skip to main content

Application of Remote Sensing and Geographic Information System in Identification of Urban Growth nodes: A Case of Surat City, 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

With the passage of time, the city's growth behavior will not change unless and until the government intervenes, and thus its identity will shift from monocentric to polycentric to meet the needs of citizens. As a result, this study is being conducted to identify emerging growth nodes within a selected area of Surat City, as well as their growth drivers over a 30-year period. Quantified built-up area within a patch size of 1km x 1km was used to compute patch density at five-year intervals from 1991 to 2021. In addition, the spatial changes that occurred within patches over the same time period were examined. Both analyses aid in determining the emerging growth nodes over a 30-year period. From 1991 to 2021, the city was driven by socioeconomic criteria such as land price, availability of good health and educational facilities, water and sewerage networks, fire stations, proximity factors such as proximity to major roads, bridges, bus stations, metro, railway stations, airport, environmental factors such as the development of riverfront and linear park, bio-diversity park, and government interventions in terms of Town Planning Schemes. This study thus aids urban planners and decision-makers in selecting which growth nodes to plan for new development and type of development, what to connect, and what to protect in the years to come.

Fulltext View|Download
Keywords: Spatial Change, Patch Density, Growth Node, Geographic Information System, Surat City

Article Metrics:

  1. Aguilera-Benavente, F., Botequilha-Leitão, A., & Díaz-Varela, E. (2014). Detecting multi-scale urban growth patterns and processes in the Algarve region (Southern Portugal). Applied Geography, 53, 234–245. https://doi.org/10.1016/j.apgeog.2014.06.019">[Crossref] 

  2. Aguilera, F., Valenzuela, L. M., & Botequilha-Leitão, A. (2011). Landscape metrics in the analysis of urban land use patterns: A case study in a Spanish metropolitan area. Landscape and Urban Planning, 99(3–4), 226–238. https://doi.org/10.1016/j.landurbplan.2010.10.004">[Crossref]

  3. 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]

  4. 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. Arabian Journal of Geosciences, 12(4). https://doi.org/10.1007/s12517-019-4261-z">[Crossref]

  5. Angel, S., Parent, J., & Civco, D. (2007). Urban Sprawl Metrics : An Analysis Of Global Urban Expansion Using Gis Introduction : The Attributes And Manifestations Of Urban ‘ Sprawl ’ Manifestations Of Urban ‘ Sprawl ,’ 1–12.

  6. 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]

  7.  Chang, Q. L. N., & Joyce, J. (2018). Predicting long-term urban growth in Beijing ( China ) with new factors and constraints of environmental change under integrated stochastic and fuzzy uncertainties, 3456789, 2025–2044. https://doi.org/10.1007/s00477-017-1493-x">[Crossref]

  8. Dadashpoor, H., Azizi, P., & Moghadasi, M. (2019). Analyzing spatial patterns, driving forces and predicting future growth scenarios for supporting sustainable urban growth: Evidence from Tabriz metropolitan area, Iran. Sustainable Cities and Society, 47(March), 101502. https://doi.org/10.1016/j.scs.2019.101502">[Crossref]

  9. 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]

  10. Dendoncker, N., Rounsevell, M., & Bogaert, P. (2007). Spatial analysis and modelling of land use distributions in Belgium. Computers, Environment and Urban Systems, 31(2), 188–205. https://doi.org/10.1016/j.compenvurbsys.2006.06.004">[Crossref]

  11. Directorate of Census Operations, G. (2011). District census handbook.

  12. Fonji, S. F., & Taff, G. N. (2014). Using satellite data to monitor land-use land-cover change in North-eastern Latvia. Springer Plus, 1–15.

  13. 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]

  14. 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]

  15. Hasnine, M., & Rukhsana. (2020). An Analysis of Urban Sprawl and Prediction of Future Urban Town in Urban Area of Developing Nation: Case Study in India. Journal of the Indian Society of Remote Sensing, 48(6), 909–920. https://doi.org/10.1007/s12524-020-01123-6">[Crossref]

  16. Hassan, Z., Shabbir, R., Ahmad, S. S., Malik, A. H., Aziz, N., & Butt, A. (2016). Dynamics of land use and land cover change (LULCC) using geospatial techniques : a case study of Islamabad Pakistan. SpringerPlus. https://doi.org/10.1186/s40064-016-2414-z">[Crossref]

  17. 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]

  18. Herold, M., Couclelis, H., & Clarke, K. C. (2005). The role of spatial metrics in the analysis and modeling of urban land use change, 29, 369–399. https://doi.org/10.1016/j.compenvurbsys.2003.12.001">[Crossref]

  19. Huang, Q., & Song, W. (2019). Computers , Environment and Urban Systems A land-use spatial optimum allocation model coupling a multi-agent system with the shu ffl ed frog leaping algorithm. Computers, Environment and Urban Systems, 77(June), 101360. https://doi.org/10.1016/j.compenvurbsys.2019.101360">[Crossref]

