Need for Pan India Compatibility of Geospatial Databases in Terms of Map Projections and Parameters (1).pdf

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Need for Pan-India Compatibility of Geospatial
Databases in Terms of Map Projections and
Parameters
Mallikarjun Mishra, K.N. Prudhvi Raju & Prem Chandra Pandey
To cite this article: Mallikarjun Mishra, K.N. Prudhvi Raju & Prem Chandra Pandey (2023):
Need for Pan-India Compatibility of Geospatial Databases in Terms of Map Projections and
Parameters, Geocarto International, DOI: 10.1080/10106049.2023.2215206
To link to this article: https://doi.org/10.1080/10106049.2023.2215206
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1
NEED FOR PAN-INDIA COMPATIBILITY OF GEOSPATIAL DATABASES IN
TERMS OF MAP PROJECTIONS AND PARAMETERS
Mallikarjun Mishra1
, K.N.Prudhvi Raju2
, and Prem Chandra Pandey3,*
1
Department of Geography, Ravenshaw University, Cuttack-753003, India
e-mail: mallikarjungeobhu2016@gmail.com/mallikarjungeog@ravenshawuniversity.ac.in
ORCID ID: 0000-0002-8601-255X
2
Department of Geography, Institute of Science, Banaras Hindu University, Varanasi-221005, India
e-mail: knpraju1954@gmail.com
3
Department of Life Sciences, School of Natural Sciences, Shiv Nadar Institution of Eminence, Gautam Budhha
Nagar, Uttar Pradesh. Corresponding author e-mail: prem26bit@gmail.com / prem.pandey@snu.edu.in
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ABSTRACT
The present study is taken up to record variations in the extent of area of two polygons--(i)a
ground measured area of a university campus, (ii)enclosing the Ganga basin and a polygon
covering (iii)India to examine the changes in both shape and area--under different map
projections with various parameters. The exercise brought forth interesting results. Depending
on final ranks worked out based on minimum differences in extent of areas and shape
distortion in the case of India, it is suggested to adopt either (i)LCC projection with Everest
India-Nepal datum, First Standard Parallel (FSP) 24.50, Second Standard Parallel (SSP) 28.50,
Latitude of Origin (LO) 16.253259, Central Meridian (CM) 80.8749 or (ii)LCC projection with
WGS 84 datum, FSP 24.50, SSP 28.50, LO 16.253259, CM 80.8749 or (iii)Polyconic with
Everest India-Nepal datum, CM 84.50, LO 13.00, for mapping both smaller areas on larger
scales and larger areas on smaller scales.
Key words: Map Projection; Datum; Parameters; Pan-India
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1. INTRODUCTION
With the advent of Remote Sensing and Geographical Information Systems (GIS) in the
early 1970s, there is an explosion of geospatial data and information. By the end of 20th
century with remote sensing satellites of several countries in space, the availability and use of
remote sensing data has increased manifold. Though remote sensing data is basically meant for
resource surveys and environmental monitoring and mapping, it is extensively being used for
various multifarious purposes. With growth and innovations in technology, various other data
gathering and information processing methods came into being and consequently, geospatial
information grew leaps and bounds in recent times. Nowadays computer-based GIS data bases
have almost replaced earlier computer-based data management systems. The GIS data bases are
different from computerized data bases in that the GIS displays and presents spatial units,
features, phenomena as maps as well as the qualitative and quantitative data and information
connected with the same maps and/or units. In GIS one can visually appreciate and digitally
interact with the spatial circumstances while taking decisions. Geographical Information
Systems (GISs) have helped and are helping in not only efficiently storing the invaluable old
maps and other archival data and information but also in updating the old maps and processing
the data into useful information (Krejci, 2008). The real power and purpose of GISs are in
their ability to work as decision support systems.
Everything under the sky comes within the ambit of geography and in every walk of
life, be it, individually or collectively and personally or professionally, geospatial information
matters to those who care. Maps are invaluable and indispensable tools to study and
understand the earth along with its complex network of features and phenomena. With maps
and geospatial information going digital, what matters to most for us, is taking the right
decisions at the right time quickly to get the best and maximum of benefit out of geospatial
circumstances/information and that is where GISs come into picture. Ultimately all this boils
down to the so-called Information Technology and its efficient use to draw benefits. In the
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evolution of human civilization, like copper-age, bronze-age, iron-age, etc., the current stage
can be called as information-age. One who has information in his/her hand readily and one
who can take quick, good and appropriate decisions ‘under the various geospatial
circumstances’ is and will be the winner. That is why geospatial information has become
global with local, regional and global implications and requirements. This is the crux of the
point in the present experiment--there must be global compatibility among data sets coming in
from different sources.
