Urban Dynamics of Gurugram City over the Period 1990-2017: A Geospatial Approach

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Suman Chauhan, Ph.D. and Sunil Kumar
Institute of Town Planners, India Journal 18 x 4, October - December 2021 ISSN:L0537-9679
21
Suman Chauhan, Ph.D.; Assistant Professor, Department of Geography, Kurukshetra
University, Kurukshetra
Sunil Kumar; Research Scholar, Department of Geography, Kurukshetra University,
Kurukshetra
Abstract
As a response to the challenges of the rapid pace of urbanization and lack of reliable
data for environmental and urban planning, especially in the developing countries, this
paper evaluates Land Use / Land Cover change (LULCC) and urban spatial change, from
1990 to 2017, in the Gurugram city, Haryana, using Landsat satellite images and eld
observations. This study applied supervised classication-maximum likelihood algorithm
in ERDAS imagine 15.0 to detect Land Use / Land Cover changes and urban growth
observed in Gurugram city, using multispectral satellite data obtained from Landsat 5,
Landsat 7, Landsat 5 and Landsat 8 for the years 1990, 2000, 2010 and 2017 respectively.
The results reveal that dramatic growth of built-up areas has led to a signicant decrease
in agricultural lands, from 1990 to 2017. The relative entropy values have shown that
the Gurugram city has experienced increasing urban growth.
1. INTRODUCTION
Most of the Indian cities have developed through expansion of the earliest
urban cores encroaching into the adjoining rural or suburban areas. With
one third of the country’s population already living in the expanded urban
areas, the trend of urban growth is haphazard along urban-rural fringe areas
or suburban areas in most Indian cities (Farooq and Ahmad, 2008). The outer
spread of cities is accompanied by many environmental problems, for example,
changes in land use patterns, fragmentation of wildlife habitats, discharge of
polluted runo water into streams and surface water bodies and pollution
of groundwater resources, etc. Increase in urban growth results increases in
land consumption, often agricultural, for housing construction and multi-story
buildings for industries.
Land Use / Land Cover (LULC) change is an important eld in global
environmental change research. Inventory and monitoring of land-use /
land-cover changes are indispensable aspects for further understanding of
change, mechanism and modelling the impact of change on the environment
and associated ecosystems at dierent scales (Turner et al., 1995; William et
al., 1994). Remote sensed data is a valuable data source from which land use
/ land cover changes can be extracted eciently. In the past two decades,
Urban Dynamics of Gurugram City over the Period
1990-2017: A Geospatial Approach
Suman Chauhan, Ph.D. and Sunil Kumar

Suman Chauhan, Ph.D. and Sunil Kumar
Institute of Town Planners, India Journal 18 x 4, October - December 2021 ISSN:L0537-9679
22
there has been a growing trend in the development of change detection
techniques using remote sensing data (Singh, 1989; Jensen, 1996; Coppin and
Bauer, 1996; Ding et al., 1998). The remote sensing technique easily measures
the conversion of agricultural land to urban uses for economic development.
Losing arable land may conict with national food security goals, especially
if urban areas expand through a low density, sprawling type of development
on highly productive soils. In fact, over much of the world urban growth rates
exceed the growth rate of urban population (Seto et al., 2011). The problem
with growth is that it involves possibly greater loss of productive land and
greater risk to food security.
2. LITERATURE SURVEY
Several studies use remote sensing methods to study expansion of certain Indian
cities. Examples include Farooq and Ahmad (2008) for Aligarh, Sudhira and
Ramchandra (2009) for Bengaluru city, Chatterjee et al. (2016) for Bhubaneswar,
Sokhi et.al. (1989) for Delhi, Amarawickrama et al (2015) for the Colombo
Metropolitan region, Sri Lanka, Mosammam et al (2016) for Qom City. While
these studies may provide valuable planning information to local ocials, their
specicity and their use of diverse methods make it dicult to compare results
and avoid systematic study of the determinants of urban expansion and growth
of the city. The major diculty with this existing city level literature for India
is that it presents a somewhat negative view of the costs of urban expansion
without considering the potential benets. For example, according to Bhatta
(2009) ‘‘Rapid urban growth in the world is alarming, especially in developing
countries like India”. Similarly, Sudhira et al (2004) talk about the ‘‘alarming
rate of urbanization and the extent of sprawl that could take place” and note
that ‘‘urban Growth is taking its toll on the natural resources at an alarming
pace”.
This study pursues two objectives. First, to examine the land use / land cover of
the Gurugram city from 1990 to 2017 and second, to measure the urban growth
of the Gurugram city by using Shannon entropy approach.
3. METHODOLOGY
Gurugram is located on the intersection of 28
0
45’ North latitude and 77
0
02’
East longitude at a distance of 30 km to the south of Delhi, the national
capital and 282 km to the south of the Chandigarh, capital city of Haryana
(Fig. 1). Gurugram is the administrative headquarters of a district and a
tehsil of the same name.
We have followed few methodological steps for the delineation of the study area.
For example, we have used a topographical sheet from the Survey of India for the