  20. Islam, R., Khanam, R., Zaman, A. K. M. M., Observation, E., Management, D., Science, P., et al. (2021). Analysis of Land Use and Land Cover Changing Patterns of Bangladesh Using Remote Sensing Technology. American Journal of Environmental Sciences, 71–81. https://doi.org/10.3844/ajessp.2021.71.81">[Crossref]

  21. Japelaghi, M., Gholamalifard, M., & Shayesteh, K. (2019). Spatio-temporal analysis and prediction of landscape patterns and change processes in the Central Zagros region, Iran. Remote Sensing Applications: Society and Environment, 15(June), 100244. https://doi.org/10.1016/j.rsase.2019.100244">[Crossref]

  22. 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]

  23. Jiao, L., Dong, T., Xu, G., Zhou, Z., Liu, J., & Liu, Y. (2021). Geographic micro-process model: Understanding global urban expansion from a process-oriented view. Computers, Environment and Urban Systems, 87(January), 101603. https://doi.org/10.1016/j.compenvurbsys.2021.101603">[Crossref]

  24. Jiao, L., Mao, L., & Liu, Y. (2015). Multi-order Landscape Expansion Index: Characterizing urban expansion dynamics. Landscape and Urban Planning, 137, 30–39. https://doi.org/10.1016/j.landurbplan.2014.10.023">[Crossref]

  25. Jokar, J., Helbich, M., Kainz, W., & Darvishi, A. (2013). International Journal of Applied Earth Observation and Geoinformation Integration of logistic regression , Markov chain and cellular automata models to simulate urban expansion. International Journal of Applied Earth Observations and Geoinformation, 21, 265–275. https://doi.org/10.1016/j.jag.2011.12.014">[Crossref]

  26. Jun, M. (2021). A comparison of a gradient boosting decision tree , random forests , and artificial neural networks to model urban land use changes : the case of the Seoul metropolitan area. International Journal of Geographical Information Science, 00(00), 1–19. https://doi.org/10.1080/13658816.2021.1887490">[Crossref]

  27. Kantakumar, L. N., Kumar, S., & Schneider, K. (2020). What drives urban growth in Pune? A logistic regression and relative importance analysis perspective. Sustainable Cities and Society, 60(April), 102269. https://doi.org/10.1016/j.scs.2020.102269">[Crossref]

  28. 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]

  29. Li, X., Zhou, W., & Ouyang, Z. (2013). Forty years of urban expansion in Beijing: What is the relative importance of physical, socioeconomic, and neighborhood factors? Applied Geography, 38(1), 1–10. https://doi.org/10.1016/j.apgeog.2012.11.004">[Crossref]

  30. Liang, X., Guan, Q., Clarke, K. C., Liu, S., Wang, B., & Yao, Y. (2021). Computers , Environment and Urban Systems Understanding the drivers of sustainable land expansion using a patch-generating land use simulation (PLUS) model : A case study in. Computers, Environment and Urban Systems, 85(November 2020), 101569. https://doi.org/10.1016/j.compenvurbsys.2020.101569">[Crossref]

  31. 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]

  32. Lv, J., Wang, Y., Liang, X., Yao, Y., Ma, T., & Guan, Q. (2021). Simulating urban expansion by incorporating an integrated gravitational field model into a demand-driven random forest-cellular automata model. Cities, 109(October 2020), 103044. https://doi.org/10.1016/j.cities.2020.103044">[Crossref]

  33. Maina, J., Wandiga, S., Gyampoh, B., & Kkg, C. (2020). Assessment of Land Use and Land Cover Change Using GIS and Remote Sensing : A Case Study of Kieni , Central Kenya Journal of Remote Sensing & GIS. Journal of Remote Sensing & GIS Research, 1–5. https://doi.org/10.35248/2469-4134.20.9.270">[Crossref]

  34. Mandal, J. (2019). Urban Growth Dynamics and Changing Land-Use Land-Cover of Megacity Kolkata and Its Environs. Journal of the Indian Society of Remote Sensing, 47(10), 1707–1725. https://doi.org/10.1007/s12524-019-01020-7">[Crossref]

  35. Ministry of Housing and Urban Affairs, I. (2021). Bengaluru , Pune , Ahmedabad best cities in EoLI 2020 ( Million Plus Category ) Shimla ranked first in EoLI 2020 ( Less than Million Category ) Indore and NDMC leading municipalities in MPI 2020 Both indices provide holistic assessment of cities Rankings.

  36. Mohammadian, H., Tavakoli, J., & Khani, H. (2017). Monitoring land use change and measuring urban sprawl based on its spatial forms The case of Qom city. The Egyptian Journal of Remote Sensing and Space Sciences, 20(1), 103–116. https://doi.org/10.1016/j.ejrs.2016.08.002'">[Crossref]

  37. Roy, P. S., & Roy, A. (2014). Land Use and Land Cover Change: A Remote Sensing & GIS Perspective. Journal of the Indian Institute of Science, 90:4(May), 489–502.

