DDDIIISSSSSSEEERRRTTTAAATTTIIIOOONNN PPPAAAPPPEEERRR OOONNN::: “““ MMMAAALLLAAARRRIIIAAA EEEPPPIIIDDDEEEMMMIIIOOOLLLOOOGGGYYY OOONNN JALPAIGURI DISTRICT APPLYING REMOTE SENSING & GEOGRAPHIC INFORMATION SYSTEM ”.
SUBMITTER BY: NAME: SANTANU DUTTA ROLL NO-16 SESION:2005 -06 SUPERVISED BY: DR.SUSHMA ROHATGI CENTRE FOR REMOTE SENSING APPLICATION,N.B.U CONTENTS:
CHAPTER NO NAME PAGE NO.
LIST OF MAPS 1.(i) LIST OF TABLES 1.(ii) ACKNOWLEDGEMENT 2 PREFACE 3-4 CHAPTER I INTRODUCTION 5-11
CHAPTER II REMOTE SENSING & GIS- A VISIONARY TOOL IN MALARIA EPIDEMIOLOGY 12-19
CHAPTER III THE DISEASE: MALARIA & IT’S HISTORY 20-37
CHAPTER IV GEO ENVIRONMENTAL STATUS OF JALPAIGURI 38-45 DISTRICT.
CHAPTER V DISTRIBUTIONAL ASPECT OF MALARIA 46-49
CHAPTER VI SPATIAL DISTRIBUTION & TREND OF MALARIA IN JALPAIGURI DISTRICT 50-66
CHAPTER VII NATIONAL MALARIA CONTROL PROGRAMME 67-71
CHAPTER VIII MALARIA CONTROLM ACTION PLAN IN 72-88 JALPAIGURI DISTRICT.
CONCLUSION
REFERRANCE
APPENDIX
LIST OF MAPS: -
1. LOCATION MAP.
2. BLOCK MAP OF JALPAIGURI DISTRICT.
3. HEALTH STATUS OF JALPAIGURI DISTRICT.
4. LAND USE MAP OF JALPAIGURI DISTRICT.
5. SETTLEMENT MAP OF JALPAIGURI DISTRICT.
6. MALARIA POSITIVE RATE IN THREE YEARS IN JALPAIGURI DISTRICT.
7. IRRIGATION & HYDROLOGY MAP OF JALPAIGURI DISTRICT.
8. REGIONWISE A.P.I MAP OF JALPAIGURI DISTRICT.
9. POPULATION MAP OF JALPAIGURI DISTRICT.
10. YEARWISE ANNUAL PARASITIC INCIDENCE MAP OF JALPAIGURI DISTRICT.
11. SPECIESWISE MALARIA POSITIVE RATE IN JALPAIGURI DISTRICT.
LIST OF TABLES:
1. REMOTE SENSING APPLICATIONS FOR MALARIA SURVEILLANCE
2. DEGREE OF ENDEMICITY & SPLEEN RATE
3. TREND OF MALARIA CASE & DEATHS IN INDIA.
4. BLOCKWISE & YEARWISE MALARIA POSITIVE CASES IN DIFFERENT BLOCKS OF JALPAIGURI DISTRICT.
5. REGIOWISE ANNUAL PARASITIC INCIDENCE.
6. IDENTIFICATION OF HIGH RISK AREAS.
7. AGE & SEXWISE DEATH REPORT OF MALARIA IN JALPAIGURI.
8. EXAMPLE OF CORRELATION TABLE.
ACKNOWLEDGEMENT:
The format of the project has been designed to cover such important areas operating to malaria as its scientific aspects, history, global position, position in India, & position in West Bengal with special reference to JALPAIGURI.malaria control & eradication programmes. On the basis of the study, the researcher has recorded their findings & ultimately attempted to suggest remedial measures & make maps of malaria epidemiology to control the malaria in Jalpaiguri, West Bengal.