Maps which show locations of features and phenomena, and their spatial distributions
and patterns are an essential source and component of GIS databases (Pearson, 1990). While
making maps manually or digitally what is required is geo-referencing the map or remote
sensing data, a process by which remote sensing data and other archival maps are brought into
the spherical earth’s framework of longitudes and latitudes and then transformed or projected
onto two-dimensional plane or surface to ultimately make maps and information. The
transformation/projection of the three-dimensional spherical earth or parts of it onto a two-
dimensional surface/paper is what is called map projection (Lapaine, et al., 2017).
Surveying to take measurements of locations, distances and heights to make maps
involves two reference planes or surfaces—geoidal datum and spheroidal datum to mark
elevations and to locate the objects/features respectively on near-spherical earth. A detailed
discussion on geoidal and spheroidal datums is outside the purview of this study. A datum is a
model surface of the earth that is used as an ‘origin surface’ for mapping. The datum defines
the shape and size of the earth ellipsoid (because earth which is compressed at the poles is not a
perfect sphere) through a so-called origin/reference surface. A datum is chosen and constructed
so as to fit to the true shape of the Earth vis-à-vis the land area of a specific country (Mailing,
1992). The best fit between the geoidal datum and spheroidal datum and the mean sea level of
any country is finally decided as the origin/reference surface for mapping any part/country of
the world. A reference surface and mean sea level of one country need not match with the
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reference surface/mean sea level of other countries. That is exactly the reason why, there are
many a local datum/reference surfaces like for example, Australian Datum (GDA 94),
European Datum (ETRS 89), North American Datum (NAD 83), Japanese Datum (JGD 2011),
Everest 1830 etc., etc., and world level reference surfaces/datums like WGS 84, WGS 72,
WGS 64, etc., (Lapaine, et al., 2017). With any single country, say India, changing the datum
while generating spherical coordinates as well as while assigning map projection, will give
different results in respect of areas, distances, directions and shapes. So, finally, each and
every country works out the best-fit reference surface/datum and best-fit projection to make
their maps in order to have the best possible measures of length, width, height and directions.
Survey of India (SOI) went with Polyconic projection with Everest 1830 and Everest
1830 Modified datum for all its large- and small-scale maps (Ghosh and Dubey, 2009). With
the implementation of India’s New Map Policy 2005, Survey of India has started producing
Open Series Maps (OSMs) with Universal Transverse Mercator (UTM) projection system and
WGS-1984 datum (Ghosh and Dubey, 2009; National Map Policy, 2005). National Spatial
Framework (NSF) released by National Remote Sensing Centre of Indian Space Research
Organization (NRSC-ISRO) has suggested two projections for maps of India—(i) Polyconic
projection with Everest Spheroid with 84o
30’ as Central Median (CM) and 13o
00’ as Latitude
of Origin (LO) and (ii) Lambert Conformal Conical (LCC) projection with Modified Everest
datum with 13o
45’ 00” as First Standard Parallel (FSP), 18o
45’ 00” as the Second Standard
Parallel (SSP), 80o
52’ 30” as Central Meridian and 16o
15’ 19.557972” as Latitude of Origin.
National Spatial Framework (NSF) also suggested Lambert Conformal Conic (LCC)
projection with World Geodetic System-84 (WGS 84) datum for 1:50,000 and larger scale
maps with local standard parallels, central meridians and latitudes of origin depending upon the
latitudinal and longitudinal extent, for different states and regions of India.
In the process of projection, the spherical three-dimensional model along with its x, y
and z coordinates is converted into a flat earth model. That means, a curved surface of the
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spherical earth is transformed into a flat surface. As it is, in this process, the length and breadth
are the first casualties—they get decreased from the original/real world measurements. That
means, there can’t be a true one to one representation—a map cannot be a true representation
of the real world. A map is only an abstraction of the real world at a reduced scale (Kennedy
and Kopp, 2000) with some inherent errors.
