Simulating Urban Growth Using the Cellular Automata Markov Chain Model in the Context of Spatiotemporal Influences for Salem and Its Peripherals, India

Abstract

Urbanization is one of the biggest challenges for developing countries, and predicting urban growth can help planners and policymakers understand how spatial growth patterns interact. A study was conducted to investigate the spatiotemporal dynamics of land use/land cover changes in Salem and its surrounding communities from 2001 to 2020 and to simulate urban expansion in 2030 using cellular automata (CA)–Markov and geospatial techniques. The findings showed a decrease in aerial vegetation cover and an increase in barren and built-up land, with a rapid transition from vegetation cover to bare land. The transformed barren land is expected to be converted into built-up land in the near future. Urban growth in the area is estimated to be 179.6 sq km in 2030, up from 59.6 sq km in 2001, 76 sq km in 2011, and 133.3 sq km in 2020. Urban sprawl is steadily increasing in Salem and the surrounding towns of Omalur, Rasipuram, Sankari, and Vazhapadi, with sprawl in the neighboring towns surpassing that in directions aligned toward Salem. The city is being developed as a smart city, which will result in significant expansion and intensification of the built-up area in the coming years. The study’s outcomes can serve as spatial guidelines for growth regulation and monitoring.

Projecting Urban Expansion by Analyzing Growth Patterns and Sustainable Planning Strategies—A Case Study of Kamrup Metropolitan, Assam, North-East India

“…Urbanization, a phenomenon that has rapidly altered the landscape in an unplanned manner, leads to various issues such as landscape fragmentation, decreased arable land, increased urban poverty, and environmental degradation [1,2]. The United Nations predicts that by 2050, urban areas will cover 60% of the world's rural populations [3].…”