Simulation of urban expansion and encroachment using cellular automata and multi-agent system model—A case study of Tianjin metropolitan region, China

Abstract The combination model of cellular automata and multi-agent system were used in this paper for simulating spatio-temporal dynamics of urban expansion and its encroachment on other lands at the regional scale. The human system(contain authorities and residents) and their behavior, the landscape system and its behavior as well as the inter actions between them were all simulated in this paper. The behavior of human system is established based on multi-agent system, the authority agent and the resident agent were both regarded as abstract entities. The cellular automata is embedded into the model for simulating the spontaneous urban growth. The impact of neighbor cells were considered so that the expansion of urban lands can be limited near the existed urban lands. Moreover, the rural residential lands have higher probability to convert to urban lands if they were close to the cities or towns. Simulation of urban expansion is undertaken on the time series from 2000 to 2020 for Tianjin metropolitan region, the largest open coastal city in northern China. The results show that Tianjin’s urban lands focus on the epitaxial expansion around the central city accompanied with the growing exurb expansion distributing in multiple districts and counties. The croplands are taken the most area of the land-use types, 1764.03 km2 are converted to urban lands, and more than one fourth of the rural residential lands are changed to urban lands in 2000–2020. The urban development and the cropland protection should be both taken into account to minimize the threats on food security and ecological environment.

[1]  J. Rademacker,et al.  Cross section for forwardJ/Ψ production in p anti-p collisions at sqrt[s]=1.8 TeV , 2002 .

[2]  Isabelle Reginster,et al.  Scenarios of Future Urban Land Use in Europe , 2006 .

[3]  J. R. Landis,et al.  The measurement of observer agreement for categorical data. , 1977, Biometrics.

[4]  Xiaolu Gao,et al.  Rural settlement land dynamic modes and policy implications in Beijing metropolitan region, China , 2014 .

[5]  Jianguo Wu,et al.  Modeling urban landscape dynamics: A case study in Phoenix, USA , 2004, Urban Ecosystems.

[6]  Model for urban and indoor cellular propagation using percolation theory , 2000 .

[7]  Jacques Ferber,et al.  Multi-agent systems - an introduction to distributed artificial intelligence , 1999 .

[8]  Ashton Shortridge,et al.  Complex systems models and the management of error and uncertainty , 2008 .

[9]  M. Janssen,et al.  Multi-Agent Systems for the Simulation of Land-Use and Land-Cover Change: A Review , 2003 .

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

[11]  A Veldkamp,et al.  Modelling land use change and environmental impact. , 2004, Journal of environmental management.

[12]  R. Hobbs,et al.  Key issues and research priorities in landscape ecology: An idiosyncratic synthesis , 2002, Landscape Ecology.

[13]  Jianguo Wu,et al.  Making the Case for Landscape Ecology , 2008, Landscape Journal.

[14]  Xia Li,et al.  Defining agents' behaviors to simulate complex residential development using multicriteria evaluation. , 2007, Journal of environmental management.

[15]  Dominique Peeters,et al.  Spatial configurations in a periurban city. A cellular automata-based microeconomic model , 2007 .

[16]  Michael Batty,et al.  Agents, Cells, and Cities: New Representational Models for Simulating Multiscale Urban Dynamics , 2005 .

[17]  A. Veldkamp,et al.  Pixels or Agents? Modelling land-use and land-cover change , 2005 .

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

[19]  J. Gareth Polhill,et al.  Agent-based land-use models: a review of applications , 2007, Landscape Ecology.

[20]  E. Lambin,et al.  Dynamics of Land-Use and Land-Cover Change in Tropical Regions , 2003 .

[21]  Jianguo Wu,et al.  Simulating spatiotemporal dynamics of urbanization with multi-agent systems—A case study of the Phoenix metropolitan region, USA , 2011 .

[22]  W. Zipperer,et al.  Urban ecological systems: linking terrestrial ecological, physical, and socioeconomic components of metropolitan areas , 2001 .