Depending upon the location of the area/country on the globe, the final maps of the
respective area/country if finally produced on different projections with different parameters
will have scale, shape, area and directional distortions when compared with each other. Some
projections retain true directions, some produce near-true scale, some portray true shape, some
give near-true areas. No single map projection truly facilitates to get true directions, distances,
areas and shapes of any country or any part of the globe. That is why there are specific
projections for specific areas and also specific projections for specific purposes--for example
navigation requires maps with true directions. The general-purpose maps on large scales
(1:50,000 and larger) go with projections that give near-true distances and areas.
2. OBJECTIVES
The present study is aimed at coming up with suggestion on the best fit projections and
parameters in Indian context after examining the differences in the extent of the area and shape
of a polygon. A total of three cases are taken up in this study. The first two cases--(i) a
University campus area measured on the ground and (ii) the Ganga river basin, to note the area
differences and the third is a polygon covering India including its islands to record area and
shape differences, under various projections and parameters. Yes, it is known and obvious,
when the datum and projections change, the extent of area is likely to change. But how much,
is the first question. The second question is whether the projections and related parameters used
for India are good enough for the geospatial databases that are being generated day in and day
out by many individuals and institutions. The final question is what projections, parameters
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one should go with in case of India. These questions are discussed, and the present experiment
threw up some interesting results to ponder over. There are several similar to near similar
exercises by a few scholars done for different areas/regions/parcels outside India. Kimerling
(1984), Danielsen (1989), and Gillissen (1993) used ellipsoidal geographic coordinates for
calculation of area of a polygon. An exercise on the selection of map projection with minimum
area difference especially on ArcGIS 9.0 platform has been carried out by the Yildirim and
Kaya (2007 & 2008). In this study authors compared the differences between real area and
projection area using different projections--UTM, Albers, Behrmann, Bonne, Cylinder, Craster
etc.. Also Usery and Seong (2000); Seong, Mulcahy, and Usery (2002) and Usery et al.,
(2003) have conducted exercises for different regions/polygons to make a correct decision
about which projection should be chosen or which projection provides minimum area
difference. Yang, Snyder and Tobler (1999) have demonstrated areal and shape distortions
under various map projections by taking examples from different regions outside India. Al
Hameedwani (2018) compared the accuracy of different map projections and datums using
ground-truth data. Sjöberg (2006) tried to determine the area of a region on a plane, a sphere
and an ellipsoid.
3. DATA AND METHODOLOGY
In the present study, a smaller area of 121.4058 ha/300 acres which is measured on the ground
manually is converted into a map and given various standard projections normally used in and
for India (details in the following text). The second case of the present study is a polygon (of
the Ganga river basin) digitised manually over very high resolution (1x1m) Google Earth
Image data, scaling/enlarging the Image data to around 1:2500 to 1:5000 and the KMZ/KML
line files generated were converted into shape files (.shp) in Global Mapper after assigning
Geographic Lat/Long with WGS 84 datum. The basin is then split into UTM zone-wise
subsets by giving the respective UTM zones (43, 44, 45) bounds (72o
-78o
, 78o
-84o
and 84o
-90o
)
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in ArcMap. The Ganga river basin measured 1031082.7531326 sq. km. The third case is a
digital boundary of India (shape file) borrowed from open source GitHub - datameet/maps:
Repository for all spatial data (GitHub, 2022). After assigning different projections with
different parameters (details in the following pages) extent of areas are calculated and
tabulated. In the case of the polygon of university campus, the ground measured area is taken
as a ‘standard’ and the area differences from each of the map projections and their percentages
are worked out. Then the order of difference is decided in ascending order assigning a score of
1 to the minimum difference and 2 to the next higher difference and so on. A similar exercise
is carried out for Ganga basin polygon with a small difference; here, the ‘standard’ area is the
total area of three separate UTM zones within which the Ganga basin is confined. Same is the
case with the polygon covering India too in which the areas of all the zones added together
becomes the ‘standard’. Further, in case of India, the official area of India is taken as the
‘standard’ and order of differences with areas from other projections is worked out. Finally,
the scores obtained thus from similar projections for all the three cases of study are added
together to decide the ranks--rank 1 is for minimum difference and rank 2 is for the next higher
difference and so on. At the end of it all, the best fit projections are decided going by the
ranks—the one with the lowest rank (1) the best fit and the one with the highest rank (6) is
unfit.