I was inspired to go through a book by SAROJINI PACHOLI, “MEDICAL GEOGRAPHY OF MALARIA IN MADHYA PRADESH”. It occurred to me that new vistas of enquiry might be found with respect to the health pf people even in the field of my own subject of specialization-ZOOLOGY. My curiosity found further, when I saw huge amount of deaths due to malaria in Jalpaiguri district in last few months. Then as a student of M.PHIL in REMOTE SENSING & GIS, I talked to my head of the department & Joint director & Academic coordinator DR.SUSHMA ROHATGI. This further encouraged me to request her to take the responsibility of supervising my project work, which she very kindly agreed to. Without her constant encouragement & guidance at all levels this work would not have seen the light of the day. I have no words to appropriately express my sense of gratitude to DR.SUSHMA ROHATGI.
In connection with my data collection campaign I had the good support of meeting Deputy Director Of Health Sastha Bhavan & Malaria Inspector Of Sastha Bhavan.
I am very obliged to Dr.Bishwajit Roy, ACMOH of Jalpaiguri Hospital, without his help, it was impossible to complete the work.
I am also grateful to CMOH of Jalpaiguri hospital. His inspiration gave a life to work in this field.
So, at last again I offer my sincere thanks to one & all.
PREFACE Malaria is endemic in India. It has made people suffer for centuries and has claimed millions of life. The discovery of malaria parasite, nearly a century ago in Algiers by Lavarian; relationship of malaria parasite, Anopheline mosquito and man was found out in the last year of nineteenth century by Ronald Ross in India. Ever since the unending battle between man & is on. In 1939 after the insectisidal properties of DDT was discovered by Paul Muller, a new strategy began between the two. Venezuella was the first country to launch an eradication programme against malaria in 1945.In 1955, the Eighth World Health Assembly recommended the eradication of malaria as an international objective, following the reported development of resistance by the vector to the insecticides in many countries. The credit of co-ordinating the global campaign against malaria goes to the World Health Organization.
In India the fight against malaria was started in 1953 with the National Malaria Control Programme & before the vectors could develop resistance, the shift to the National Malaria Eradication was undertaken in 1958. Out of the 390 units, 250 units were declared free from this disease by 1966. Due to this phenomenal success it was thought that the country has nearly eradicated the disease. But as the efforts in this field slackened down the whole fortification turned turtle, of course due to various disease. The reappearance of malaria in many areas is bewildering the experts. The same is the case of West Bengal; the area of research of the present work is Jalpaiguri district. Jalpaiguri, the name is derived for Jalpai olive trees, which once abounded the town of Jalpaiguri. It is situated between 26º16’ & 27º0’ in the northern hemisphere. The easternmost extremity of the district is marked by 88º25'.The chief town & the administrative headquarters of the district & also of the Jalpaiguri division, is Jalpaiguri, situated on the west or the right bank of the Tista river in 26º32' north & 88º43' east. It contains total 2905.64 square miles area & a total 4108,048 souls population.
Jalpaiguri has undergone several administrative changes in its boundary & area in the past. The present status of the Jalpaiguri is the result of reorganization of boundary commissioner of Bengal in 1876.It contains total 13 blocks, of which the main focus of the research work is Alipurduar 1, Alipurduar 2 & Kalchini & few tea gardens in Alipurduar, which are malaria prone area.
The vicious circle of disease- low income, bushy area, poor health services, more disease & more poverty not only poses a problem of health & sanitation but also the welfare of the society at large. In order to avoid such a situation it seems pertinent to attempt an interdisciplinary study, which may help in safe guarding the health of the people.
Jalpaiguri is one of those districts of West Bengal where occurrence & recurrence of malaria has been reported on a large scale. This calls for serious diagnostic study. Since physical factors do contribute to the incidence of malaria & there may be various patterns of the prevalence of disease.
Chapter wise Description:
CHAPTER 1 deals with INTRODUCTION
CHAPTER 2 deals with – REMOTE SENSING & GIS-A VISIONARY TOOL IN MALARIA EPIDEMIOLOGY.
