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Planning for Future Urban Development: Land Suitability Analysis

5.1 INTRODUCTION

Urban centres in less developed countries like India have witnessed tremendous changes

in terms of population growth and aerial expansion. In the absence of proper urban

management practices, uncontrolled and rapid increase in population pose enormous

challenges to governments in providing adequate shelter to the millions of homeless and

poor in urban areas. Urban growth due to in-migration has led to increase in population

density. The migration of people from rural to urban areas for better job opportunities,

better standard of living and higher level of education is expected to continue in coming

future. This will lead to shortage of facilities and increasing demand of land for

residential purposes.

In the last 200 years, the Earth’s urban population has increased by over 100 times

while the total population has increased only six times (Hauser et al., 1982 quoted in Jain

and Subbaiah, 2007:2576). According to United Nation's Population Division report

published in 1975, about 38 percent of the earth's population was living in urban areas

and by 2025 this proportion is expected to rise to 61 percent. This implies that about 5

billion people out of a total world population of 8 billion will be living in urban areas

(UNPD 1995, quoted in WRI 1996). This rapid increase in urban population accompanied

by fast transforming urban economy lead to an ever-increasing load on the urban

environment in terms of unplanned sprawl, inadequate housing facilities, traffic

congestion, insufficient drainage, and lack of sewerage and other facilities (Liu, 1998).

Also, this growth is accompanied by increasing demand of land for future development.

As urban regions grow, more land will be needed to satisfy further growth of urban

population in the future (Yeh, and Li, 1998:172). Much of the increased demand of land

for urban development is met out of the surrounding agricultural land. Increase in

population also means increase in demand of food. It is therefore, very important that the

best and most suitable land remains under agricultural uses. Not only this urban

expansion in the ecologically fragile areas be avoided. In this context it is very important

to plan for appropriate and judicious allocation of lands for urban development to

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overcome the problems arising out of rapid growth of any urban centre. This requires a

careful planning for land allocation.

Land suitability refers to the ability of a particular type of land to support a

specific use, and the process of land suitability classification involves the evaluation and

grouping of particular land areas in terms of their suitability for a defined use (Prakash,

2003:2). Land suitability analysis is thus concerned with evaluation of the fitness of a

given tract of land for a defined use (Steiner, et al. 2000:200). In other words, it is the

process to determine whether the land resource is suitable for some specific uses. It is also

undertaken to determine the suitability level. In order to determine the most desirable

direction for future development, the suitability for various land uses should be carefully

examined with the aim of directing growth to the most appropriate sites. Establishing

appropriate suitability factors is the construction of suitability analysis. Initially,

suitability analysis was developed as a method for planners to connect spatially

independent factors within the environment and, consequently to provide a more unitary

view of their interactions. Suitability analysis techniques integrate three factors of an

area: location, development activities, and biophysical/ environmental processes (Miller

et al., 1998). Land use suitability analysis aims at identifying the most appropriate spatial

pattern for future land uses according to specific requirements, preferences, or predictors

of some activity (Collins et al., 2001:611).

Acquiring new site for urban development or improvement is becoming

increasingly challenging, particularly in a growing real estate market and with stringent

environmental standards or regulations. The results of the site suitability analysis produce

a detailed display of the most-suitable areas for consideration of placement of a certain

facility, while filtering out unusable or less desirable sites. Certain aspects are more

important than others in determining the best location for each facility. The selection of

suitable sites for specific uses must be based upon a set of criteria depending on local

norms. A scoring system can be applied to the various aspects of suitability to assess the

overall suitability for a specific urban use (Kumar and Shaikh, 2012:1).

5.2 ANALYTIC HIERARCHY PROCESS (AHP)

The Analytic Hierarchical Process (AHP) is one of the methodological approaches that

may be applied to resolve highly complex decision-making problems (Saaty 1980). AHP

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was proposed in the 1970s by Thomas L. Saaty. Saaty, in his initial formulation, proposed

a four-step methodology comprising modelling, valuation, prioritization and synthesis. At

the modelling stage, a hierarchy representing relevant aspects of the problem (criteria, sub

criteria, attributes and alternatives) is constructed. The underlying goal or mission is

placed at the top of this hierarchy. Other relevant aspects (criteria, sub-criteria, and

attributes) are placed at the remaining levels (Altuzarra et al. 2007). The AHP method

commonly used in multi-criteria decision making exercises was found to be a useful

method to determine the weights, in comparison with other methods used for determining

weights. When applying AHP, constraints are compared to each other to determine the

relative importance of each variable in accomplishing the overall goal.

