Journal of Geographic Information System, 2012, 4, 412-424 Published Online October 2012 (
Assessment of School Infrastructure at Primary and Upper
Primary Level: A Geospatial Analysis
Gouri Sankar Bhunia1, Pravat Kumar Shit2*, Soumen Duary3
1Rajendra Memorial Research Institutes of Medical Sciences (ICMR), Bihar, India
2Department of Geography & Environment Management, Vidyasagar University, Medinipur, India
3Office of the Sarva-Siksha-Mission (SSM), Paschim Medinipur, India
Email:, *,
Received July 27, 2012; revised August 28, 2012; accepted September 24, 2012
With the introduction of powerful and high-speed personal computers, proficient techniques for infrastructure develop-
ment and management have advanced, of which Geoinformatics technology is of great significance. An attempt has
been made for broad mapping and analysis of existing infrastructures in the context of planning scheme in Paschim
Medinipur district, and to delineate the development zones of educational infrastructure facilities. The thematic layers
considered in this study are infrastructure accessibility, type and condition of classroom and number of classroom allo-
cated for the educational system at primary and upper primary level. Morans I statistics was used to estimate the spatial
distribution of elementary infrastructu re across the district. All these themes and their individual features were then as-
signed weights according to their relative importance in educational development and corresponding normalized
weights were obtained based o n the Saaty’s analytical hierarchy process. The thematic layers were finally integrated in
GIS software based on multi-criteria approach to yield educational development infrastructure index. Morans I statis-
tics shows girl’s toilet, electric and boundary wall facility within the district are clustered in pattern at primary level. At
the upper primary level, only electric and computer facilities shows the clustered distribution across the district. How-
ever, four different zones have been delineated, namely “very good”, “good”, “moderate” and “poor”. The block cov-
ered by very good elementary educational infrastructure facility is Daspur-I and Dantan-II at primary level and Ke-
shiary block at upper primary level in Paschim Medinipur district. Finally, it is concluded that the Geoinformatics
technology is very efficient and useful for the identification of infrastructure development.
Keywords: Educational Infrastructure; Spatial Auto-Correlation; Saaty’s Analytical Hierarchy Process;
Multi-Criteria Approach
1. Introduction
Education is a decisive determinant of economic and
social expansion, and also household livelihoods and
food security status. Empirical studies also demonstrated
that investment in elementary education amplifies the
productivity in all the secto rs of the econ omy much more
than other levels of education, and that economic returns
to investment in primary education are greater than those
arising from other levels of schooling [1]. The develop-
ment of education depends on large number of factors
including the infrastructure resources available to a
school. School infrastructure, such as the site, buildings,
furniture and equipments contribute to a learning envi-
ronment [2]. Study conducted by Ayeni and Adelabu [2],
mentioned that the classrooms in most of the schools
were inadequate in terms of decency, space, ventilation
and insulation from heat; the incinerators and urinal were
not conveniently placed, and the school plant was poorly
maintained; these combined deficiencies constituted a
major gap in the quality of learn ing environment. Hence,
the school infrastructure management and planning is
signifying the regional planning agencies to improve the
educational facility in a particular area.
After nineties, secondary and higher education in India
is experiencing structural changes due to the process of
globalization [3]. The post nineties period witnessed a
considerable increase in the infrastructure at tertiary level
[4,5]. Nevertheless, any fundamental change has not
been found in the structure and organisation of elemen-
tary education system which lags in the quality also. In
India nearly 90 percent of schools at elementary level are
run by government [6]. Most of the schools are situated
in rural areas, and not have average criteria for quality
education. It may be due to poor infrastructure such as
*Corresponding a uthor.
opyright © 2012 SciRes. JGIS
shelter-less school building, insufficient building, and
high ratio of pupil-teacher, traditional methods of teach-
ing and high absentee rate. Ever since its commencement
by the Government of India and State Government, the
Sarva Shiksha Abhiyan (SSA) has accentuated decen-
tralized planning of education with a focus on district
planning. It envisages achieving goal of universal ele-
mentary education by 2010. A phenomenal growth in the
education system has made in India at elementary level
under SSA, in spite of paucity of resources. The number
of primary schools (Government) is highest in Paschim
Medinipur (4672). In general, existing condition of edu-
cational services in Paschim Medinipur show that in
some parts of the region is suffering in educational infra-
structure provision. The spatial distribution of those fa-
cilities is also not equitable across the district, and mostly
located away from the urban centers. Consequently, there
is a need to plan the distribution of educational infra-
structure in Paschim Medinipur to assign great impor-
tance for planning and appraisal activities. So that infor-
mation derived from the stu dy may be helpful for quality
implementation of the programme in Paschim Medinipur
“Optimally”, not fragmented as it is now.
There is actually a general belief that the condition of
school’s learning environment has an important impact
on elementary infrastructure effectiveness and students’
academic concert and or enrollment ratio. The facilities
that are needed to facilitate effective education develop-
ment and learning in an educational institution includes
the girl’s toilet, library, boundary wall, computer, play
ground, classrooms, offices and other buildings structure.
