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Measuring urban sprawl: A case study of Hyderabad
Further, measurement of the difference of entropy between time t and t+1 can be used to indicate the magnitude of change of urban sprawl, i.e.,
Results and Discussion Remote sensing data is capable of detecting and measuring a variety of elements relating to the morphology of cities, such as the amount, shape, density, textural form and spread of urban areas (Webster, 1995;Mesev etal.1995, Yeh and Li, 2001). Hyderabad occupies fifth position in terms of area and population in the country. The city has been witnessing rapid growth in urban population between 1981 and 1999 (Mary and Raghavaswamy, 2000). The urban population of the city has increased by 41.57% as against 43% of the total Andhra Pradesh state and 36% of total country. In such a scenario, studies on land use cover dynamics over the Hyderabad and its environs gain importance. Land use / cover analysis from the Remote sensing data suggested different land use cover classes viz., residential, industrial, public, semi-public, water bodies and forest (Fig. 1). The detailed land use cover estimates obtained from IRS-1C (LISS III + PAN) data for the years, 1980, 1992 and 1999 (Mary and Raghavaswamy, 2000) are given in Table 2. Analysis of the results suggests a clear increase in residential, commercial, industrial and transportation in the urban area. In the non-urban area, there is a clear reduction in agriculture area and also in vacant land suggesting the increased intensity with urbanization activities. In the present study, the areal estimates of 1999 of Hyderabad and its environs have been used for studying the urban sprawl intensity at different zones, viz., Residential (Banjara Hills), Industrial (Balanagar), Commercial (Paradise, Panjagutta, Abids, Charminar) Sensitive (Zoo park) and Mixed zone (Uppal). The classified data obtained from remote sensing has been transferred to GIS domain for performing the spatial operations. Relative entropy of two types of buffer zones viz., based on the site (site buffer) and road (road buffer) respectively, for each site has been calculated to measure the degree of urban sprawl in each of the buffer zone (Fig.2). Density of land development (%) defined as the amount of land developed divided by the land area in each of the buffer zone has been calculated following Yeh and Li approach (2001). Apart from the above analysis for the year 1999, the entropy values have been calculated for the years of 1980 and 1992 also. The results suggest that there is substantial variation in the patterns of urban sprawl among the different zones of the study area corresponding to residential, industrial, commercial, sensitive and mixed zones. The pattern of land development away from city center is slightly different from that along the roads. The detailed analysis has been carried with respect to density of land development and the road distance for each of the sites. Analysis of the results suggest that in case of residential areas, as the road distance increased, the density of land development also increased and vice versa. This relationship has been found to be high for site of Banjara hills and lowest for Paradise. The density of land development (%) declined rapidly as the distance from road increased for Zoopark and Charminar in residential sites (Fig.3). When compared to residential sites which showed a positive correlation of r2 = 0.72 with respect to density of land development, negative correlation has been noted for remaining industrial, public and forest areas. This suggests that, public amenities such as colleges, university and industrial and forest areas are not in proportion i.e., as the road distance from the city center increased, the above amenities also decreased considerably, suggesting the aggregation of the above amenities at some localities. The temporal change of spatial patterns of urban development can be easily measured from the change of entropy equation. Urban sprawl as reflected from the entropy values in different sites for Hyderabad and its environs suggest that the buffer zones 1(10m) higher entropy at all sites compared to buffer zone2 and buffer zone 3. This indicates that around the center of city, the entropy values are high suggesting highly dispersed nature of the environs. It is found that the average increase in entropy from city center is around 0.55 during the year, 1999. Results of the analysis suggested that in the Hyderabad city, higher entropy is noted for Paradise area followed by Banjara hills and Balanagar (Fig.4). Using the areal estimates of the years 1980-1999, entropy values have been calculated for urban and sub-urban areas. The entropy for urban areas increased from 0.75 in 1980 to 0.80 in 1999 compared to increase of entropy in non-urban areas from 0.55 in 1980 to 0.69 in 1999 (Fig 5). The increase in the values of entropy indicates that there is an increase in urban sprawl and development tend to be more dispersed over a period of time. This indicates rapid increase of urban sprawl. The entropy values in urban areas are much higher than sub-urban areas indicating rapid urbanization process. Overall analysis suggests that among the different zones classified as Industrial, Commercial, Sensitive and Residential, the entropy values are considerably high in the Residential zones suggesting high rate of urban sprawl over a period of time.
The present study has demonstrated the utility of entropy approach to identify, measure and monitor spatio-temporal patterns of urban sprawl in Hyderabad city and its environs, by integrating with remote sensing and GIS techniques. The entropy method can be easily implemented within GIS to facilitate the measurement of urban sprawl. The study suggests that entropy is a good indicator for identifying the spatial processes in land development.
Acknowledgements Authors are grateful to Dr. R. R. Navalgund, Director, NRSA and Prof.S.K.Bhan, Associate Director, NRSA (Applications) for the guidance and encouragement.V.Krishna Prasad and and M.Lata thank ISRO-GBP for providing the fellowship. References
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