Exploring the effects of partitioned transition rules upon urban growth simulation in a megacity region: a comparative study of cellular automata-based models in the Greater Wuhan Area

The question of where to accommodate future urban expansion has become a politically sensitive issue in many regions. Against the backdrop of ‘urban compaction’ policy, this study uses population surface modelling and cellular automata (CA) to conduct an empirical urban growth simulation for Southeast England. This implementation leads to a consideration of the proper balance between the theoretical abstraction of self-organised growth and empirical constraints to land development. Specifically, we use 1991 and 1997 postcode directories to construct population surfaces. From these, the distributions of developed and vacant (rural) land are derived. Development potential is assessed through accessibility surfaces, which are constructed from the travel/commuting time to major London rail termini, to motorway junctions, and to principal settlements. Through investigating the frequencies of land development in relation to the accessibility surfaces, we can begin to understand the distribution of land development in this region. Based on this empirical relationship, the transition rules of a CA simulation of future urban expansion are constructed. In addition, government population projections at the county level are used to constrain simulation to the year 2020. The study demonstrates the utility of empirical CA in urban growth modelling; in particular the importance of empirically informed CA simulation rules in characterising the distribution of land development.

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Implementation and calibration of a new irregular cellular automata-based model for local urban growth simulation: The MUGICA model

Environment and Planning B Urban Analytics and City Science ◽

10.1177/2399808317709280 ◽

2017 ◽

Vol 46 (2) ◽

pp. 243-263 ◽

Cited By ~ 1

Author(s):

Pablo Barreira-González ◽

Francisco Aguilera-Benavente ◽

Montserrat Gómez-Delgado

Keyword(s):

Cellular Automata ◽

Urban Growth ◽

Confusion Matrix ◽

Good Choice ◽

Random Component ◽

Simulation Accuracy ◽

Growth Simulation ◽

Geographic Space ◽

Irregular Structure ◽

Geographical Environment

Cellular automata-based models have traditionally employed regular grids to represent the geographical environment when simulating urban growth or land use change. Over the last two decades, the scientific community has introduced the use of other spatial structures in an attempt to represent the processes simulated by these models more realistically. Cadastre parcels are a good choice when simulating urban growth at local scales, where pixels or regular cells do not represent the geographic space properly. Furthermore, the implementation and calibration of key factors such as accessibility and suitability have not been sufficiently explored in models employing irregular structures. This paper presents a fully calibrated model to simulate urban growth: Model for Urban Growth simulation using Irregular Cellular Automata. The model uses the irregular structure of the cadastre and its smallest unit: the cadastral parcel. The factors included are based on the traditional Neighbourhood, Accessibility, Suitability and Zoning Status modelling schema, frequently employed in other models. Each factor was implemented and calibrated for the irregular structure employed by the model, and a new approach was explored to introduce a random component that would reproduce illegal growth. Several versions of Model for Urban Growth simulation using Irregular Cellular Automata were produced to calibrate the model within the period 2000–2010. The results obtained from the simulations were compared against observed growth for 2010, adapting the traditional confusion matrix to irregular space. A new metric is proposed, called growth simulation accuracy, which measures how well the model locates urban growth.

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Urban Growth Modeling and Future Scenario Projection Using Cellular Automata (CA) Models and the R Package Optimx

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi7100387 ◽

2018 ◽

Vol 7 (10) ◽

pp. 387 ◽

Cited By ~ 3

Author(s):

Yongjiu Feng ◽

Zongbo Cai ◽

Xiaohua Tong ◽

Jiafeng Wang ◽

Chen Gao ◽

...

Keyword(s):

Cellular Automata ◽

Urban Growth ◽

Growth Dynamics ◽

R Package ◽

Future Scenarios ◽

Good Ability ◽

Gradient Based ◽

Transition Rules ◽

Alternative Scenarios ◽

Spatially Explicit Modeling

Cellular automata (CA) is a spatially explicit modeling tool that has been shown to be effective in simulating urban growth dynamics and in projecting future scenarios across scales. At the core of urban CA models are transition rules that define land transformation from non-urban to urban. Our objective is to compare the urban growth simulation and prediction abilities of different metaheuristics included in the R package optimx. We applied five metaheuristics in optimx to near-optimally parameterize CA transition rules and construct CA models for urban simulation. One advantage of metaheuristics is their ability to optimize complexly constrained computational problems, yielding objective parameterization with strong predictive power. From these five models, we selected conjugate gradient-based CA (CG-CA) and spectral projected gradient-based CA (SPG-CA) to simulate the 2005–2015 urban growth and to project future scenarios to 2035 with four strategies for Su-Xi-Chang Agglomeration in China. The two CA models produced about 86% overall accuracy with standard Kappa coefficient above 69%, indicating their good ability to capture urban growth dynamics. Four alternative scenarios out to the year 2035 were constructed considering the overall effect of all candidate influencing factors and the enhanced effects of county centers, road networks and population density. These scenarios can provide insight into future urban patterns resulting from today’s urban planning and infrastructure, and can inform future development strategies for sustainable cities. Our proposed metaheuristic CA models are also applicable in modeling land-use and urban growth in other rapidly developing areas.

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