4. THE EXPERIMENT
4.1 Experiment with Area of a Polygon-Case of a ground measured area of a
UniversityCampus:
A polygon enclosing an area of 121.4058 ha/300 acres of a university campus (Fig.1) measured
on the ground, has been assigned projections C1 to C8 (Table 1) to see how the extent of area
varies from projection to projection. Table 2 and Figure 2 reveal that projection ‘C8’ shows
minimum positive difference of 2.2517% followed by projection ‘C6’ with +2.2750% of
difference. The difference is the largest with projection ‘C7’ (4.8565%). So, in this
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experiment, projections C8 and C6 stand out as the best fit ones with minimum differences
from the actual ground measured area.
Fig.1 Area of a Polygon-Case of a ground measured area of a University Campus (Central
University of South Bihar, Gaya, Bihar, India).
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Fig.2 Bar diagrams showing area of a ground measured polygon under different Map
projections and datums and its comparison with ground measured (Y) of a polygon- a
university campus from India.
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Table 1: Example of a ground measured area/polygon. The extent of area under different
projections and parameters
Projections and Parameters Area of the ground
measured polygon
(in hectares / acres)
UTM Zone 44 with WGS 84
C1
125.0555 / 309.0185
C2
124.7171 / 308.1821
Polyconic with Everest 1969
Central Meridian (CM) 84.50
Latitude of Origin (LO) 13.00
C3
124.6517 / 308.0213
Polyconic with WGS 84
CM 82.50 / LO 16.00
C4
124.7677 / 308.3080
Lambert Conformal Conic(LCC)with WGS 84
LO 16.00 / CM 81.00
First Standard Parallel (FSP) 12.00
Second Standard Parallel (SSP) 25.00
C5
124.6172 / 307.9359
LCC with WGS 84
FSP 13.00 / SSP 26.00
LO 16.00 / CM 82.50
C6
124.1676 / 306.8250
LCC with Everest 1969
FSP 13.75 / SSP 18.75
LO 16.25 / CM 80.87
C7
127.3017 / 314.5695
FSP 13.00 / SSP 26.00
LO 16.25 / CM 82.50
C8
124.1394 / 306.7553
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Table 2: Differences in Area from other projections compared with the ground measured
polygon
Areas from other
projections compared
with the ground measured
polygon
Y = 121.4058 ha / 300 acres
Difference in Area
(ha / acres)
Percentage of
difference
compared with
the ground
measured
polygon ‘Y’
Order of
difference
compared
with the
standard
C1 (125.0555) - Y +3.6497 / 9.0185 +3.0061 7
C2 (124.7171) –Y +3.3113 / 8.1827 +2.7275 5
C3 (124.6517) – Y +3.2459 / 8.0212 +2.6727 4
C4 (124.7677) – Y +3.3619 / 8.3079 +2.7693 6
C5 (124.6172) – Y +3.9359 / 7.9359 +2.6453 3
C6 (124.1676) – Y +2.7618 / 6.8250 +2.2750 2
C7 (127.3017) – Y +5.8959 / 14.5695 +4.8565 8
C8 (124.1394) – Y +2.7336 / 6.7553 +2.2517 1
4.2 Experiment with Area of a Polygon--Case of the Ganga Basin:
The boundary/perimeter of the Ganga River basin extends approximately between 21o
30’
N
and 31o
45’
N Latitudes and between 73o
E and 90o
E Longitudes. The three UTM within which
the Ganga river basin falls were given UTM projection with WGS 84 datum (separately for
each of the three zones). Area of each of the polygons was then calculated (Fig.3 and Table 3).
Similarly, the whole Ganga basin as a single polygon was given UTM projection with Zone 44
(the central zone of the Ganga River basin) and WGS 84 datum (Fig.3). The same process is
repeated for the whole basin giving different projections with different parameters and datums
(Table 3). When the area of the Ganga basin is split into 3 UTM zones with WGS 84 datum
the resultant total area of all the three zones put together came to 1031082.7531326 sq. km.
This is designated as ‘A1’ in Tables 3 & 4 and is taken here as a ‘standard’ as the deviations if
any are expected to be minimal in UTM zones as each zone accounts for only 6o
of longitude.
Compared with this ‘standard’ area, the same Ganga River basin with Polyconic projection
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with Everest India-Nepal datum (the actual NSF suggested datum is Everest Modified)
produced a larger area by 4384.195814 sq. km. (A3 in Table 4). Similarly, with each case of
other projections, parameters and datums (Table 3) the resultant area of the Ganga River basin
has come to be larger than the ‘standard’.
