Mohammad Reza Mobasheri
Assistant Professor
Faculty of Geodesy & Geomatics Engineering
K. N. Toosi University of Technology (KNTU)

Mohammad javad Valadan Zoej
Associate Professor
Faculty of Geodesy & Geomatics Engineering
K. N. Toosi University of Technology (KNTU)

Yousef Rezaei
Phd Student of Remote Sensing
Faculty of Geodesy & Geomatics Engineering
K. N. Toosi University of Technology (KNTU)

Abstract:
Malaria has been found in the vast areas in different regions of the world. Particularly many people in the tropical and subtropical regions suffer from this disease. 40 percent of the earth’s population lives in zones where malaria exists. In Iran, Malaria is one of the main public health concerns mostly in south and southeast regions of the country. Malaria outbreak is profoundly correlates with the environmental and climatic conditions of a region. Due to the vastness of the potential area, Remote Sensing is a useful tool for detection of the conditions appropriate for malaria outbreaks and consequently helping managing it. This could be done through estimation of environmental information and climate parameters using satellite imageries. Thus, it can be used for organizing a controlling system for malaria outbreaks.

In this study, a methodology is suggested in which at the first step, based on the biology of the insect, the minimum requirements of the environmental and climatological parameters for the incidence of this phenomenon will be determined. This study showed that some parameters such as air temperature, relative humidity, vegetation cover and lagoons and basins are the most influential parameters in creation of potential for of epidemy outbreaks.

In the modeling section, different methods in extraction of environmental parameter were thoroughly studied. Comparison of different methods leaded to identification of the most appropriate strategy for each parameter extraction using Landsat images. Then, high risk regions were located for each parameter.

In the next step, the selected regions were imported in to a GIS (Geographical Information System) environment as independent layers. Weighted overlay method was implemented and finally, high risk regions were determined. Also, for model evaluation, some ground truth data has been collected and the work has so far shown good applicability.

Keywords: Hygiene, Malaria Outbreak, Remote sensing, Geographical Information System.

1.Introduction
Malaria disease could be found in the evasive regions of the world. Furthermore, many people of the various parts of the world live in the high risk regions (Fig 1). Every year, numerous amounts of people are being infected and some even die due to this disease. This disease can be spread out by variety of Anopheles insects each in an appropriate natural condition. Despite of many researches in malaria, this disease is still one of the main threats for global health and still there are many unsolved problems in this regards. Nowadays, it has been understood that the most important way to fight against this disease is controlling it by prediction of its outbreaks. In this research we tried to find an answer to this fundamental question by deployment of remote sensing technology as well as auxiliary environmental data (weather parameters).

Malaria was endemic in most parts of Iran around 100 years ago based on the periodical reports from Iran to WHO/EMRO1. As the result of extensive malaria control programs in the last 5 decades, the malaria incidence rate has dropped dramatically. However, malaria is still one of the most common parasitic diseases in Iran and one of the main public health concerns in the southeast of the country that is Sistan and Baloochestan, Hormozgan and southern parts of Kerman provinces [1].

In this research it is tried to review the application of RS and GIS techniques in identification of the regions with the potential of malaria outbreak. The RS data can help in identifying the relation between the environmental condition (climate) and the outbreak of malaria. Based on this knowledge one can buildup a controlling system that is able to forecast the probability of the outbreak of the disease through determination of suitable environmental condition. Images of 7ETM+ sensors are used for the southeast regions of Iran including the cities of Kahnooj and Minab (Fig 2). Also some ground truth in the year 2003 would be used for model evaluation.

Fig 1.The Distribution of Malaria In The World (WHO/TDR 2003) [7]

Fig 2. The Study Area In Iran

2. Effects of Environmental Parameters on Malaria

Climatic conditions directly influence mosquito and parasite development and the duration of the incidence of the disease [2]. Parameters like air temperature, relative humidity, vegetation cover and basin existence are the most influential parameters on epidemy outbreaks.

2.1. Air Temperature
Temperature is important because it governs the rate at which mosquitoes develop into adults, how frequently they need blood feed (and, therefore, acquire parasites) and the incubation time of parasites in the mosquito [3]. Also Temperature has an effect on the survival rate of adult mosquitoes. Considering this, the suitable temperature limit for incidence of Malaria is between 20-35 degrees centigrade[3].