This required a comprehensive mapping of the obtainable
infrastructure to scrutiny the gap in educational infra-
structure. On the basis of that, the objective of the pre-
sent study is to provide a broad mapping and analysis of
existing infrastructures in the context of plann ing scheme
in Paschim Medinipur district, West Bengal (India).
2. Materials and Methods
2.1. Study Area
The entire district of Paschim Medinipur in the state of
West Bengal, India is considered to conduct the present
study, extended between 21˚46'N and 22˚57'N between
6˚33'E and 87˚44'E (Figure 1). The district has 4 8
Figure 1. Location map of the study area.
Copyright © 2012 SciRes. JGIS
sub-divisions: Kharagpur, Medinipur Sadar, Ghatal and
Jhargram. The district is bounded on the north by Bank-
ura and Purulia districts, on east by Purba Medinipur
district, on the south by Purba Medinipur district and
Balasore district of Orissa and on the west by May-
urbhanj district of Orissa and Singbhum districts of
Jharkhand. The total geographical area of Paschim
Medinipur district is 9345 sq km. According to the 2011
census, Paschim Medinipur district has a population of
5,943,300. The district has a population density of 636
inhabitants per square kilometre (1650/sq mi). Its popu-
lation growth rate over the decade 2001-2011 was
14.44%. The district has a sex ratio of 960 females for
every 1000 males, and a literacy rate of 79.04%; where,
the literacy rate of male and females are 81.13% and
56.90% respectively.
Individually the overall literacy rate is found maxi-
mum at Daspur II block (81.7%), where the male and
female literacy rate is maximum at Sabang (89.3%) and
Daspur-I (70.7%) block respectively. On the other hand
individually the overall literacy rate is found minimum at
Binpur II, (55.7%), Gopiballavpur I (56.5%) and Naya-
gram (40.6%) block respectively. The Sabang block is
comparatively developed in respect to the other blocks.
This may be due to the availability of the edu cation facil-
ity but the stunning feature is present at Da spur-I I, wh ere
educational facility is inadequate but the consciousness
of people of these blocks has lead to reach at such a high
level of literacy. In Binpur II, Nayagram and Gopibal-
lavpur I the literacy figure is very poor for the domi-
nance of the poor tribal population (e.g., Sabar, Santal,
Gond etc.) marked by a fragile financial base, lack of
banking facility, predominance of low yielding agricul-
ture and forest resource collection. The block, Keshpur
enjoys the most intensive educational facility but could
not come at the top according to the literacy rate 67%
2.2. Methodology
The analysis is made on the basis on District Information
System for Education (DISE) report on schools at pri-
mary and upper primary level of academic year 2009-
2.2.1. Base layer Creation and Database Generation
The district and block maps of Paschim Medinipur were
collected from the Office of the District Land and Land
Reforms (DL & LRO Office), Medinipur and were
geo-reference with the survey of India Topographical
(SOI) sheet (scale 1:50,000). The process of geo-coding
was done by the ground control points which were col-
lected uniformly over the whole block and then mathe-
matically warped to fit into the real world co-ordinates
using Universal Transverse Mercator (UTM) co-ordinate
system and the World Geodetic System (WGS) 84 datum
by ERDAS Imagine Software (Version 9.2). Block and
district boundary layers were digitized and the vector
layers were generated from the rectified base map. A
database were generated on the excel sheet considering
the infrastructure facility of the school, such as, girls toi-
let, play ground, library, electric, kitchen shed, computer,
ramp, drinking water, boundary wall available for each
school per block and their percentage have been calcu-
lated. The excel files were merged together and exported
to geodatabase.
2.2.2. Geogra phi c Visu al i zation and Data E xpl oration
For processing visual information, and for formulating
research questions of infrastructure facility available at
primary and upper primary level, geographic visualiza-
tion procedure was adopted. Geographic visualization
emphasizes map-based manipulations as data visualiza-
tion process [7,8]. The day-metric maps were derived to
delineate areas of homogeneous values, rather than fol-
lows administrative boundaries [8]. The derived data for
each theme at primary and upper primary level was clas-
sified separately prior to mapping based on geometric
interval in ArcGIS v9.3 software, and were symbolized
using colour scheme for quantitative data.
2.2.3. Ge ne ration of Thematic Layers
In order to differentiate infrastructure facility block in the
study area, a multiparametric data were used. The the-
matic layers of type of classroom (e.g., good condition,
need of minor repair, need of major repair, pucca house,
partially pucca house, kuncha house), condition of class-
room (e.g., number of classroom) and school infrastruc-
ture availability (e.g. girls toilet, drinking water, library,
electric, ramp, boundary wall, play ground, kitchen shed,
computer facility) were prepared at primary and upper
primary level separately. All the digitized coverages
were spatially organized in the GIS environment with the
same resolution and coordinate system. The checking of
these spatial maps was done with respect to other data-
base layers by the overlaying technique, and refined mu-
tually as part of standardization of the database. The er-
rors due to digitization and mismapping were removed in
this process.
