Introduction

Understanding the phenomenon of urbanisation and analyses of patterns of urbanisation would help in addressing the needs of the present and future needs of a region. This plays a key role in planning for infrastructure and becomes crucial in regional planning especially when resources are scarce. Unchecked urbanisation is often referred colloquially as sprawl poses serious problems in infrastructure planning and implementation that leads to unforeseen consequences. In this context, prior knowledge of patterns of sprawl and its trend would help the development machinery in planning the basic necessities of a region. This requires spatial and statistical data for a different time period. Temporal data acquired remotely (i.e. remote sensing data) for a region along with the historical data of a region (such as population growth patterns, etc.) would help in finding out the patterns and trends of sprawl.  Geographic Information System or GIS would help in integrating both spatial and statistical data and generate themes based on various growth trends.

The process of urbanisation is fairly contributed by population growth, migration and infrastructure initiatives resulting in the growth of villages into towns; towns into cities and cities into metros. However in such a phenomenon for ecologically feasible development, planning requires an understanding of the growth dynamics. Nevertheless in most cases there is lot of inadequacies to ascertain the nature of uncontrolled progression of urban sprawls. Sprawl is considered to be an unplanned outgrowth of urban centers along the periphery of the cities, along highways, along the road connecting a city, etc. Due to lack of prior planning these outgrowths are devoid of basic amenities like water, electricity, sanitation, etc. Provision of certain infrastructure facilities like new roads and highways, fuel such sprawls that ultimately result in inefficient and drastic change in land use affecting the ecosystem.

Usually sprawls take place on the urban fringe, at the edge of an urban area or along the highways in most parts of the globe. The need for understanding urban sprawl is already stressed (Sierra Club, 1998; 2001; The Regionalist, 1997) and attempted in the developed countries (Batty et al., 1999; Torrens and Alberti, 2000; Barnes et al., 2001, Yeh and Li, 2001; Hurd et al., 2001; Epstein et al., 2002).  Typically conditions in environmental systems with gross measures of urbanization are correlated with population density with built-up area (The Regionalist, 1997; Berry, 1990). Added to this, recently concluded 2001 national census show that with the current trend, at least 33% of the Indian population would be in urban centers by 2016. This substantiates the need to analyze and understand the urban sprawl phenomenon in the context of a developing country to address effective resource utilization and infrastructure allocation. The most common form of sprawl either radial (across a city) or along the highways is being investigated by many. In addition to these sprawls, there is a need to understand the sprawl that is taking place, when a city / town is connected by a road, which is most common in developing countries.

Normally, when a rural pockets are connected to a city by a road. At initial stages, development in the form of service centers such as shops, cafeteria, etc. can be seen on the roadside, which eventually become the hub of rural economic activity leading to sprawl. An enormous amount of upsurge could be observed along these roads. This type of upsurge caused by a road network between urban / semi-urban / rural centers is very much prevalent and persistent at most places in India. These regions are devoid of any infrastructure, since planners are unable to visualize this type of growth patterns. This growth is normally left out in all government surveys (census), as this cannot be grouped under either urban or rural centre.  The investigation of patterns of this kind of growth is very crucial from regional planning point of view to provide basic amenities in these regions. Further, with the Prime Minister of India’s pet project, “Golden Quadrilateral of National Highways Development Project” initiative of linking villages, towns and cities and building 4-lane roads, this investigation gains importance and significance. Prior visualising the trends and patterns of growth enable the planning machineries to plan for appropriate basic infrastructure facilities (water, electricity, sanitation, etc.). The study of this kind reveals the type, extent and nature of sprawl taking place in a region and the drivers responsible for the growth. This would help developers and town planners to project growth patterns and facilitate various infrastructure facilities. In this paper, an attempt is made to identify the sprawl pattern, quantify sprawl across roads in terms of Shannon’s entropy, and estimate the rate of change in built-up area over a period with the help of spatial and statistical data of nearly three decades using GIS.

The physical expressions and patterns of sprawl on landscapes can be detected, mapped, and analyzed using remote sensing data and geographical information system (GIS) (Barnes et al., 2001) with image processing and classification. The patterns of sprawl could be described using a variety of metrics and through visual interpretation techniques. Characterization of urbanized landscapes over time and computation of spatial indices that measure dimensions such as contagion, the patchiness of landscapes, fractal dimension, and patch shape complexity are done statistically by Northeast Applications of Useable Technology In Land Use Planning for Urban Sprawl (Hurd et al., 2001; NAUTILUS, 2001).

In the recent years understanding the dynamics of sprawl, quantifying them and subsequently predicting the same for a future scenario has gained significant importance.
Batty et al. (2001) are successful in analyzing this phenomenon using differential equations and developing a model to simulate sprawl using cellular automata for the Ann Arbor, Michigan region. Various issues concerned with quantifying urban sprawl phenomenon are addressed (Torrens and Alberti, 2000; Barnes et al., 2001) to arrive at a common platform for defining the process. Most of these studies quantify sprawl considering the impervious or the built-up as the key feature of sprawl.

The Shannon’s entropy index reflects the concentration of dispersion of spatial variable in a specified area, to measure and differentiate types of sprawl would be useful in quantifying the sprawl (Yeh and Li, 2001). This measure is based on the notion that landscape entropy or disorganization increases with sprawl. The urban land uses are viewed as interrupted and fragmented previously homogenous rural landscapes, thereby increasing landscape disorganization. Similar approach was adopted to quantify urban sprawl in Udupi – Mangalore highway (Sudhira et al., 2003) and for Hyderabad City, India (Lata et al., 2001).

Built-up area as an indicator of urban sprawl

The percentage of an area covered by impervious surfaces such as asphalt and concrete is a straightforward measure of development (Barnes et al., 2001) and these surfaces can be easily detected and interpreted in remotely sensed data. This is based on the assumption that developed areas have greater proportions of impervious surfaces, i.e. the built-up areas as compared to the lesser-developed areas. Further, the population in the region also influences sprawl. The proportion of the total population in a region to the total built-up of the region is a measure of quantifying sprawl. Epstein et al. (2002) also employ a similar technique for mapping suburban sprawl and compared the result with rural and urban centers. Thus the sprawl is characterized by an increase in the built-up area along the urban and rural fringe and this attribute gives considerable information for understanding the behaviour of such phenomenon. Earlier studies carried out in parts of the world highlight and conform that the built-up area could be used as fairly accurate parameter for urban sprawl analyses. The other parameter that is considered as an indicator of urban sprawl is the nighttime data captured with the help of radars or geo-stationary satellites.