Rapid Urban Expansion in the metropolitan cities like Pune, result in indiscriminate land use changes transforming natural landscapes. Such haphazard spatial development poses a risk to depletions of resources along with the disarray of the ecological system. A proactive approach to predicting and analyzing such changes is necessary in promoting the well-managed growth of cities. For the complex nature of urban growth, simulation method is used for analyzing spatial patterns, evaluating future scenarios, and aiding urban planning decisions. Bavdhan, Sus, and Mahalunge, which lie towards the western side of Pune and are witnessing significant urbanization are selected for the study. This research investigates the use of Cellular Automata (CA) urban growth modeling for simulating and anticipating urban expansion by calibrating the model against previous development patterns and testing it using real-world data in the selected suburbs. Cellular Automata – CA integrates various urban growth parameters of spatial dynamics viz Land Use Classification, regression Transition Probability Matrices and spatial dependency factors to simulate urban sprawl process close to the reality. CA-based simulation is used in combination with GIS data, remote sensing satellite images and land use classification to Markov chains, machine learning and socio-economic methodologies for predictive urban modelling. Socio-economic and policy driven factors are incorporated that influence urban expansion to enhance prediction. The findings highlight expanding urban hotspots, infrastructure stress areas, and emerging environmental concerns. The outcomes will contribute to enhance stakeholders’ understanding of dynamics of spatial & temporal urban development, facilitating data driven land-use planning that ensures harmony between environment and infrastructural development. These findings will offer urban planners and policymakers a data-driven framework for reducing unregulated growth and optimizing zoning restrictions.

Keywords: Cellular automata, environmental concerns, land use change, predictive modeling,

urban expansion

🔒 This is a subscription article

Full text is available to subscribers and institutional members. Please choose an option below to access it.

1. United Nations. World urbanization prospects: The 2018 revision. New York: United Nations Department of Economic and Social Affairs, Population Division; 2018. Available from: https:// un.org/wup/Publications/Files/WUP2018-Report.pdf.
2. Sudhira HS, Ramachandra TV, Jagadish KS. Urban sprawl: Metrics, dynamics and modelling using GIS. Int J Appl Earth Obs Geoinf. 2004;5(1):29–39. doi: 1016/j.jag.2003.08.002.
3. Clarke KC, Gaydos LJ. Loose coupling a cellular automaton model and GIS: Long-term urban growth prediction for San Francisco and Washington/Baltimore. Int J Geogr Inf Sci. 1998;12(7):699–714. doi: 1080/136588198241617.
4. Lu H, Wang R, Ye R, Fan J. Monitoring long-term spatiotemporal dynamics of urban expansion using multisource remote sensing images and historical maps: A case study of Hangzhou, China. Land. 2023;12(1):144. doi: 3390/land12010144.
5. Al-Darwish A. Predicting the future urban growth and its impacts on the surrounding environment using urban simulation models: Case study of Ibb city – Yemen. Alexandria Eng J. 21(3):265–272. Available from: https://www.sciencedirect.com/science/article/pii/S1110016818300863.
6. Ravetz J, Fertner C, Nielsen TS. The dynamics of peri-urbanization. In: Peri-urban futures: Scenarios and models for land use change in Europe. Springer; 2013. p. 13–44. doi: 1007/978-3-642-30529-0_2.
7. Sivaramakrishnan KC. Re-visioning Indian cities: The urban renewal mission. New Delhi: Sage Publications India; 2011.
8. White R, Engelen G. Cellular automata and fractal urban form: A cellular modelling approach to the evolution of urban land-use patterns. Environ Plan A. 1993;25(8):1175–1199. doi: 1068/a251175.
9. Batty M, Xie Y, Sun Z. Modeling urban dynamics through GIS-based cellular automata. Comput Environ Urban Syst. 1999;23(3):205–233. doi: 1016/S0198-9715(99)00015-0.
10. Landis JR, Koch GG. The measurement of observer agreement for categorical data. Biometrics. 1977;33(1):159–174. doi: 2307/2529310.

