Spatial Information Research

, Volume 27, Issue 1, pp 37–48 | Cite as

Modeling urban dynamics along two major industrial corridors in India

  • T. V. RamachandraEmail author
  • Jefferey M. Sellers
  • H. A. Bharath
  • S. Vinay
Part of the following topical collections:
  1. Academia and Industry collaboration on the Spatial Information


Rapid urban growth and consequent sprawl have been a major concern in urban planning towards the provision of basic amenities and infrastructure. The current research was undertaken as per the recommendations of brainstorming session involving stakeholders from academia, government agencies and industry. The outcome of this study is expected to provide the vital inputs to the federal government to provision basic amenities and smart infrastructure, to boost the industrial growth, while maintaining the local ecology and environment and support local livelihood. Spatial patterns of land use dynamics have been analysed in two major corridors (with 10 km buffer on either side). During the past two decades, the urban growth is about 441% along Mumbai–Pune Industrial corridor and 276% along Chennai–Bangalore–Mangalore corridor. The prediction of likely growth has been done using Markov-cellular automation model, accounting fuzzy behavior of agents. Spatial metrics confirm that the core urban areas of major cities have concentrated growth and sprawl at the outskirts. Prediction model estimates that urban area would increase to 47.1% by 2027 in Mumbai–Pune corridor and to 35.4% in 2029 in Chennai–Mangalore corridor. This study aids in pre-visualising the urban growth to evolve appropriate management strategies to mitigate environmental impacts.


Cellular automata Chennai–Bangalore Fuzzy Markov chains Mumbai–Pune 



We are grateful to (1) APN Network for climate change [ARCP2012-FP03-Sellers] for the financial support to carryout research—Mega Regional Development and Environmental change in India and China, (2) the NRDMS Division, The Ministry of Science and Technology, Government of India; (3) Indian Institute of Science and (4) Indian Institute of Technology, Kharagpur for the infrastructure support. We thank (1) United States Geological Survey and (2) National Remote Sensing Centre (NRSC-Hyderabad) for providing temporal remote sensing data. Ms. Revathi N. and Brigit M. Baby worked as interns in this research as part of their respective master’s dissertation work and took part in data mining and spatial data analyses. We thank the participants of stakeholder meeting. And this research was undertaken as per the recommendations of stakeholder interaction meeting involving academia, government agencies and industry.