2.2.4. Spatial Autocorrelation Analysis
Spatial autocorrelation was measured based on the Mo-
ran’s Index (Morans I) spatial statistics to evaluate
whether the pattern expressed is clustered, dispersed, or
random [9]. Using the spatial statistical tool of ArcGIS
software the Morans I value and Z score were calculated
to assess the significance of that index. In general, a Mo-
rans I value near +1.0 indicates clustering while an in-
Copyright © 2012 SciRes. JGIS
dex value n e a r –1.0 indicates dispersion.
2.2.5. Integration of Thematic Layers
The thematic layers of type of classroom, condition of
classroom and school infrastructure availability were
used for the delineation of suitable school infrastructure
management system block at primary and upper primary
level in the Paschim Medinipur district. To differentiate
development block, all these thematic layers were inte-
grated using ArcGIS software. The weights of the dif-
ferent themes were allocated on a scale of 1 to 5 based on
their influence on the educational development. Different
features of each theme were assigned weights on a scale
of 1 to 9 according to their relative influence on educa-
tional development. Based on this scale, a qualitative
assessment of different features of a given theme was
performed, with: poor (weight = 1 - 1.5); moderate
(weight = 2 - 3.5); good (weight = 4 - 5.5); very good
(weight = 6 - 7.5); and excellent (weight = 8 - 9). There-
after, a pairwise comparison matrix was constructed us-
ing the Saaty’s analytical hierarchy process Saaty [10], to
compute normalized weights for individual themes and
their features. To differentiate better infrastructure facil-
ity available block, all the ninteen thematic layers after
assigning weights were integrated step by step using
multi-criteria approach in ArcGIS software. Figure 2
shows the methodological flow chart for Education De-
velopment Infrastructure In dex (EDII).
Educational development infrastructure index (EDII)
is a dimensionless quantity that helps in indexing uni-
versal accessibility of school infrastructure development
across the district. The range of EDII values were di-
vided into four equal classes (called zones) based on
geometric interval, and the EDII of different blocks fal-
ling under different range were grouped into one class.
Thus, the entire stud y area was qualitatively divid ed into
four zones based on the universal accessibility of school
infrastructure. The final output showing these blocks
which were performed very well to success the SSA mis-
sion for achieving goals of univers alization of element a r y
ducation at grass root level using ArcGIS software. e
Figure 2. Methodological flow chart for Education Development Infrastructure Index (EDII).
Copyright © 2012 SciRes. JGIS
3. Results
The details of elementary infrastructure with their sp atial
distribution in the study area are presented below.
3.1. School Infrastructure
Educational infrastructure at grass root level is responsi-
ble for growth in secondary and tertiary education. A
total of 4359 schools were surveyed at the primary level
across the district. On the other hand, at the upper pri-
mary level, a total of 670 schools were surveyed. Thus,
linking of infrastructure availability with the educational
system provides a simple way to understand achievement
of the SSA mission for the development process across
the district. Figure 3 shows the status of infrastructure
facilities at block level in Paschim Medinipur district.
Figure 3. Infrastructure facility in Paschim Medinipur district (A) primary level; (B) Upper primary level.
Copyright © 2012 SciRes. JGIS
Given that health issues are known to impact on school
attendance and completion [11], establishing an impact
of separate-sex toilets on girl’s health could build indi-
rect evidence of an impact of separate toilets on girl’s
educational outcomes. Going to a school lacking proper
basic facilities, like toilets, could be one of the most frus-
trating situations for many children in the rural and urban
schools. A school environment, often hostile or unap-
pealing, with no special facilities for girls (toilets, a pro-
tective wall around the school, etc.) is conducive to the
enrolment of girls. It is very common for boys not only
to intimidate and make fun of girls, but also attack and
beat them up. In our study area, with regard to the avail-
ability of girl’s toilet, the n umbers of schools were 15.84
percent at primary level, and 92.14 percent at upper
pr imary level. The highest girl’s toilet facility was found
in the Daspur-I block (29.46 percent), while, lowest fa-
cility was available in Gopiballavpur-I (3.82 percent) at
the primary level. Moreover, the average distribution of
girl’s toilet facility at primary level showed 14.67 per-
cent (standard deviation ±7.48). In the upper primary
level, the numbers of schools were 92.14 percent of sep-
arate girl’s toilet facility. In Gopiballavpur-II block area,
lowest numbers of girl’s toilet facility were available
(70.59 percent) at the upper primary level. However, in
Pignla, Sabang, Salbani, Sankril, Narayangarh, Mohan-
pur, Kharagpur-II, Keshiary, Jambani, Ghatal, Garhbe-
ta-III, and Debra all these school having girl’s toilet fa-
The building of toilets in scho ols has not only brought
about a change at the school compounds, but lack of
girl’s toilet facility may act as a deterrent to girls’ atten-
dance in schools or impact negatively on their learning
[12]. The spatial distribution of the availability of girls
toilet at the primary level shows clustered pattern (Mo-
rans I 0.29, Z score 2.72), whereas, at the upper primary
level random pattern has been found (Morans I 0.01, Z
score 0.35).