CHAPTER 3 deals with – THE DISEASE: MALARIA & IT’S HISTORY.
CHAPTER 4 deals with – GEOENVIRONMENTAL STATUS OF JALPAIGURI DISTRICT.
CHAPTER 5 deals with – DISTRIBUTIONAL ASPECT OF MALARIA.
CHAPTER 6 deals with – SPATIAL DISTRIBUTION & TREND OF MALARIA IN JALPAIGURI,WEST BENGAL.
CHAPTER 7 deals with – NATIONAL MALARIA CONTROL PROGRAMME. CHAPTER 8 deals with – MALARIA CONTROL ACTION PLAN IN JALPAIGURI DISTRICT.
CHAPTER 1- INTRODUCTION
(i)Problem:
Malaria history is not a new phenomenon of India. Some of the earliest references to this fever occur in the Atharva Veda believed to have been composed about 1500 B.C. Vndyke Carter & others verified the discovery of malarial parasite by Laveran in 1881 quickly in India, but it is not till later that minds of research workers began to be directed towards the association of the insects with disease. In India the fight against malaria was started in 1953 with the National Malaria Eradication Programme & before the vectors could develop resistance, the shift to the National Malaria Eradication was undertaken in 1958, Again that was converted into National Vector Borne Disease Control Programme. Jalpaiguri district of West Bengal is basically a malaria prone zone. The geographical status of this district ids mainly a causative phenomenon of this disease.
The GIS database used for relationship of health condition with land use and other spatial and non-spatial data of the district, which show positive relationship with poor health condition and tea garden belts than other part ofdistrict.
The primary causes of the poor health are – (a) Poor drinking facility due to the contamination of ground and surface water and waste management of tea garden belts because do not have concert platform of tube well and deep tube well. (b)Land use pattern, geomorphology, hydrology condition and drainage networks all are directly or indirectly related with marshy land which are more favorable condition for malaria disease. The secondary cause of the poor health condition –
(a)Literacy Literacy is one of the prime factors of human interference hazard and creating awareness. Health and literacy both are complementary step for better future. But unfortunately literacy rate is very poor in the tea garden area compare to the district. In recent census literacy rate is 63.62% in whole district where as 37.48% in tea garden area. Illiterate people do not know how to combat with the hazard.
(b)Lack of awareness Being a backward district Jalpaiguri is confronted with a very major social problem which is a great concern for the programmed of eradication water- borne diseases like malaria, The entire problem is alarming in this region due to lack of awareness about their health. The rural families, majority of whom are living under the poverty line, they are aware about their sanitation
(c) Poverty Poverty of the Tea Garden belt area: More than 70% of the total populations in Tea garden area are working as daily-labourer. The percentage of Below Poverty Line (BPL) families have decreased from 62.01(1997) to 59.53 (2002) in all over the district. But the percentage of BPL families in Tea Garden area is 67.07%. Tea garden labourer gets minimum wage in relation to labourers of other spheres. The feudalistic character of tea garden management still treats them as their bonded labors. For this reason huge amount of deaths are reported from this district during last four months. In order to avoid such a situation it seems pertinent to attempt an interdisciplinary study, which may help in safe guarding the health of the people.
(ii) Review of literature:
(Malaria incidence & vector density in relation to climetogical factors in Western Doon Valley, Uttaranchal). A study on malaria incidence & vector density in relation to climetogical factors in Western Doon Valley in Uttaranchal, conducted by R.K.Mahesh & R.K.Jahuri during January 1999 to December 2002 revealed considerably high infection of P.vivax. The study was done on the basis of 2402 Anopheline mosquito specimens’ comprosing 10 different species of which A.stephensi recorded most no & then A.nigerrimus. During 1999 to December 2002 total 374 A.stephensi were collected & then 341 A.nigerrimus were collected.During these 6 years less amount of A.vagus were collected, which was only 46. But in case of yearwise analysis A.fluviatilis were mostly collected in the year 1999, A.subpictus were mostly collected in the year 2000. In the year 2001 & 2002,mostly dominent species were respectively A.nigerrimus & A.stephensi. According to the epidemiological data analysis it is clearly evident that July, August & September is the most peak period of malaria positive cases. Due to huge rainfall, July, August & September is the peak period of different Anophelines.