In day to day life the pair-wise comparison is undertaken to a certain number of

options to select the most appropriate one from a given number of alternatives. However,

this process includes errors and limitations. It is so because the capacity of the human

brain does not allow evaluating each and every given alternative as a result selection is

narrowed down to a fewer once. Though this reduces the load on our brain and makes the

process extremely simple, the rationality of the process based upon intuitive selection

may produce unwanted results choosing a wrong alternative and overlooking the best

solution (Kinoshita, 2005:3).

Evaluation of the suitability of lands for urban development plays a fundamental

role in regional and urban land-use planning. Its major objective is to evaluate the

advantages and disadvantages of certain areas for urban development, so as to find out

places which are most suitable for urban development in the future (Dai, 2001). Land

suitability analysis mainly deals with a large amount of data on several dimensions.

Analytic hierarchy process (AHP) is a classical land suitability analysis procedure, which

gives a systematic approach in making proper decisions for site selection and appropriate

allocation of lands for different uses. It also suggests the integration of the GIS-based

land suitability model for site selection (Mendoza, 1997).

Integration of GIS for land suitability analysis serves three objectives (Malczewski,

2004:3). First, to provide an introduction to geographical information technology along

with an historical perspective on the evolving role of Geographic Information Systems

(GIS) in planning. Second, to overview relevant methods and techniques for GIS based

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land-use suitability mapping and modelling. And finally to identify the trends, challenges

and prospects of GIS-based land-use suitability analysis.

5.2.1 Scale for pair wise comparison: In AHP all identified criteria are compared to

each other in a pair-wise comparison matrix, which is a measurement to express the

relative preference among the factors.

Table 5.1 Nine point weighting scale for pair-wise comparison (Based on Saaty, 2008)

Intensity of Importance Definition Explanation

1 Equal Importance Two activities contribute equally to the objective 2 Weak or slight -

3 Moderate importance

Experience and judgement slightly favour one activity over another

4 Moderate plus -

5 Strong importance

Experience and judgement strongly favour one activity over another

6 Strong plus -

7

Very strong or demonstrated importance

An activity is favoured very strongly over another; its dominance demonstrated in practice

8 Very, very strong -

9 Extreme importance

The evidence favouring one activity over another is of the highest possible order of affirmation

Reciprocals

of above

If activity i has one of the above non-zero numbers assigned to it when compared with activity j, then j has the reciprocal value when compared with i

Thus, numerical values express a judgment of the relative preference of one factor against

another. Saaty (1977) suggested a scale for comparison consisting of values ranging from

1 to 9 which describe the intensity of importance. In this a value of 1 expresses “equal

importance” and a value of 9 is given to those factors having an “extreme importance”

over another factors (Saaty and Vargas 1991; Marinoni 2004). Table 5.1 shows the scale

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used for the comparison. The factors under consideration were compared to each other

using the pair–wise comparison matrix.

The selection of suitable sites is based upon a specific set of local criteria. The

characteristics of a site (e.g., present land use, slopes, water availability, geology,

geomorphology, etc.) influence its suitability for a specific land use type. To assess the

overall suitability a scoring and weighting system is applied to the various aspects of

suitability. Site suitability is the process of understanding existing site qualities and

factors, which will determine the location of a particular activity. The purpose of

selecting potential areas for residential development depends upon the relationship of

different factors, like location of available sites, extent of the area, accessibility, etc. and

site association factors like slope, soil etc. The analysis may also determine how those

factors will fit into the design process to evaluate site suitability (Hofstee and Brussel,

1995).

5.2.2 Selection of different Parameters for Suitability: As noted already, land

suitability assessment is a multiple criteria evaluation process. The attributes of land

suitability criteria are to be derived from spatial and non-spatial, qualitative and

quantitative information under diverse conditions’’ (Chen et al. 2010a:175). In land

suitability analysis, each evaluation criterion is represented by a separate map in which a

‘degree of suitability’ with respect to that particular criterion is ascribed to each unit of

area (Sehgal, 1996, Prakash, 2003). These ‘degrees of suitability’ then need to be rated

according to the relative importance of the contribution made by that particular criterion,

achieving the ultimate objective. Different land qualities, which can be considered for

suitability modelling relate to present land use/land cover, proximity of transportation

network, groundwater depth and quality condition etc. The characteristics of a site (e.g.,

present land use, water availability, road accessibility, flood hazard, etc.) influence its

suitability for further Urban Development (Sunil, 1998 quoted in Jain and Subbaiah,