Drinking water (DW) is another important elementary
aspect for the school infrastructure development. At the
primary level and the upper primary level, percent of
schools with drinkable water available is almost same
across the district. The overall percentage of DW avail-
ability showed 98.24 percent (SD ±1.75) and 98.50 per-
cent (SD ± 2.70) at primary and upper primary level re-
spectively. However, the spatial distribution is somewhat
random for the both the level. Th e Moran’s I statistics of
the spatial distribution of drinking water facility for the
entire district showed 0.05, with Z score of 0.01 at the
primary level. In the upper primary level, the Morans I
values showed 0.01, with a Z score of 0.34 that represent
the blocks are randomly distributed based on their avail-
ability of DW facilities.
Library is a very important element in any education
level as they serve as knowledge source for the students.
The availability of library facility has been derived
through the number of primary schools having library
facility expressed as percentage of total number of
schools. It was observed that facility of libraries at upper
primary levels is comparatively good acro ss the Paschim
Medinipur district (68.34 percent), in respect to the upper
primary level (91.34 percent). More than 95 percent
school in Keshiary and Mohanpur blocks having the li-
brary facility at primary level. However, Debra (26.21
percent), Binpur-II (33.86 percent), Kharagpur-I (33.67
percent), and Pingla (31.03 percent) blocks have re-
corded the lowest percentage of library facility. More-
over, the spatial distribution of highest and lowest facili-
ties of library is partially random (Morans I 0.11, Z
score 1.10). Alternatively, in Narayangarh, Mohanpur,
Kharagpur-I, Datna-II and Keshiary block has the 100
percent availability of library. The lowest availability of
library facility was recorded from the Binpur-II (77.27
percent) and Garhbeta-II (77.78 percent) blocks. The
spatial auto correlation between highest and lowest li-
brary facilities at upper primary lev el also illustrated par-
tially random pattern (Morans I 0. 1 1, Z score 1.18).
Percentage of schools having electricity may be con-
sidered an indicator of elementary infrastructure. At Pri-
mary level, majority of the school in Paschim Medinipur
district, do not have electricity. The overall percentage of
electricity availability acro ss the district is 10.68 p ercent.
Highest percent of electricity availability has been re-
corded from Daspur-I block (44.19 percent), while, low-
est percent have been documented in the Binpur-I block
(0.73 percent). The spatial distribution between highest
and lowest percent of electricity availability across the
district showed clustered pattern (Morans I 0.49, Z score
4.71). The percentage of electricity availability at the
upper primary level varied enormously in the district,
ranging from a low 36.36% in Binpur-II to 100 % in
Kharagpur-I. The overall availability of electricity in the
district is 76.22% (Standard deviation ±15.88). The spa-
tial distribution pattern of electricity at the upper primary
level showed clustered pattern (Mora ns I 0.40, Z score
For the assessment and management of student’s
mental health in wellbeing in primary and secondary
schools, it is necessary that every school should have
ramp. In general, at the primary level, availability of
ramp facility was 55.81% across the district. However,
the highest facility of ramp was recorded from Keshiary
block (90.16%), and the lowest ramp facility was re-
corded from Chandrakona-I (30.17%). The spatial auto-
correlation of ramp facilities between the blocks showed
random pattern (Morans I 0.09, Z score 0.40). At the
upper primary level, 63.78% schools across the district
had provision of ramps. The availability of ramp per-
Copyright © 2012 SciRes. JGIS
centage school is ranging from a low 16.67% in Gopi-
ballavpur-I to 100% in Keshiary block. However, these
blocks were randomly distributed across the district
(Morans I 0.09, Z score 1.01).
Availability of boundary wall is also other very sig-
nificant issues for cater education. The problem of
schools building and boundary wall is not so good in
several elementary schools in Paschim Medinipur district.
The issue of boundary wall is very severe that most of
the schools do not have boundary walls. The lowest per-
centage of boundary wall availability (6.09%) was re-
corded from the Sabong block, and the highest percent-
age was documented in Keshiary block (52.46%). The
spatial distribution of boundary wall facilities between
the blocks showed clustered pattern (Morans I 0.40, Z
score 3.55) at the primary level; whereas, at the upper
primary level it showed somewhat clustered pattern
(Morans I 0.23, Z score 2.14). Additionally, the highest
percentage of boundary wall facilities block has been
documented as Gopiballavpur-I (91.67%), and the lowest
percentage were recorded as Sabong block (20.83%) at
the upper primary level. Moreover, at the primary level,
the facilities of boundary wall availability in the schoo l is
found higher in southeast part of the district, while low
facilities are presented in the western pockets.
