DOI: https://doi.org/10.14710/geoplanning.10.1.23-36

An Application of Cellular Automata (CA) and Markov Chain (MC) Model in Urban Growth Prediction: A case of Surat City, Gujarat, India

*Kaushikkumar Prafulbhai Sheladiya orcid  -  Urban Planning Section, Department of Civil Engineering, S. V. National Institute of Technology,Surat,Gujarat,India-395007, India
Chetan R. Patel  -  Urban Planning Section, Department of Civil Engineering, S. V. National Institute of Technology,Surat,Gujarat,India-395007, India

Citation Format:
Abstract

The main purpose of this study is to detect land use land cover change for 1990-2000, 2000-2010, and 2010-2020 using multispectral Landsat images as well as to simulate and predict urban growth of Surat city using Cellular Automata-based Markov Chain Model. Maximum likelihood supervise classification was used to generate LULC maps of the years 1990,2000,2010, and 2020 and the overall accuracy of these maps were 90%, 95%, 91.25%, and 96.25%, respectively. Two transition rules were commuted to predict the LULC of 2010 and 2020. For validation of these LULC maps, the Area Under Characteristics curve was used, and these maps' accuracy was 95.30% and 86.90%. This validation predicted LULC maps for the years 2035 and 2050. Transition rules of 2010-2035 showed that there will be a probability that 36.33% of vegetation area and 40.27% of the vacant land area will be transited into built-up by the year 2035, and it will be 49.20 % of the total area. Also, 57.77% of the vegetation area and 60.24% of the built-up area will be transformed into urban areas by the year 2050, almost 62.60 %. Analysis of LULC maps 2035 and 2050 exhibits that there will be abundant growth in all directions except the South Zone and Southwest Zone. Therefore, this study helps urban planners and decision-makers decide what to retain, where to plan for new development and type of development, what to connect, and what to protect in coming years.

Fulltext View|Download
Keywords: Cellular Automata(CA); Markov Chain(MC); Urban Growth Modelling; Geographic Information System; Surat City

Article Metrics:

Article Info
Section: Original Research
Language : EN
Recent articles
The Effects of Green Open Spaces on Microclimate and Thermal Comfort in Three Integrated Campus in Yogyakarta, Indonesia Impact Deforestation on Land Surface Temperature: A Case Study Highland Kundasang, Sabah Shoreline Dynamics in the Very Small Islands of Karimunjawa – Indonesia: A Preliminary Study More recent articles
Most cited articles
QUALITY ANALYSIS OF SINGLE TREE OBJECT WITH OBIA AND VEGETATION INDEX FROM LAPAN SURVEILLANCE AIRCRAFT MULTISPECTRAL DATA IN URBAN AREA SLUM REVITALIZING PLAN OF BAGHDADIYAH BY SPATIAL RE-MODELING CONFIGURATION DETEKSI PERUBAHAN PENGGUNAAN LAHAN DENGAN CITRA LANDSAT DAN SISTEM INFORMASI GEOGRAFIS: STUDI KASUS DI WILAYAH METROPOLITAN BANDUNG, INDONESIA MODEL SPASIAL STATISTIK KEPEMILIKAN SEPEDA MOTOR DI KECAMATAN BANYUMANIK, KOTA SEMARANG THE DEVELOPMENT OF MARINE SPATIAL PLANNING AND ITS APPLICATION FOR FLOATING FISH NET CULTURE More cited articles

  1. Aburas, M. M., Ho, Y. M., Ramli, M. F., & Ash’aari, Z. H. (2016). The simulation and prediction of spatio-temporal urban growth trends using cellular automata models: A review. International Journal of Applied Earth Observation and Geoinformation, 52, 380–389. https://doi.org/10.1016/j.jag.2016.07.007">[Crossref] 

  2. Al-Ahmadi, K., See, L., Heppenstall, A., & Hogg, J. (2009). Calibration of a fuzzy cellular automata model of urban dynamics in Saudi Arabia. Ecological Complexity, 6(2), 80–101. https://doi.org/10.1016/j.ecocom.2008.09.004">[Crossref] 

  3. Barredo, I., & Demicheli, L. (2003). Urban sustainability in developing countries ’ megacities : modelling and predicting future urban growth in Lagos, 20(5), 297–310. https://doi.org/10.1016/S0264-2751(03)00047-7">[Crossref]