2007:2578). To assess the overall suitability a scoring and weighting system is applied to

the various aspects of suitability. Suitable sites are found out by adding all layers which

are affecting site suitability. Slope is one of the major factors taken into consideration in

any land suitability analysis. It may be noted here that Rohtak city is situated on a plane

area and the influence of slope in suitability analysis becomes negligible. That is why

slope has not been taken into consideration for land suitability analysis for urban

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development. On the whole, the following parameters have been considered for the

suitability analysis in the present case:

Land use/land cover

Proximity to major road

Proximity to city urban built up land

Soil salinity

Ground water table depth

Ground water quality

Land use/Land cover map is a comprehensive expression of land use/land cover

classification. This map has been prepared by using Google earth data and the same has

been shown in Map 5.1. The main classes which affect the planning aspect, such as, built-

up land, industrial land, agriculture land, vegetation, Parks/gardens, water bodies, village

pasture land and vacant land are considered here and the area covered under each of these

is given in Table 5.2.

Table 5.2 Land Use/ Land Cover of Rohtak City (2011)

Land Use Land Cover Classes Area in Ha. Area in Percent Built Up Area 2358.49 5.90 Rural Area 878.34 2.20 Water Body 822.47 2.06 Open Area 3501.04 8.75 Forest Area 306.72 0.77 Agricultural Land 31382.52 78.46 Industrial Area 155.12 0.39 Parks 30.82 0.08 Village Pasture Land 564.48 1.41 Total 40000 100

Source: calculated by the researcher from Google earth Image

Information on land use/land cover classes is crucial in locating suitable sites for

urban development. It may be noted that already built up area is not suitable for the future

development because once a building is constructed, it remains for minimum 50-75 years.

Likewise, water body, forest area and parks are not suitable for future development for

residential and other urban uses. Rural area and their surrounding pasture lands are called

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‘Lal Dora’ which is always avoided by government in future plans for urban growth.

Therefore, these areas are not considered suitable for future growth. Thus, open land and

agricultural land both within and in immediate peripheral areas are the most suitable land

for urban development.

The road network is one of the important parameters in identifying the areas for

urban development as it provides accessibility to different parts of the city. In this

chapter, in order to find out the accessibility of the region, National Highway and State

Highways, which provide connectivity to different areas, have been digitized from

Google earth image 2011 of Rohtak city. Effort has been made here to locate the site

nearer to any existing road if possible. In order to find out better accessibility to the

existing road, buffer zones have been created by taking distances 250, 500, 750 and 1000

metres and so on from the road centre to generate road proximity map. Proximity to

National and State Highways are given higher value in AHP and as we move away from

road the value decreases. Map 5.2 shows the National Highway and State Highways in

the study area.

Proximity to already built up land is an important determinant of the cost of future

urban development depends. That is why proximity to urban built up land is assigned

higher importance than the area which located away from the built up land. On the basis

of accessibility to ‘built-up’ land buffer zones were prepared. The buffer which is near to

already build up land has been given higher suitable value and as we go away from built

up land the value decreases. Proximity to urban built up land is shown in Map 5.3.

Increasing population leads to growing demand to food. Therefore, areas with

fertile soils should not be encroached for urban development. In other words, areas with

less fertile soil should be preferred for urban development, while areas with fertile soil

should be left for agricultural purposes. In the present case land suitability analysis is

undertaken for urban development, therefore, lands with low fertile soil has been assigned

higher value than those with fertile soil for urban development. As already mentioned

earlier, ‘open area’ and ‘agricultural land’ offer the best choice for urban development.