Basically according to the report of Singh(1984), A.culcifacies recorded most number in the month of September due to high incidence of Plasmodium vector during June to October.
On the other hand according to the report of Hati, Chatterjee & Biswas (1992), A.stephensi recorded most in the beginning of the monsoon & decreased after September & in the Winter & Summer few specimens were recorded in Calcutta. So it is clear that raise in density of species the number of malaria cases also increase.
So conclusively it can be interpreted that malaria incidence is totally depend on temperature & rainfall. This climatologically variation influence the breeding status of mosquito population. Plasmodium vivax recorded most from Jan-May & Plasmodium falciperum recorded most from April to June. So temperature is the key factor of the mosquito transmission.
(iii)Objectives:
The main objective of this work is Malaria endemicity zone mapping.of JALPAIGURI DISTRICT. This work will help to manipulate the area & interprets the affected area in in future.
(iv)Study area:
Jalpaiguri a district of the combined Rajshahi Kuch Bihar commissionership or division, is situated between 26 16’ & 27 in the northern hemisphere. He easternmost extremity of the district is marked by 88 25’. The chief town & the administrative headquarters of the district & also of the Jalpaiguri division, is Jalpaiguri, situated on the west or the right bank of the Tista river in 26 32’ north & 88 east. It contains total 2905.64 square miles area & total 4108,048 souls populations.
This district contains 13 blocks, namely, Sadar, Rajganj, Maynaguri, Dhupguri, Mal, Metali, Nagrakata, Falakata, Madarihat, Klachini, APD I, APDII & Kumargram. There are total 150 tea gardens in Jalpaiguri district. Topography of this district and its environs is characterized by uneven elevation of this region varies form 62m to 350m. The altitude falls from 350m to above mean sea level at the foot of the Himalayas to 150m above mean sea level over a distance of 25km and then falls to about 60m above mean sea level over a distance 110 km further south. The climate of the area is characterized by a sub-topical & humid, the maximum, minimum temperature 370 C and 60C respectively. The average annual rainfall of this district is 3736mm.The storm rainfall is of hydro-meteorological significance causing inundation and flood of the area.
(v)Data source:
Data’s are collected from various sources namely:
(a) The Deputy Chief Medical Officer Of Health, Jalpaiguri. (b) The Chief Medical Officer Of Health, Jalpaiguri. (c) Sastha Bhavan, Kolkata. (d) Website Of National Vector Borne Disease Control Programme .
(vi)Methodology:
Jalpaiguri is one of those districts where malaria has claimed millions of lives during the course of history & the study of the disease in its geographical perspective appeared tempting enough. In fact, following methodologies have been adopted for the study of malaria epidemiology in Jalpaiguri.
(1) Remote Sensing (2) Geographic Information System. (3) Cartograms
LOCATION MAP
1)REMOTE SENSING:
Remote sensing is the science & art of obtaining information about an object, area or phenomenon through the analysis of data acquired by a device that is not in contact with the object, area or phenomenon under investigation.
This is a broad definition, but we generally use this term for observing our earth’s surface from space using satellites or from the air using aircraft, which have been modified suitably.
The elements of the data acquisition process are energy sources.
(A)Propagation of energy through the atmosphere. (B)Energy interactions with earth surface features. ©Retransmission of energy through the atmosphere. (D)Airborne/space borne sensors.
(E)Resulting in the generation of sensor data in pictorial or digital form. (F)In short, sensors record variations in the way earth surface features reflect & emit electromagnetic energy. (G)The data analysis process involves examining the data using various viewing & interpretation devices to analyze pictorial data & a computer to analyze digital sensor data. (H)Reference data about the resources being studied (such as soil maps, crop statistics,) are used when & where available to assist in the data analysis. (I)With the aid of reference data, the type, extent, location & condition of the various resources are extracted, which the sensor data were collected. (J)Then this information’s are compiled in the form of hardcopy maps & tables or as computer files that can be merged with other layers of information in a Geographic Information System (GIS).