The area differences, the percentage of differences and the order of difference compared with
the ‘standard’ area, are presented in Tables 3 & 4 and Figure 4. Projections A8 (+0.13%) and
A5 (+0.16) came up with minimum positive differences from the standard (Table 4). The area
differences from projections A4 (+0.45%), A3 (+0.42%) and A2 (+0.46%) though on higher
side from A8 and A5, fall in a group with insignificant differences from each other.
Projections A7 (+3.27%) and A6 (+3.30%) are exceptions with very large differences from the
‘standard’. So, A8 and A5 with minimum differences from the ‘standard’, stand out as the
best fit ones in this experiment (Table 4 and Fig.4).
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Fig.3 Map showing the Ganga river basin with UTM 44N zone and WGS 84 datum (top);
parts of the Ganga basin with separate UTM zones- 43, 44 & 45 with WGS 84 datum
(bottom).
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Fig.4 Bar diagrams showing area of Ganga basin under different Map projections and datums
and its comparison with standard area (A1).
Table 3: Example of the Ganga Basin. The extent of area under different projections and
parameters.
Projections and Parameters Area of the Ganga
River basin
(sq.km.)
Universal Transverse Projection (UTM)
3 zones (43, 44 & 45) separately with World
Geodetic System (WGS) 84
‘Standard’
A1
1031082.7531326
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A2
1035881.56566
Polyconic with India Everest India-Nepal
Latitude of Origin (LO) 13.00 (NRSC)
A3
1035466.94894
CM 84.50 / LO13.00
A4
1035702.70884
Lambert Conformal Conic (LCC) with WGS 84
LO 16.253259 / CM 80.8749
A5
1032687.21106
LCC with WGS 84
FSP 13.75 / SSP 18.75/
LO 16.253259 / CM 80.8749
A6
1065112.70181
LCC with Everest India-Nepal
FSP 13.75 / SSP 18.75
LO 16.253259 / CM 80.8749 (NRSC)
A7
1064871.92716
LCC with Everest India-Nepal
FSP 24.50 / SSP 28.50
LO 16.253259 / CM 80.8749
A8
1032452.28527
Table 4: Differences in Areas from other projections compared with ‘standard’ area. Example
of the Ganga Basin.
Areas from other projections
compared with the standard
area
(A1 = 1031082.7531326)
Difference in
Area (sq. km.)
Percentage of
difference
compared with
the standard
area ‘A1’
Order of
difference
compared
with the
standard
A2 (1035881.56566) – A1 4798.8125274 +0.46 5
A3 (1035466.94894) – A1 4384.1958074 +0.42 3
A4 (1035702.70884) – A1 4619.9557074 +0.45 4
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A5 (1032687.21106) – A1 1604.4579274 +0.16 2
A6 (1065112.70181) – A1 34029.9486774 +3.30 7
A7 (1064871.92716) – A1 33789.1740274 +3.27 6
A8 (1032452.28527) – A1 1369.5321374 +0.13 1
4.3 Experiment with Area and Shape of a Polygon-Case of whole India:
In this experiment, whole of India including all its islands are taken as an example of a
polygon. As in the case of the first the experiment described above, the GIS shape file of India
is cut into six polygons coinciding with six (Zones 42 to 47) UTM zones within which the area
of India is covered (Fig.5) and were projected with UTM WGS 84 datum. Then, the area
falling within each zone is separately calculated. The areal extent of all zones together
covering India totalled up to 3232158.095668 sq. km. (B1 in Table 5). This area (B1) is taken
as the ‘standard’.
Table 5: Projections, Parameters and resultant areas. Example of India
Projections and Parameters Area of
India(sq.km.)
Universal Transverse Projection (UTM) 6 zones
(42 to 47) separately with World Geodetic System
(WGS) 84
‘Standard’
B1
3232158.095668
B2
3296424.842714
Polyconic with Everest 1969
Latitude of Origin (LO) 13.00
B3
3292646.392607
CM 82.50 / LO 16.00
B4
3286710.860431
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Lambert Conformal Conic (LCC) with WGS84
LO 16.00 / C81.00
B5
3285233.22112
LCC with WGS 84
FSP 13.00 / SSP 26.00
LO 16.00 / CM 82.50
B6
3276541.590867
FSP 13.75 / SSP 18.75
LO 16.25 / CM 80.87
B7
3348620.532232
FSP 13.00 / SSP 26.00
LO 16.25 / CM 82.50
B8
3275694.311204
Further, the same map of India referenced with UTM projection with Zone 44 and WGS 84
datum has given an area of 3296424.842714 sq. km. Also, the same map of India was given
different projections with different data (datums) and parameters (Table 5), and the respective
areas were calculated and presented in Table 5. It is interesting to note that in the case of entire
India too, the areal extent compared with the so called ‘standard’ (B1), is greater in all cases of
other projections (Table 6). Projection B7 is an exception with the largest difference of
+3.47%. The pair of projections B6 (+1.35) and B8 (+1.32) make up one group with
minimum difference. Projection B4 (+1.65) and B5 (+1.61) with insignificant difference
between them is the second group with a moderate difference from the standard. The pair of
projections B8 and B6 with minimum difference (Table 6 and Fig.6) prove to be best fit in
this experiment.