2.2. Relative Humidity
Humidity is one of the factors that have a direct effect on the survival of the mosquitoes [4]. In other words, suitable humidity is one of the major factors for developing anopheles. Different species need different degrees of humidity. If the average relative humidity per month is below 55% and above 80%, the life duration of mosquitoes will be decreased and thus the amount of malaria incidence reduces. The amount of optimized humidity is between 60-65%.

2.3. Vegetation Cover
Vegetation cover has an important but indirect role on the abundance of malaria [5]. Various vegetation covers and the density and species regarding the kind of anopheles can be a good resting place for transfer of the disease. It almost can be said that all vegetation cover are suitable for anopheles.

2.4. Basins existence

Breeding and early prevalence of anopheles as larva is done in water basins and lagoons [6]. Since the fly range of mosquitoes is limited and breeding should be done in water, then the abundance of mosquitoes can be found around the places where there are patches of stilled waters. The dams built by humans, watering plans and developing agricultural projects can produce patches of stilled water and as a result changing ecosystem which in turn can cause the increase in abundance of mosquitoes.

3. Ways of acquiring parameters

In this section, the possible ways of the estimation of different parameters was studied comprehensively. A comparison of different methods leads us to identify the most appropriate strategies for each section. Timeline comparison for Existing process Vs Automated process

3.1. Air Temperature

To recognize the high risk regions in the terms of air temperature, the SEBAL method was chosen [8]. In this method, surface temperature was calculated in the first step and after that, air temperature was derived from the surface temperature. The following formula is used for computing surface temperature.

3.2. Relative Humidity

In the next step and regards to obtain the high risk regions due to relative humidity, a combination of air temperature image and meteorological parameters was utilized. The following formula is used for acquiring relative humidity [9]:

Where e and s e are water vapor pressure and saturated water vapor pressure respectively. The water vapor pressure values are provided by synoptic weather stations in study area. Also, the saturated water vapor pressure can be calculated through traditional equations using air temperature image which calculated in the previous step.

3.3. Vegetation Cover and Water Basins Detection

In the next step and regards to determine the vegetation cover and localize the water basins, NDVI index was used. The structure of this index is as follow [10]:

Where RED is reflectance in red band and NIR is reflectance in near infrared band. The limit between 0.2 to 1 displays the vegetation cover and the negative amounts stands for water surfaces. Also for acquiring better result a combination of NDVI and EVI can be used. The structure of EVI index is as follow [11]:

Where RED is reflectance in red band, BLUE is reflectance in blue band and NIR is reflectance in near infrared band.

4. Results

All of the previous steps were modeled in ENVI software. After calculating the parameters, thresholding method was used and the suitable limits of each parameter for incidence of Malaria in the study area were determined as information layers (Fig 3).

Fig 3. a.The suitable temperature limit (20-35 °C) in study area.b.The suitable relative humidity limit (55%-80%) in study area.c.Vegetation cover in study area.d.Water basins and wet surfaces in study area

In the next step, the selected regions were imported to a GIS work station as independent layers. Weighted overlay was implemented. Then at the last step the final high risk regions was recognized (Fig 4).

Fig 4.Malaria incidence risk map in study area

After recognizing the high risk regions, the statistical results of the suffered area were compared to the derived raster map.

5. Conclusion

As shown in this paper, Malaria incidence depends on the environmental parameters. Air Temperature, Relative Humidity, Vegetation cover and basins are discussed as the most influential parameters. By using satellite data, these parameters could be extracted, assessed and by GIS’s analysis, the high risk regions could be recognized. Therefore, a comprehensive model could be developed to produce an output to recognize the high risk regions of malaria incidence. As discussed in this paper, due to 15 degree centigrade temperature difference and 25 percent relative humidity difference, the uncertainties due to the remote sensing technique could be considered acceptable.

