Delineating multi-scenario urban growth boundaries with a CA-based FLUS model and morphological method

Abstract Urban growth boundaries (UGBs) have been commonly regarded as a useful tool for controlling urban sprawl. There is a need to create models that can establish plausible UGBs for fast growing regions. Previous methods have merely focused on establishing a single UGB scenario over different time intervals, but rarely considered the influences of macro policy (e.g., future urban demand) and spatial policy (e.g., master plan) for regional planning. However, the spatial patterns of urban expansion are significantly affected by regional planning. In this paper, a CA-based method called the future land use simulation (FLUS) is applied to the delineation of UGBs. We argue that the delineation needs to integrate the top-down approach with CA for projecting complex land use changes under designed scenarios. The system dynamics model (SD) and cellular automaton model (CA) were interactively coupled in the FLUS model during the projection period. The top-down SD is used to project scenarios that relate to macro policy and socioeconomic status, and the bottom-up CA accounts for urban growth simulations under the influence of different driving factors and spatial planning policies. A morphological technology based on erosion and dilation is further proposed to generate the UGBs from the FLUS model’s simulated urban forms. The proposed UGB-FLUS model was applied to the establishment of UGBs in the Pearl River Delta region (PRD) from 2020 to 2050. The results demonstrate that the method can support urban planning by generating feasible patterns for UGBs under different planning scenarios.

[1]  Myung-Jin Jun,et al.  The Effects of Portland's Urban Growth Boundary on Urban Development Patterns and Commuting , 2004 .

[2]  Zhiyong Hu,et al.  Modeling urban growth in Atlanta using logistic regression , 2007, Comput. Environ. Urban Syst..

[3]  A. M. Hersperger,et al.  Containing urban sprawl—Evaluating effectiveness of urban growth boundaries set by the Swiss Land Use Plan , 2009 .

[4]  Yan Liu,et al.  Modeling dynamic urban growth using cellular automata and particle swarm optimization rules , 2011 .

[5]  A. Yeh,et al.  Economic Development and Agricultural Land Loss in the Pearl River Delta , 1997 .

[6]  Yizhen Gu,et al.  Spatiotemporal heterogeneity of urban planning implementation effectiveness: Evidence from five urban master plans of Beijing , 2012 .

[7]  Stefan Coe,et al.  Erratum to: Urban growth patterns and growth management boundaries in the Central Puget Sound, Washington, 1986–2007 , 2011, Urban Ecosystems.

[8]  Xia Li,et al.  Sustainable Land Development Model for Rapid Growth Areas Using GIS , 1998, Int. J. Geogr. Inf. Sci..

[9]  Y. Wei,et al.  Economic transition and urban land expansion in Provincial China , 2014 .

[10]  Xiaoping Liu,et al.  Simulating urban growth by integrating landscape expansion index (LEI) and cellular automata , 2014, Int. J. Geogr. Inf. Sci..

[11]  B. Bhatta,et al.  Modelling of urban growth boundary using geoinformatics , 2009, Int. J. Digit. Earth.

[12]  Keith C. Clarke,et al.  Loose-Coupling a Cellular Automaton Model and GIS: Long-Term Urban Growth Prediction for San Francisco and Washington/Baltimore , 1998, Int. J. Geogr. Inf. Sci..

[13]  Ying Long,et al.  Urban growth boundaries of the Beijing Metropolitan Area: Comparison of simulation and artwork , 2013 .

[14]  Xiaoping Liu,et al.  A future land use simulation model (FLUS) for simulating multiple land use scenarios by coupling human and natural effects , 2017 .

[15]  Peter H. Verburg,et al.  Predictive ability of logistic regression, auto-logistic regression and neural network models in empirical land-use change modeling – a case study , 2011, Int. J. Geogr. Inf. Sci..

[16]  Peter H. Verburg,et al.  Simulating feedbacks in land use and land cover change models , 2006, Landscape Ecology.

[17]  Eddo John Coiacetto,et al.  Residential Sub-market Targeting by Developers in Brisbane , 2007 .

[18]  Tan Yua.  Return to the nature of city: Also on the layout of streets in permeable city , 2005 .

[19]  Bryan C. Pijanowski,et al.  An urban growth boundary model using neural networks, GIS and radial parameterization: An application to Tehran, Iran , 2011 .

[20]  Amin Tayyebi,et al.  Predicting the expansion of an urban boundary using spatial logistic regression and hybrid raster–vector routines with remote sensing and GIS , 2014, Int. J. Geogr. Inf. Sci..

[21]  Yongjiu Feng,et al.  Modeling dynamic urban land-use change with geographical cellular automata and generalized pattern search-optimized rules , 2017, Int. J. Geogr. Inf. Sci..

[22]  Shih-Kung Lai,et al.  Effectiveness of urban construction boundaries in Beijing: an assessment , 2009 .

[23]  Peter H Verburg,et al.  Land cover change or land‐use intensification: simulating land system change with a global‐scale land change model , 2013, Global change biology.

[24]  Xiaoping Liu,et al.  Capturing the varying effects of driving forces over time for the simulation of urban growth by using survival analysis and cellular automata , 2016 .