Few Remote Sensing software’s have been used to construct the land use land cover maps. EASI PACE is one of the most important Remote Sensing software’s. Digital images can be processed with this software. Digital Image Processing involves the manipulation & interpretation of the digital images with the aid of a computer.
Total procedure has few steps: Image rectification & restoration. Image enhancement. Image classification. Data merging & GIS interpretation. Hyperspectral image analysis. Biophysical analysis. Image transmission & compression.
(2) Geographic Information System (GIS)
A system for input, storage, manipulation & output of geographic information. A class of software. A practical instance of a GIS combines software with hardware data, a user, etc, to solve a problem, support a decision, help in planning.
A geographic Information System is a computer-based tool for mapping & analyzing geographic phenomenon that exist, & events that occur, on earth. GIS technology integrates common database operations such as query & statistical analysis with the unique visualization & geographic analysis benefits offered by maps.
A GIS has four main functional subsystems, these are:
A data input subsystem. A data storage & retrieval subsystem. A data manipulation & analysis subsystem. A data output & display subsystem.
So, a common accepted definition of a GIS is “a system of hardware, software, data, people, organizations & institutional arrangements for collecting, storing, analyzing, & disseminating information about areas of the earth.”
(3) CARTOGRAMES:
Charts & Diagrams are effective devices for vivid presentation of statistical datas. The main objective of diagrammatic representation is emphasis the relative position of different subdivisions & not simply to record details. The charts & diagrams are used here:
(a)LINE DIAGRAM:
Line diagram is most effective method to representing statistical data, specially used where data are shown in according with the time of occurrence.
(B)BAR DIAGRAM:
Bar diagram consists of group of equisaped rectangular bars, one for each category of given statistical data. Bars starting from a common base line must be of equal width & their lengths represent the value of statistical data.
(4) SOFTWARES:
There are several software’s, which have been used here: 1. ARC VIEW. 2. SPANS. 3. MAP INFO.
CHAPTER 2 -
RS & GIS – A visionary tool in Malaria Epidemiology
Malaria is a major public health problem in India. Nearly 2-3 million cases occur every year with about 1000 deaths in India. Control of malaria requires case detection; treatments of affected individuals, & for curtailment of malaria transmission, control of malaria vectors are undertaken. Vector control requires knowledge of the ecology of breeding & resting habitats & behavior of various specious of mosquitoes. Periodical surveys are essential for arriving of any conclusion for developing vector control strategy. Routine entomological surveys over vast geographic areas are impractical, time consuming & expensive & therefore are confined to limited areas.
(I) REMOTE SENSING: Remote sensing is a tool for the surveillance of habitats, densities of vector species & even prediction of the incidence of the diseases, has opened up new vistas in the epidemology of the malaria & other vector borne disease. Several characteristics of remote sensing data help to study environmental process. Imagery acquired from aircraft or satellite platforms provides a synoptic view of earth’s surface. Sensors can be calibrated to record in spectral region beyond those to which the human eye is sensitive. Data are commonly available in digital format for computer analysis & integration with other digital databases. They provide a historical record of conditions for a particular area or region.
The digital image generated by multispectral scanner system is actually a two dimensional array of discrete picture elements or pixels. The sensors for each array are calibrated to record reflected or emitted energy in a specific spectral region. Individual pixels in this array may be from a few meters to several kilometers. The DN value of each pixel, which corresponds to photographic gray level, represents the average reflectance over the ground area being measured. The interpretation of enhanced multispectral imagery or aerial photography that has been photographically processed, involves the visual identification of objects & the determination of their meaning or significance based on colour, tone, texture, shape, shadow, pattern, location & association.