Distribution of schools having play ground in primary
level were 32.95 percent across the district; however, at
the upper primary level had 79.49 percent schools had
the play ground. While lowest percent of play ground
(13.86 percent) had in Garhbeta-II block and the Debra
had the highest percent (69.42 percent) of play ground in
the school at the primary level. It is further observed that
play ground facilities in highest and lowest percent
schools were distributed in clustered pattern across the
district (Morans I 0.31, Z score 2.81). On the other hand,
at the upper primary level, Chandrakona-II block had the
highest percent (100 percen t) of play ground facility, and
the lowest percentage has been recorded from Ghatal
(60.61 percent). The spatial autocorrelation of play
ground facilities block at upper primary level showed
random pattern (Morans I 0.16, Z score 0.96).
The percentage of school with kitchen shed facilities
had shown a very interesting result. The majority of su ch
schools, 94.65 percent, at primary level had kitchen shed
facility. Though their distribution pattern across the dis-
trict is slightly random (Morans I 0.09, Z score 1.67),
however, only Daspur-II block documented as lowest
percent (39.81 percent) of schools having the kitchen
shed facility. The majority of the school in Paschim
Medinipur district had kitchen shed, more than 90 per-
cent connection with the school at primary level. Alter-
natively, at the upper primary level, almost an equ al per-
centage (90.41 percent) had the kitchen shed facility. In
the study area, the percentage of such upper primary
schools having kitchen shed facility varying from 31.58
percent in Datan-II block to 100 percent in Sankril,
Kharagpur-I, Garhbeta-II, Garhbeta-III, Datan-I, Chandra-
kona, I, II and Binpur-II block. The spatial distribution
pattern of kitchen sh ed facility in the study area is totally
random (Morans I 0.03, Z score 0.08).
The availability of computer facility in the schools of
the study area showed much lower percentage of such
school. The lowest percentage of schools with computer
facility in case of the primary level is shown in Datan-II
(2.17 percent). The percentage of such school is also high
in the district of Mohanpur block (34.57 percent). How-
ever, blocks had computer facility, randomly distributed
in the study area (Morans I 0.01, Z score 0.54). Com-
paratively, the percentage of upper primary school with
computer facility was found to be higher (19.57 percent)
than the primary levels. In upper primary level, Nara-
yangarh block had maximum percentage of school hav-
ing computer facility (37.84 percent). The lowest per-
centage of block in upper primary school having com-
puter facility is notified as Chandrakona-I (4.17 percent).
Nevertheless, the clustered pattern has been observed
among the blocks connection with computer facility in
upper primary level of the study area (Morans I 0.25, Z
score 2.26).
3.2. Condition of Class Room
The condition of classrooms at primary and upper pri-
mary level also reveals that it is an important factor for
elementary school development. The conditions of
schools building is not so good in several elementary
schools in Paschim Medinipur district (Figure 4). In our
study we considered the pucca, partially pucca, kuncha,
need minor repair and need major repair classrooms for
EDII model development. It is observed that in the pri-
mary level 40.90 percent building had good condition,
while in the upper primary level, the percentage was
54.72 percent. Further it is observed that Garhbeta-III
block; more than 60 percent of all such schools have
good condition buildings. On the other hand, in Sabong
less than 30 percent schools have good condition. While
at the upper primary level, Garhbeta-III, Gopiballavpur-I,
Kharagpur-II, Mohanpur have more than 60 percent
school in good condition. However, the percentage of
minor repaired school at the primary level was 26.84
percent, and at the upper primary level, the percentage
was 20.33. The provision of major repaired percentage of
school building in primary level varies from 15.84 per-
cent (Garhbeta-III) to 48.46 percent (Datan-I). Addition-
ally, it was also observed that at the upper primary level,
13.22 percent of such school in Gopiballavpur-I block
have need to be major repaired, whereas in Chandra-
kona-II, 42.75 percent schools have need for major re-
Copyright © 2012 SciRes. JGIS
Copyright © 2012 SciRes. JGIS
centage was 1.53 percent only. Narayangarh, and Garh-
beta-III block have worst position in respect of % of
pucca classroom i.e., 41.25 and 59.74 respectively. The
percentage of schools having kuncha buildings is some-
what high (e.g., 3.07 percent) at pr imary level, in respect
to the upper primary level (e.g., 1.10 percent). The per-
centage is as high as 14.29 percent in Narayangarh and
9.69 percent in Dantan-I at the primary level. In the up-
per primary level, Chandrakona-I having the highest per-
cent of kuncha building schools (e.g. 8.40 percent). The
spatial autocorrelation of classroom type among the
blocks of Paschim Medinipur district is shown in Table
pairing. However, the spatial relationship among the dis-
tribution of classroom condition of Paschim Medinipur
district is shown in Table 1.
3.3. Type of Classroom
Brief looks at the block-specific percentages of schools
having type of classroom were also examined at the pri-
mary and upper primary level of Paschim Medinipur
district. The result of the study reveals that 85.24 per-
cent at the primary and 97.37 percent at the upper pri-
mary level schools of the Paschim Medinipur district
have provided pucca buildings. It varies from 41.25
perc ent in Narayangarh to 100.00 percent in Kharagpur-I
of schools having pucca buildings at the primary level.