Prof Eng Mojmir Kyselka
Faculty of Architecture, VUT Brno, Czech republic

Dr Peter Spièa
Associate Prof, Arch. Dept. Faculty of Civil Slovak Technical University

The Cities are like trees; both of them grow under natural limits. These limits affect in the formulation of a city’s master plan. The historical urban development of cities is usually used for defining the main direction of a city’s development. One of the objectives of any master plan is to guide urban development by studing the natural properties of the city borders and to determine a suitable direction of city growth. (Antar Korain 1997, P. 153). Rodgers has suggested that the second part of a master plan must be a historical background of the city, aiming to define the effective factors in urban development. (A K Alam, 1983). The historical background should include general information for understanding the effective factors on the city’s form. (Antar Korain 1997, P. 157). The need for GIS is driven by factors such as population growth and urbanisation, which in turn create various types of geo-referenced data. Information of this kind lends itself well to the analytical capabilities of GIS. (Henk J. Scholten and John C. H. Stillwell, 1990, P. 30). GIS has the ability to create, store, edit, visualise, analyse, and present the data which is needed for carrying out the historical and future studies of the urban growth of the city.

Objective
This paper aims at supporting the master plan of Kharga City by defining the main direction of growth of Kharga City by analysing its old and recent historical urban growth.

The old historical growth
The oases area, in Egypt’s western desert, has played a significant role over various ages of the old Egyptian history. Kharga’s long history and ancient civilisation is described in several monuments going back to many ages starting from B.C. until the Islamic age.

The 2nd dynasty (5000 B.C.) had registered in Gabal El-Tayer monuments that Kharga was famous for its agricultural activity.

In the Pharaonic era Kharga oasis was called a “plough “,the Dakhla oasis was termed as “southern oasis”, while Farafra oasis was named a “cow”, which clearly speaks of welfare-as such areas were fully cultivated. Kharga and Dakhla oases, in the Pharaonic era, were one region affiliated to “Thani” region near Suhag serving the as vital defense front-line of Egypt to stand against any offensive either from the west or the south. The 26th Pharaonic dynasty(650:565B.C.) constructed Hebas temple in Kharga whereas the 27th dynasty (since 522 B.C.) built the Ghewata temple.

Persian Qambiz invaded Egypt in the 6th Century B.C.,but the King the first Dara had finished the engraving of Hebas and Ghewata temples which were like castles, overlooking any invaders coming through the Darb AL Arbaen.

In the Greek-Roman era, agriculture had developed.The Romans had dug wells in fertile lands which were known as “Roman eyes”. The Romans also had created in architectural field, during Antonus’s reign at the beginning of the 2nd century many monuments that had been built such as the AL-Nadoura temple, EL-Dear fortressand the, Qaser AL-Zian temple. (Ashraf M. S. Mahrous, 1998)

In third and fourth centuries B.C., when Roman suppression towards of Christians increased, too many of Egypt’s Copts (Christians) fled to such oases, EL-Bagawat cemetery and church, Aen Mostfa AL-Kashef monastery, AL-Monera monastery, and Shams AL-Dean monastery all indicate Christian presence on Kharga at the time of advent of Islam to Egypt.

Fig. (1): Development curve of land uses areas

Since the Islam’s entry into Egypt life started to become stable and safe.Simplicity of life was the main feature of the oases, which were considered separated for mainstream Egyptians life. It was named “nomad’s life” until the beginning of the domination republicans in Egypt.

The recent historical growth: -
It was clear that old history had been preserved on the walls of temples and monuments. But recent history must be preserved on computer systems in a digital form, which is the modern view. The next section explains as to how to create a comprehensive historical background and define the main direction of urban growth of the Kharga City using the GIS techniques.

Methodology
Creating base-map and database for Kharga City
The base-map has been created as a set of theme map-each one describes different land-use.The base-map consists of a total of 7322 land parcels, each of them has been joined with its attribute record in the database that contains for each parcel, tabular data such as ID-Cod, land-use activity, its area, its perimeter, number of floors, total floor area, construction state, registration number in the governat cadastre, building tax collection method, ownership, construction material, number of people in the building, and the year of construction,…. etc.

Experimental Work with ArcView GIS: -
To analyse a historical growth of a city using GIS techniques, we use the following procedures: -

Merging all themes into each other for carrying out the land-use map for the whole of the Kharga City.

