Abstract
Urban growth does not always strictly follow historical trends; the government may reshape urban growth patterns with considerations of ecological conservation or other plans. Both urban dynamic rules and landscape characteristics are the two main factors influencing the spatial patterns of cities, and obtaining an optimized spatial pattern is very important for sustainable urban growth. Therefore, in this study, we integrated logistic regression (LR) with the ant colony optimization (ACO) model to analyze the optimal scenario for smart urban growth. The LR model was used to discuss the relationship between urban patterns and environmental variables such as topography, development centers, and traffic conditions. Then, the urban growth probability was generated using the parameters obtained from LR. The ACO model was further integrated to optimize urban land allocation, which can meet the requirement of high growth probability, and a connected and compacted landscape pattern. This can solve the problem of urban land only being allocated by LR from being distributed fragmentarily in the space. With this integrated model, Guangzhou City, a rapidly developing area in China, was selected as a case study. The urban patterns derived from LR, as well as a simulation scenario using logistic regression-based cellular automata (LR-CA), were used in the comparison. Six landscape metrics were chosen to validate the performance of this proposed model at the pattern level. The results show that the LR-ACO model has a better performance in urban land allocation. This study demonstrated that models that couple dynamic rules and planning objectives can provide plausible scenarios for smart urban growth planning.
Similar content being viewed by others
References
Abbott C, Margheim J (2008). Imagining Portland’s urban growth boundary: planning regulation as cultural icon. J Am Plann Assoc, 74(2): 196–208
Aerts J C J H, Eisinger E, Heuvelink G B M, Stewart T J (2003). Using linear integer programming for multi-site land-use allocation. Geogr Anal, 35(2): 148–169
Aljoufie M, Zuidgeest M, Brussel M, van Vliet J, van Maarseveen M (2013). A cellular automata-based land use and transport interaction model applied to Jeddah, Saudi Arabia. Landsc Urban Plan, 112: 89–99
Cao K, Batty M, Huang B, Liu Y, Yu L, Chen J (2011). Spatial multi-objective land use optimization: extensions to the non-dominated sorting genetic algorithm-II. Int J Geogr Inf Sci, 25(12): 1949–1969
Cao K, Huang B, Wang S, Lin H (2012). Sustainable land use optimization using boundary-based fast genetic algorithm. Comput Environ Urban Syst, 36(3): 257–269
Cerreta M, De Toro P (2012). Urbanization suitability maps: a dynamic spatial decision support system for sustainable land use. Earth Syst Dynam, 3(2): 157–171
Chen Y M, Li X, Liu X P, Liu Y L (2010). An agent based model for optimal land allocation (Agent LA) with a contiguity constraint. Int J Geogr Inf Sci, 24(8): 1269–1288
Clarke K C, Gaydos L J (1998). Loose-coupling a cellular automata model and GIS: long-term urban growth prediction for San Francisco and Washington/Baltimore. Int J Geogr Inf Sci, 12(7): 699–714
Dorigo M, Maniezzo V, Colorni A (1996). The ant system: optimization by a colony of cooperating agents. IEEE Trans Syst Man Cybern A Syst Hum, 26(1): 1–13
Feng Y, Liu Y, Tong X, Liu M, Deng S (2011). Modeling dynamic urban growth using cellular automata and particle swarm optimization rules. Landsc Urban Plan, 102(3): 188–196
Godschalk D R (2004). Land use planning challenges: coping with conflicts in visions of sustainable development and livable communities. J Am Plann Assoc, 70(1): 5–13
Gounaridis D, Chorianopoulos I, Koukoulas S (2018). Exploring prospective urban growth trends under different economic outlooks and land-use planning scenarios: the case of Athens. Appl Geogr, 90: 134–144
Hess P M, Sorensen A (2015). Compact, concurrent, and contiguous: smart growth and 50 years of residential planning in the Toronto region. Urban Geogr, 36(1): 127–151
Holzkämper A, Seppelt R (2007). A generic tool for optimizing land-use patterns and landscape structures. Environ Model Softw, 22(12): 1801–1804
Huang K, Liu X, Li X, Liang J, He S (2013). An improved artificial immune system for seeking the Pareto front of land-use allocation problem in large areas. Int J Geogr Inf Sci, 27(5): 922–946
Jantz C A, Goetz S J, Donato D, Claggett P (2010). Designing and implementing a regional urban modeling system using the SLEUTH cellular urban model. Comput Environ Urban Syst, 34(1): 1–16
Jokar Arsanjani J, Helbich M, Kainz W, Darvishi Boloorani A (2013). Integration of logistic regression, Markov chain and cellular automata models to simulate urban expansion. Int J Appl Earth Obs Geoinf, 21: 265–275
Lambin E F, Meyfroidt P (2011). Global land use change, economic globalization, and the looming land scarcity. Proc Natl Acad Sci USA, 108(9): 3465–3472
