3.2 Case study
3.2.3 Model development for the study area
significant and 5 average levels), gets the highest weight (=1). Next, AOI of university hill which contains 3 significant institutes acquires the second highest weight (=0.6).
Accordingly, weights applied to every average and significant level institute is obtained as 0.12 and 0.2, respectively. Similarly, Kamakhya temple, located on the top of the Kamakhya hill, is the most prominent tourist place in Guwahati, and hence, it gets the highest weight (=1) in the expert opinion survey. Weights to the other tourist places were assigned by experts based on the role of tourist places on providing job/business opportunities and growing developmental activities in the neighbouring area.
Depending on the degree of economic activeness, weights have been applied by the experts to these places. On the other hand, weights for the location of the hill with respect to the core city area have been assigned based on the percentage of AOI lying within the core city area. Here, the old municipal area of Guwahati city has been considered as the core city area. Finally, ‘‘F’’ for every hill is calculated by taking the average of the weights given to all of the four components. These values are displayed in Table 3.5.
Table 3.5: Favouring indices for hills of Guwahati city
Hill ID. Name of hills
Total weight for educational
facilities in AOI of hill
Weight for Major tourist places in
AOI
Weight indicating
present economic activities in
AOI
Weight indicating Location of hill in heart
of city
Favouring index, F
1 University 0.60 0.35 0.00 0.00 0.24
2 Fatasil 0.36 0.00 0.40 0.30 0.27
3 Kalapahar 0.00 0.00 1.00 0.70 0.43
4 Sonaighuli 0.00 0.00 0.30 0.00 0.08
5 Sarania 1.00 0.20 1.00 1.00 0.80
6 Kharguli 0.40 0.90 0.90 1.00 0.80
7 Japorigog 0.00 1.00 0.40 0.30 0.43
8 Burhagosain 0.00 0.00 0.00 0.00 0.00
9 Khanapara 0.00 0.60 0.00 0.00 0.15
10 Garbhanga 0.00 0.30 0.00 0.00 0.08
11 Kamakhya 0.24 1.00 1.00 0.80 0.76
12 Kahilipara 1.00 0.00 1.00 1.00 0.75
13 Betkuchi 0.00 0.00 0.90 0.00 0.23
14 Chunsali 0.00 0.00 0.00 0.00 0.00
15 Koinadhara 0.40 0.00 0.80 0.00 0.30
validation is the process of application of the calibrated model and comparison of the model results with a set observed data other than those used for calibration of the model. In this study, the model is calibrated with respect to data of first ten numbers of hills (from Hill ID 1 to 10) and is validated against the remaining five numbers of hills (from Hill ID 11 to 15). For the study area, the independent variables assumed to influence the urban settlement in hills are- (1) average slope of hill (G1) (2) average elevation of hill (G2) (3) commercial unit density in AOI of hill (Cu) (4) free space available in AOI of hill (Af) (5) land value in AOI of hill (Lv) and (6) favouring index (F). Values of the potential influencing factors of urban settlement in hills of Guwahati city are presented in Table 3.6. For better interpretation of variability of these variables, these are normalized (feature scaling) to a range of (1, 10). Before modelling, it is important to check how and whether all these potential factors really influence the urban settlement in the hills of Guwahati city. Therefore, the relation of each of these factors with urban settlements in hills (hill ID from 1 to 10) is analysed by fitting both linear and various non-linear forms of equations. Fig. 3.8 (a)-(f)shows the dependency of hill settlement on each of these factors.
Table 3.6: Values of potential influencing factors of urban settlement in hills of Guwahati city.
Hill
ID Hill name G1
(Degree) G2 (m)
Cu (numbers/sq km)
Af (%)
Lv (Rs/sq km)
F
1 University 13.78 90.85 31.39 80.91 3109.70 0.24
2 Fatasil 12.96 127.72 179.09 67.93 4131.16 0.27
3 Kalapahar 9.74 90.59 154.55 49.30 6033.58 0.43
4 Sonaighuli 11.62 77.86 182.34 78.86 5223.88 0.08
5 Sarania 19.68 99.35 234.94 34.63 11791.04 0.80
6 Kharguli 10.94 98.91 371.81 47.95 10421.64 0.80
7 Japorigog 13.76 119.13 67.77 51.35 6014.93 0.43
8 Burhagosain 13.67 108.01 34.34 81.41 1847.01 0.00
9 Khanapara 14.11 153.21 51.35 76.00 3694.03 0.15
10 Garbhanga 10.97 88.60 121.26 66.30 4626.87 0.08
11 Kamakhya 18.68 137.05 119.34 50.41 9119.40 0.76
12 Kahilipara 13.12 118.37 224.33 33.24 8832.09 0.75
13 Betkuchi 10.98 76.81 106.31 70.37 5044.78 0.23
14 Chunsali 12.84 117.39 9.49 76.06 2388.06 0.00
15 Koinadhara 10.57 85.03 88.24 52.39 5014.93 0.30
(a) (b)
(c) (d)
(e) (f)
Fig. 3.8: Urban settlement in hill versus- (a) average slope of hill (b) average elevation of hill (c) commercial unit density in AOI (d) available free space in AOI
(e) average land value in AOI (f) favouring index.