More than 95.00 percent schools in each block of Pa-
schim Medinipur district also have pucca school build-
3.4. Number of Classroom Distribution
There are 13.10% of schools having one room, 33.10%
of schools having two rooms, 24.94% of schools having
three rooms and 28.84% of schools having above three
rooms at primary level in the district. It is also observed
hat Binpur-II and Keshpur have the highest number of
The percentage of primary schools having partially
pucca buildings were 11.69 percent in Paschim Medini-
pur district, whereas, at the upper primary level the per- t
Figure 4. Type and condition of classroom availability in Paschim Medinipur district (A) Primary level; (B) Upper primary
Table 1. Spatial autocorrelation of type and condition of classroom facilities distribution across the Paschim Medinipur dis-
Primary Upper primary
Variables Moran’s I Z score Distribution pattern Moran’s I Z score Distribution pattern
Pucca 0.13 1.45 Random 0.14 1.53 Slightly random
Partially pucca 0.08 1.00 Random 0.05 0.73 Random
Kuncha 0.22 2.27 Somewhat clustered –0.14 1.00 Slightly random
Good condition 0.41 3.73 Clustered –0.08 0.42 Random
Need minor repair 0.04 0.66 Random –0.07 0.30 Random
Need major repair 0.26 2.52 Somewhat clustered 0.05 0.72 Random
school with single room. Moreover, in Narayangarh, Jhar-
gram and Binpur-II has been recorded maximum number
of schools with two classrooms. The minimum number of
schools with three classrooms has been recorded from
Datan-I, Datan-II, and Mohanpur block. Furthermore,
Daspur, Debra and Sabong have the maximum number of
schools with four classrooms at the primary level. The
spatial relation of the distribution of schools in each block
having number of classroom at the primary and upper
primary level was also examined by Morans I statistics,
and the result has been given in Table 2.
There are 0.40% of schools having less than four
rooms, 4.93% of schools having four rooms, 4.39% of
schools having five rooms and 90.15% of schools having
above five rooms at upper primary level in the district.
However, maximum number of schools of above five
rooms was recorded from Sobang block, while lowest
number was docum en t e d in Kharagpu r-I block.
3.5. Weight Assignment for EDII and
Geoinformatics Based Modeling
Suitable weights were consigned to the nineteen themes
at primary and upper primary level and their individual
features after understanding their infrastructural signify-
cance in causing educational development in the study
area. The normalized weights of the individual themes
and their different features were obtained through the
Saaty’s analytical hierarchy process (AHP). The weights
assigned to different themes are presented in Table 3.
The process of attaining the normalized weights of the
themes is presented in Table 4 for primary level and in
Table 5 for upper primary level. The normalized weights
of different features of the nineteen themes were ob-
tained in the similar app roach.
After deriving the normal weights of all the thematic
layers and each feature under indivial mes, all the the-
matic layers were incorporated with one another using
ArcGIS software in order to segregate EDII zones in the
study area. The final weights of each polygon in the final
integrated layer were derived by summing up the weights
of polygons from individual layers and the highest de-
rived sum of the weights in the final integrated layer was
divided into four equal classes, i.e. “very good”, “good”,
“moderate” and “poor”, in order to mark out educational
infrastructure development zones as per SSA mission.
The delineation of EDII zones was done by grouping the
polygons in the final integrated layer having weights of
ny of the four classes. a
Table 2. Spatial autocorrelation of classroom facilities distribution across the Paschim Medini pur district.
Primary Upper primary
Variables Moran’s I Z score Distribution pattern Variables Moran’s I Z score Distribution pattern
Single room 0.01 0.36 Random Less than 4 room0.23 2.93 Somewhat cluster ed
Double room –0.08 0.36 Random 4 room 0.21 2.18 Somewhat clustered
Triple room 0.23 2.27 Some w h a t c l ustered5 room 0.04 0.64 Random
Above 3 room 0.41 3.80 Cl u s t e red Above 5 room 0 .36 3.41 Clustered
Table 3. Weights of the infrastructural facilities themes for educational development zoning in Paschim Medinipur district.
Primary Upper Primary
Variable Weightage Variable Weightage
Girl's toilet 3 Girl’s toilet 4
Drinking water 6 Drinking water 4.5
Library 5 Library 6
Electricity 2.5 Electricity 3.5
Ramp 2 Ramp 3
Boundary wall 3.5 Boundary wall 4
Play ground 4.5 Play ground 3.5
Kitchen shed 4 Kitchen shed 3
Computer 4.5 Computer 5
Single classroom 3 Less than 4 classroom 1
Double classroom 2 4 classroom 2.5
3 Classroom 1.5 Above 5 classroom 3
Above 3 Classroom 1 Pucca houses 2.5
Pucca houses 2 Partially pucca houses 1.5
Partially pucca houses 1.5 Kuncha 2.5
Kuncha 1 Good condition 5
Good condition 3 Need minor repair 2
Need minor repair 2 Need major repair 1.5
Need major repair 1.5
Copyright © 2012 SciRes. JGIS
GT 1.000.50 0.60 1.201.500.860.670.750.671.001.502.003.001.502.