“Merge themes together” operation in “Geo-processing” tool has been used to merge all land use themes. This operation appends the features of the merged themes into a single theme, the attributes are retained because the attributes of each land use theme have the same names. The output file of this operation is the all land-use theme, which has been named as “Kharga Land Use (KLU)”.

Dissolving features of land-use map
Construction time of each building is the individual indicator for knowing the historical growth of the city. Therefore, dissolving features of KLU theme based on the “Year of Construction” attribute is the operation used-a Geo-processing tool- to create the Historical Land Use (HLU) theme by carrying out some additional fields in the output tabular data file such as average, sum, minimum, maximum of each field attribute, etc.

This theme is presented by the “Graduated Color legend” type, according to the classification of the Construction Year attribute field. The range of construction field has been divided into 10 grades; each grade is presents one decade starting from 1900 to 2000, in addition to two grades presenting the constructed buildings since b.c. and also the under-construction buildings.

Fig. (2): The historical land use map (HLU) and Kharga land use (KLU)

Querying and summarizing data
The attributes data of KLU theme has been summarised for the development of Land use areas, and peoples activities shown in Fig. 1, and Fig. 3 respectively. The summarising operation depended on the Count of parcels, Sum-Area, and Sum-People in each land use activity. This operation has been repeated for each decade, selected on the basis of querying operation.
Presenting the result as charts, tables, and figures
Fig. 1 and 3 have been drawn using Microsoft Excel software with the resultant data of querying and summarising operations in ArcView GIS. Figure 2 has been derived by using ArcView GIS as a layout of achieved maps.

Fig. (3): Development curve of people’s activities in Kharga City

Result and discussion
Historical Land use development: Fig. 1 shows that according to the registration of cadastre- the largest area of total land use is used for palm agriculture and agricultural field during the twentieth century. However, these land uses had been developed only during the fourth and the seventh decades of the last century. This is a fact because in 1958 the first construction caravan had arrived to Kharga and then it was changed into the capital city of the new valley governorat. At that time the urban area of Kharga was limited to the old-Kharga site. Aen AL-Dar well was the main water source for agriculture and drinking in addition to some wells which served as water source for agriculture at Aan AL-Shakh, Aen AL-Gadida, Aen AL-Malek, Aen AL-Kalaa, AL-Berba, and AL-Khalefa. This construction caravan developed the wells and doubled the agricultural land from 2 Km2 to 4 Km2 In addition the palm land was increased from 3.8 Km2 to 4.3 Km2. Since 1970 until now, the extension development of agriculture and palm Standstill and the Kharga oasis have begun to change to city-form. The curve also shows that land-use of public services has developed more than other land uses, the reason for this also is the arrival of construction the caravan. It is quite clear -in Fig. 1- that since 1910 to 2002 the area of houses land-use is nearly equal to the area of streets land-use. In addition, the development of such houses or streets land uses is more homogenous than other land uses where the individual affecting factor has been the rate of increasing population. Fig. 1-A – an enlargement of the lower right corner of Fig. 1- shows that emergency, educational, and management land uses have experienced growth as well as the others.

As shown in Fig. 2 each color degree in the legend of historical land use map indicates one decade and each color in the legend of Kharga Land Use map shows one land use.

During 1900-1920 the Kharga oasis has been divided into two parts, (see 1 in fig.2) the first part is agricultural land beside the monuments at the northward part of the city. The second part is residential land with agricultural land in the southward segment of the city. This is explained by the fact that during 1900-1920 people had continued farming the cultivated area –which was occupied since B.C. till the beginning of the nineteenth century-using the wells beside the monuments and started farming the land beside the residential site. It means that the people’s activity was only farming during this decade.

During 1921-1940 rresidential neighborhood-now named “Old Kharga”- had been built into two parts of the city (see 2) using clay as an appropriate material for the hot climate. The houses assimilated each other. This neighborhood is going vanishing as residents are replacing the old houses by new limited-story buildings without following any planning principles.