  1. 1.
    Wang, H., He, S., Liu, X., Dai, L., Pan, P., Hong, S., et al. (2013). Simulating urban expansion using a cloud-based cellular automata model: A case study of Jiangxia, Wuhan, China. Landscape and Urban Planning, 110(1), 99–112. Scholar
  2. 2.
    Hu, S., Tong, L., Frazier, A. E., & Liu, Y. (2015). Urban boundary extraction and sprawl analysis using Landsat images: A case study in Wuhan, China. Habitat for International, 47, 183–195. Scholar
  3. 3.
    United Nations. (2014). World urbanization prospects: The 2014 revision, highlights. Department of Economic and Social Affairs, Population Division. Retrieved from
  4. 4.
    Office of the Registrar General and Census Commissioner. (2011). Census of India. Ministry of Home Affairs, Government of India. Retrieved February 7, 2017 from
  5. 5.
    Planning Comission. (2012). The challenges of urbanization in India. Retrieved from
  6. 6.
    Vickerman, R. (2007). Gateways, corridor and compitativeness: An evolution of trans-european networks and lessons for Canada. In International conference on gateways and corridors. Vancouver. Retrieved from
  7. 7.
    Govenment of India. (2014). Make in India. Retrieved January 13, 2018 from
  8. 8.
    De, P., & Iyengar, K. (2014). Developing economic corridors in South Asia. Mandaluyong City, Philippines: Asia Development Bank. Retrieved from
  9. 9.
    Centre for Urban Research. (2015). Delhi Mumbai Industrial corridor. Retrieved January 5, 2018 from
  10. 10.
    Banerjee, P. (2017). Development of east coast economic corridor and Vizag: Chennai industrial corridor. Mandaluyong City, Philippines.
  11. 11.
    Ministry of Commerce and Industry, and Government of India. (2017). National Industrial Corridor Development and Implementation Trust. Press Information Bureau. Retrieved January 15, 2018 from
  12. 12.
    Bhatta, B., Saraswati, S., & Bandyopadhyay, D. (2010). Urban sprawl measurement from remote sensing data. Applied Geography, 30(4), 731–740. Scholar
  13. 13.
    Ramachandra, T. V., Bharath, H. A., Vinay, S., Joshi, N. V, Kumar, U., & Venugopal Rao, K. (2016). Modelling and visualization of urban trajectory in 4 cities of India. In 32nd annual in-house symposium on space science and technology ISRO-IISc space technology cell. Retrieved from
  14. 14.
    Ji, C. Y., Liu, Q., Sun, D., Wang, S., Lin, P., & Li, X. (2001). Monitoring urban expansion with remote sensing in China. International Journal of Remote Sensing, 22(8), 1441–1455. Scholar
  15. 15.
    NASA. (2001). Satellite maps provide better urban sprawl insight. NASA, News Release 2. Retrieved January 23, 2018 from
  16. 16.
    Chang, K. T. (2015). Introduction to geographic information system (8th ed.). New York: McGraw Hill Education.Google Scholar
  17. 17.
    Islam, W., & Sarker, S. C. (2016). Monitoring the changing pattern of land use in the Rangpur City corporation using remote sensing and GIS. Journal of Geographic Information System, 08(04), 537–545. Scholar
  18. 18.
    Jasim, M. A., Shafri, H. Z. M., Hamedianfar, A., & Sameen, M. I. (2016). Land transformation assessment using the integration of remote sensing and GIS techniques: A case study of Al-Anbar Province, Iraq. Arabian Journal of Geosciences. Scholar
  19. 19.
    Batty, M. (2009). Urban modeling. International Encyclopedia of Human Geography. Scholar
  20. 20.
    Santé, I., García, A. M., Miranda, D., & Crecente, R. (2010). Cellular automata models for the simulation of real-world urban processes: A review and analysis. Landscape and Urban Planning, 96(2), 108–122. Scholar
  21. 21.
    Crooks, A. T. (2010). Constructing and implementing an agent-based model of residential segregation through vector GIS. International Journal of Geographical Information Science, 24(5), 661–675. Scholar
  22. 22.
    Guidolin, M., Chen, A. S., Ghimire, B., Keedwell, E. C., Djordjević, S., & Savić, D. A. (2016). A weighted cellular automata 2D inundation model for rapid flood analysis. Environmental Modelling and Software, 84, 378–394. Scholar
  23. 23.
    Johnston, K. M., North, M. J., & Brown, D. G. (Eds.). (2013). Introducing agent based modeling in the GIS environment. California: ESRI Press.Google Scholar
  24. 24.
    Jokar Arsanjani, J., Helbich, M., Kainz, W., Darvishi Boloorani, A., Arsanjani, J. J., Helbich, M., et al. (2012). Integration of logistic regression, Markov chain and cellular automata models to simulate urban expansion. International Journal of Applied Earth Observation and Geoinformation, 21(1), 265–275. Scholar
  25. 25.
    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. Scholar
  26. 26.
    Matthews, K. B., Craw, S., & Sibbald, A. R. (1999). Implementation of a spatial decision support system for rural land use planning. Integrating Geographic Information System and Environmental Models with Search and Optimisation Algorithms, Computers. Scholar
  27. 27.
    Bharath, H. A., Vinay, S., Venugopal Rao, K., & Ramachandra, T. V. (2015). Prediction of spatial patterns of urban dynamics in Pune, India. In 11th IEEE India conference: Emerging trends and innovation in technology, INDICON 2014.
  28. 28.
    Chen, L. (2012). Agent-based modeling in urban and architectural research: A brief literature review. Frontiers of Architectural Research, 10, 10. Scholar
  29. 29.
    Zadeh, L. A. (1975). The concept of a linguistic variable and its application to approximate reasoning-I. Information Sciences, 8(3), 199–249. Scholar
  30. 30.
    Zimmermann, H. J. (2010). Fuzzy set theory. Wiley Interdisciplinary Reviews: Computational Statistics. Scholar
  31. 31.
    Arsovski, S., Todorovic, G., Lazić, Z., Arsovski, Z., Ljepava, N., & Aleksic, A. (2017). Model for selection of the best location based on fuzzy AHP and Hurwitz methods. Mathematical Problems in Engineering, 2017, 1–12. Scholar
  32. 32.
    Saaty, T. L. (2004). Decision making: The analytic hierarchy and network processes (AHP/ANP). Journal of Systems Science and Systems Engineering, 13(1), 1–35. Scholar
  33. 33.
    Bharath, H. A., Chandan, M. C., Vinay, S., & Ramachandra, T. V. (2017). Modelling urban dynamics in rapidly urbanising Indian cities. Egyptian Journal of Remote Sensing and Space Science. Scholar
  34. 34.
    United States Geological Survey. (2015). Earthexplorer. United States geological survey. Retrieved December 23, 2015 from
  35. 35.
    Geological Survey of India. (n.d.). Topographical maps. Government of India. Retrieved November 9, 2016 from
  36. 36.
    Google. (2016). Google earth. Retrieved from
  37. 37.
    National Remote Sensing Centre. (2016). Bhuvan. Indian Space Research Organisation, Government of India. Retrieved from
  38. 38.
    Jensen, J. R. (1996). Introductory digital image processing: A remote sensing perspective (2nd ed.). London: Pearson. Scholar
  39. 39.
    Congalton, R. G., & Green, K. (2009). Assessing the accuracy of remotely sensed data: Principles and practices. The photogrammetric record (Vol. 2). Boca Raton: CRC Press. Scholar
  40. 40.
    Saaty, T. L. (2011). Fundamentals of decision making and priority theory with the analytic hierarchy process (3rd ed., Vol. 6). Pittsburgh: RWS Publications.Google Scholar
  41. 41.
    Chang, D.-Y. (1996). Applications of the extent analysis method on fuzzy AHP. European Journal of Operational Research, 95(3), 649–655. Scholar
  42. 42.
    Lillesand, T. M., Kiefer, R. W., & Chipman, J. W. (2004). Remote sensing and image interpretation. Lloydia Cincinnati (Vol. 3). Retrieved from

Copyright information

© Korean Spatial Information Society 2018

Authors and Affiliations

  1. 1.Energy and Wetland Research Group, CES TE 15, Centre for Ecological Sciences, New Bioscience Building, Third Floor, E-Wing [Near D-Gate]Indian Institute of ScienceBangaloreIndia
  2. 2.Department of Political AffairsUniversity of Southern California (USC)Los AngelesUSA
  3. 3.Ranbir and Chitra Gupta School of Infrastructure Design and Management (RCGSIDM)Indian Institute of Technology KharagpurKharagpurIndia

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