Suman Chauhan, Ph.D. and Sunil Kumar
Institute of Town Planners, India Journal 18 x 4, October - December 2021 ISSN:L0537-9679
23
Fig. 1: Location Map of Gurugram City Study Area
delineation of the study area (Fig. 2). After this process we used Landsat satellite
imagery data (Table - 1) for extracting the land use / land cover of the study
area which was obtained from the https://earthexplorer.usgs.gov. Some erdas
15.0 and ArcGIS 10.4 Tools are used to prepare these maps. We also did land
transformation work on these maps. The vector map of Gurugram city (Municipal
Commission area) was used for sub-setting the satellite images. Other ancillary
data such as topographical maps of Survey of India is used. The images were
obtained as standard products, i.e. geometrically and radio-metrically corrected.
Images from dierent sensors have dierences in their spatial resolution e.g.
Landsat TM, ETM+ and OLI. One approach to encounter a problem is to re-sample
the higher resolution images so they match the resolution of the lowest spatial
resolution image.
Shannan’s entropy (H,) can measure the degree of spatial concentration or
dispersion of a geographical variable. Then Shannon Diversity Index was calculated
using the following equation (Yeh and Li, 2001):

Suman Chauhan, Ph.D. and Sunil Kumar
Institute of Town Planners, India Journal 18 x 4, October - December 2021 ISSN:L0537-9679
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Fig. 2: Steps for Delineation of Study Area
Data Description Path /
Row Resolution Source
Landsat 05 TM 05-12-1990
12-12-1990 146/40
147/40 30 Meter USGS / Earth Explorer
(United State Geological
Survey)
Landsat 07 ETM+ 08-10-2000
15-10-2000 146/40
147/40 30 Meter/15
Meter Pan
USGS / Earth Explorer
(United State Geological
Survey)
Landsat 05 TM 03-10-2010
12-10-2010 146/40
147/40 30 Meter USGS / Earth Explorer
(United State Geological
Survey)
Landsat 08 OLI 15-10-2017
12-10-2017 146/40
147/40 30 Meter/15
Meter Pan
USGS / Earth Explorer
(United State Geological
Survey)
Topo sheet 1967 ----------- Scale
1:50000 Survey of India
Table 1: Landsat Satellite Imagery Data