Since 1941-1960, with the arrival of first construction caravan, great changes have been occurred in land use areas. Agriculture land use has been extended northward and southward of the city (see 3). The management buildings had built in between the two parts of city (see 4), At the same time the new residential site for caravan of construction had been selected near the management buildings (see 5). The old Kharga neighborhood had extended in all directions (see 6). Durig 1961-1980 Kharga land-use pattern began to become an urbanised area. It started to take the form of a city where the local government developed and created public services, health and luxury facilities and religious places etc. in addition to the residential sites that had grown in all directions of the city (see 7).

During 1981-2002 the land had been registered, as owned by the government. Therefore the residential growth was limited to the roles of the governorat and the residential society under different administrations. In other words, the governorat built the houses by financing through housing bank, then the people dwelt in them and paid the cost of buildings on monthly installment basis. This system was applied on AL-AMAL, AL-Zohur, and AL-Marwa sites (see 8). According to the second system, the governorat specified an area for every residential society, then the society divides the area and specifics parcels for each member who paid the price the of parcel by the way of monthly installment . This system was applied to Engineers’ land, farmers’ land, AL-Slam’s Land, and AL-Mohafza’s land (see 9). Most of these sites are under construction. The industrial and workshops and crafts areas have been specified in west direction around the Kharga-Dakhla road. Some of the parcels have been built, and most of them are under-construction (see 10 in fig.2). In the tourist field, the governorat and private companies have built hotels such as AL-Kharga Hotel, AL-Waha hotel, Hamad Allah Hotel, AL-Dar AL-Bidaa Hotel and AL-Rouad Hotel. Regarding transportation, the governorat has built a railroad system joining Kharga with Safaga on the Red Sea beach for the export of phosphates from Abou Tartour. It also joins the Kharga City with its villages for public travel.

Development of Public activites: Fig. 3 shows that during 1900-1960 the main activity of the people was in the field of agriculture until the change from an oasis to city took place. Then administrative jobs became the main activity of the people, morever, people working in educational services out numbered those working in the agricultural field since 1980s.

Conclusion and recommendation
On the basis of our research presented in this paper we conclude that: though urban planning has used computer model and information system but the local governorat use a traditional methods for the planning process and it also depended on a few inexpert engineers for urban planning of Kharga city. The GIS technique is a powerful tool for analysing the historical urban development.

The year 1958 was turning point for Kharga City when all land use of the city was been developed as well as before. In 1958 the construction caravan developed the wells and doubled the agricultural land from 2 Km2 to 4 Km2. In addition, It increased the palm land from 3.8 Km2 to 4.3 Km2. The main growth direction of Kharga City is northward to southward, the city has grown around two centers: the agricultural land and the archaeological monuments. During the twentieth century the area of houses land uses is nearly equalise the area of streets land uses. Since 1970 until now, the extension development of agriculture and palm Standstill and the Kharga oasis have been undergoing a continuous process of urbanisation. Urban planning management of Kharga City doesn’t obey any planning principles and the land parcels specialisation process can be considered as a random method of land distribution. Thus we strongly recommend that the local government to transfer the urban planning process to such specialists as the National Planning Institute or the universities of planning.

Abstract
The purpose of this paper is to provide a set of guidelines by which health information system can be refocused using GIS as a monitoring tool to improve the timeliness, quality, access and use of Health Management information. This exercise also shows that an alternative way of improving the flow of Health management information is to dedicate resources specifically to co-ordinate access, use and ongoing development of relevant information.

Introduction
Spatial analysis and mapping in epidemiology have a long history but until recently, their use in public health has been limited. However, recent advances in GIS and mapping technologies and increased awareness have created new opportunities for public health administrators to enhance their planning, analysis and monitoring capabilities.

In the context, the reliable and timely information can be used to:

• design the functions of health care services and administrative services.
• monitor health status and service need.
• set priorities for the allocation of health care resources.
• evaluate health programmes & health care outcomes e.g. changes in health status as a result of intervention on health care programme.
• identify environmental, socio-economic and other risk factors, which influence health, under serviced, poor, inaccessible areas and other geographic and demographic factors.
• project perceived health problems with incidence rate.
• focus population sub groups with specific health problems, needs & demands.