[25]  David L. A. Gordon,et al.  GROSS DENSITY AND NEW URBANISM , 2005 .

[26]  Xiaoping Liu,et al.  Sensing spatial distribution of urban land use by integrating points-of-interest and Google Word2Vec model , 2017, Int. J. Geogr. Inf. Sci..

[27]  A. Yeh,et al.  An integrated remote sensing and GIS approach in the monitoring and evaluation of rapid urban growth for sustainable development in the Pearl River Delta, China , 1997 .

[28]  D. Clapham,et al.  The Reform of Housing in Eastern Europe and the Soviet Union , 1994 .

[29]  Shaowen Wang,et al.  Sustainable land use optimization using Boundary-based Fast Genetic Algorithm , 2012, Comput. Environ. Urban Syst..

[30]  Arthur Christian Nelson,et al.  Assessing urban growth management ?: The case of Portland, Oregon, the USA's largest urban growth boundary , 1993 .

[31]  Madalasa Venkataraman,et al.  Analyzing Urban Growth Boundary Effects in the City of Bengaluru , 2014 .

[32]  Lucy R. Hutyra,et al.  Urban growth patterns and growth management boundaries in the Central Puget Sound, Washington, 1986–2007 , 2011, Urban Ecosystems.

[33]  Xiaocong Xu,et al.  A New Global Land-Use and Land-Cover Change Product at a 1-km Resolution for 2010 to 2100 Based on Human–Environment Interactions , 2017 .

[34]  Xia Li,et al.  Delimiting the urban growth boundaries with a modified ant colony optimization model , 2017, Comput. Environ. Urban Syst..

[35]  Tong Zhang,et al.  Proximate control stream assisted video transcoding for heterogeneous content delivery network , 2014, 2014 IEEE International Conference on Image Processing (ICIP).

[36]  Maria Cerreta,et al.  Urbanization suitability maps: a dynamic spatial decision support system for sustainable land use , 2012 .

[37]  Tiyan Shen,et al.  Evaluation of plan implementation in the transitional China: A case of Guangzhou city master plan , 2011 .

[38]  Justice Nana Inkoom,et al.  Explicit Modeling of Spatial Growth Patterns in Shama, Ghana: an Agent-Based Approach , 2017, Journal of Geovisualization and Spatial Analysis.

[39]  Perumal Balakrishnan,et al.  Change analysis of land use/land cover and modelling urban growth in Greater Doha, Qatar , 2015, Ann. GIS.

[40]  Haoying Han,et al.  Evaluating the effectiveness of urban growth boundaries using human mobility and activity records , 2015 .

[41]  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..

[42]  Yatao Zhang,et al.  Mapping fine-scale population distributions at the building level by integrating multisource geospatial big data , 2017, Int. J. Geogr. Inf. Sci..

[43]  Qiping Shen,et al.  Driving force of urban growth and regional planning: A case study of China's Guangdong Province , 2013 .

[44]  P. Verburg,et al.  From land cover change to land function dynamics: a major challenge to improve land characterization. , 2009, Journal of environmental management.

[45]  Xiaohu Zhang,et al.  Simulating land-use dynamics under planning policies by integrating artificial immune systems with cellular automata , 2010, Int. J. Geogr. Inf. Sci..

[46]  Bryan C. Pijanowski,et al.  Two rule-based Urban Growth Boundary Models applied to the Tehran Metropolitan Area, Iran , 2011 .

[47]  K. Overmars,et al.  Combining top-down and bottom-up dynamics in land use modeling: exploring the future of abandoned farmlands in Europe with the Dyna-CLUE model , 2009, Landscape Ecology.

[48]  Ajay Narayanan.  Fast Binary Dilation/Erosion Algorithm Using Kernel Subdivision , 2006, ACCV.

[49]  Michel Savy,et al.  High speed rail in China and its potential impacts on urban and regional development , 2011 .

[50]  Yu Liu,et al.  Simulation of Dynamic Urban Growth with Partial Least Squares Regression-Based Cellular Automata in a GIS Environment , 2016, ISPRS Int. J. Geo Inf..

[51]  Linda See,et al.  A Fuzzy Cellular Automata Urban Growth Model (FCAUGM) for the City of Riyadh, Saudi Arabia. Part 2: Scenario Testing , 2009 .

[52]  Erle C. Ellis,et al.  A global assessment of market accessibility and market influence for global environmental change studies , 2011, Environmental Research Letters.

[53]  Xia Li,et al.  Combining system dynamics and hybrid particle swarm optimization for land use allocation , 2013 .

[54]  Andres Monzon,et al.  Efficiency and spatial equity impacts of high-speed rail extensions in urban areas , 2013 .

[55]  Keith C. Clarke,et al.  Why simulate cities? , 2014 .

[56]  Keith C. Clarke,et al.  The Use of Scenarios in Land-Use Planning , 2003 .

[57]  PETER H. VERBURG,et al.  Modeling the Spatial Dynamics of Regional Land Use: The CLUE-S Model , 2002, Environmental management.

[58]  Anthony Gar-On Yeh,et al.  Neural-network-based cellular automata for simulating multiple land use changes using GIS , 2002, Int. J. Geogr. Inf. Sci..