[23]  Quang Bao Le,et al.  Land-Use Dynamic Simulator (LUDAS): A multi-agent system model for simulating spatio-temporal dynamics of coupled human-landscape system. I. Structure and theoretical specification , 2008, Ecol. Informatics.

[24]  Corentin M. Fontaine,et al.  An agent-based approach to model future residential pressure on a regional landscape , 2009, Landscape Ecology.

[25]  E. Lambin,et al.  The emergence of land change science for global environmental change and sustainability , 2007, Proceedings of the National Academy of Sciences.

[26]  S. Carpenter,et al.  Global Consequences of Land Use , 2005, Science.

[27]  Jianguo Wu,et al.  Comparing urbanization patterns in Guangzhou of China and Phoenix of the USA: The influences of roads and rivers , 2015 .

[28]  Arnold K. Bregt,et al.  An agent-based approach to model land-use change at a regional scale , 2010, Landscape Ecology.

[29]  Jianguo Wu,et al.  A spatially explicit hierarchical approach to modeling complex ecological systems: theory and applications , 2002 .

[30]  Wen Dong,et al.  Expansion of Ürümqi urban area and its spatial differentiation , 2007 .

[31]  Paul M. Torrens,et al.  Ucl Centre for Advanced Spatial Analysis Can Geocomputation save Urban Simulation? Throw Some Agents into the Mixture, Simmer and Wait , 2022 .

[32]  P. McCullagh,et al.  Generalized Linear Models , 1984 .

[33]  Wenli Huang,et al.  Monitoring Urban Expansion in Beijing, China by Multi-Temporal TM and SPOT Images , 2008, IGARSS 2008 - 2008 IEEE International Geoscience and Remote Sensing Symposium.

[34]  J. Casti.  Would-Be Worlds: How Simulation Is Changing the Frontiers of Science , 1996 .

[35]  Nicholas Mark Gotts,et al.  Comparison of empirical methods for building agent-based models in land use science , 2007 .

[36]  Steen Rasmussen,et al.  Urban growth simulation from "first principles". , 2002, Physical review. E, Statistical, nonlinear, and soft matter physics.

[37]  P. McCullagh,et al.  Generalized Linear Models , 1992 .

[38]  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..

[39]  A. M. Hersperger,et al.  Driving forces of landscape change - current and new directions , 2004, Landscape Ecology.

[40]  Lin Liu,et al.  A bottom‐up approach to discover transition rules of cellular automata using ant intelligence , 2008, Int. J. Geogr. Inf. Sci..

[41]  S. An,et al.  The spatiotemporal dynamics of rapid urban growth in the Nanjing metropolitan region of China , 2007, Landscape Ecology.

[42]  R. Gil Pontius,et al.  Modeling the spatial pattern of land-use change with GEOMOD2: application and validation for Costa Rica , 2001 .

[43]  Jianguo Wu,et al.  A gradient analysis of urban landscape pattern: a case study from the Phoenix metropolitan region, Arizona, USA , 2004, Landscape Ecology.

[44]  Brian Muller,et al.  Residential Location and the Biophysical Environment: Exurban Development Agents in a Heterogeneous Landscape , 2007 .

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

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

[47]  Michael Batty,et al.  SERIES Key Challenges in Agent-Based Modelling for GeoSpatial Simulation , 2007 .

[48]  Juval Portugali,et al.  Self-Organization and the City , 2009, Encyclopedia of Complexity and Systems Science.

[49]  Guangjin Tian,et al.  Modeling urban expansion policy scenarios using an agent-based approach for Guangzhou Metropolitan Region of China , 2014 .

[50]  P. Stern,et al.  A second environmental science: human-environment interactions. , 1993, Science.

[51]  François Bousquet,et al.  Multi-agent simulations and ecosystem management: a review , 2004 .

[52]  J. R. Landis,et al.  The analysis of longitudinal polytomous data: generalized estimating equations and connections with weighted least squares. , 1993, Biometrics.