M. Tech Student, Dept. of Remote Sensing
BIT Mesra, Ranchi
M. Tech Student, Dept. of Remote Sensing
BIT Mesra, Ranchi
Dr. M. S. Nathawat
Prof. & HOD, Dept. of Remote Sensing
BIT Mesra, Ranchi
In a developing country like India where 73% of the population reside in rural area and 27% in urban areas, we need a very structured planning procedure such that the development activities and infrastructure facilities are available at both urban and rural area. However, in such a condition where majority of people leave in rural area and are provided with the least infrastructure facilities, creates a regional imbalance in development, causing shift in population from rural to urban areas. Hence administrators or decision-makers require an efficient GIS based tool which will assist them to get the updated scenario of the region.
Recently after the creation of Jharkhand as 28th state, it faces a number of challenges in the path of development. One of the main causes is absence of accurate digital data in the form of maps. The data generated by various state government departments such as Education, Health, PWD e.t.c are enormous but poorly maintained, particularly the spatial data shows the maximum inaccuracy.
The present study emphasises the power of GIS technology which will help the state government of Jharkhand to better understand and evaluate spatial data by creating graphic displays using information stored in the database .As GIS does more than just display the data; it enables the user to dynamically analyse and update the information linked to those locations spatially and can further strengthen the e-governance.
Ranchi district was taken as a case study covering all the 20 blocks with 2154 villages. The administrative maps were digitised and non-spatial attribute data, prepared on MS-Excel, were incorporated to each of the villages in spatial data. In the present study two prime parameter – health end education were taken as a model to demonstrate the GIS based e-governance. Similarly other amenities can also be linked and a holistic analysis of the regional development can be found out. The purpose of this study is to locate existing health and education facilities and indicate upgradation /new creation of such facilities require as per the norms. An interface was customised where the user can query on the datasets to retrieve tabular and spatial information.ArcInfo8.01 software were used for creating maps (to the scale) and Arcview3.2a for creating the GIS based information system. Provision is made for hosting the maps on the Internet in such a way clients can view the information query using Arcexplorer.
It is concluded that a state like Jharkhand which has immense potential of development and has maximum tribal population residing in rural area urgently needs a GIS based e-governance system such that it will help the government in planning, implementation and monitoring of various projects for development in different fields at much faster rate which in turn will make the state technologically more developed.
- To provide the planners an accurate spatial view of the district at different levels such as district, block, village level as well as road and rail network, drainage network e.t.c.
- To provide the planners detailed demographic data and education & health related data on desktop in a GIS environment.
- To assists the planners in finding out the possible locations for the schools and health centers depending on several parameters such as for health; population density ,number of health centers required and its optimum location ,number of disease infected persons etc. and similarly for education; percentage literates, number of primary schools and middle schools required and its optimum location, number of teachers posted, vacancy and required as per norms etc. in a village.
Ranchi district is situated in Chotanagpur plateau. Chotanagpur is a vast undulating plateau studded with hills, which were once covered with dense forests, but with influx of population, rapid industrialisation and extensive mining the forest cover has decreased. Ranchi district of Jharkhand has been selected as a case study. The reasons behind selecting Ranchi as study area are as follows:
- After the declaration of Ranchi as the capital of newly created Jharkhand state, economic activities have increased manifold hence developmental activities have also increased but side by, it is facing challenges for providing better quality basic infrastructure facility such as health and education.
- The district is the largest in the state and rich in minerals therefore after successful functioning of SDSS created for such a big district, will open the way for creating GIS based SDSS for remaining districts of Jharkhand state to take Jharkhand a step forward in the path of development through e-governance.
(per Sq. Km)
|% Male Literacy||78||65|
|% Female Literacy||53||37|
Fig 2: Block Map of Ranchi District
Spatial Extent of the District
Ranchi district in Jharkhand lies between
- 84o 52′ 7″ and 85o 54′ 15″ East Longitude
- 22o 34′ 13″ and 23o 43′ 5″ North Latitude
It is bounded on the north by Hazaribagh district and portion of Chatra district, on the east by the district of Purulia (in West Bengal) and part of Pashchimi Singhbhum and on the west by the district of Palamu, Lohardaga and Gumla.
Total area of the district is 7698 Sq.Kms. The district headquarter is located at Ranchi.
Fig 3: Village Map of Ranchi District along with PHC locations
According to Census-91, Ranchi district is comprised of one sub-division and, 20 community development blocks. It has 9 towns and 2057 villages (2038 inhabited villages and 19 uninhabited villages).
Ranchi is basically a tribal belt. The main tribal groups residing here are the -‘Mundas’, ‘Oraon’, Kharias, and Birhore. The district can be divided into five distinct linguistic cum social zones as follows:
- Panch Pargania areas.
- Isolated Mandari speaking areas.
- Church influenced areas.
- Urban areas.
- Areas having sizeable Muslim population.
First of all study was done for defining and understanding the current problems regarding health and education in Jharkhand state. In order to achieve the objective set, the following methodology was adopted.
Fig 4: Malaria affected places of Ranchi District along with drainage map
Spatial Database Design & Organisation
- Delineating the study area spatially.
- Identification and collection of spatial data consisting of SOI Toposheets of 1:250,000 scale and maps from Census-handbook.
- Creation of maps using cartographic techniques.
- Creation of digital maps using Arc/Info software , which involves
- Digitisation of scanned maps.
- Coverage editing to remove digitisation errors such as dangles, overshoots or undershoots and labels for each of the polygons.
- Performing clean build operation to create topology.
- Projection and transformation of coverage into realworld co-ordinates following the same projection system as adopted by SOI Toposheets i.e. Polyconic Projection and choosing spheroid as Modeverest.