  4. Bharath, H. A., Chandan, M. C., Vinay, S., & Ramachandra, T. V. (2018). The Egyptian Journal of Remote Sensing and Space Sciences Modelling urban dynamics in rapidly urbanising Indian cities. The Egyptian Journal of Remote Sensing and Space Sciences, 21(3), 201–210. https://doi.org/10.1016/j.ejrs.2017.08.002">[Crossref] 

  5. Chaudhuri, G., & Clarke, K. C. (2019). Modeling an Indian megalopolis– A case study on adapting SLEUTH urban growth model. Computers, Environment and Urban Systems, 77(June 2019), 101358. https://doi.org/10.1016/j.compenvurbsys.2019.101358">[Crossref]

  6. Deep, S. (2014). Urban sprawl modeling using cellular automata. The Egyptian Journal of Remote Sensing and Space Sciences, 17(2), 179–187.[https://doi.org/10.1016/j.ejrs.2014.07.001">Crossref] 

  7. Gao, C., Feng, Y., Tong, X., Lei, Z., Chen, S., & Zhai, S. (2020). Modeling urban growth using spatially heterogeneous cellular automata models: Comparison of spatial lag, spatial error and GWR. Computers, Environment and Urban Systems, 81(January), 101459. https://doi.org/10.1016/j.compenvurbsys.2020.101459">[Crossref] 

  8. Gharaibeh, A., Shaamala, A., Obeidat, R., & Al-Kofahi, S. (2020). Improving land-use change modeling by integrating ANN with Cellular Automata-Markov Chain model. Heliyon, 6(9), e05092. https://doi.org/10.1016/j.heliyon.2020.e05092">[Crossref] 

  9. Guan, D., He, X., He, C., Cheng, L., & Qu, S. (2020). Does the urban sprawl matter in Yangtze River Economic Belt , China ? An integrated analysis with urban sprawl index and one scenario analysis model. Cities, 99(November 2019), 102611. https://doi.org/10.1016/j.cities.2020.102611">[Crossref]

  10. He, C., Okada, N., & Zhang, Q. (2006). Modeling urban expansion scenarios by coupling cellular automata model and system dynamic model in Beijing , China, 26, 323–345. https://doi.org/10.1016/j.apgeog.2006.09.006">[Crossref]

  11. He, Q., He, W., Song, Y., Wu, J., Yin, C., & Mou, Y. (2018). The impact of urban growth patterns on urban vitality in newly built-up areas based on an association rules analysis using geographical ‘big data.’ Land Use Policy, 78(July), 726–738. https://doi.org/10.1016/j.landusepol.2018.07.020">[Crossref] 

  12. Hu, Z., & Lo, C. P. (2007). Modeling urban growth in Atlanta using logistic regression. Computers, Environment and Urban Systems, 31(6), 667–688. https://doi.org/10.1016/j.compenvurbsys.2006.11.001">[Crossref]

  13. Jokar Arsanjani, J., Helbich, M., & de Noronha Vaz, E. (2013). Spatiotemporal simulation of urban growth patterns using agent-based modeling: The case of Tehran. Cities, 32, 33–42. https://doi.org/10.1016/j.cities.2013.01.005">[Crossref]

  14. Kallvetty, S., & Bandopadhyay, S. (2018). Spatial Explicit Modeling To Understand the Dynamics of Landuse Switch Using Open Source Satellite Data. Geoplanning: Journal of Geomatics and Planning, 5(1), 1https://doi.org/10.14710/geoplanning.5.1.1-16">.[Crossref]

  15. Kamusoko, C., Aniya, M., Adi, B., & Manjoro, M. (2009). Rural sustainability under threat in Zimbabwe – Simulation of future land use / cover changes in the Bindura district based on the Markov-cellular automata model. Applied Geography, 29(3), 435–447. https://doi.org/10.1016/j.apgeog.2008.10.002">[Crossref]

  16. Lagarias, A. (2012). Urban sprawl simulation linking macro-scale processes to micro-dynamics through cellular automata, an application in Thessaloniki, Greece. Applied Geography, 34, 146–160. https://doi.org/10.1016/j.apgeog.2011.10.018">[Crossref] 