DW 2.001.00 1.20 2.403.001.711.331.501.332.
Lib 1.670.83 1.00 2.002.501.431.
Electric 0.830.42 0.50
Ramp 0.670.33 0.40 0.801.000.570.440.500.440.671.001.332.001.001.332.000.671.001.330.810.04
BW 1.170.58 0.70 1.401.751.000.780.880.781.171.752.333.501.752.333.501.171.752.331.410.07
PG 1.170.75 0.90 1.802.
KS 1.330.67 0.80 1.602.001.140.891.000.891.332.002.674.002.002.674.001.332.002.671.610.07
Computer 1.500.75 0.90 1.802.
Sin room1.000.50 0.60 1.201.500.860.670.750.671.001.502.003.001.502.
Doub room0.670.33 0.40 0.801.000.570.440.500.440.671.001.332.001.001.332.000.671.001.330.810.04
trip room0.500.25 0.30 0.600.750.430.330.380.330.500.751.001.500.751.001.500.500.751.000.610.03
Abv3 room0.330.17 0.20 0.400.500.
Pucca 0.670.33 0.40 0.801.000.570.440.500.440.671.001.332.001.001.332.000.671.001.330.810.04
Par_pucca 0.500.25 0.30 0.600.750.430.330.380.330.500.751.001.500.751.001.500.500.751.000.610.03
kuncha 0.330.17 0.20 1.250.500.
gd_cond 1.000.50 0.60 1.201.500.860.670.750.671.001.502.003.001.502.
need min0.670.33 0.40 0.801.000.570.440.500.440.671.001.332.001.001.332.000.671.001.330.810.04
need maj0.500.25 0.30 0.600.750.430.330.380.330.500.751.001.500.751.001.500.500.751.000.610.03
GTDWLibElect RampBWPGKSCompSin.
room Dob.
room Trip.
room >3
room Puccca
house Par_
pucca Kuncha Gd.
cond Need
minor Need
major Nth rootofthe
product valuesEigenv-
ect or
GT = Girl’s toilet, DW = Drinking water, Lib = library, Elct = Electric, BW = Boundary wall, PG = Play ground, KS = Kitchen Shed, Comp = Computer, Sin. Room = Single room, Dob room = Double room, Trip
room = Triple room, Par Pucca = Partially pucca houses, Gd. Cond = Good condition.
Table 4. Pair-wise comparison matrix of the thematic layers for EDII at primary level in Paschim Medinipur district.
Copyright © 2012 SciRes. JGIS
GT 1.00 0.890.67 1.14 1.33 1.60 1.33 1.60 2.671.600.802.002.671.370.07
DW 1.13 1.000.75 1.29 1.50 1.80 1.50 1.80 3.001.800.902.253.001.540.08
Library 1.50 1.331.00 1.71 2.00 1.501.712.001.206.00 2.40 2.00 2.40 4.002.401.
Electric 0.88 0.780.58 1.00 1.17 0.881.001.170.703.50 1.40 1.17 1.40 2.331.400.701.752.331.200.06
Ramp 0.75 0.670.50 0.86 1.00 0.750.861.000.603.00 1.20 1.00 1.20
BW 1.00 0.890.67 1.14 1.33 1.60 1.33 1.60 2.671.600.802.002.671.370.07
PG 0.88 0.780.58 1.00 1.17 0.881.001.170.703.50 1.40 1.17 1.40 2.331.400.701.752.331.200.06
KS 0.75 0.670.50 0.86 1.00 0.750.861.000.603.00 1.20 1.00 1.20
Computer 1.25 1.11 0.83 1.431.67 1.251.431.671.005.00 2.00 1.67 2.00 3.332.001.002.503.331.710.09
Less than4 room0.25 0.220.17 0.29 0.33 0.40 0.33 0.40 0.670.400.200.500.670.340.02
4room 0.63 0.560.42 0.42 0.83 0.630.710.830.502.50 1.00 0.83 1.00 1.671.000.501.251.670.830.04
Abv5 room0.75 0.670.50 0.86 1.00 0.750.861.000.603.00 1.20 1.00 1.20
Puccaroom0.63 0.560.42 0.71 0.83 0.630.710.830.502.50 1.00 0.83 1.00 1.671.000.501.251.670.850.04
Partially_pucca 0.38 0.330.25 0.43 0.50 0.380.430.500.301.50 0.60 0.50 0.60 1.000.600.300.751.000.510.03
Kuncha0.63 0.560.42 0.71 0.83 0.630.710.830.502.50 1.00 0.83 1.00 1.671.000.501.251.670.850.04
Good_cond 1.25 1.11 0.83 1.43 1.67 1.251.431.671.005.00 2.00 1.67 2.00 3.332.001.002.503.331.710.09
Need minor0.50 0.440.33 0.57 0.67 0.500.570.670.402.00 0.80 0.67 0.80 1.330.800.401.001.330.680.03
Need major0.38 0.330.25 0.43 0.50 0.380.430.500.301.50 0.60 0.50 0.60 1.000.600.300.751.000.510.03
GTDWLibElectRampBWPGKS Computer Less
room Pucca Par_
pucca Kuncha Gd
cond Need
minor Need
maj Nthrootofthe
product valuesEigenvec-tor
GT = Girl’s toilet, DW = Drinking water, Lib = library, Elct = Electric, BW = Boundary wall, PG = Play ground, KS = Kitchen Shed, Comp = Computer, Abv5 room = Above 5 room, houses, Good Cond = Good
Table 5. Pair-wise comparison matrix of the thematic layers for EDII at upper primary level in Paschim Medinipur district.