The fertility status of soils can be determined with the electrical conductivity

(EC). Electrical conductivity (EC) is a measurement of the dissolved material in an

aqueous solution which relates to the ability of the material to conduct electrical current

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through it. Electrical conductivity is measured in units called Siemens per unit area (dS/m

or mS/cm). The higher the dissolved salts/ions concentration, the more conductive the

sample has. Electrical conductivity is a gross measure of dissolved salts in soil solution,

but provides no information as to which salts are present and in what proportion. Salinity

is a soil property referring to the amount of soluble salt in the soil. High saline soil is not

suitable for agricultural purposes and therefore offers a very good choice for urban

development. High salinity levels adversely impact crop yields and reduce overall soil

quality. The presence of a saline shallow water table can be a major contributor to this

problem. The problem of salinity is associated with the growth cycle of rice plant, poor

irrigation practice or insufficient irrigation water, alkaline soils in inland areas, increase in

the level of saline groundwater, intrusion of saline seawater in coastal areas and

associated with phosphorous, zinc, iron deficiency or boron toxicity. Soil salinity is a very

common problem in today's irrigated agriculture. The electrical conductivity of soils

varies depending on the amount of moisture held by soil particles. Sands have a low

conductivity, silts have a medium conductivity, and clays have a high conductivity.

Consequently, EC correlates strongly to soil particle size and texture. Soils in the middle

range of conductivity, which are both medium-textured and have medium water-holding

capacity, may be the most productive ones. For the present purpose Soil electrical

conductivity map has been taken from Department of Agriculture, Haryana. Areas

overlaid with soil that is not suitable for crops can best be taken for urban development.

That is why it is an important indicator for land suitability analysis for urban growth.

Electrical conductivity map divides the soil in four categories viz. non saline, slightly

saline, moderate saline and saline (Map 5.4). In the present study, higher values in AHP

are given to areas with saline soil and the values decline as we progress to the lower

category of salinity.

Ground water is also an important resource for urban existence and growth. In

Rohtak city, growing population coupled with desire for better quality of life is placing an

ever increasing demand on good water resource of city. At the same time, however, depth

of water table also plays an important role. It goes without saying that higher ground

water depth is more suitable for built up area than low water table depth. It is because low

water table areas are prone to damage of buildings through seepage. It also poses risk of

flood which make it less suitable for urban expansion. The ground water map of the study

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area (Map 5.5) was obtained from Ground Water Cell, Haryana. Weightage have been

assigned accordingly to different categories of water table depth.

The quality of ground water is of great importance in determining the suitability

of particular ground water for a certain use (public water supply, irrigation, industrial

applications, power generation etc.). Rohtak city is located in an area whose economy is

predominately based on agricultural activity. For sustainable agricultural development, it

is essential to utilize the irrigation potential. The quality of ground water is the result of

all the processes and reactions that have acted on the water from the moment it condensed

in the atmosphere to the time it is discharged by a well. Salt content is an important factor

in water use. Salinity can be technically defined as the total mass in grams of all the

dissolved substances per Kilogram of water. Salinity always exists in ground water but in

variable amounts. It is mostly influenced by aquifer material, solubility of minerals,

duration of contact and factors such as the permeability of soil, drainage facilities,

quantity of rainfall and above all, the climate of the area. The groundwater quality map

(Map 5.6) is taken from Ground Water Cell, Haryana. The groundwater quality map of

the study area shows the spatial extent of the groundwater quality zones mainly based on

electrical conductivity (EC). On the basis of EC the groundwater quality can be

categorized in to four classes i.e. fresh groundwater, marginal groundwater, saline

groundwater and highly saline ground water. Areas with good quality of underground

water corresponds in to lower values of EC should be left exclusively for agricultural

purposes. Thus, areas with poor quality of groundwater offer good choice before planners

for future urban development. It may also be noted that demand of water for domestic

consumption can be taken care of by piped water supply in an urban area. Therefore, in

AHP higher values have been assigned to higher values of electrical conductivity.

5.3 PAIR WISE COMPARISON OF CRITERIA

As input, AHP takes the pair-wise comparisons of the parameters and produces their

relative weights as output. Once the relevant factors are identified, hierarchical

relationships based on the respective importance are computed and quantified through the

assessment of numerical scores. These values are based on subjective determination of

the relative importance of each factor by the investigator (Saaty, 2003). The relative

ranking of the importance of each factor is accomplished through the construction of a

pair-wise comparison matrix. Each cell of the matrix represents the rating of one factor

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against another. Because the matrix is symmetric, one half of the matrix contains all

possible pair-wise comparisons, and the remaining cells are simply the reciprocals of

these comparisons. The main diagonal of the pair-wise matrix is always equal to unity. If

the row factor is relatively more important than the column factor, the matrix cell value

varies between 1 and 9, depending on how much more relatively important the row factor

is perceived to be. Conversely, if the column factor is perceived to be relatively more

important, a reciprocal value ranging between 1:2 and 1:9 are considered. The principal

eigenvector of the pair-wise comparison matrix is then computed to produce a best-fit set

of weights. The eigenvector corresponding to the largest eigen value of the AHP matrix

has been demonstrated to provide the correct relative priorities of the selected factors, i.e.

if a factor is preferred to another, then its eigenvector component is larger than that of the

other (Saaty and Vargas, 1991; Saaty, 2003). Because the components of the eigenvector

sum to unity, the developed weights reflect the relative importance of the various factors

involved in the pair-wise comparison matrix, and they are used to create the final map of

site suitability.