(I.i) Detection of habitats of mosquito vectors: In India, a feasibility study using multidate IRS 1A & 1B satellite data was undertaken in collaboration with the Indian Space Research Organization in & around Delhi by selecting six states including West Bengal with different ecological features.
It was found that false colour composite images can help in the development of base maps of the study area & macro stratification of mosquitogenic conditions is possible. The limitation of satellite resolution (36.5m) was felt, as the smaller habitats of anopheles mosquitoes were not detectable. Correlation of changes in the area of land use features; water bodies &vegetation with mosquito density was found significant in some places. Based on IRS LISS II data, mosquito larval production can be estimated.
(I.ii) Monitoring of environmental parameters affecting populations of mosquito vectors:
The role of environmental factors; temperature, rainfall & relative humidity in the epidemology of vector- borne diseases is well known. Meteorological data obtained from different places are not uniform & therefore limit their use for modeling of diseases, The advanced high resolution radiometer (AVHRR) sensor, on polar orbiting meteorological satellites of National Oceanic & Atmospheric Administration (NOAA) & Meteostat satellite provide data about rainfall (based on cold cloud duration), vegetation state (NDVI), land surface temperature & soil & vegetation moisture contents. The Normalized Difference Vegetation Index (NDVI) is a reliable indicator of rainfall. The NDVI values are obtained in the range of –1.0 to 1.0. The values of 0 to 0.2 indicate bare soils (with scanty vegetation), 0.2 to 0.7 different categories of green vegetation & negative value indicates the presence of water. The NDVI data are usually composited fortnightly or monthly. High NDVI values generally corresponded with high rainfall. The ability to foresee flooding of mosquito habitats by remote sensing was found to have importance bearing on developing strategies for mosquito control & disease prevention.
(I.iii) Future Perspective :
The foregoing account indicates that RS technology has provided a tool for mapping the breeding habitats of anopheline mosquitoes, prediction of densities of vector species & even development of risk maps of malaria. The purpose of RS is not to detect the mosquitoes, but the indirect parameters of their ecology & behavior, which help in thriving of vector species. Remote sensing is likely to become a rapid epidemiological tool for surveillance of vector borne diseases & malaria in particular. sA number of studies have established the potential of remote sensing for detection of malaria. Studies on the use of remote data to identify mosquito- breeding sites have also been carried out. The findings of the studies cited on the following table illustrate hoe geospatial technologies can provide scientists with a new perspective with which to study the factors influencing the patterns of malaria at a variety of landscapes levels.
Remote Sensing Applications For Malaria Surveillance
Remote sensing Parameter Disease Reference data used derived from remote sensing data Colour infrared Land & water Mosquito Welch et.al aerial photography cover TM simulator/ Water & Mosquito larvae Wood et.al Landsat TM vegetation cover Landsat TM Land cover Malaria/ Mosquito Beck et.al
SPOT HRV Vegetation Malaria/Mosquito Roberts et.al amount
AVHRR Vegetation Malaria/Mosquito Hay et.al NDVI,(8km) amount Temperature,(MIR) AVHRR NDVI (1.1 Vegetation Malaria/ Mosquito Thomson km) amount et.al IRS 1A & 1B Vegetation/ Malaria/ Mosquito Sharma et.al Ecotype IRS 1D Eco- Malaria/Mosquito Srivastava epidemiological et.al classes
(II) GEOGRAPHIC INFORMATION SYSTEM
Many misconceptions exist as to the meaning of the term GIS, particularly in disciplines where the use of such technology has not been firmly established. The phrase “Geographic Information System” was first used in the 1960s to refer to a computerized system for asking questions of maps showing current & potential land use. A GIScan be defined as a ‘set of tools for collecting, storing, retrieving, transforming, displaying spatial data from the real world for a particular set of purposes’. A typical GIS comprises an organized collection of computer hardware, software, geographic data, & personnel data, designed to efficiently capture, store, analyze, & display all forms of geographically referenced information. Each piece of information is related in the system through specific geographic coordinates to a geographical entity (e.g. health center, school, dam, drainage, village or state). The information can be displayed in the forms of maps, charts, graphs, and tables. GIS adds the dimension of geographic analysis to information technology by providing an interface between data & maps. GIS has several advantages over conventional methods used in health planning, management & research.