The range of expectations about the performance of health information systems will depend on the roles of the people involved. This ‘people’ comprises three categories – Doers, users and viewers.

Doers are GIS specialists involved in GIS creation and maintenance.

Users are decision makers, planners who are interested in analysing the GIS data that have been created. For instance, Executives will need summary information on the achievement of aims and objectives, the costs and the efficiency of services. Mid-level Managers will want information relating to performance indicators, activity levels, resource used and relative effectiveness of care on treatment. Clinicians will need information to assist with the management of individual patients and compare treatment outcomes. Public Health staff will need information to assist with the management of individual patients and the delivery of public health services.

Viewers are interested in the final viewing of the GIS analysis results. For instance, epidemiologists need information on disease patterns while top level programme managers require specific details on health care on administrative programmes.

A GIS can help to focus the Health Management Information for the above groups and perform the following key functions:

• Generate “thematic maps” (ranged colour maps on proportional symbal maps to denote the intensity of a mapped variable).
• Allow for overlaying of different pieces of information.
• Create buffer areas around selected features (eg. a radius of 10 Km around a health centre to denote a catchment area)
• Calculate distances between two points
• Permit dynamic link between databases and maps so that data updates are automatically reflected on maps.
• Permit interactive queries of information contained within the map, table or graph.

Mark the Special Action Project areas (hill areas, tribal packets, coastal islands etc)

Fig. 5: Districtwise Prevalence Rates of Leprosy – An example for ranged pattern map

For any health management information in the Public Health, the data is collected at the PHC level and later consolidated at block level, Health Unit District level (HUD), District level and finally at state level. In the above example, distribution of Leprosy Cases by typewise in Rural and Urban areas of Ramnad district detected is simulated. Cases have been classified as Pauci Bacillary (PB) and Multi Bacillary (MB). In fig 1, the district map of Ramnad with the geographic data is shown. The distribution of cases at the HUD level (Fig 2), at the block level (Fig 3) and at PHC level (Fig 4) are depicted.

Next example is a ranged pattern map. (Fig. 5) It presents the prevalence rates of leprosy in the 29 districts at the start of the Multi Drug Therapy (MDT) during mid 80’s, at the time of integration of leprosy services with the Primary Health Care and during March 2001. The prevalence rate has reached to 3.7 leprosy affected individuals per 10,000 population in Mar 2001 against 117 per 10,000 as recorded in mid 80’s. While satisfactory progress continues to be made towards the elimination of leprosy as public health problem, a “Final push” is being given to reduce the prevalence below 1 per 10,000 population by the year 2004.

Summary and conclusion
GIS and maps are valuable in strengthening the whole process of HMIS and analysis. It serves as a common platform for convergence of multi-disease surveillance activities standardised georeferencing of epidemiological data facilitates standardised approaches to data management. The process provides an excellent means of analysing epidemiological data, revealing trends, dependencies and inter-relationships that would otherwise remain hidden in data shown only in tabular format. A GIS can serve as an entry point for integrating disease surveillance activities where appropriate.

Tahsin Yomralioglu, PhD
Karadeniz Technical University
Department of Geodesy and Photogrammetry Engineering, GISLab
61080 Trabzon-Turkey

A Case Study of Trabzon, Turkey

Abstract
Forming a cancer control program and putting strategic action plans into practice became an important matter for the health industry. Especially in cancer cases, the correlation of variations in different societies and environmental factors should be examined spatially with reliable data. To achieve this, cancer occurrence density maps have to be created. In this study, a database was built with the ability of GIS to examine the distribution of cancer cases, and maps relating to cancer events in allocation units were created. The Trabzon province of Turkey has been used as a case study. Cancer cases data registered in 2004 by the Cancer Struggle Department of Health Directorate of Trabzon of Turkey were used. Using ArcGIS software, the distribution of cancer cases was presented on cancer maps including allocation units and incidence values, which were calculated for each town-based region. According to the World Health Organization standards cancer rates were determined and examined by the spatial analysis power of GIS.