Suman Chauhan, Ph.D. and Sunil Kumar
Institute of Town Planners, India Journal 18 x 4, October - December 2021 ISSN:L0537-9679
25
n
Hn =⅀ Pi log (1/Pi)
I
It is calculated by where pi is the probability or proportion of a phenomenon
(variable) occurring in the ith zone (pi = xi12 xi), xi is the observed value of the
phenomenon in the ith zone, and n is the total number of zones. The value of
entropy ranges from zero to log (n1. If the distribution is maximally concentrated
in one zone, the lowest value, zero, will be obtained. Conversely, an evenly
dispersed distribution among the zones will give a maximum value of log (n) (Yeh
and Li, 2001).
Landsat Thematic Mapper (TM) images, Landsat Enhanced Thematic Mapper
(ETM+) and Landsat (Operational Land Imager) were used to estimate the amount
of urban growth and to measure and compare the spatial patterns of land use /
land cover during 1990 to 2017, respectively.
4. LAND USE / LAND COVER CHANGE DETECTION AND ANALYSIS
The results obtained through the analysis of multi-temporal satellite imageries
were diagrammatically illustrated in Fig. 3 and it registers data in Table 2.
Land Use
Classes
1990 2000 2010 2017
Area
(ha) Area
(%) Area (ha) Area
(%) Area
(ha) Area
(%) Area
(ha) Area
(%)
Agricultural
Land 18887.8 91.63 17597.6 85.38 14049.8 68.16 11526.5 55.92
City Area 586.64 2.85 1559.23 7.56 2777.33 13.47 4000.03 19.41
Suburban Area 535.45 2.60 890.42 4.32 3305.42 16.04 4619.8 22.41
Vegetation
Cover 464.02 2.25 415.26 2.01 378.87 1.84 321.07 1.56
Water bodies 2.64 0.01 21.36 0.10 18.77 0.09 62.7 0.30
Others 136.35 0.66 127.91 0.62 82.57 0.40 82.23 0.40
Total Area 20612.87 100 20611.77 100 20612.8 100 20612.29 100
Source: USGS, Earth explorer, Landsat Satelite-1990, 2000, 2010 and 2017.
Table 2: Land Use / Land Cover Statistics of the Gurugram City; in 1990, 2000,
2010, and 2017
Table 2 and Fig. 3 gives above constraints, and details out about six categories
during the year 1990, 2000, 2010 and 2017 pertaining to the Gurugram City.
The six categories of land use / land cover comprising agricultural land,
city area, suburban area, vegetation, water bodies and others are spread
over 20,612 ha land of study area. In 1990, agricultural land had a cover of

Suman Chauhan, Ph.D. and Sunil Kumar
Institute of Town Planners, India Journal 18 x 4, October - December 2021 ISSN:L0537-9679
26
This change shows a drastic decline in the agricultural land in Gurugram city
because multinational and international industries have taken this land as a
built-up area, on the contrary the city area and suburban area increased from
586.64 hectare (2.85 percent) to 4000.03 ha (19.41%) and 535.45 ha (2.60%)
Fig. 3: Gurugram City Land Use / Land Cover and Land Use Classes
Fig. 4: Ground Survey Pictures, 2017
Land use Classes
Agricultural Land
City Area
Suburban Area
Others
Vegetation
Waterbodies
2000
1990
2010 2017
0 12.5 25
Kilometers
0 5 10
Kilometers
0 5 10
Kilometers
0 5 10
Kilometers
0 5 10
Kilometers
18,887.8 hectare (91.63 percent) of the total area but reduced to 11,526.5
ha (55.92 percent) in 2017.