Rapid Urban Expansion in the metropolitan cities like Pune, result in indiscriminate land use changes transforming natural landscapes. Such haphazard spatial development poses a risk to depletions of resources along with the disarray of the ecological system. A proactive approach to predicting and analyzing such changes is necessary in promoting the well-managed growth of cities. For the complex nature of urban growth, simulation method is used for analyzing spatial patterns, evaluating future scenarios, and aiding urban planning decisions. Bavdhan, Sus, and Mahalunge, which lie towards the western side of Pune and are witnessing significant urbanization are selected for the study. This research investigates the use of Cellular Automata (CA) urban growth modeling for simulating and anticipating urban expansion by calibrating the model against previous development patterns and testing it using real-world data in the selected suburbs. Cellular Automata – CA integrates various urban growth parameters of spatial dynamics viz Land Use Classification, regression Transition Probability Matrices and spatial dependency factors to simulate urban sprawl process close to the reality. CA-based simulation is used in combination with GIS data, remote sensing satellite images and land use classification to Markov chains, machine learning and socio-economic methodologies for predictive urban modelling. Socio-economic and policy driven factors are incorporated that influence urban expansion to enhance prediction. The findings highlight expanding urban hotspots, infrastructure stress areas, and emerging environmental concerns. The outcomes will contribute to enhance stakeholders’ understanding of dynamics of spatial & temporal urban development, facilitating data driven land-use planning that ensures harmony between environment and infrastructural development. These findings will offer urban planners and policymakers a data-driven framework for reducing unregulated growth and optimizing zoning restrictions.

Keywords: Cellular automata, environmental concerns, land use change, predictive modeling,

urban expansion

🔒 This is a subscription article

Full text is available to subscribers and institutional members. Please choose an option below to access it.

1. United Nations. World urbanization prospects: The 2018 revision. New York: United Nations Department of Economic and Social Affairs, Population Division; 2018. Available from: https:// un.org/wup/Publications/Files/WUP2018-Report.pdf.
2. Sudhira HS, Ramachandra TV, Jagadish KS. Urban sprawl: Metrics, dynamics and modelling using GIS. Int J Appl Earth Obs Geoinf. 2004;5(1):29–39. doi: 1016/j.jag.2003.08.002.
3. Clarke KC, Gaydos LJ. Loose coupling a cellular automaton model and GIS: Long-term urban growth prediction for San Francisco and Washington/Baltimore. Int J Geogr Inf Sci. 1998;12(7):699–714. doi: 1080/136588198241617.
4. Lu H, Wang R, Ye R, Fan J. Monitoring long-term spatiotemporal dynamics of urban expansion using multisource remote sensing images and historical maps: A case study of Hangzhou, China. Land. 2023;12(1):144. doi: 3390/land12010144.
5. Al-Darwish A. Predicting the future urban growth and its impacts on the surrounding environment using urban simulation models: Case study of Ibb city – Yemen. Alexandria Eng J. 21(3):265–272. Available from: https://www.sciencedirect.com/science/article/pii/S1110016818300863.
6. Ravetz J, Fertner C, Nielsen TS. The dynamics of peri-urbanization. In: Peri-urban futures: Scenarios and models for land use change in Europe. Springer; 2013. p. 13–44. doi: 1007/978-3-642-30529-0_2.
7. Sivaramakrishnan KC. Re-visioning Indian cities: The urban renewal mission. New Delhi: Sage Publications India; 2011.
8. White R, Engelen G. Cellular automata and fractal urban form: A cellular modelling approach to the evolution of urban land-use patterns. Environ Plan A. 1993;25(8):1175–1199. doi: 1068/a251175.
9. Batty M, Xie Y, Sun Z. Modeling urban dynamics through GIS-based cellular automata. Comput Environ Urban Syst. 1999;23(3):205–233. doi: 1016/S0198-9715(99)00015-0.
10. Landis JR, Koch GG. The measurement of observer agreement for categorical data. Biometrics. 1977;33(1):159–174. doi: 2307/2529310.