DOI: https://doi.org/10.14710/geoplanning.10.2.135-150

Comparative Study for Understanding the Spatial Growth Pattern of Pune and Jaipur City from 1990 to 2020

*Puja Rouhit Lohiya  -  Urban Planning Section, Department of Civil Engineering, Sardar Vallabhbhai National Institute of Technology, Surat, India-395007, India
Kaushikkumar Prafulbhai Sheladiya  -  Urban Planning Section, Department of Civil Engineering, Sardar Vallabhbhai National Institute of Technology, Surat, India-395007, India
Chetan R Patel  -  Urban Planning Section, Department of Civil Engineering, Sardar Vallabhbhai National Institute of Technology, Surat, India-395007, India

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Abstract

Understanding the urban form, conducting spatial change analysis of an urban area using time-series data, and identifying urban growth drivers play a crucial part in framing policies for sustainable planning practices. In this research, an inverse S-curve function is employed to examine Urban Land Densities (ULD) derived from concentric divisions of urban regions in Pune and Jaipur. The inverse S-curve quantitatively describes variations in Urban Land Density (ULD) from the urban center to the outskirts. Consequently, the parameters identified during the curve-fitting process offer information about the urban form of the cities, shedding light on their rate of expansion, level of compactness, and the nature of sprawl. Built-up area is determined from the Landsat datasets for the years 1991, 1996, 2001, 2006, 2011,2016, and 2021. The analysis confirmed that Pune revealed an increase in sprawling, expansive, and low-density development. As a city that has grown linearly, Jaipur has experienced more constrained growth than Pune. Additionally, the fitted ULD equation provided an accurately fitted radius for Jaipur, but not for Pune, highlighting the equation's shortcomings. The direction analysis and understanding of the change in the slopes of the S curve further led to identifying growth drivers, broadly classified into proximity, government intervention, socioeconomic, and physical factors. The study can help achieve future research objectives in simulating and modeling urban growth and creating policies to deal with related problems.

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Keywords: Urban Land density, Density fit curve, Urban Character, Urban Sprawl, Compactness, Urban Growth drivers

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Section: Original Research
Language : EN
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  1. Abiden, M. Z. Z., Abidin, S. Z. Z., & Jamaluddin, M. N. F. (2010). Pixel based urban growth model for predicting future pattern. 1–5. https://doi.org/10.1109/cspa.2010.5545324">[Crossref] 

  2. Altuwaijri, H. A., Alotaibi, M. H., Almudlaj, A. M., & Almalki, F. M. (2019). Predicting urban growth of Arriyadh city , capital of the Kingdom of Saudi Arabia , using Markov cellular automata in TerrSet geospatial system.

  3. 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] 

  4. Catal, B., Saur, D., & Serra, P. (2008). Urban sprawl in the Mediterranean ? Patterns of growth and change in the Barcelona Metropolitan Region 1993 – 2000. 85, 174–184. https://doi.org/10.1016/j.landurbplan.2007.11.004">[Crossref]

  5. Cho, J. (2005). Urban planning and urban sprawl in Korea. Urban Policy and Research.

  6. Dadras, M., Shafri, H. Z. M., & Ahmad, N. (2015). Spatio-temporal analysis of urban growth from remote sensing data in Bandar Abbas city , Iran. The Egyptian Journal of Remote Sensing and Space Sciences, 18(1), 35–52. https://doi.org/10.1016/j.ejrs.2015.03.005">[Crossref]

  7. Daneshpour, A., & Shakibamanesh, A. (2011). Compact city ; dose it create an obligatory context for urban sustainability ? 21(2).

  8. Development, D., For, P., & Pune, O. L. D. (2013). Draft Development Plan Pune Municipal Corporation -2013 Report for Preparation of Development Plan of Old City Limit. 26(1).

  9. Dietzel, C., Herold, M., Hemphill, J. J., & Clarke, K. C. (2005). Spatio-temporal dynamics in California’s Central Valley: Empirical links to urban theory. International Journal of Geographical Information Science, 19(2), 175–195. https://doi.org/10.1080/13658810410001713407">[Crossref]

  10. Getu, K., & Bhat, H. G. (2021). Analysis of spatio-temporal dynamics of urban sprawl and growth pattern using geospatial technologies and landscape metrics in Bahir Dar, Northwest Ethiopia. Land Use Policy, 109(August), 105676. https://doi.org/10.1016/j.landusepol.2021.105676">[Crossref]

  11. Hakim, A. M. Y., Baja, S., Rampisela, D. A., & Arif, S. (2019). Spatial dynamic prediction of landuse / landcover change (case study: Tamalanrea sub-district, makassar city). IOP Conference Series: Earth and Environmental Science, 280(1), 0–8. https://doi.org/10.1088/1755-1315/280/1/012023">[Crossref]

  12. Howley, P. (2009). Attitudes towards compact city living: Towards a greater understanding of residential behaviour. Land Use Policy, 26(3), 792–798. https://doi.org/10.1016/j.landusepol.2008.10.004">[Crossref]

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

  14. Ismael, H. M. (2021). Urban form study: the sprawling city—review of methods of studying urban sprawl. GeoJournal, 86(4), 1785–1796. https://doi.org/10.1007/s10708-020-10157-9">[Crossref]

  15. Jain, A. S. and K. (2020). Comparison of Spatial Modelling Approaches to Predict Urban Growth of Lucknow City , India. 2(63), 4267–4270.

