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Urban sprawl pattern recognition and modeling using GIS


Measuring Urban Sprawl
To understand the complexity of a dynamic phenomenon such as urban sprawl; land use change analyses, urban sprawl pattern and computation of sprawl indicator indices were determined.

The characteristics of land use / land cover, drainage network, roads and railway network and the administrative boundaries from the toposheets were digitised. Individual layers for each character were digitized. The highway passing between the two cities was digitized separately and a buffer region of 4 km around this was created using MAPINFO 5.5. This buffer region demarcates the study region around the highway.

Urban sprawl over the period of three decades (1972-99) was determined by computing the area of all the settlements from the digitized toposheets of 1971-72 and comparing it with the area obtained from the classified satellite imagery for the built-up theme.

The vector layers were digitized from the toposheets of 1972, included themes as; highway in the buffer region, built-up area, drainage (sea, rivers, streams and water bodies), administrative boundaries, and road network.

The toposheets as mentioned in Table 1, were first geo-registered. Since urban sprawl is a process, which can affect even the smallest of villages, each and every village was analyzed. Details of villages like taluk it belongs to, village name, population density, distance to the cities, were extracted from census books of 1971 & 1981 and were added to the attribute database. The area under built-up (for 1972) was later added to this attribute database after digitization of the toposheets for the built-up feature for each village.

Satellite image – IRS data for Path 97 and Row 67 dated 29th March 1999 was procured from NRSA, Hyderabad. From the LISS imagery available the analysis for 1999 was undertaken using Idrisi 32.

The standard processes for the analyses of satellite imagery such as extraction, restoration, classification, and enhancement were applied for the current study. The Maximum Likelihood Classifier (MLC) was employed for the image classification. The original classification of land-use of 16 categories was aggregated to vegetation, built-up (residential & commercial), agricultural lands & open, and water bodies. Area under built-up theme was recognized and the whole built-up theme from that imagery was digitized; this vector layer gave the urban area of 1999. Further, by applying vector analyses, the built-up area under each village was calculated.

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). It can be safely considered 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.

Considering the built-up area as a potential and fairly accurate parameter of urban sprawl has resulted in making considerable hypothesis on this phenomenon. Since the sprawl is characterized by an increase in the built-up area along the urban and rural fringe, this attribute gives considerable information for understanding the behaviour of such sprawls. This is also influenced by parameters such as, population density, population growth rate, etc.

Pattern recognition helps in finding meaningful patterns in data, which can be extracted through classification. Digital image processing through spectral pattern recognition wherein the spectral characteristics of all pixels in an image were analysed. By spatially enhancing an image, pattern recognition can also be performed by visual interpretation.

Shannon’s Entropy
As an important exercise, the Shannon’s entropy approach (Yeh and Li, 2001) was quantified to detect the urban sprawl phenomenon. The Shannon’s entropy, Hn is given by,

Hn = - S Pi log (Pi)---------------------- 1

Pi = Proportion of the variable in the ith zone
n = Total number of zones

The value of entropy ranges from 0 to log n. If the distribution is very compact then the entropy value would be closer to 0 and when the distribution is very dispersed the value will be closer to log n. Large value of entropy indicates the occurrence of urban sprawl.

Results and Discussion

Image Analysis and Interpretation
The standard image processing techniques such as, image extraction, rectification, restoration, and classification were applied in the current study. The image obtained from the NRSA in three bands, viz., Band 2 (green), Band 3 (red) and Band 4 (near infrared), were used to create a False Colour Composite (FCC) as shown in Figure 2. Training polygons were chosen from the composite image and corresponding attribute data was obtained in the field using GPS. Based on these signatures, corresponding to various land features, image classification was done using Guassian Maximum Likelihood Classifier. From the original classification of land-use of 16 categories the image was reclassified to four broader categories as vegetation, water bodies, open land, and built-up. The classified image is shown in Figure 3.

From the classified image the area under the built-up theme was computed. Area under built-up theme for each village in the study area was also computed by overlaying a vector layer with village boundaries and tabulated accordingly for further analyses.

Figure 2 False Colour Composite
Figure 3 Classified Image

Table 2 shows the built-up area, population and Shannon’s entropy for 1972 and 1999.

Table 2: Built-up Area, Population and Shannon’s Entropy for the Study Area
Segment Built-up Area (sq. km) Population Shannon’s Entropy
1972 1999 1972 1999 1972 1999 Log N
Udupi – Mangalore 25.1383 61.7603 312003 483183 1.7673 1.673 1.9138

Population Growth and Built-up Area The rate of development of land in the region of Udupi – Mangalore is far outstripping the rate of population growth. This implies that the land is consumed at excessive rates and probably in unnecessary amounts as well. Between 1972 and 1999, population in the region grew by about 54% (Census of India, 1971 and 1981) while the amount of developed land grew by about 146%, or nearly three times the rate of population growth. (Figure 4) This means that the per capita consumption of land has increased markedly over three decades. The per capita land consumption refers to utilization of all the land development initiatives like the commercial, industrial, educational, and recreational establishments along with the residential establishments per person. Since most the initiatives pave way for creation of jobs and subsequently helps in earning livelihood, the development of land is seen as a direct consequence of this and hence one can conclude that the per capita land consumption is inclusive of all the associated land development.

Figure 4 Rates of Growth in Population and Built-up from 1971 – 1999

Shannon’s entropy was calculated from the built-up area for each village wherein each village was considered as an individual zone (n = total number of villages). From the Shannon’s entropy calculation, it revealed that the distribution of built-up in the region in 1972 was more dispersed than in 1999. However the degree of dispersion has come down marginally and that distribution is predominantly dispersed or there is the presence of sprawl. It should be recollected that the entropy value indicates the degree of dispersion or compaction of the built-up in the region. The values obtained here being 1.7673 in 1972 to 1.673 in 1999, are closer to the upper limit of log n, i.e. 1.914, thus showing the degree of dispersion of built-up in the region.

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