Suman Chauhan, Ph.D. and Sunil Kumar
Institute of Town Planners, India Journal 18 x 4, October - December 2021 ISSN:L0537-9679
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Table 3: Builtup Area Change in Gurugram City
Source: USGS, Earth explorer, Landsat Satelite-1990, 2000,
2010 and 2017.
Years Builtup Area (ha) Increase (%)
Till-1990 1122.10 ------
1990-2000 2449.65 11.88
2000-2010 6082 29.50
2010-2017 8617.98 41.81
to 4619.8 ha (22.41%) respectively in
2017. The water bodies encompassed
62.7 hectare (0.30 percent) in the year
2017 from 2.64 hectare (0.01 percent)
in the year 1990.
Ground truth survey pictures are
showing the multi-storey buildings in
the study area and some slum areas,
which occupied the vacant land as
settlements with plastic covers. The
Built-up area Fig. 5 and the detailed
description of the cover area for
the year 1990, 2000, 2010 and 2017
are given in Fig. 5 and area percent
change for the year 1990, 2000, 2010
and 2017 is shown in Table 3. In this
Map we found a drastic increasing
trend in built-up area during 1990 to
2017. It converts agricultural lands
into an urban area for residential
and industrial purposes. In 1990 only
1122.10 hectare land lie under built-
up area but in 27 years, it increased
nearly about 8 times as compared to
1990 i.e. 8617.98 hectare.
Table 4 contains details about changes
noticed in six categories taken in the study to nd out land use land cover
changes in the Gurugram district during the years from 1990 to 2017. As per
Fig. 5: Gurugram City Builtup Area 1990-2017
Table 4: Land Transformation of Gurugram city from 1990 to 2017
Source: USGS, Earth explorer, Landsat Satelite-1990, 2000, 2010 and 2017. Computed by Author.
2017 Classes 1990 (Area in ha)
Agricultural
Land Built up
City Neighboring
Built up Others Vegetation Water
Bodies Grand
Total
Agricultural land 11311.1 0 0 29.8 170.7 1.4 11512.9
Built up city 2997.9 586.6 283.3 105.6 26.5 1.3 4001.1
Neighboring built up 4564.7 0 246.6 9.1 12.1 0 4832.5
Others 82.2 0 0 0 0 82.2
Vegetation 66.2 0 0 0 254.8 0 321.1
Water bodies 62.7 0 0 0 0.0 62.7
Grand Total 19084.8 586.6 529.9 144.6 464.0 2.6 20812.5

Suman Chauhan, Ph.D. and Sunil Kumar
Institute of Town Planners, India Journal 18 x 4, October - December 2021 ISSN:L0537-9679
28
the table, there is maximum change in city area class and neighboring built up
class. 586 hectare land is under the city area in 1990 and 246 hectare land under
neighboring built up area but in 2017 city area have 4001 ha and neighboring built
up area 4832 ha land in built up category because of the huge agricultural land
converted into both categories due to rapid urbanization and industrialization
in Gurugram. In 1990 there was 19,084.8 hectare land under agriculture but in
2017 it is only 11,512.9 hectare land under this class. Near about 2,997 hectare
agricultural land got converted into city area and 4,564 hectare agricultural land
was converted into neighboring built up area in the span of 27 years. Figure - 6
Fig. 6: Land Transformation Gurugram City 1990-2017
Land use Classes
Agricultural land to Agricultural land
Agricultural land to Neighbouring_builtup
Agricultural land to Builtup_city
Agricultural to Waterbodies
Neighbouring builtup to Builtup city
Agricultural to Vegetation
Agricultural to Others
Builtup city to Builtup city
Neighbouring builtup to Neighbouring builtup
Other to Agricultural land
Others to Neighbouring builtup
Others to Builtup city
Vegetation to Neighbouring builtup
Vegetation to Builtup city
Vegetation to Vegetation
Vegetation to Agricultural land
Waterbodies to Waterbodies
Waterbodies to Builtup city
Waterbodies to Agricultural land

Suman Chauhan, Ph.D. and Sunil Kumar
Institute of Town Planners, India Journal 18 x 4, October - December 2021 ISSN:L0537-9679
29
showing 19 categories of land transformations in dierent classes from 1990 to
2017. Maximum area converted from agricultural land to two major categories,
which is city area and neighboring built up area. Some patches of agricultural
land also transformed into water bodies, which are water reservoirs near the
city area. These are the main sources of drinking water for the Gurugram city
because the level of the city’s groundwater table has fallen below 33 m, though
in the neighboring areas of Sohna and Farrukhnagar, the level still hovers around
22.79 m and 17.69 meter respectively. Over the next seven years, the city’s
groundwater level is expected to fall to about 40 meter by 2030, the city’s
groundwater levels may plummet to 50 meter (Narain, 2016).
Table 5 indicates that Shannon entropy valued four zones of Gurugram city in
1990. In 1990 only the second zone showing some concentration of built up area
in 1990. The overall entropy value, is 0.21 i.e. less concentration of built up area
in overall selected zones of Gurugram city. The value of entropy ranges from
zero to log (n1. If the distribution is maximally concentrated in one zone, the
lowest value, zero, will be obtained. Conversely, an evenly dispersed distribution
among the zones will give a maximum value of log (n) (Yeh and Li, 2001). In
1990 there was less urban area in Gurugram city because primary activity was
there. Maximum area fell under the agricultural land in 1990 because that time
agriculture activity is the basic need of the people of these area but after
the globalization and economic revolution maximum agricultural land was
occupied by multi-nationals or national industries in Gurugram city. If we see
the Figure 7 and 8 there is a huge concentration of built up area. It calculates
Fig. 7: Gurugram City Builtup Area 1990 Fig. 8: Gurugram City Builtup Area 2017
Concentric circles distance
2 Km
4 Km
6 Km
8 Km
Builtup Area
City Center
Concentric circles distance
2 Km
4 Km
6 Km
8 Km
Builtup Area
City Center

