Journal of the Indian Society of Remote Sensing

, Volume 31, Issue 4, pp 299–311 | Cite as

Urban growth analysis using spatial and temporal data

  • H. S. Sudhira
  • T. V. Ramachandra
  • Karthik S. Raj
  • K. S. Jagadish


Urban growth identification, quantification, knowledge of rate and the trends of growth would help in regional planning for better infrastructure provision in environmentally sound way. This requires analysis of spatial and temporal data, which help in quantifying the trends of growth on spatial scale. Emerging technologies such as Remote Sensing, Geographic Information System (GIS) along with Global Positioning System (GPS) help in this regard. Remote sensing aids in the collection of temporal data and GIS helps in spatial analysis. This paper focuses on the analysis of urban growth pattern in the form of either radial or linear sprawl along the Bangalore — Mysore highway. Various GIS base layers such as built-up areas along the highway, road network, village boundary etc. were generated using collateral data such as the Survey of India toposheet, etc. Further, this analysis was complemented with the computation of Shannon’s entropy, which helped in identifying prevalent sprawl zone, rate of growth and in delineating potential sprawl locations. The computation Shannon’s entropy helped in delineating regions with dispersed and compact growth. This study reveals that the Bangalore North and South taluks contributed mainly to the sprawl with 559% increase in built-up area over a period of 28 years and high degree of dispersion. The Mysore and Srirangapatna region showed 128% change in built-up area and a high potential for sprawl with slightly high dispersion. The degree of sprawl was found to be directly proportional to the distances from the cities.


Global Position System Geographic Information System Urban Sprawl Buffer Region Basic Amenity 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. Barnes, K.B., Morgan III, J.M., Roberge, M.C. and Lowe, S. (2001). Sprawl development: Its patterns, consequences, and measurement. Towson University, urbansprawl/ download/Sprwal_white_paper.pdfGoogle Scholar
  2. Batty, M., Xie, Y. and Sun, Z. (1999). The dynamics of urban sprawl. Working Paper Series, Paper 15, Centre for Advanced Spatial Analysis, University College London.Google Scholar
  3. http://www/ Scholar
  4. Berry, B.J.L. (1990). Urbanisation. In: The Earth as Transformed by Human Action (Eds.: B. L. Turner II, W.C. Clark, R.W. Kates, J.F. Richards, J.T. Mathews and W.B. Meyer). Cambridge University Press, U.K., pp. 103–119.Google Scholar
  5. Census of India. (1971). District Census Handbook — South Kanara District. Series —14, Mysore, Directorate of Census Operations.Google Scholar
  6. Census of India. (1981). District Census Handbook — Dakshin Kannada District. Series —9, Karnataka, Directorate of Census Operations.Google Scholar
  7. Epstein, J., Payne, K. and Kramer, E. (2002). Techniques for mapping suburban sprawl. Photogrammetric Engineering and Remote Sensing,63(9): 913–918.Google Scholar
  8. Hurd, J.D., Wilson, E.H., Lammey, S.G. and Civeo, D.L. (2001). Characterization of forest fragmentation and urban sprawl using time sequential Landsat Imagery. Proc. ASPRS Annual Convention, St. Louis, MO. April 23–27, 2001.Google Scholar
  9. Scholar
  10. Lata, K.M., Sankar Rao, C.H., Krishna Prasad, V., Badrinath, K.V.S., Raghavaswamy. (2001). Measurin urban sprawl: a case study of Hyderabad. GISdevelopment,5(12): 26–29.Google Scholar
  11. NAUTILUS (2001). Characterization of Urban Sprawl. Scholar
  12. The Regionalist (1997). Debate on Theories of David Rusk.2(3).Google Scholar
  13. Sierra Club (1998). The Dark Side of the American Dream: The Costs and Consequences of Suburban Sprawl. Google Scholar
  14. Sudhira, H.S., Ramachandra, T.V. and Jagadish, K.S. (2003). Urban sprawl pattern recognition and modeling using GIS. Proc. Map India — 2003, New Delhi, January 28 —31, 2003.Google Scholar
  15. Sutton, P., Roberts, D., Elvidge, C. and Meij, H. (1997). A comparison of night-time satellite imagery and population density for the continental United States. Photogrammetric ENgineering and Remote Sensing,63(11): 1303–1313.Google Scholar
  16. Torrens, P.M. and Alberti, M. (2000). Measuring sprawl. Working PaperNo. 27, Centre for Advanced Spatial Analysis, University College London. papers/ Google Scholar
  17. Welch, R. (1980). Monitoring urban population and energy utilization patterns from satellite data. Remote Sensing of Environment,9: 1–9.CrossRefGoogle Scholar
  18. Yeh, A.G.O. and Xia Li (2001). Measurement and monitoring of urban sprawl in a rapidly growing region using entropy. Photogrammetric Engineering and Remote Sensing,67(1): 83p.Google Scholar

Copyright information

© Springer 2003

Authors and Affiliations

  • H. S. Sudhira
    • 1
  • T. V. Ramachandra
    • 1
  • Karthik S. Raj
    • 1
  • K. S. Jagadish
    • 1
    • 2
  1. 1.Energy and Wetlands Research GroupCentre for Ecoloical Sciences Indian Institute of ScienceBangaloreIndia
  2. 2.Department of Civil EngineeringIndian Institute of ScienceBangaloreIndia

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