  16. Jiao, L. (2015). Landscape and Urban Planning Urban land density function : A new method to characterize urban expansion. Landscape and Urban Planning, 139, 26–39. https://doi.org/10.1016/j.landurbplan.2015.02.017">[Crossref]

  17. Karimi, M., Sedighi, A., & Argany, M. (2019). A geographical direction-based approach for capturing the local variation of urban expansion in the application of CA-Markov model. Cities, 93(May), 120–135. https://doi.org/10.1016/j.cities.2019.05.001">[Crossref]

  18. Kazemzadeh-zow, A., Shahraki, S. Z., & Salvati, L. (2016). International Journal of Geographical Information A spatial zoning approach to calibrate and validate urban growth models. International Journal of Geographical Information Science, 0(0), 1–20. https://doi.org/10.1080/13658816.2016.1236927">[Crossref]

  19. Kim, Y., & Newman, G. (2020). in Urban Land Change Models. 1–22.

  20. Kumar, M., Singh, R. B., Pravesh, R., Kumar, P., Tripathi, D. K., & Sahu, N. (2022). statistical techniques Urban growth dynamics and modelling usi remote sensing data and multivariate stati techniques.

  21. la Luz Hernández-Flores, M., Otazo-Sánchez, E. M., Galeana-Pizana, M., Roldán-Cruz, E. I., Razo-Zárate, R., González-Ramirez, C. A., Galindo-Castillo, E., & Gordillo-Mart’inez, A. J. (2017). Urban driving forces and megacity expansion threats. Study case in the Mexico City periphery. Habitat International, 64, 109–122. https://doi.org/10.1016/j.habitatint.2017.04.004">[Crossref]

  22. Li, Y., Li, Y., Qureshi, S., Kappas, M., & Hubacek, K. (2015). On the relationship between landscape ecological patterns and water quality across gradient zones of rapid urbanization in coastal China. 318, 100–108.

  23. Lv, J., Ma, T., Dong, Z., Yao, Y., & Yuan, Z. (2018). Temporal and spatial analyses of the landscape pattern of Wuhan city based on remote sensing images. Canadian Historical Review, 7(9). https://doi.org/10.3390/ijgi7090340">[Crossref]

  24. Mahriyar, M. Z., & Rho, J. H. (2014). The Compact City Concept in Creating Resilient City and Transportation System in Surabaya. Procedia - Social and Behavioral Sciences, 135, 41–49. https://doi.org/10.1016/j.sbspro.2014.07.323">[Crossref]

  25. Milad, M., Ming, Y., Firuz, M., & Hanan, Z. (2017). International Journal of Applied Earth Observation and Geoinformation Improving the capability of an integrated CA-Markov model to simulate spatio-temporal urban growth trends using an Analytical Hierarchy Process and Frequency Ratio. 59, 65–78. https://doi.org/10.1016/j.jag.2017.03.006">[Crossref]

  26. Mouratidis, K. (2019). Compact city, urban sprawl, and subjective well-being. Cities, 92(April), 261–272. https://doi.org/10.1016/j.cities.2019.04.013">[Crossref]

  27. Neuman, M. (2005). The compact city fallacy. Journal of Planning Education and Research, 25(1), 11–26. https://doi.org/10.1177/0739456X04270466">[Crossref]

  28. Okwuashi, O., & Ndehedehe, C. E. (2021). Remote Sensing Applications : Society and Environment Integrating machine learning with Markov chain and cellular automata models for modelling urban land use change. Remote Sensing Applications: Society and Environment, 21(December 2020), 100461. https://doi.org/10.1016/j.rsase.2020.100461">[Crossref]

  29. Plan, C. M. (2008). Pimpri-Chinchwad Municipal Corporation ( PCMC ) Comprehensive Mobility Plan ( CMP ) for PCMC. November.

  30. Shafizadeh-Moghadam, H., Asghari, A., Tayyebi, A., & Taleai, M. (2017). Coupling machine learning, tree-based and statistical models with cellular automata to simulate urban growth. Computers, Environment and Urban Systems, 64, 297–308.

  31. Subrahmanya, B., & Hillemane, M. (2009). Urban Sprawl Management : Need for an Integrated Spatial Planning Support System. January.

  32. Yadav, P., Deshpande, S., Centre, D., Area, H. I., Machine, S. V., & Growth, U. (2015). Spatio-Temporal Assessment of Urban Growth Impact in Pune. 2014.

  33. Yin, H., Kong, F., Yang, X., James, P., & Dronova, I. (2018). Exploring zoning scenario impacts upon urban growth simulations using a dynamic spatial model. Cities, 81(May), 214–229. https://doi.org/10.1016/j.cities.2018.04.010">[Crossref]

  34. Zhang, K., Zhao, Y., & Zhang, H. (2012). Monitoring land use/land cover changes of Kunming city based on remote sensing technology and spatial metrics. Proceedings - 2012 20th International Conference on Geoinformatics, Geoinformatics 2012, 2, 5–9. https://doi.org/10.1109/Geoinformatics.2012.6270335">[Crossref]

  35. 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]

  36.  


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