Lawler J J, Lewis D J, Nelson E, Plantinga A J, Polasky S, Withey J C, Helmers D P, Martinuzzi S, Pennington D, Radeloff V C (2014). Projected land-use change impacts on ecosystem services in the United States. Proc Natl Acad Sci USA, 111(20): 7492–7497
Li F, Gong Y, Cai L, Sun C, Chen Y, Liu Y, Jiang P (2018). Sustainable land-use allocation: a multiobjective particle swarm optimization model and application in Changzhou, China. J Urban Plann Dev, 144(2): 04018010
Li X, Chen Y, Liu X, Li D, He J (2011). Concepts, methodologies, and tools of an integrated geographical simulation and optimization system. Int J Geogr Inf Sci, 25(4): 633–655
Li X, He J, Liu X (2009). Intelligent GIS for solving high-dimensional site selection problems using ant colony optimization techniques. Int J Geogr Inf Sci, 23(4): 399–416
Li X, Lao C, Liu X, Chen Y (2011). Coupling urban cellular automata with ant colony optimization for zoning protected natural areas under a changing landscape. Int J Geogr Inf Sci, 25(4): 575–593
Li X, Yeh A G (2002). Neural-network-based cellular automata for simulating multiple land use changes using GIS. Int J Geogr Inf Sci, 16(4): 323–343
Ligmann-Zielinska A, Church R, Jankowski P (2008). Spatial optimization as a generative technique for sustainable multi-objective land-use allocation. Int J Geogr Inf Sci, 22(6): 601–622
Liu R, Zhang K, Zhang Z, Borthwick A G L (2014). Land-use suitability analysis for urban development in Beijing. J Environ Manage, 145: 170–179
Liu Y, Wang H, Ji Y, Liu Z, Zhao X (2012). Land use zoning at the county level based on a multi-objective particle swarm optimization algorithm. Int J Environ Res Public Health, 9(8): 2801–2826
Ma S, Li X, Cai Y (2017). Delimiting the urban growth boundaries with a modified ant colony optimization model. Comput Environ Urban Syst, 62: 146–155
Masoomi Z, Mesgari M S, Hamrah M (2013). Allocation of urban land uses by multi-objective particle swarm optimization algorithm. Int J Geogr Inf Sci, 27(3): 542–566
Mitsova D, Shuster W, Wang X (2011). A cellular automata model of land cover change to integrate urban growth with open space conservation. Landsc Urban Plan, 99(2): 141–153
Olsen L M, Dale V H, Foster T (2007). Landscape patterns as indicators of ecological change at Fort Benning, Georgia, USA. Landsc Urban Plan, 79(2): 137–149
Persson C (2013). Deliberation or doctrine? Land use and spatial planning for sustainable development in Sweden. Land Use Policy, 34: 301–313
Klepeis P, Turner II B L (2001). Integrated land history and global change science: the example of the Southern Yucatán Peninsular Region project. Land Use Policy, 18(1): 27–39
Poelmans L, Van Rompaey A (2010). Complexity and performance of urban expansion models. Comput Environ Urban Syst, 34(1): 17–27
Rindfuss R R, Walsh S J, Turner B L, Fox J, Mishra V (2004). Developing a science of land change: challenges and methodological issues. Proc Natl Acad Sci USA, 101(39): 13976–13981
Santé I, García A M, Miranda D, Crecente R (2010). Cellular automata models for the simulation of real-world urban processes: a review and analysis. Landsc Urban Plan, 96(2): 108–122
Santé-Riveira I, Boullón-Magán M, Crecente-Maseda R, Miranda-Barrós D (2008). Algorithm based on simulated annealing for land-use allocation. Comput Geosci, 34(3): 259–268
Silva E A, Ahern J, Wileden J (2008). Strategies for landscape ecology: an application using cellular automata models. Prog Plann, 70(4): 133–177
Stewart T J, Janssen R (2014). A multiobjective GIS-based land use planning algorithm. Comput Environ Urban Syst, 46: 25–34
Stewart T J, Janssen R, van Herwijnen M (2004). A genetic algorithm approach to multiobjective land use planning. Comput Oper Res, 31(14): 2293–2313
Tudes S, Yigiter N D (2010). Preparation of land use planning model using GIS based on AHP: case study Adana-Turkey. Bull Eng Geol Environ, 69(2): 235–245
Turner M A (2007). A simple theory of smart growth and sprawl. J Urban Econ, 61(1): 21–44
Verburg P H, de Koning G H J, Kok K, Veldkamp A, Bouma J (1999). A spatial explicit allocation procedure for modelling the pattern of land use change based upon actual land use. Ecol Modell, 116(1): 45–61
Wei Y, Ye X (2014). Urbanization, urban land expansion and environmental change in China. Stochastic Environ Res Risk Assess, 28(4): 757–765
Wu F (2002). Calibration of stochastic cellular automata: the application to rural-urban land conversions. Int J Geogr Inf Sci, 16(8): 795–818
Wu F, Webster C J (1998). Simulation of land development through the integration of cellular automata and multicriteria evaluation. Environ Plann B Plann Des, 25(1): 103–126
Acknowledgements
The authors would like to thank the anonymous reviewers for their suggestions and comments. This research was supported by the National Natural Science Foundation of China (Grant No. 41901311).
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Ma, S., Liu, F., Ma, C. et al. Integrating logistic regression with ant colony optimization for smart urban growth modelling. Front. Earth Sci. 14, 77–89 (2020). https://doi.org/10.1007/s11707-018-0727-7
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11707-018-0727-7