Predictive Modelling of Urban Growth Pattern: A Study of Burdwan City

Abstract

Indian cities, currently accommodating 377 million people, constituting 31.16% of the nation's population, are projected to reach 675 million by 2035, marking 43.2% of the total populace as per the UN-Habitat report 2021. Despite the traditional role of major cities in fueling urbanization, growth rates in these centers have diminished since 1991, leading to the expansion of urban footprints into adjacent districts and causing significant urban sprawl. The World Bank's 'Leveraging Urbanization in South Asia' report 2022 underscores a notable discrepancy, with urban sprawl affecting 55.3% of India's population, surpassing the official census figure of 31%. This has led to large Indian cities like Kolkata, Mumbai, and Delhi spilling over their administrative boundaries. While Greater Mumbai experiences a modest 0.4% annual urbanization rate, areas like Thane to the north thrive at 3.1%, highlighting shifting dynamics. Similarly, Burdwan in West Bengal registers a higher 4.9% urbanization rate compared to Kolkata's 1.2%. Of all the aforementioned cities undergoing rapid urban growth, the city of Burdwan in the state of West Bengal has the lowest Human Development Index (HDI) as per United Nations Development Programme (UNDP) report of 2021 and lowest Infrastructure Index as per the Ministry of Statistics and Programme Implementation (MoSPI). The aforementioned issues stem from several factors such as delayed planning proposals, deficient monitoring, complex institutional frameworks, irregular land zoning, and ineffective urban development and master plans. Therefore, there's a pressing need for more efficient planning systems, requiring fewer resources and less time. Predictive modelling is one such process which is used to predict future events or outcomes by analyzing patterns in a given set of input data and statistical models. This research aimed to develop a Predictive Model for mapping the future Urban Growth patterns of Burdwan and determine its practical application. In the pursuit of comprehensively understanding and effectively managing urban growth, this study tried to discern and refine the multitude of factors influencing this phenomenon. These encompass demographic dynamics such as population variations and migration trends, as well as spatio-temporal alterations in land cover, the ramifications of road network expansions, the availability and distribution of essential amenities such as healthcare and education facilities, the trajectory of industrial development, and the trends .Predictive Modelling of Urban Growth Pattern: A Study of Burdwan City housing demand and supply. Once these critical determinants of urban growth were identified, the research constructed a predictive model that can forecast future urban expansion patterns through statistical and GIS analysis. This predictive model was prepared and refined utilizing a diverse array of methodologies, including Shannon’s Entropy to gauge the future pattern of built-up land cover, level of disorder and predictability within urban systems, the SLEUTH model to simulate future Developmental Probability of land, regression models to establish statistical relationships between various factors and urban growth, and metrics such as Urbanization Rate, Growth Ratio, Urbanization Index, and Geographic Information System (GIS) analysis to quantify and visualize urbanization trends. Through the synthesis of insights gleaned from these analyses, the research developed a Rapid Planning Toolkit tailored for adoption by Urban Local Bodies (ULBs). This toolkit would serve as a practical guide, delineating the integration of the predictive model into the urban planning framework. By facilitating the incorporation of predictive analytics into urban planning processes, the toolkit seeks to enhance the efficacy and sustainability of urban development initiatives. Ultimately, the overarching goal of the predictive model is to inform strategic decisions for the Urban Local Bodies (ULBs). By providing stakeholders with actionable insights into optimal locations for development, the research attempted to contribute to the realization of more efficient, equitable, and resilient urban planning systems