Copyright © 2012 SciRes. JGIS
Copyright © 2012 SciRes. JGIS
The EDII zone map of the Paschim Medinipur district
reveals four distinct classes (zones), representing very
good’, ‘good’, ‘moderate’ and ‘poor’ educational devel-
opment infrastructure availability in the area (Figure 5).
The v er y g oo d ED II zo n e ma i n ly en co mp a s se s max i mum
availability of type of classroom (e.g., pucca houses,
kuncha houses, room facilities, etc.), condition of class-
room (e.g., good condition, repair etc.) and school infra-
structure (e.g., girl’s toilet, computer, library, boundary
wall, play ground ramp etc. availability) facilities. The
block covered by very good educational infrastructure
facility is Daspur-I and Dantan-II at primary level. In
the upper primary level, only keshiary block showed
highest facilities for educational development. However,
western, central and central-north shown the good facili-
ties of infrastructures are available at the primary level.
Central-north and southwest part of Paschim Medinipur
district showed good availability of educational infra-
structure at upper primary level. Conversely, the poor
EDII zone has been delineated where the availability of
these above mentioned facilities is less in number and/or
percent. Binpur-II, Jamboni, Nayagram, and Chadra-
kona-I showed the low availability of infrastru cture at the
primary level. The distribution of low availability of in-
frastructure at the upper primary level is much higher
than the primary level in Paschim Medinipur district.
Figure 5. Educational development infrastructure index (EDII) map of Paschim Medinipur district (A) Primary level, (B)
upper primary level.
In Binpur-II, Jahrgram, Gopiballavpur-I, II, Datan-I, Kesh-
pur, Debra, Garhbeta-II and Chandrakona-I blocks has
the low facilities of infrastructure availability at the up-
per primary level.
4. Conclusion
The main concern in this study h as been on infrastructur e
availability stratification at micro level in the education
system, its impact on educational process and to a lesser
extent outlining block to educational disparities. Gov-
ernment of India instigated a flagship system Sarva
Shksha Abhiyan (SSA) particularly meant for increasing
infrastructure up to elementary education for develop-
ment and increasing literacy rate. However, our results
recommend that the availability of infrastructure ele-
ments such as availability of toilets, electricity, library,
computers, type and condition of classroom is very of
great significance for improving the learning environ-
ment. As a process that produces specific functional
products, EDII is fundamentally an educational mi-
cro-planning effort focused on increasing school resource
efficiency and equity. An ample number of thematic lay-
ers and proper assignment of weights are the keys to the
accomplishment of Geoinformatics techniques in identi-
fying infrastructure development process. However,
based on our analysis, we found that the availability of
infrastructure is not well distributed everywhere across
the district. Some of the blocks (e.g., north-central and
southeastern part) are having the good facilities, while
north western and south western does not. This demand
has a particular importance when it is connected with the
area of education which is sens itive and imp ortant for the
future progress of societies. The relationship between
student achievement and infrastructure condition has
been perfectly expressed in the phrase of Prof. Berner
“Good infrastructure is truly at th e base of quality educa-
tion”. This may be due to the reason of socio-economic
barrier, political problem and low accessibility of the
area. The present approach may help facilitate more re-
levant and effective educational micro-planning based on
logical conditions and reasoning, it can also be applied in
other regions of India or abroad. Moreover, this study
may also help the investors and funding bodies, as well
as those who are responsible for planning, managing and
designing educational facilities to take necessary action
in those areas suffering from the deficiencies of elemen-
tary school infrastructure in the near future. Overall, the
results of this study demonstrated that the Geoinformat-
ics technology is a powerful tool for assessing the current
status of infrastructure development zone, based on
which concerned decision makers can formulate an effi-
cient elementary infrastructure development plan for the
tudy area to achieve the success of SSA mission. s
5. Acknowledgements
We are very much thankful to the Office of the Sarva-
Siksha-Mission of Paschim Medinipur for freely provid-
ing the data.
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