  17. Li, G., Sun, S., & Fang, C. (2018). The varying driving forces of urban expansion in China: Insights from a spatial-temporal analysis. Landscape and Urban Planning, 174(February), 63–77. https://doi.org/10.1016/j.landurbplan.2018.03.004">[Crossref] 

  18. Li, X., Yang, Q., & Liu, X. (2008). Landscape and Urban Planning Discovering and evaluating urban signatures for simulating compact development using cellular automata, 86, 177–186. https://doi.org/10.1016/j.landurbplan.2008.02.005">[Crossref] 

  19. Lu, Q., Chang, N. Bin, Joyce, J., Chen, A. S., Savic, D. A., Djordjevic, S., & Fu, G. (2018). Exploring the potential climate change impact on urban growth in London by a cellular automata-based Markov chain model. Computers, Environment and Urban Systems, 68(March 2017), 121–132. https://doi.org/10.1016/j.compenvurbsys.2017.11.006">[Crossref]

  20. Milad, M., Ming, Y., Firuz, M., & Hanan, Z. (2016). International Journal of Applied Earth Observation and Geoinformation The simulation and prediction of spatio-temporal urban growth trends using cellular automata models : A review. International Journal of Applied Earth Observations and Geoinformation, 52, 380–389. https://doi.org/10.1016/j.jag.2016.07.007">[Crossref] 

  21. Mitsova, D., Shuster, W., & Wang, X. (2011). A cellular automata model of land cover change to integrate urban growth with open space conservation. Landscape and Urban Planning, 99(2), 141–153. https://doi.org/10.1016/j.landurbplan.2010.10.001">[Crossref] 

  22. Mosammam, H. M., Nia, J. T., Khani, H., Teymouri, A., & Kazemi, M. (2017). Monitoring land use change and measuring urban sprawl based on its spatial forms: The case of Qom city. Egyptian Journal of Remote Sensing and Space Science, 20(1), 103–116. https://doi.org/10.1016/j.ejrs.2016.08.002">[Crossref] 

  23. Mustafa, A., Cools, M., Saadi, I., & Teller, J. (2017). Coupling agent-based, cellular automata and logistic regression into a hybrid urban expansion model (HUEM). Land Use Policy, 69(October), 529–540https://doi.org/10.1016/j.landusepol.2017.10.009">.[Crossref] 

  24. Myint, S. W., & Wang, L. (2006). Multi-criteria Decision Approach for Land Use Land Cover Change Using Markov Multicriteria decision approach for land use land cover change using Markov chain analysis and a cellular automata approach, (December). https://doi.org/10.5589/m06-032">[Crossref] 

  25. Okafor, G. C., Annor, T., Odai, S. N., & Larbi, I. (2020). Land Use Landcover Change Monitoring and Projection in the Dano Catchment , Southwest Burkina Faso. International Journal of Advanced Remote Sensing and GIS, 9(1), 3185–3204.

  26. Rahnama, M. R. (2021). Forecasting land-use changes in Mashhad Metropolitan area using Cellular Automata and Markov chain model for 2016-2030. Sustainable Cities and Society, 64, 102548. https://doi.org/10.1016/j.scs.2020.102548">[Crossref]

  27. Sahana, M., Hong, H., & Sajjad, H. (2018). Analyzing urban spatial patterns and trend of urban growth using urban sprawl matrix: A study on Kolkata urban agglomeration, India. Science of the Total Environment, 628–629, 1557–1566. https://doi.org/10.1016/j.scitotenv.2018.02.170">[Crossref]

  28. Sang, L., Zhang, C., Yang, J., Zhu, D., & Yun, W. (2011). Simulation of land use spatial pattern of towns and villages based on CA – Markov model. Mathematical and Computer Modelling, 54(3–4), 938–943. https://doi.org/10.1016/j.mcm.2010.11.019">[Crossref]

  29. Sarkar, A., & Chouhan, P. (2020). Modeling spatial determinants of urban expansion of Siliguri a metropolitan city of India using logistic regression. Modeling Earth Systems and Environment, 6(4), 2317–2331. https://doi.org/10.1007/s40808-020-00815-9">[Crossref]