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Table 6: Differences in Areas from other projections compared with ‘standard’ area. Example
of India
Areas from other
projections
compared with the
standard area
(B1=3232158.095668)
Difference in
Area (sq. km.)
Percentage of
difference
compared with the
standard area
‘B1’
Order of
difference
compared
with the
standard
B2 (3296424.842714) – B1 64266.747046 +1.95 6
B3 (3292646.392607) – B1 60488.296939 +1.83 5
B4 (3286710.860431) – B1 54552.764763 +1.65 4
B5 (3285233.22112) – B1 53075.125452 +1.61 3
B6 (3276541.590867) – B1 44383.495199 +1.35 2
B7 (3348620.532232) – B1 116462.436564 +3.47 7
B8 (3275694.311204) – B1 43536.215536 +1.32 1
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Fig.5 Map showing whole India with six UTM zones with WGS 84 datum.
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Fig.6 Map projections and datums with respective areas of whole India and its comparison
with standard area (B1).
The areas of India map obtained under different projections (B1 to B8 in Tables 5 & 6) are
compared with the official area of India (X in Table 7) (https://www.india.gov.in/india-
glance/profile) (NPI, 2002) which is 3,287,263 sq. km. (Table 7). A glance at Table 7 and
Figure 7 reveals that projection B3 (+0.16%) has come up with minimum difference followed
by B2 (+0.28%). With Projection B7 (+1.87%) the difference is relatively larger. On
projections B1, B4, B5, B6 and B8 the calculated area of India came up lesser than ‘X’ the
official area of India. Though the difference in area is on the negative side (Table 7 and Fig.7),
there is almost a close match in areal extent between ‘X’ (3287263 sq. km.) and ‘B4’
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(3286710.86 sq. km.) (Table7). Projections B3 (+0.16%) and B4 (-0.0175%), with the former
showing minimum positive difference and the latter showing minimum negative difference
stand up as the best fit ones in this experiment.
Fig.7 Boundaries of Maps with various projections and datums. Insets 1 to 3 are enlargements
of segments (from inset blocks) of boundaries at different scales.
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To examine the shape differences, all the maps of India on projections B1 to B8, are composed
together (Fig.7). It is interesting to note that there are no major differences of shape among the
maps. Boundaries of maps B1, B2, B4, B5 and B6 superposed exactly one over the other and
boundaries of maps B3, B7 and B8 coincided exactly with each other and finally both the
groups show up as only two lines (Fig.7). And, the difference in the boundaries between the
two groups of maps in terms of distance is in the range of 0 to 100 metres only. The difference
in boundaries between the two groups can only be seen at larger scales starting from 1:100,000
(see insets in Fig.7).
Table 7: Differences in Areas from other projections compared with the official area of India.
Areas from other
projections
compared with the
official area of India
(X=3287263 sq. km.)
Difference in
Area (sq. km.)