From Fig. 3.8, it is observed that urban settlement in hills of Guwahati city has no linear or nonlinear relation with average slopes and average elevations of hills. Hence, these two variables are avoided in the model specifically developed for Guwahati city.
0 5 10 15 20 25 30
0 2 4 6 8 10
Urban settlement in hill (% of hill area)
Average slope of hill (normalized)
0 5 10 15 20 25 30
0 2 4 6 8 10
Urban settlement in hill (% of hill area)
Average elevation of hill (normalized)
y = 2.4624x + 4.1208 R² = 0.662
0 5 10 15 20 25 30 35
0 2 4 6 8 10
Urban settlement in hill (in % of hill area)
Commercial unit density in AOI (normalized)
y = -2.2528x + 29.716 R² = 0.754
0 5 10 15 20 25 30
0 2 4 6 8 10
Urban settlement in hill (in % of hill area)
Free space in AOI (normalized)
y = 2.6857x + 2.7168 R² = 0.908
0 5 10 15 20 25 30 35
0 2 4 6 8 10
Urbansettlement in hill (in % of hill area)
land value in AOI (normalized)
y = 2.2352x + 4.3284 R² = 0.811
0 5 10 15 20 25 30
0 2 4 6 8 10
Urban settlement in hill (in % of hill area)
Favouring index (normalized)
Again, all the other four factors are found to have fairly good linear correlations with the urban settlement in hills of Guwahati city. This indicates that other factors being fulfilled, people are ready to compromise with steepness and height of a hill present in the study area. Another probable reason for having poor correlation with the average slope of a hill is that people usually cut the steep portion of a plot of land and make it plain before settling in that plot. On the other hand, land value in AOI of a hill has the best correlation (R2 = 0.908) with urban settlement in the hill. This is obvious as high land value indicates more facilities in an area. The low-income group of people though gets attracted towards that AOI, cannot afford land in it and hence settle in the nearby hills. The good linear correlations with the variables other than average slope and average elevation indicate that multi-linear regression analysis can be used to develop the proposed model for Guwahati city. Normalized data used for calibration and validation of the model are displayed in Table 3.7. The model can be expressed as,
(3.4) where, B0 is the constant term (intercept) in the model, B1, B2, B3, and B4 are the coefficients of independent variables Cu, Af, Lv and F, respectively.
Table 3.7: Data (normalized) used for calibration and validation of the model.
Hill
ID Hill name
Cu
(numbers/sq km)
Af (%) Lv (Rs/sq km)
F
Observed urban settlement in hill (%)
Computed urban settlement in hill (%)
1 University 1.54 9.91 2.14 3.67 7.85 7.81
2 Fatasil 5.21 7.48 3.07 3.98 12.47 12.40
3 Kalapahar 4.60 4.00 4.79 5.78 18.56 16.85
4 Sonaighuli 5.29 9.52 4.06 1.84 16.54 12.65
5 Sarania 6.60 1.26 10.00 10.00 28.31 28.51
6 Kharguli 10.00 3.75 8.76 10.00 25.87 27.16
7 Japorigog 2.45 4.38 4.77 5.78 17.49 15.51
8 Burhagosain 1.62 10.00 1.00 1.00 5.70 5.36
9 Khanapara 2.04 8.99 2.67 2.69 6.44 9.12
10 Garbhanga 3.78 7.18 3.52 1.84 8.21 12.04
11 Kamakhya 3.73 4.21 7.58 9.55 22.63 21.64
12 Kahilipara 6.34 1.00 7.32 9.44 20.91 24.03
13 Betkuchi 3.40 7.94 3.89 3.53 14.36 12.49
14 Chunsali 1.00 9.00 1.49 1.00 6.82 6.29
15 Koinadhara 2.96 4.58 3.87 4.38 14.03 13.91