As already mentioned the criteria taken into consideration while locating

appropriate land for urban development included land use land cover, distance from the

main roads viz. National and State Highways, distance from built up area, fertility status

of soil, depth of ground water and ground water quality.

Clearly, each criterion should be carefully examined and properly adjusted with

respect to the local conditions. The above listed parameters have been used because they

hold a significant place in the land suitability analysis for urban development. Then pair-

wise comparisons of all related attribute values were used to establish the relative

importance of hierarchy elements. In order to determine the relative preferences for the

two elements of the hierarchy in the pair-wise comparison matrix, an underlying

semantically scale is employed with values ranging from 1 to 9 (Table 5.1). As can be

seen, nearness to NH/SH and soil electrical conductivity which indicates the fertility

status emerge as the most important elements. Rohtak city is located on fertile agricultural

land so, lands with fertile soil and good water quality should at any cost be spared for

agriculture uses. Thus, for urban development these lands should get the least priority.

Keeping the above in mind, zones with varying distance from National Highways

and State Highways were demarcated and weightages were assigned in such a way that

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the zone closed to the roads gets the highest value and vice-versa. Similarly, higher values

were given to nearness to the built up area and vice-versa. Here a value of 9 is given to

the next zones nearest to main road and built up area and a value of 8 was given to the

zone in the next distance category and so on. As already mentioned earlier, on the basis of

electrical conductivity soil are grouped under three categories viz. highly saline soil,

moderately saline soil and non saline soil. The last of the three is the best suited for

agricultural purposes. Keeping this in mind a value of 8 is given to saline soil, 7 to

moderate saline soil and given 3 to non saline soil.

Likewise zones with varying water-table depth were demarcated and weightages

were assigned in such a way that the zone with maximum depth gets the highest value and

vice-versa. Here a value of 9 is given for a depth of 10 - 20 m, 7 for a depth between 2

and 6 m and 2 for a depth below 2 m.

Table 5.3 Pair-wise Comparison matrix

Land use land cover

Proximity to NH/SH

Proximity to Built up area

Soil Salinity

Ground Water Depth

Ground Water

Quality Land use

land cover 1 0.2 1 0.111 0.333 1

Proximity to NH/SH 5 1 4 0.333 5 4

Proximity to Built up area 1 0.25 1 0.2 0.25 1

Soil Salinity 9 3 5 1 5 7 Ground

Water Depth 3 0.2 4 0.2 1 3

Ground Water

Quality 1 0.25 1 0.143 0.333 1

The raster GIS themes of the six selected factors were classified according to the

standard values. Then the AHP pair-wise comparison matrix was constructed based on

the preferences of each factor to the others as shown in Table 5.3.

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Table 5.4 Normalized pair-wise comparison matrix

Land use land cover

Proximity to NH/SH

Proximity to Built up area

Soil Salinity

Ground Water Depth

Ground Water

Quality Land use

land cover 0.05

0.04

0.06

0.06

0.03

0.06

Proximity to NH/SH

0.25

0.20

0.25

0.17

0.42

0.24

Proximity to Built up area 0.05

0.05

0.06

0.10

0.02

0.06

Soil Salinity 0.45

0.61

0.31

0.50

0.42

0.41

Ground Water Depth

0.15

0.04

0.25

0.10

0.08

0.18

Ground Water

Quality 0.05

0.05

0.06

0.07

0.03

0.06

After the formation of ratio matrix all criteria were normalised and weights have

been computed for each criteria using pair-wise comparison method (Table 5.4).

After these spatial datasets were prepared, including all necessary geometric and

thematic editing of the original datasets, a topology was created. Different criteria maps

were converted into raster data environment for further analysis because in raster data

format computation is less complicated than that in vector data format (Chang, 2006). All

vector layers were converted into raster format with 24 m resolution.