  38. 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]

  39. Shafizadeh-Moghadam, H., Tayyebi, A., & Helbich, M. (2017). Transition index maps for urban growth simulation: application of artificial neural networks, weight of evidence and fuzzy multi-criteria evaluation. Environmental Monitoring and Assessment, 189(6). https://doi.org/10.1007/s10661-017-5986-3">[Crossref]

  40. 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]

  41. Sheladiya, K. P., & Patel, C. R. (2023a). Leveraging Urban Growth Models ( UGM ) for Sustainable Urban Planning and Climate Resilient Cities : A Bibliometric Analysis, 12(3), 585–595. https://doi.org/10.5530/jscires.12.3.056">[Crossref]

  42. Sheladiya, K. P., & Patel, C. R. (2023b). An Application of Cellular Automata (CA) and Markov Chain (MC) Model in Urban Growth Prediction: A case of Surat City, Gujarat, India. Geoplanning: Journal of Geomatics and Planning, 10(1), 23–36. https://doi.org/10.14710/geoplanning.10.1.23-36">[Crossref]

  43. Sisodia, P. S., Tiwari, V., & Kumar, A. (2014). Analysis of Supervised Maximum Likelihood Classification for remote sensing image. International Conference on Recent Advances and Innovations in Engineering, ICRAIE 2014, 9–12. https://doi.org/10.1109/ICRAIE.2014.6909319">[Crossref]

  44. Tang, D., Liu, H., Song, E., & Chang, S. (2020). Urban expansion simulation from the perspective of land acquisition-based on bargaining model and ant colony optimization. Computers, Environment and Urban Systems, 82(December 2019), 101504. https://doi.org/10.1016/j.compenvurbsys.2020.101504">[Crossref]

  45. Taubenböck, H., Wegmann, M., Roth, A., Mehl, H., & Dech, S. (2009). Urbanization in India - Spatiotemporal analysis using remote sensing data. Computers, Environment and Urban Systems, 33(3), 179–188. https://doi.org/10.1016/j.compenvurbsys.2008.09.003/">[Crossref]

  46. 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]

  47. 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]

  48. United Nations, U. (2018). World Urbanization Prospects. NY, USA.

  49. Wang, J., Maduako, I. N., & Wang, J. (2018). Spatio-temporal urban growth dynamics of Lagos Metropolitan Region of Nigeria based on Hybrid methods for LULC modeling and prediction Spatio-temporal urban growth dynamics of Lagos Metropolitan Region of Nigeria based on Hybrid methods for LULC modeling . European Journal of Remote Sensing, 51(1), 251–265. https://doi.org/10.1080/22797254.2017.1419831">[Crossref]

  50. Wang, W., & Jiao, L. (2020). Delineating urban growth boundaries under multi-objective and constraints. Sustainable Cities and Society, 61(January), 102279. https://doi.org/10.1016/j.scs.2020.102279">[Crossref]

  51. Wulder, M. A., White, J. C., Goward, S. N., Masek, J. G., Irons, J. R., Herold, M., et al. (2008). Landsat continuity: Issues and opportunities for land cover monitoring. Remote Sensing of Environment, 112(3), 955–969. https://doi.org/10.1016/j.rse.2007.07.004">[Crossref]

  52. Xu, G., Zhou, Z., Jiao, L., & Zhao, R. (2020). Compact Urban Form and Expansion Pattern Slow Down the Decline in Urban Densities: A Global Perspective. Land Use Policy, 94(January), 104563. https://doi.org/10.1016/j.landusepol.2020.104563">[Crossref]

  53. Yadav, V., & Ghosh, S. K. (2019). Assessment and prediction of urban growth for a mega-city using CA-Markov model. Geocarto International, 0(0), 1–33. https://doi.org/10.1080/10106049.2019.1690054">[Crossref]

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

  55. Yilmazer, S., & Kocaman, S. (2020). A mass appraisal assessment study using machine learning based on multiple regression and random forest. Land Use Policy, 99(September 2019), 104889. https://doi.org/10.1016/j.landusepol.2020.104889">[Crossref]

  56. 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(April), 214–229. https://doi.org/10.1016/j.cities.2018.04.010">[Crossref]

  57. You, H., & Yang, X. (2017). Land Use Policy Urban expansion in 30 megacities of China : categorizing the driving force pro fi les to inform the urbanization policy. Land Use Policy, 68(18), 531–551. https://doi.org/10.1016/j.landusepol.2017.06.020/">[Crossref]

  58. 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]

  59.  

  60.  

  61.  


Last update:

No citation recorded.

Last update: 2024-05-11 10:36:38

No citation recorded.