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Article

Parallel Cellular Automata Markov Model for Land Use Change Prediction over MapReduce Framework

1
School of Earth Sciences, Zhejiang University, Hangzhou 310027, China
2
School of Architectural and Surverying & Mapping Engineering, Jiangxi University of Science and Technology, Ganzhou 341000, China
3
Department of Environmental Science and Engineering, Fudan University, Shanghai 200438, China
*
Author to whom correspondence should be addressed.
ISPRS Int. J. Geo-Inf. 2019, 8(10), 454; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi8100454
Received: 12 September 2019 / Revised: 29 September 2019 / Accepted: 11 October 2019 / Published: 13 October 2019
(This article belongs to the Special Issue Big Data Computing for Geospatial Applications)
The Cellular Automata Markov model combines the cellular automata (CA) model’s ability to simulate the spatial variation of complex systems and the long-term prediction of the Markov model. In this research, we designed a parallel CA-Markov model based on the MapReduce framework. The model was divided into two main parts: A parallel Markov model based on MapReduce (Cloud-Markov), and comprehensive evaluation method of land-use changes based on cellular automata and MapReduce (Cloud-CELUC). Choosing Hangzhou as the study area and using Landsat remote-sensing images from 2006 and 2013 as the experiment data, we conducted three experiments to evaluate the parallel CA-Markov model on the Hadoop environment. Efficiency evaluations were conducted to compare Cloud-Markov and Cloud-CELUC with different numbers of data. The results showed that the accelerated ratios of Cloud-Markov and Cloud-CELUC were 3.43 and 1.86, respectively, compared with their serial algorithms. The validity test of the prediction algorithm was performed using the parallel CA-Markov model to simulate land-use changes in Hangzhou in 2013 and to analyze the relationship between the simulation results and the interpretation results of the remote-sensing images. The Kappa coefficients of construction land, natural-reserve land, and agricultural land were 0.86, 0.68, and 0.66, respectively, which demonstrates the validity of the parallel model. Hangzhou land-use changes in 2020 were predicted and analyzed. The results show that the central area of construction land is rapidly increasing due to a developed transportation system and is mainly transferred from agricultural land. View Full-Text
Keywords: CA Markov; land-use change prediction; Hadoop; MapReduce; cloud computing CA Markov; land-use change prediction; Hadoop; MapReduce; cloud computing
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MDPI and ACS Style

Kang, J.; Fang, L.; Li, S.; Wang, X. Parallel Cellular Automata Markov Model for Land Use Change Prediction over MapReduce Framework. ISPRS Int. J. Geo-Inf. 2019, 8, 454. https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi8100454

AMA Style

Kang J, Fang L, Li S, Wang X. Parallel Cellular Automata Markov Model for Land Use Change Prediction over MapReduce Framework. ISPRS International Journal of Geo-Information. 2019; 8(10):454. https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi8100454

Chicago/Turabian Style

Kang, Junfeng, Lei Fang, Shuang Li, and Xiangrong Wang. 2019. "Parallel Cellular Automata Markov Model for Land Use Change Prediction over MapReduce Framework" ISPRS International Journal of Geo-Information 8, no. 10: 454. https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi8100454

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