Introduction

Urbanisation is a form of paved surface growth in response to technological, economic, social, and political forces and to the physical geography of an area [1], [2]. The increase in human population coupled with enhanced economic activities often leads to further development of town and urban agglomerates, and agrarian dominant regions evolve to industrially dominant regions. This has given impetus to the spread of the city towards outskirts or urban sprawl [3]. Sprawl often takes place in the urban fringe, resulting in radial development of urban areas, or along major transport infrastructure, resulting in the elongated development of urban forms [4], which has been investigated in the developed countries [5], [8] and in developing countries such as China [9], [10] and India [11], [12], [13], [14], [15], [16], [17]. Ciscel (2001) examined sprawl visualising and quantifying three major components for the cause, the jobs, business and housing and government infrastructure capital costs [18]. Urban sprawl was also captured indirectly through socio-economic indicators such as population growth, employment opportunity, number of commercial establishments, etc. [19]. Nevertheless, these techniques cannot effectively identify the impacts of urban sprawl in a spatial context. In this context, availability of spatial data at regular intervals through space-borne remote sensors are helpful in effectively inventorying, mapping and monitoring land use changes [20], [21]. Galster et al. (2001) have quantified sprawl earlier using parameters such as density, continuity, concentration, clustering, centrality, nuclearity, proximity and mixed uses [22]. Tsai (2005) employed four quantitative variables (i.e. metropolitan size, activity intensity, distribution degree and clustering extent) to differentiate compactness from sprawl [23]. Others [24], [25], [26] employed multidimensional indicators to measure compactness within specific neighbourhoods or cities. Computation of landscape metrics with the multi-resolution remote sensing data aid in quantifying the spatial pattern of land use patches in geographical area to understand the patterns of variations in peri-urban regions [1]. However, these approaches still lack spatially explicit urban expansions in the past with the related urban planning policies and predict possible expansion scenarios in the future [27]. Attempts linking land use changes and transportation to predict urbanisation have been done through cellular automata (CA) modelling, ABM, etc. In India, several studies attempted urbanisation and urban growth in relation to transportation, energy, land use, climate, etc. and many studies have not addressed the problem of urban sprawl. Recent research [28], [29], [30], [31] assert urban systems are complex systems, while acknowledging the self-organisation in urban systems. Capturing urban systems as discrete models gained further momentum with the popularity of the cellular automata (CA) based techniques [33]. Yakoub (2005) has adopted a change detection approach to evaluate, detect, and estimate the areas of land use change in parts of the Delta area in Egypt [32]. Further, Courage et.al, (2009) [34] Ramachandra et al., (2013) [3] used Markov chain for generating transition probability matrices based on the understanding of past land use changes and could easily establish the developments in the region based on current land use with CA and Markov provided vital insights to the understanding of spatio-temporal patterns of urbanisation. There have been many other methods that use various techniques to model the land use changes [35]. However, these approaches do not take into account agent’s interaction in the urban space for modelling the likely growth.  This communication is to understand the spatio-temporal patterns of urbanisation in Bhopal city with a buffer of 10 km and to predict likely future urban growth based agent’s interactions with land use change process and current rate of transitions.