Suman Chauhan, Ph.D. and Sunil Kumar
Institute of Town Planners, India Journal 18 x 4, October - December 2021 ISSN:L0537-9679
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Table 5: Shannon Entropy Value in Study Area
during 1990
Zones 1990
N1 pi ln pi pi*ln pi
Zone 1 6.05 0.029 -3.53 0.10
Zone 2 2.66 0.013 -4.35 0.06
Zone 3 1.27 0.006 -5.09 0.03
Zone 4 0.74 0.004 -5.63 0.02
Total 206 H= 0.21
Source: Computed by Author
Zones 2017
N1 Pi ln Pi Pi*ln Pi
Zone 1 11.81 0.06 -2.86 0.16
Zone 2 28.77 0.14 -1.97 0.27
Zone 3 22.02 0.11 -2.24 0.24
Zone 4 17.9 0.09 -2.44 0.21
Total Area 206 H= 0.89
Source: Computed by Author
Table 6: Shannon Entropy Value in Study Area
during 2017
relative entropy Hi which is based on
town buer in 1990, 2000, 2010 and
2017. The result shows that there is
substantial variation in the pattern of
urban growth of the study area. The
average entropy of urban growth from
town center was 0.76 (Maximum is 1),
that is the major reason in 1990, which
showes less entropy value in the study
area. Further it can be observed that in the year 2017 relative entropy value i.e.
H=0.89 which is four times higher than comparative 1990 relative entropy value
(Table - 6). Large numbers of industries, multinational companies are established
there after the post-liberalization period in Gurugram city.
Fig. 9: Builtup Area change by Distance
3500
3000
2500
2000
1500
1000
500
0
Area in ha
2017
2010
2000
1990
2 4 6 8
Distance from City Center (Km)
5. CONCLUSIONS
This paper analyses the process and growth of the urban area in Gurugram from
1990, 2000, 2010 and 2017. Four land use and land cover maps were prepared
for each year and used to show land transformations. The study showed that
built-up area has increased in the form of city area and suburban area, which is
586.64 hectare and 535.45 hectare in 1990 and 4,001 hectare and 4,619 hectare
increased in 2017, respectively. The expansion of built up area in the city
increased rapidly after the economic reforms of 1991. It is moving from north to
south-west part of the study area because there is a counter magnet town i.e.
IMT Manesar, which has a large number of industries. In 2017 the built up area
increased four times as compared to 1990, unplanned and haphazard growth
characterized this spread of built-up area. This built-up growth pattern suggests
that the vegetation cover and agricultural land had very less inertia, which
is being transformed because of the growing population in the area. Relative
entropy used to measure, monitor and identify spatial-temporal patterns of
urban growth by the integration of Remote Sensing and GIS. The entropy method
can be easily implemented within the GIS to facilitate the measurement of urban
growth away from urban centers. This study shows that entropy is a good indicator

Suman Chauhan, Ph.D. and Sunil Kumar
Institute of Town Planners, India Journal 18 x 4, October - December 2021 ISSN:L0537-9679
31
for identifying the spatial problems of land development. It can identify a town
with better spatial eciency for land development in terms of compactness. The
method can provide useful information for government ocials and planners to
monitor the land development process and to identify land use problems.
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