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Urban sprawl pattern recognition and modeling using GIS
Epstein et al. (2002) bring out the techniques for mapping suburban sprawl. They evaluate the traditional unsupervised classification and proposed GIS buffering approach for mapping the suburban sprawl. They also discuss the problems associated with the classification of urban classes (built-up) in comparison with rural and urban centers.
Yeh and Li (2001) use Shannon’s entropy, which reflects the concentration of dispersion of spatial variable in a specified area, to measure and differentiate types of sprawl. 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. Lata et al (2001) have also employed a similar approach of characterizing urban sprawl for Hyderabad City, India. Pontius et al. (2000) studied the scenarios of land use change in the Ipswich watershed, USA over a period of two decades. This study found that a conversion of forest into residential areas is a predominant land use change. Considering this type of land use change they predict the future land use changes in the Ipswich watershed based on the model calibrated for 1971 and 1985, and validated for 1991. With this model, the extent of deforestation in the watershed is predicted under different scenarios. The results of this are verified by Kappa index. In recent years, considerable interest has been focused on the use of GIS as a decision support system. The use of GIS as a direct extension of the human decision making process—most particularly in the context of resource allocation decisions is indeed a great challenge and an important milestone. With the incorporation of many software tools to GIS for multi-criteria and multi-objective decision-making — an area that can broadly be termed decision strategy analysis there seems to be no bounds for the application of GIS. The land use changes in the region under different scenarios are done using the multi-criteria evaluation through the decision support system. The decision support is based on a choice between alternatives arising under a given set of criterion for a given objective. A criterion is some basis for a decision that can be measured and evaluated. Criterion can be of two kinds: factors and constraints, and this can pertain either to attributes of the individual or to an entire decision set. In this case the objective being to urbanize; constraints include the already existing built-up area, road-rail network, water bodies, etc., where there is no scope for further sprawl; and factors include the components of population growth rate, population density and proximity to the highway and cities. The decision support system evaluates these sets of data using multi-criteria evaluation. This predicts the possibilities of sprawl in the subsequent years using the current and historical data giving the output images for the objective mentioned. Closely associated with the decision strategy analysis is the uncertainty management. Uncertainty is not considered as a problem with data, but else, it is an inherent characteristic of the decision making process. With the increasing pressures on the resource allocation process, the need to recognize uncertainty as a fact of the decision making process that needs to be understood and carefully assessed. Uncertainty management thus lies at the very heart of effective decision-making and constitutes a very special role in GIS (Eastman, 1999). This paper focuses on the urban sprawl pattern recognition and explores the causal factors. Objectives The main objective of the study was to
This study was carried out in the region located within coordinates of latitudes 12° 49' 35'' N and 13° 22' 50" N and longitudes 74o 42' 5" E and 74° 54' 55" E surrounding the National Highway between Udupi and Mangalore (Figure 1). The National Highway (NH) no. 17 passes between Mangalore and Udupi. The distance between the two urban centers is 62 km. A buffer region of 4 km on each side is marked as the specific area for thorough investigation. The total study area is 434.2 sq. km. The annual precipitation in this area is approximately 4242.5 mm in Mangalore and 4128.1 mm in Udupi. The southwest monsoon during the months of June to October is mainly responsible for the precipitation. The next round of precipitation occurs in the months of November and December due to the northeast monsoon. The relative humidity is considerably high mainly due to the proximity of the region to the coast. Mean annual temperature ranges from 18.6° C to 34.9° C (Census of India, 1981).  Figure 1: Location of Study Area Data Collection The data collection was carried out in two phases. This involved primary data collection and secondary data collection. The nature of these data and their source are shown in Table 1.
The toposheet of 1:50000 used for the current study area has the following features:
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