Abstract

Sustainable urban planning requires an understanding of the spatial and temporal patterns of urbanization with insights to the sprawl. Indian cities have been experiencing a rapid urbanization consequent to globalization and relaxations in market economy during the past three decades. This has posed challenges to the policy makers and necessitated appropriate urban policies taking into account sensitiveness of a region and dynamic changes. Availability of remote sensing data at regular intervals with Geo-informatics has proved to be highly efficient in identifying, measuring and quantifying spatio-temporal patterns of urban growth. This communication quantifies the changes in two rapidly urbanizing landscapes in India. This involved analysis of
(i) land cover and land use changes
(ii) spatial patterns of urbanisation through zone wise density gradients
Tier I cities namely Hyderabad and Chennai, emerging IT giants in India were chosen for the analysis. Computation of Shannon’s entropy and spatial metrics ai ded in assessing the sprawl and spatial patterns of urbanisation in a landscape. Modelling and prediction of likely changes in the land use was done using an integrated approach with fuzzy, analytical hierarchical process (AHP), cellular automata (CA) and Markov chain. Results of land use analysis revealed an increase in urban area from1.46% (1991) to 18.81% (2013) for Chennai region and 1.75% (1989) to 22.19% (2014) for Hyderabad region. The spatial analysis through prioritized eight metrics reveal a fragmented or dispersed growth in the outskirts and compact growth in the core area. Modelling was performed considering a set of agents and constraints with two scenarios- implementation of city development plan (CDP) and without CDP. Modelling of urban growth in 2025 reveals urbanized landscapes of 36.6% and 51% in Chennai and Hyderabad respectively. Periphery of the major roads and outside the city jurisdiction limits are favorable areas of urban growth with land use changes from agriculture or others category to built-up category. Modelling and visualisation of urban growth equip regional decision making to provide basic amenities and appropriate infrastructure considering the likely demand with urbanisation.


Keywords : Urbanisation, Cellular automata, Markov chain, AHP, Fuzzy