(II.i) DATA MANAGEMENT:
GIS provides the user the ability to store, integrate, query, display & analyze data from the molecular level to that of satellite resolution through their shared spatial components obtained from diverse sources. Surveillance of diseases requires continuous & systemic collection & analysis of data. GIS can eliminate the duplication of effort involved in the data collection across an organization, & hence substantially reduce the cost involved. It can also serve as a common platform for convergence of multi- disease surveillance activities.
Global Positioning System (GPS) can be used to obtain the location of point features on a map such as wells or septic tanks, precisely. GIS can process aerial & satellite imageries to allow information such as temperature, soil types & land use to be easily integrated & spatial correlations between potential risk factors & the occurrence of disease to be determined. Latest & accurate maps are essential for epidemiological surveillance. GPS, high- resolution satellite imageries & aerial photographs can be used to obtain accurate & up to date maps of a region. Multitemporal satellite imageries can be used to monitor land use & land cover changes over time.
(II.ii) VISUALISATION:
GIS offers powerful tools to present spatial information to the level of individual occurrence, conduct predictive modeling. It determines geographical distribution & variation of disease, & their prevalence & incidence. In studying the surveillance of malaria in India, in studying the surveillance of malaria in India, it is important to find out which type of malaria is occurring. Such studies have important implications for the disease eradication strategy to be employed. GIS can help to generate thematic- maps, ranged colour maps or proportional symbol maps to denote the intensity of a disease. In comparison with tables & charts, maps developed using GIS are more effective means for communicating messages clearly even to those who are not familiar with the technology. GIS allows policy makers to easily understand & visualize the problems in relation to the resources & effectively target resources to those communities in need. GIS permits dynamic link between databases & maps so that data updates are automatically reflected on the maps.
(II.iii) OVERLAY ANALYSIS:
GIS can overlay different pieces of information. This helps in decision- making & medical research through multicriteria modeling. It helps to understanding the association between prevalence of certain diseases & specific geographic features.
(II.iv) BUFFER ANALYSIS:
GIS can create buffer zones around selected features. A radius of 10 km around a hospital to depict its catchments area or 1 km around an effluent discharge site or 50 met on both sides of sewerage to indicate the spread of hazardous material. The user can specify the buffer size & then combine this information with disease incidence data to determine the number of cases fall within the buffer.
(II.v) NETWORK ANALYSIS:
GIS provides the ability to quickly access the geodemographic dynamics of an organizations existing service area in contrast to the likely demand for services at a new location. It can identify catchments areas of health centers & also locate suitable site for a new health facility. GIS provides accurate & timely information about where health services are located, & instructions & maps on how to get there.
(II.vi) STATISTICAL ANALYSIS:
GIS can carry out specific calculations, such as proportion of population falling within a certain radius of a health center. It also calculates distances & areas for example distance of a community to a health center, & area covered by a particular health programme.
(II.vii) QUERY:
GIS allows interactive queries to extract information contained within the map, table or graph. It can answer queries of location, condition, trends, spatial patterns & modeling.
(II.viii) EXTRAPOLATION:
GIS provides a range of extrapolation techniques, for example, vector distribution in inaccessible & non-sampled areas can be mapped using GIS.
(II.ix) WEB GIS:
One of the recent advancements in GIS technology is web based GIS, health data is stored in central server, which can be accessed from various terminals connected to the server through internet or intranet. Dynamic web published on the web allow continuous monitoring for effective health interventions.
(II.x) Potential applications of GIS in public health:
GIS is gradually being accepted & used by public health administrators & professionals, including policy makers, statisticians, epidemiologists, regional & district medical officers. Some of its potential applications in public health are listed below: