A Cellular Automata Model for Urban Land-Use Simulation

2005 ◽  
Vol 32 (2) ◽  
pp. 247-263 ◽  
Author(s):  
Kwok Hung Lau ◽  
Booi Hon Kam
Keyword(s):  
Land Use ◽  
Cellular Automata ◽  
Distance Function ◽  
Alternative Model ◽  
Resistance To Change ◽  
Land Use Changes ◽  
Urban Land ◽  
Urban Land Use ◽  
Land Use Allocation ◽  
Ca Model

This paper presents an urban land-use simulation model using cellular automata (CA). In the model urban growth is regarded as the result of a global process underpinned by local actions and land-use change as the joint action of three different effects: attribute, heterogeneity, and gravity. The attribute and heterogeneity effects are regarded as different aspects of a local driving force for change constituted by changing accessibility and other attributes resulting from the interaction of land use and transport at the neighborhood level. The gravity effect is a universal resistance to change as a result of inertia and agglomeration of compatible land uses in the vicinity. To ensure that local actions would lead to global behavior, a multipass, in addition to a single-pass, land-use-allocation algorithm is designed to mimic land-use changes. With metropolitan Melbourne in Australia as a case study, the performance of the model in replicating land-use changes is compared with that of an alternative model developed by using only a distance function. The results of the comparison show that the proposed CA model outperforms the alternative model with only a distance function, confirming the importance of incorporating local attributes in modeling land-use changes.

scholarly journals Application of an Improved ABC Algorithm in Urban Land Use Prediction

Information ◽  
2018 ◽  
Vol 9 (8) ◽  
pp. 193 ◽  
Author(s):  
Jiuyuan Huo ◽  
Zheng Zhang
Keyword(s):  
Land Use ◽  
Cellular Automata ◽  
Land Use Changes ◽  
Remote Sensing Data ◽  
Urban Land ◽  
Urban Land Use ◽  
Simulation Accuracy ◽  
Abc Algorithm ◽  
Bee Colony ◽  
Quantification Analysis

Scientifically and rationally analyzing the characteristics of land use evolution and exploring future trends in land use changes can provide the scientific reference basis for the rational development and utilization of regional land resources and sustainable economic development. In this paper, an improved hybrid artificial bee colony (ABC) algorithm based on the mutation of inferior solutions (MHABC) is introduced to combine with the cellular automata (CA) model to implement a new CA rule mining algorithm (MHABC-CA). To verify the capabilities of this algorithm, remote sensing data of three stages, 2005, 2010, and 2015, are adopted to dynamically simulate urban development of Dengzhou city in Henan province, China, using the MHABC-CA algorithm. The comprehensive validation and analysis of the simulation results are performed by two aspects of comparison, the visual features of urban land use types and the quantification analysis of simulation accuracy. Compared with a cellular automata model based on a particle swarm optimization (PSO-CA) algorithm, the experimental results demonstrate the effectiveness of the MHABC-CA algorithm in the prediction field of urban land use changes.

scholarly journals Correction to: The impacts of agricultural and urban land-use changes on plant and bird biodiversity in Costa Rica (1986–2014)

2021 ◽  
Vol 21 (3) ◽  
Author(s):  
Andrea Montero ◽  
Joan Marull ◽  
Enric Tello ◽  
Claudio Cattaneo ◽  
Francesc Coll ◽  
...  
Keyword(s):  
Land Use ◽  
Costa Rica ◽  
Land Use Changes ◽  
Urban Land ◽  
Urban Land Use

scholarly journals Multi-Scenario Prediction of Intra-Urban Land Use Change Using a Cellular Automata-Random Forest Model

2021 ◽  
Vol 10 (8) ◽  
pp. 503
Author(s):  
Hang Liu ◽  
Riken Homma ◽  
Qiang Liu ◽  
Congying Fang
Keyword(s):  
Land Use ◽  
Random Forest ◽  
Cellular Automata ◽  
Land Use Change ◽  
Urban Land ◽  
Urban Land Use ◽  
Random Forest Model ◽  
Growth Trend ◽  
Related Factors ◽  
Forest Model

The simulation of future land use can provide decision support for urban planners and decision makers, which is important for sustainable urban development. Using a cellular automata-random forest model, we considered two scenarios to predict intra-land use changes in Kumamoto City from 2018 to 2030: an unconstrained development scenario, and a planning-constrained development scenario that considers disaster-related factors. The random forest was used to calculate the transition probabilities and the importance of driving factors, and cellular automata were used for future land use prediction. The results show that disaster-related factors greatly influence land vacancy, while urban planning factors are more important for medium high-rise residential, commercial, and public facilities. Under the unconstrained development scenario, urban land use tends towards spatially disordered growth in the total amount of steady growth, with the largest increase in low-rise residential areas. Under the planning-constrained development scenario that considers disaster-related factors, the urban land area will continue to grow, albeit slowly and with a compact growth trend. This study provides planners with information on the relevant trends in different scenarios of land use change in Kumamoto City. Furthermore, it provides a reference for Kumamoto City’s future post-disaster recovery and reconstruction planning.

scholarly journals Optimization of a Novel Urban Growth Simulation Model Integrating an Artificial Fish Swarm Algorithm and Cellular Automata for a Smart City

2021 ◽  
Vol 13 (4) ◽  
pp. 2338
Author(s):  
Xinxin Huang ◽  
Gang Xu ◽  
Fengtao Xiao
Keyword(s):  
Land Use ◽  
Sustainable Development ◽  
Urban Growth ◽  
Smart City ◽  
Urban Land ◽  
Urban Land Use ◽  
Growth Simulation ◽  
Artificial Fish Swarm Algorithm ◽  
Artificial Fish Swarm ◽  
Ca Model

As one of the 17 Sustainable Development Goals, it is sensible to analysis historical urban land use characteristics and project the potentials of urban sustainable development for a smart city. The cellular automaton (CA) model is the widely applied in simulating urban growth, but the optimum parameters of variables driving urban growth in the model remains to be continued to improve. We propose a novel model integrating an artificial fish swarm algorithm (AFSA) and CA for optimizing parameters of variables in the urban growth model and make a comparison between AFSA-CA and other five models, which is used to study a 40-year urban land growth of Wuhan. We found that the urban growth types from 1995 to 2015 appeared relatively consistent, mainly including infilling, edge-expansion and distant-leap types in Wuhan, which a certain range of urban land growth on the periphery of the central area. Additionally, although the genetic algorithms (GA)-CA model and the AFSA-CA model among the six models due to the distance variables, the parameter value of the GA-CA model is −15.5409 according to the fact that the population (POP) variable should be positively. As a result, the AFSA-CA model regardless of the initial parameter setting is superior to the GA-CA model and the GA-CA model is superior to all the other models. Finally, it is projected that the potentials of urban growth in Wuhan for 2025 and 2035 under three scenarios (natural urban land growth without any restrictions (NULG), sustainable urban land growth with cropland protection and ecological security (SULG), and economic urban land growth with sustainable development and economic development in the core area (EULG)) focus mainly on existing urban land and some new town centers based on AFSA-CA urban growth simulation model. An increasingly precise simulation can determine the potential increase area and quantity of urban land, providing a basis to judge the layout of urban land use for urban planners.

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