Simulating Urban Growth with Raster and Vector Models: A Case Study for the City of Can Tho, Vietnam

Urban growth has been widely studied and many models (in particular Cellular Automata and Agent-Based Models) have been developed. Most of these models rely on two representations of the geographic space: raster and vector. Both representations have their own strengths and drawbacks. The raster models are simpler to implement and require less data, which explains their success and why most of urban growth models are based on this representation. However, they are not adapted to microscopic dynamics such as, for example, the construction of buildings. To reach such goal, a vector-based representation of space is mandatory. However, very few vector models exist, and none of them is easily adaptable to different case studies. In this paper, we propose to use a simple raster model and to adapt it to a vector representation of the geographic space and processes allowing studying urban growth at fine scale. Both models have been validated by a case study concerning the city of Can Tho, Vietnam.

[1]  Eric Vaz,et al.  Spatiotemporal simulation of urban growth patterns using agent-based modeling: The case of Tehran , 2013 .

[2]  Anne Ruas,et al.  A Multi-Agent System for the simulation of urban dynamics , 2010 .

[3]  Roger White Modeling Multi-scale Processes in a Cellular Automata Framework , 2006 .

[4]  Elisabete A. Silva,et al.  Land use–transport interaction modeling: A review of the literature and future research directions , 2015 .

[5]  Andrew M. Colman,et al.  The complexity of cooperation: Agent-based models of competition and collaboration , 1998, Complex..

[6]  Andrew T. Crooks,et al.  Constructing and implementing an agent-based model of residential segregation through vector GIS , 2010, Int. J. Geogr. Inf. Sci..

[7]  Keith C. Clarke,et al.  A Self-Modifying Cellular Automaton Model of Historical Urbanization in the San Francisco Bay Area , 1997 .

[8]  P. Waddell UrbanSim: Modeling Urban Development for Land Use, Transportation, and Environmental Planning , 2002 .

[9]  Michel Grabisch,et al.  A decade of application of the Choquet and Sugeno integrals in multi-criteria decision aid , 2010, Ann. Oper. Res..

[10]  M. Batty,et al.  Modeling urban dynamics through GIS-based cellular automata , 1999 .

[11]  I. Benenson MULTI-AGENT SIMULATIONS OF RESIDENTIAL DYNAMICS IN THE CITY , 1998 .

[12]  Yongjiu Feng,et al.  A Logistic Based Cellular Automata Model for Continuous Urban Growth Simulation: A Case Study of the Gold Coast City, Australia , 2012 .

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

[14]  Paul M. Torrens,et al.  Simulating Sprawl , 2006 .

[15]  Dominique Peeters,et al.  Morphological similarities between DBM and a microeconomic model of sprawl , 2011, J. Geogr. Syst..

[16]  Bruce Edmonds,et al.  From KISS to KIDS - An 'Anti-simplistic' Modelling Approach , 2004, MABS.

[17]  Juste Raimbault,et al.  A Hybrid Network/Grid Model of Urban Morphogenesis and Optimization , 2016, ArXiv.

[18]  A. Hagen-Zanker,et al.  A fuzzy set approach to assess the predictive accuracy of land use simulations , 2013 .

[19]  J. Portugali,et al.  The face of the city is its information , 2003 .

[20]  M. Wegener Overview of Land Use Transport Models , 2004 .

[21]  Benoit Gaudou,et al.  GAMA 1.6: Advancing the Art of Complex Agent-Based Modeling and Simulation , 2013, PRIMA.

[22]  M. Barthelemy,et al.  Modeling the polycentric transition of cities. , 2013, Physical review letters.

[23]  Sonit Bafna,et al.  Space Syntax , 2003 .