Fig 5: Village map of Ormanjhi block along with Health facilities & Malaria affected areas
Non-Spatial Database Design and Organisation
- Identification and collection of non-spatial data elements consisting of
- Census-data of 1991 in digital form.
- Educational data (2001) containing information about number of Primary schools, Middle schools in each of the village, number of teachers and students e.t.c. in hardcopy from offices of District Commissioner of education, Ranchi and in softcopy from office of Bihar Education Project, Ratu block HQ, Ranchi.
- Health related data of year 2001 containing information about number of PHC, PHSC, APHC, and Referral hospital e.t.c. from Civil Surgeon Office, Ranchi. Malaria related data from State malaria Office, Jharkhand, Ranchi.
- Editing the non-spatial data attributes.
- Transformation of all data obtained in a suitable standard digital form i.e. dbase files such that it is compatible.
- Design and Organisation
- All the non-spatial data obtained from different sources are organised following a district-Block-Village hierarchy using MS-Excel software.
Fig 6: Village map of Torpa block along with Education facilities & range of number of students
Linkage of Spatial and Non-Spatial Data
GIS allows the linkage of spatial and non-spatial data based upon a defined relationship. A one to one relationship can be defined for each of the spatial entity with the non-spatial data. For performing the linkage operation the following steps were done:
- Identification of relation between spatial and non-spatial data such as one-to-one relationship between each of the village entity with non-spatial data for them.
- Selection of key field as linkage item which may be Census-code, polygon – id obtained after topology creation in Arc/Info or self-generated code.
- Linkage has been done in Arc View 3.2a environment of the Arc/Info coverages, as they are easily accessible through ArcVeiw by performing Join operation.
Method of Analysis
In a GIS based system any analysis can be done and its output can be shown in a much better way only by integrating non-spatial data with spatial data. The core of all analysis is:
- A combination of different parameter whether spatial or non-spatial.
- Derivation of mathematical indices from non-spatial data and representing them spatially.
In present case our main emphasis was to derive such indices which reflects the level of infrastructural development of each village, identifies the gaps so as to be taken up for development considering two basic amenities education and health.
Fig 7: Customised interface in ArcView
Simultaneously we also analysed whole district in the light of INTRA DISTRICT DISPARITIES, which includes –
- Population density.
- % SC/ST Population.
- % Literacy.
- % Distribution of workers.
- % Distribution of non-workers.
- % Distribution of agricultural workers.
- % Distribution of workers involved in Mining and Quarrying.
- % Distribution of Marginal worker.
- % Distribution of Trade and Commerce workers.
- % Distribution of Transport, Storage and Communication workers.
Finally considering all above mentioned parameter Village Development Index was calculated.
Derivation of EFI and MFI
EFI (Educational Facility Index) is the measure of the importance of a village from the educational facilities point of view whereas MFI (Medical Facility Index) is the measure of the availability of medical facilities in the village and thus level of importance based on medical facilities. Both EFI and MFI are calculated using weighted indexing method given below –
If Ii is the index of particular function ‘j’ of ith village , then
Ii = S Wj. Xj ——
Where Wj = weight of jth function.
Total no. of villages in district
Wj = __________
Villages having jth function.
Xj = Value or availability of jth function in ith village.
n = number of functions / facility available in ith village.
Hence EFIi = PI + MI + HI +……… —— (II)
Where P = Primary school and PI = Wp. Xp
M= Middle school
H= High school
Similarly MFI = DI + PHSCI + APHCI + …….. – (III)
Where DI = Dispensary
PHCI = Primary health center.
APHCI = Additional primary health center.
PHSCI = Primary health sub center.
Derivation of Village Development Index (VDI)
Village development index is a complex characteristic, which is not directly observable. It is only partially reflected by several variables such as % Literacy, population density e.t.c. Theoretically such a composite index can be obtained by weighted sum method same as expressed in equation (I) i.e.
nVDI = S Wi. Xi i =1
Where Xi = variables (i.e. Population density, % Literacy, % Distribution of workers e.t.c)
Wj = weight of ith variable
Problems faced in above-mentioned method:
- Removing the biasness of scale:
In present case the biasness of the scale was removed by dividing value of each variable of a village by its district average i.e.
Unbiased value of a variable = Original value / District average of that particular variable
- Determining the weight of the variables:
The weights of the variables were calculated using Principal Component Analysis.
Principal Component Analysis (a branch of factor analysis) is a technique designed primarily to synthesize a large number of variables into a smaller number of general components, which retain the maximum amount of descriptive ability. The mathematical formulation of the principal component analysis as developed by Hotelling (1933) is given below in a summary form-
Let X=(X1X2X3……..Xn) be a set of n vectors of standardised/Unbiased random variables having a good inter co-relations among them. The Principal Components of these n variables are such linear combinations of them, which gives the maximum variance.
Features of SDSS
SDSS plays an important role in MCE (Multi Criteria Evaluation) which is a process for combining data according to their importance in making a decision. Basically GIS provides information to fuel those part of the decision making process which are spatial in nature. The main features of our SDSS are:
- Customised interface
Several dialog boxes were created using Dialog Designer Module (A special extension of ArcView3.2a) which is helpful in creating a more effective, user friendly interface, tailored to user’s specific needs.
- Data updating provisions
Provisions were made for updatation of both spatial & non-spatial data so as to have the latest data for further analysis /modeling.
- Hotlinked views & images
Hot links were set up between different views, images, so that, for example, clicking on a block in a view showing the district map with block boundaries, can automatically display another view showing detailed information about that particular block.
- Query Cells
Query cells were generated based on precise query expressions which will provide the administrator/planner necessary information by clicking only few buttons. These query expressions can be grouped into several classes such as simple query expression, compound query expression, etc. In simple query expression, only one mathematical (=, +, -…) or relational operator (,