Simulating urban land use and cover dynamics using cellular automata and Markov chain approach in Addis Ababa and the surrounding

Urban Climate ◽  
2020 ◽  
Vol 31 ◽  
pp. 100545 ◽  
Author(s):  
Asfaw Mohamed ◽  
Hailu Worku
Keyword(s):  
Land Use ◽  
Markov Chain ◽  
Cellular Automata ◽  
Addis Ababa ◽  
Urban Land ◽  
Urban Land Use ◽  
Markov Chain Approach ◽  
Land Use And Cover

scholarly journals ESTIMATING BUILT-UP AREA CHANGE IN ULAANBAATAR CITY, MONGOLIA

2020 ◽  
Vol XLIII-B4-2020 ◽  
pp. 143-147
Author(s):  
P. Myagmartseren ◽  
I. Myagmarjav ◽  
N. Enkhtuya ◽  
G. Byambakhuu ◽  
T. Bazarkhand
Keyword(s):  
Land Use ◽  
Markov Chain ◽  
Cellular Automata ◽  
Urban Expansion ◽  
Land Development ◽  
Urban Land ◽  
Urban Land Use ◽  
Chain Model ◽  
Rapid Urbanization ◽  
Study Results

Abstract. Long-term urban built-up area changes of the Ulaanbaatar city has accelerated since the 1950s and due to rapid urbanization most of the Mongolian population, or about 68%, live in urban areas. The systematic understanding of urban land expansion is a crucial clue for urban land use planning and sustainable land development. Therefore, in this paper, we used a Markov chain model and cellular automata (CA) to simulate and predict current and future built-up areas expansion is Ulaanbaatar. Landsat imageries (Landsat TM 5, Landsat ETM 7 and Landsat OLI 8) of 1988, 1998, 2008, and 2017 were used to derive main land use classes. Clark Lab’s (Clark University) Geospatial Monitoring and Model software had been used for the urban expansion prediction. The results are innovated to comparable to validate with other study results by using a different kind of methods. Built-up area expansion modeled and predicted 2028’s trends based on a historical expansion of the Ulaanbaatar city between 1988 and 2017, which are prepared according to input model requirements. The built-up area was 7282 hectares (ha) in 1988 and has expanded to 31144 ha in 2017. The built-up area growth of the Ulaanbaatar city has reached 4.3 times over the past 30 years, and from 2017 to 2028 the expansion of the built-up area will be 1.5 times. A comparison of urban expansion from 1988 to 2017 has revealed a rapid built-up invasion to the previous areas of agriculture, grassland, and forest. Simulation performance of Markov chain with the cellular automata model can be used for an improvement in the understanding of the urban expansion processes while allowing helpful for better planning of Ulaanbaatar city, as well as for other rapidly developing towns of Mongolia.

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.

Using neural networks and cellular automata for modelling intra‐urban land‐use dynamics

2008 ◽  
Vol 22 (9) ◽  
pp. 943-963 ◽  
Author(s):  
C. M. Almeida ◽  
J. M. Gleriani ◽  
E. F. Castejon ◽  
B. S. Soares‐Filho
Keyword(s):  
Neural Networks ◽  
Land Use ◽  
Cellular Automata ◽  
Urban Land ◽  
Urban Land Use ◽  
Land Use Dynamics

scholarly journals Stochastic cellular automata modeling of urban land use dynamics: empirical development and estimation

2003 ◽  
Vol 27 (5) ◽  
pp. 481-509 ◽  
Author(s):  
Cláudia Maria de Almeida ◽  
Michael Batty ◽  
Antonio Miguel Vieira Monteiro ◽  
Gilberto Câmara ◽  
Britaldo Silveira Soares-Filho ◽  
...  
Keyword(s):  
Land Use ◽  
Cellular Automata ◽  
Urban Land ◽  
Urban Land Use ◽  
Stochastic Cellular Automata ◽  
Land Use Dynamics

Cellular Automata and Fractal Urban Form: A Cellular Modelling Approach to the Evolution of Urban Land-Use Patterns

1993 ◽  
Vol 25 (8) ◽  
pp. 1175-1199 ◽  
Author(s):  
R White ◽  
G Engelen
Keyword(s):  
Land Use ◽  
Cellular Automata ◽  
Urban Form ◽  
Structural Evolution ◽  
Fractal Dimensions ◽  
Urban Land ◽  
Urban Land Use ◽  
Land Use Patterns ◽  
Type Data ◽  
High Level

Cellular automata belong to a family of discrete, connectionist techniques being used to investigate fundamental principles of dynamics, evolution, and self-organization. In this paper, a cellular automaton is developed to model the spatial structure of urban land use over time. For realistic parameter values, the model produces fractal or bifractal land-use structures for the urbanized area and for each individual land-use type. Data for a set of US cities show that they have very similar fractal dimensions. The cellular approach makes it possible to achieve a high level of spatial detail and realism and to link the results directly to general theories of structural evolution.

scholarly journals Dynamic simulation of urban expansion based on cellular automata and logistic regression model: Case study of Hyrcanian region of Iran

2016 ◽  
Author(s):  
Meisam Jafari ◽  
Seyed Masoud Monavari ◽  
Hamid Majedi ◽  
Ali Asghar Alesheikh ◽  
Mirmasoud Kheirkhah Zarkesh
Keyword(s):  
Land Use ◽  
Logistic Regression ◽  
Cellular Automata ◽  
Urban Development ◽  
Rural Areas ◽  
Urban Expansion ◽  
Urban Land ◽  
Urban Land Use ◽  
High Rate ◽  
Hyrcanian Forests

Although, promotion of urbanization culture in recent decades has made inevitable development of cities in the world, however, the development can be guided in a direction that leave, to the extent possible, minimum socioeconomic and environmental impacts. For this, it is required to first forecast auto-spreading orientation of cities and suburbs in rural areas over time and then avoid shapeless growth of cities. This paper is an attempt to develop a dynamic hybrid model based on logistic regression (LR), Markov chain (MC), and cellular automata (CA) for prediction of future urban sprawl in fast-growing cities. The model was developed using 12 widely-used urban development criteria, whose significant coefficient was determined by logistic regression, and validated by relative operating characteristic (ROC) analysis. The validated model was run in Guilan, a tourist province in northern Iran with a very high rate of urban development. For this, changes in the area of urban land use were detected over the period of 1989 to 2013 and then, future sprawl of the province was forecasted by the years 2025 and 2037. The analysis results revealed that the area of urban land use was increased by more than 1.7 % from 36012.5 ha in 1989 to 59754.8 ha in 2013, and the area of Caspian Hyrcanian forestland was reduced by 31628 ha. The results also predicted an alarming increase in the rate of urban development in the province by the years 2025 and 2037, during which urban land use is predicted to develop 0.9 % and 1.38 %, respectively. The development pattern is expected to be uneven and scattered, without following any particular direction. The development will occur close to the existing or newly-formed urban basements as well as around major roads and commercial areas. This development, if not controlled, will lead to the loss of 13863 ha of Hyrcanian forests and if the trend continues, 21013 ha of Hyrcanian forests and 20208 ha of Barren/open lands are expected to be destroyed by the year 2037. In general, the proposed model is an efficient tool for the support of urban planning decisions and facilitates the process of sustainable development of cities by providing decision-makers with an overview on future development of cities where the growth rate is very fast.

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