a fast developing city using the modified logistic cellular automaton with a patch-based simulation strategy , 2014, Int. J. Geogr. Inf. Sci..

[31]  Xiangzheng Deng,et al.  Land-use/land-cover change and ecosystem service provision in China. , 2017, The Science of the total environment.

[32]  Mark Brussel,et al.  Logistic regression and cellular automata-based modelling of retail, commercial and residential development in the city of Ahmedabad, India , 2014 .

[33]  G H Huang,et al.  A system dynamics approach for regional environmental planning and management: a study for the Lake Erhai Basin. , 2001, Journal of environmental management.

[34]  Jian Peng,et al.  Spatial identification of multifunctional landscapes and associated influencing factors in the Beijing-Tianjin-Hebei region, China , 2016 .