Planning for Future Urban Development: Land Suitability...
Transcript of Planning for Future Urban Development: Land Suitability...
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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.