  30. Sheladiya, K. P. (2023). The Impacts of Urban Growth Drivers on the Spatial and Temporal Pattern of City Expansion. Journal of the Indian Society of Remote Sensing, 6. https://doi.org/10.1007/s12524-023-01729-6">[Crossref]

  31. Shu, B., Zhu, S., Qu, Y., Zhang, H., Li, X., & Carsjens, G. J. (2020). Modelling multi-regional urban growth with multilevel logistic cellular automata. Computers, Environment and Urban Systems, 80(December 2019). https://doi.org/10.1016/j.compenvurbsys.2019.101457">[Crossref] 

  32. Taubenböck, H., Esch, T., Felbier, A., Wiesner, M., Roth, A., & Dech, S. (2012). Monitoring urbanization in mega cities from space. Remote Sensing of Environment, 117, 162–176. https://doi.org/10.1016/j.rse.2011.09.015">[Crossref] 

  33. Thapa, R. B., & Murayama, Y. (2020). Computers , Environment and Urban Systems Urban growth modeling of Kathmandu metropolitan region , Nepal. Computers, Environment and Urban Systems, 35(1), 25–34. https://doi.org/10.1016/j.compenvurbsys.2010.07.005">[Crossref] 

  34. Tripathy, P., & Kumar, A. (2019). Monitoring and modelling spatio-temporal urban growth of Delhi using Cellular Automata and geoinformatics. Cities, 90(January), 52–63. https://doi.org/10.1016/j.cities.2019.01.021">[Crossref]

  35. Wang, S. Q., Zheng, X. Q., & Zang, X. B. (2012). Procedia Environmental Sciences Accuracy assessments of land use change simulation based on Markov-cellular automata model, 13(2011), 1238–1245. https://doi.org/10.1016/j.proenv.2012.01.117">[Crossref]

  36. Ward, D. P., Murray, A. T., & Phinn, S. R. (2000). A stochastically constrained cellular model of urban growth. Computers, Environment and Urban Systems, 24(6), 539–558https://doi.org/10.1016/S0198-9715(00)00008-9">.[Crossref]

  37. Weng, Q. (2002). Land use change analysis in the Zhujiang Delta of China using satellite remote sensing , GIS and stochastic modelling, 273–284. https://doi.org/10.1006/jema.2001.0509">[Crossref] 

  38. White, R., & Engelen, G. (2000). High-resolution integrated modelling of the spatial dynamics of urban and regional systems, 24, 383–400.

  39. Xian, G., & Crane, M. (2005). Assessments of urban growth in the Tampa Bay watershed using remote sensing data, 97, 203–215. https://doi.org/10.1016/j.rse.2005.04.017">[Crossref]

  40. Xin, Y., Xin-qi, Z., & Li-na, L. (2012). A spatiotemporal model of land use change based on ant colony optimization , Markov chain and cellular automata, 233, 11–19https://doi.org/10.1016/j.ecolmodel.2012.03.011">.[Crossref]

  41. Xu, T., & Gao, J. (2019). Directional multi-scale analysis and simulation of urban expansion in Auckland, New Zealand using logistic cellular automata. Computers, Environment and Urban Systems, 78(August), 101390https://doi.org/10.1016/j.compenvurbsys.2019.101390">.[Crossref]

  42. Yang, Y., Bao, W., & Liu, Y. (2020). Scenario simulation of land system change in the Beijing-Tianjin-Hebei region. Land Use Policy, 96(April), 104677https://doi.org/10.1016/j.landusepol.2020.104677">.[Crossref] 

  43. Yin, J., Yin, Z., Zhong, H., Xu, S., Hu, X., Wang, J., & Wu, J. (2011). Monitoring urban expansion and land use/land cover changes of Shanghai metropolitan area during the transitional economy (1979-2009) in China. Environmental Monitoring and Assessment, 177(1–4), 609–621https://doi.org/10.1007/s10661-010-1660-8">.[Crossref] 

  44. Zhou, L., Dang, X., Sun, Q., & Wang, S. (2020). Multi-scenario simulation of urban land change in Shanghai by random forest and CA-Markov model. Sustainable Cities and Society, 55(January), 102045. https://doi.org/10.1016/j.scs.2020.102045">[Crossref] 

  45.  

  46.  

  47.  


Last update:

No citation recorded.

Last update: 2024-11-22 05:20:10

No citation recorded.