Article Open Access http://dx.doi.org/10.26855/jamc.2018.05.002
Urban Growth Modelling Using Cellular Automata – Markov (CA-Markov) Technique
Kamal Pandey1,*, Diptarshi Mitra2, Sandeep Maithani3, Harish Karnatak1
1GeoWeb Services, IT and Distance Learning, Indian Institute of Remote Sensing (IIRS), 4, Kalidas Road, Dehradun-248001
2Geoinformatics, IIRS, 4, Kalidas Road, Dehradun-248001
3Urban and Regional Studies, IIRS, 4, Kalidas Road, Dehradun-248001
*Corresponding author: Kamal Pandey
Published: May 24,2018
Abstract
Nowadays, rapid urbanization is destabilizing
the environment and the economy of a place. For undertaking sustainable
urbanization (to protect the environmental balance), we first need to model and
predict the urban growth in the near future. In this study, Cellular Automata –
Markov (CA-Markov) model has been used to predict the urban growth of Dehradun
(planning area) in 2035. For this, the classified images of Dehradun (showing
urban and non-urban classes), for the years 1995 and 2015, have been taken as
input. Along with these two images, three more classified images of Dehradun
(showing urban and non-urban classes), pertaining to the years 2000, 2003 and
2010, have been used to draw a percentage urban area vs. year graph to find out
the trend of urban growth. The percentage urban area for the year 2035 has been
calculated both from the output obtained from the CA-Markov model (≈38%) and
from this graph (≈32%). The values are more or less similar. Now, five factors
influencing urban growth, viz., distance from the roads, distance from the
rivers, distance from the residential areas, distance from the city centre and
slope of the area, have been considered. Maps representing these parameters are
created and weighted overlay is performed with them to find out the suitable
areas of growth. The map obtained from weighted overlay shows the central part
of Dehradun as the most suitable area for growth. This result agrees well with
the classified images and the output from CA-Markov model.
References
[1] Maithani S (2010) Cellular Automata Based Model of Urban Spatial Growth. J Indian Soc Remote Sens 38(4): 604-610. URL: https://www.researchgate.net/publication/225469064_Cellular_Automata_Based_Model_of_Urban_Spatial_Growth
[2] Maithani S, Arora M, Jain R (2010) An Artificial Neural Network Based Approach for Urban Growth Zonation in Dehradun City, India. Geocarto Int 25(8): 663-681. URL: http://www.tandfonline.com/doi/full/10.1080/10106049.2010.524313
[3] Maithani S (2010) Application of Cellular Automata and GIS Techniques in Urban Growth Modelling: A New Perspective. Inst T Planners, India J 7(1): 36-49. URL: http://www.itpi.org.in/uploads/journalfiles/jan3_10.pdf
[4] Subedi P, Subedi K, Thapa B (2013) Application of a Hybrid Cellular Automaton – Markov (CA-Markov) Model in Land-Use Change Prediction: A Case Study of Saddle Creek Drainage Basin, Florida. Appl Ecol Environ Sci 1(6): 126-132. URL: http://pubs.sciepub.com/aees/1/6/5/index.html
[5] Zhang X, Lin X, Zhu S (2015) Modelling Urban Growth by Cellular Automata: A Case Study of Xiamen City, China. The 10th International Conference on Computer Science and Education (ICCSE), Cambridge (UK). URL: https://www.researchgate.net/publication/308856936_Modeling_urban_growth_by_cellular_automata_A_case_study_of_Xiamen_City_China
[6] National Capital Region Planning Board (2013) Draft Revised Regional Plan 2021: National Capital Region. URL: http://www.indiaenvironmentportal.org.in/files/file/Draft Revised Regional plan.pdf
How to cite this paper
Urban Growth Modelling Using Cellular Automata – Markov (CA-Markov) Technique
How to cite this paper: Kamal Pandey, Diptarshi Mitra, Sandeep Maithani, Harish Karnatak. (2018) Urban Growth Modelling Using Cellular Automata – Markov (CA-Markov) Techniquet. Journal of Applied Mathematics and Computation, 2(5), 178-187.
DOI: http://dx.doi.org/10.26855/jamc.2018.05.002