Integrating cellular automata and Markov techniques to generate urban development potential surface: a study on Kolkata agglomeration

Mondal, Biswajit; Das, Dipendra Nath; Bhatta, Basudeb

doi:10.1080/10106049.2016.1155656

Cited by 60 publications

(23 citation statements)

References 58 publications

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“…Existing scholarship on urban growth uses biophysical factors, social factors, economic factors, and spatial policies as the drivers of urbanization (Li, Zhou, & Ouyang, 2013;Mondal, Das, & Bhatta, 2017;Poelmans & Van Rompaey, 2010;Shafizadeh-Moghadam & Helbich, 2015;Verburg, de Nijs, Ritsema van Eck, Visser, & de Jong, 2004). Both, locational and socio-demographic factors provide a measure of urban growth suitability and relative accessibility in a region.…”

Section: Drivers Of Urbanization In Kolkata Uamentioning

confidence: 99%

Modeling an Indian megalopolis– A case study on adapting SLEUTH urban growth model

Chaudhuri

Clarke

2019

Computers, Environment and Urban Systems

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Section: Drivers Of Urbanization In Kolkata Uamentioning

confidence: 99%

Modeling an Indian megalopolis– A case study on adapting SLEUTH urban growth model

Chaudhuri

Clarke

2019

Computers, Environment and Urban Systems

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“…They are widely used in the simulation and prediction of land use [38]. According to the previous studies [12,14,38,39], the CA model can be expressed as:…”

Section: Ca-markov Modelmentioning

confidence: 99%

“…The Cellular Automata-Markov (CA-Markov) combines the ability to simulate the spatial variation of complex systems of CA model and the long-term prediction of the Markov model. Therefore, it has been used in land use simulation [12][13][14], species distribution [15], and ecological environment [16,17].…”

Section: Introductionmentioning

confidence: 99%

Integration of Boosted Regression Trees and Cellular Automata—Markov Model to Predict the Land Use Spatial Pattern in Hotan Oasis

et al. 2020

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The simulation and prediction of the land use changes is generally carried out by cellular automata—Markov (CA-Markov) model, and the generation of suitable maps collection is subjective in the simulation process. In this study, the CA-Markov model was improved by the Boosted Regression Trees (BRT) to simulate land use to make the model objectively. The weight of ten driving factors of the land use changes was analyzed in BRT, in order to produce the suitable maps collection. The accuracy of the model was verified. The outcomes represent a match of over 84% between simulated and actual land use in 2015, and the Kappa coefficient was 0.89, which was satisfactory to approve the calibration process. The land use of Hotan Oasis in 2025 and 2035 were predicted by means of this hybrid model. The area of farmland, built-up land and water body in Hotan Oasis showed an increasing trend, while the area of forestland, grassland and unused land continued to show a decreasing trend in 2025 and 2035. The government needs to formulate measures to improve the utilization rate of water resources to meet the growth of farmland, and need to increase ecological environment protection measures to curb the reduction of grass land and forest land for the ecological health.

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“…[22,45,[55][56][57]. Especially, socio-economic data have a confirmatory impact to the simulation of the CA-Markov model [58]. However, little information has been focused on landscape metrics.…”

Section: Relationship Between Kappa Coefficient and Landscape Populamentioning

confidence: 99%

Landscape pattern and economic factors’ effect on prediction accuracy of cellular automata-Markov chain model on county scale

Wang

Zhao

et al. 2020

Open Geosciences

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Understanding and modeling of land use change is of great significance to environmental protection and land use planning. The cellular automata-Markov chain (CA-Markov) model is a powerful tool to predict the change of land use, and the prediction accuracy is limited by many factors. To explore the impact of land use and socio-economic factors on the prediction of CA-Markov model on county scale, this paper uses the CA-Markov model to simulate the land use of Anren County in 2016, based on the land use of 1996 and 2006. Then, the correlation between the land use, socio-economic data and the prediction accuracy was analyzed. The results show that Shannon’s evenness index and population density having an important impact on the accuracy of model predictions, negatively correlate with kappa coefficient. The research not only provides a reference for correct use of the model but also helps us to understand the driving mechanism of landscape changes.

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Integrating cellular automata and Markov techniques to generate urban development potential surface: a study on Kolkata agglomeration

Cited by 60 publications

References 58 publications

Modeling an Indian megalopolis– A case study on adapting SLEUTH urban growth model

Modeling an Indian megalopolis– A case study on adapting SLEUTH urban growth model

Integration of Boosted Regression Trees and Cellular Automata—Markov Model to Predict the Land Use Spatial Pattern in Hotan Oasis

Landscape pattern and economic factors’ effect on prediction accuracy of cellular automata-Markov chain model on county scale

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