Percentage of
difference
compared with
the official area
of India ‘X’
Order of
difference
compared
with the
standard
B1 (3232158.095668 -- X -55104.904332 -1.67 7
B2 (3296424.842714) – X +9161.842714 +0.28 4
B3 (3292646.392607) –X +5383.392607 +0.16 3
B4 (3286710.860431) – X -552.139569 -0.017 1
B5 (3285233.22112) – X -2029.77888 -0.06 2
B6 (3276541.590867) – X -10721.409133 -0.33 5
B7 (3348620.532232) – X +61357.532232 +1.87 8
B8 (3275694.311204) – X -11568.688796 -0.35 6
5. DISCUSSION AND CONCLUSIONS
As has been mentioned earlier, map projections depend upon the location of one’s area of
interest over the globe. As far as India is concerned, polyconic projection had long been
decided as the best projection because of minimum area error and shape distortion. In fact, in
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the case of India, zones-wise projection with UTM WGS 84 gives much accurate measurement
of area than with polyconic or any other conical projections because the longitudes spread out
wider towards the south in conical projections unlike in UTM zones. Lambert Conformal
Conic projection too which is basically a conical projection is also suitable for India. As it is,
UTM projection is unsuitable for India as India falls in six UTM zones. In such a case, though
theoretically the area measurements (of a large area within 20o
of latitudinal width range), can
be much more accurate than with polyconic projection, there will be problem of combining and
merging data of different UTM zones. That means, though strips of maps of adjoining UTM
zones can be mosaicked together but cannot be merged as each zone has a different origin and
parameters. So geospatial information generated by various individuals and organizations for
areas falling in different UTM zones cannot be brought together in a single data frame for
many operations in GIS software. This difficulty forecloses the use of UTM projection to
generate geospatial data in case of India. But, still, several spatial data creators including
several governmental organizations use UTM projection with WGS 84 datum to be one with
the rest of the world (as UTM with WGS 84 is a very popular projection worldwide) and geo-
reference the data (of large areas within India and of whole India) with UTM WGS 84
projection by using parameters of UTM Zone 44 (in Indian case). Here, in the present
experiment, it has been observed that area calculated separately zones-wise (3232158.095668
sq.km) is less than the official area of India (X = 3287263 sq. km. in Table 7). The difference
(9161.84 sq. km accounting for 0.28%) between the official area of India (3287263 sq. km. - X
in Table 7) and the area of India with UTM Zone 44 projection (3296424.84 sq. km. - B2 in
Table 7) is not significant especially because it is a little more (not less) than the official area of
India (Table 7).
As has been mentioned in data and methodology, the percentage of differences from the
‘standard areas’ are given scores in increasing order starting from 1 for the minimum with
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maximum difference going up to 8 (Tables 2, 4, 6 and 7) and all the scores for similar
projections are added up to prepare ranks (Table 8). It is to be noted here that projections
designated A1 to A8 are similar to B1 to B8 and C1 to C8. Finally, based on ranks (Table 8)
A8, B8, C8; A5, B5, C5; A3, B3, C3; A4, B4, C4 are selected as the best fit projections with
minimum differences from the ‘standard’ areas.
If the states go with their local parameters and other national institutions go with all-India
parameters, there would be a problem of compatibility in putting together different sets of
geospatial data. Whether it is local or national, it is advisable to implement a single all-India
projection with common parameters so that there is data compatibility which facilitates all
India mosaic in a single dataframe. All funding organizations dealing with any kind of spatial
data generation should insist on a common projection with common parameters so that there is
data compatibility between the data sets. As for shape of India under various projections with
various parameters (Tables 6& 7 and Fig.7), there is no significant distortion at all (maximum
100 metres of shift with minimum being zero). Finally, based on ranks (Table 8) the authors
suggest (i) LCC with Everest India-Nepal datum (A8,B8,C8), (ii) LCC with WGS 84 datum
(A5,B5,C5), (iii) Polyconic with Everest 1969 datum (A3,B3,C3), and (iv) Polyconic with
WGS 84 datum (A4,B4,C4), both for mapping smaller areas on larger scales (excluding
cadastral maps) and larger areas on smaller scales, as the best fit ones in case of India. The
difference in extent of areas from the ‘standard’ increase from (i) to (iv). In all the studies,
quoted in the paper, conducted for different areas outside India, there were variations obviously
in extent of area and they suggested/used best fit projections based on minimum area
differences after comparing their results with either ground measured areas or available official
areas.
Table 8: Order of differences in area compared with the standard of the three experiments—
the Ganga River basin, India, and the ground measured area and final ranks based on
differences
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Projections
From Tables
1, 3 & 5
Ganga River
Basin
From Table
4
India Ground
Measured
Area
From Table 2
Total
Score
4, 6, 7
& 2
Rank
based on
differences
From
Table
6
From
Table
7
C1 A1 B1 --- --- 7 7 -- ---
C2 A2 B2 5 6 4 5 20 5
C3 A3 B3 3 5 3 4 15 3
C4 A4 B4 4 4 1 6 15 3
C5 A5 B5 2 3 2 3 10 2
C6 A6 B6 7 2 5 2 16 4
C7 A7 B7 6 7 8 8 29 6
C8 A8 B8 1 1 6 1 09 1
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