Agent Based Modelling Urban Dynamics of Bhopal, India
Aithal H. BHARATH1, 2, S. VINAY1, T. V. RAMACHANDRA1, 2, 3
1 Indian Institute of Science, Centre for Ecological Sciences (CES), Energy & Wetlands Research Group, Bangalore, INDIA
2 Indian Institute of Science, Centre for Sustainable Technologies (ASTRA), Bangalore, INDIA
3 Indian Institute of Science, Centre for infrastructure, Sustainable Transportation and Urban Planning (CiSTUP), Bangalore, INDIA
E-mail: firstname.lastname@example.org, email@example.com, firstname.lastname@example.org
Abstract. Urbanization involves the transformation of traditional agrarian economy to urban economies dominated by industries and other commercial activities. Our study focuses on the urbanization process in Bhopal (India), a prominent Tier I city, during the last four decades and the visualisation of future growth in 2018 and 2022 with an understanding of urban morphology dynamics through spatial analysis of time-series (of 1977 - 2014) remote sensing data with spatial metrics, density gradients and Markov - Cellular automata. Zone-wise urban density gradients recorded between 1977 and 2014 aided in understanding the urban morphology with intense urbanization at core regions and sprawl at outskirts in NW and SE regions. Our study shows an increase of built up ranges by 162% (from 1977 to 1992), by 111% (from 1992 to 2000), by 150% (from 2000 to 2010) and by 49% (from 2010 to 2014). CA-Markov based urban growth modelling indicates urban changes of 50% (2018) and 121% (2022), while agent based modelling (ABM) indicates urban changes of 57% (2018) and 243% (2022) with increasing urban population as compared to 2014. ABM simulations captured reality more effectively and provided the flexibility to vary quantities and characteristics based on proximity of various amenities generating probability surface influenced by various agents, indicating urban development. Simulation of urban growth based on ABM can help planning infrastructure and basic amenities and support the sustainable management of natural resources.
K e y w o r d s: modelling, Markov-Cellular Automata, urban, spatial metrics, Agent Based Modelling