Each cell in the study area now has a value for each input criteria. One has to

combine the derived datasets so as to create the suitability map that will identify the

potential locations for urban development. However, it is not possible to combine them in

their present form - for example, combining a cell value in which ground water depth

equals 15 meters with a cell value for land use under forest and get a meaningful answer

that will enable a comparison with other locations. To combine the datasets, first a

common measurement scale, such as 1 to 10 is to be set. That common measurement

scale is what determines how suitable a particular location, represented by a cell for

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future urban development is. Higher values indicate more suitable locations for urban

development.

Using ArcGIS extension spatial analyst tool, we can weight the values of each

dataset, and then combine them. However, the inputs for the spatial analyst tool must

contain discrete, integer values. Land use data is already categorized into discrete values;

for example, forest is represented by a value of 5, so this dataset can be simply added

directly into the spatial analyst tool and each cell can be assigned a new value on the

common measurement scale of 1 to 10. Values representing areas of built up, rural area

and water body will be restricted. The values in the datasets thus are all floating-point,

continuous datasets, categorized into ranges, and they must first be reclassified so that

each range of values is assigned one discrete integer value. However, it is easier to weight

the cell values for derived datasets while reclassifying. A model has been made with the

help of spatial analyst tool in ArcGIS. Thus, the final output derived is depicted in Map

5.7.

For each level in the hierarchy it is necessary to know whether the pair-wise

comparison has been consistent in order to accept the results of the weighting. To ensure

the credibility of the relative significance used, the consistency ratio (CR) was also

calculated. This value indicates the probability that the ratings were randomly assigned. It

is suggested that if the CR is smaller than 0.10 then the degree of consistency is fairly

acceptable. But if it is larger than 0.10 then there are inconsistencies in the consideration,

and the AHP may not yield meaningful results (Saaty, 1980 quoted in Tienwong et al.,

2009:173). In this case, consistency ratio is less than 0.1 which is 0.0551. This indicates

that the comparisons of criteria were consistent, and the relative weights were suitable for

use in the suitability analysis.

5.4 LAND SUITABILITY FOR URBAN DEVELOPMENT

Land suitability map for urban development has been extracted using weighted overlay

techniques. As described in the previous section, application of GIS and AHP in the

process of land suitability analysis is an effective way for the urban land suitability

assessment. This assessment creates an index of the influencing factors for the land-use

suitability based on the literature review. Overlay mapping is the basic method applied in

GIS and helps the planners to obtain the final suitability map. On the other hand, the AHP

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method is used to combine attribute scores with weights or preferences that should be

used in the process of weight value calculations, so we can avoid some subjective ideas

affecting the results and combine the quantitative and qualitative methods.

The final output has been shown in land suitability map (Map 5.7). As could be

seen the study area was divided into four different suitable categories viz. high suitable,

moderate suitable, low suitable and not suitable. The scores derived by these four levels

of suitability are 8, 7-6, 5 and 0 respectively. Absolute values and percentage share of

these categories are shown in Table 5.5. High suitable zone covers a geographical area of

10943.53 hectares of land in absolute terms and accounts for 17.23 percent of that total

suitable area. The area is surrounded by exiting built up area of the city except some

portion in the southern part of the city.

Table 5.5 Land suitability for Urban Growth in Rohtak city

Land Suitability Classes

Area (in Hectares)

Area (in percent)

High Suitable 6893.92 17.23

Moderate High Suitable 167554.63 41.89

Low Suitable 5407.80 13.52

Not Suitable* 4433.57 11.08

Unclassified 6509.08 16.27

Total 40000.00 100.00

* Indicate land under built up area, village area (include village pasture), water body that is not suitable for future growth.

The extension of the zone is demarcated by transport network i.e. along NH-10

towards Delhi and SH-18 towards Sonipat in the eastern part, along NH-71 towards Jind

in the north eastern part and along SH-16 towards Bhiwani. ‘High suitable’ areas in the

form of patches scattered all around the city can be seen on the map. Most of them are

however, connected by road network. The largest among them can be seen in the northern

parts of the city along NH-71A near Bahmanwas village. The ‘high suitable’ zone is

surrounded by ‘moderate suitable zone’. This is elongated in the east west direction of the

city and covers around one fourth of the total area. Thus, approximately 60 percent of the

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total area falls under high and moderate suitable zones. Only 40 % of land falls under

‘low’, ‘not suitable’ and unclassified categories.