1. Introduction
Cancer is one of the most important health phenomenons of today. Its high mortality rate, the disabilities it leaves behind and the high costs of medication are the causes of heavy losses in terms of both national economy and labor. In Turkey, for example, cancer is the second mortal disease leading to deaths (Özet, 2005). In the country, 100-150 thousand cancer cases are observed, and prevalence of cancer is increasing 6% rate in every year. It must take a measures against cancer, otherwise, 5 million people will be cancer in the future 20 years and 3,5 million of these people will lose their life (Tuncer, 2005).

Precaution policy against cancer is tried to form in Turkey. Firstly, available dimension of cancer is required to reveal. Therefore, cancer statistics is tried to obtain reliably. In order for the Cancer Control Programme (CCP) which is prepared to cancer by World Health Organization (WHO) to be implemented in Turkey, the frequency of the cancer disease, the number of patients and the cancer types should be known (URL-1). In order to develop control strategies for cancer, firstly, there is need for descriptive statistics defining dimension of the disease (Sengelen, 2002).

In 1992, “Turkish Cancer Record and Incidence” project was started to make cancer statistics and control programs available. For this purpose, Cancer Record Centers (CRC) were founded in 11 provinces of Turkey and the cancer cases was started to be recorded in the country. One of these centers is located in the Trabzon province. The cancer cases in the Eastern Black Sea Region are recorded by Trabzon Cancer Recording Center (URL-2).

This study is a pilot application regarding the cancer maps which are guiding base maps in order for the cancer control program to be prepared. It will be tested in view of reaching effective results in the usage of cancer maps for nationwide cancer controls. When cancer maps are produced, there will be opportunities for determining the regions with a considerable density and studying the factors triggering the cancer cases in these regions (Colak, E., 2005).

Trabzon province of Turkey is selected as a study area. In this study, firstly, a database using Geographical Information Systems (GIS) set up and then the statistical maps displaying cancer density of settlement areas were displayed. In constitution of statistical cancer maps, the cancer cases data recorded in the Cancer Struggle Department of Health Directorate of Trabzon for the year 2004 was used. The cancer cases and related information were displayed on the maps by GIS techniques to make cancer data in spatial meaning. Therefore, distribution of the cancer cases could have been investigated in spatial base.

2. Aim of Study

• To be determined cancer density areas and to be guiding component for geographical analysis research to factors causing cancer on these areas.
• To be determined relationship between cancer types and environmental threat areas.
• To be formed guiding based maps for implementation of Cancer Control Programme.
• To be supplied necessary information for epidemiological study
• To be tested an application methodology on Health GIS works.

3. Material and Methods
Study Area
The study area is Trabzon where located in the North Eastern part of Turkey and having coast to the Black Sea (Figure 1). The province has 17 towns and 537 villages with 4 664 square km. Its population in 2000 is recorded as 975 137, and according to the data of 2000; the population density of Trabzon is 209. The annual rate of increase in population in 1990- 2000 is nearly 20.3% (DIE, 2002).

Figure 1: The location of Trabzon Province in Turkey

Providing Cancer Data and Based Maps
Cancer statistics concerning Trabzon province are provided from the Cancer Struggle Department of Health Directorate of Trabzon Province of Turkey. The cancer cases data recorded for the year 2004 was used. During 2004 year, the center has recorded 1939 cancer cases for the Eastern Black Sea Region. Out of these dataset, 1216 cases occurred within the administrative boundaries of Trabzon province were selected for this study. However, as a result of the data quality analysis of these cases, some records of cancer cases lacking adequate address information were excluded. After this elimination, a total of 1150 cancer cases were used in the production of cancer maps.

Administrative unit map of Trabzon was used as the base map for the application to cancer cases is demonstrated on it. In this base map, there are graphical information representing the boundaries and the centers of administrative units. The data in the map includes boundaries of counties and villages, and their centers including their populations. The base map was transferred into topological data structure using ArcGIS 9.x software and the data was stored in the shape (.shp) files. This dataset comprises of two data layers, one is administrative boundaries in polygons and the other is administrative centers in points.

4. Creating GIS-based Cancer Density Maps
The cancer cases applicable for the study have been determined and the data have been arranged in a database in the Microsoft Office Excel program. Afterwards, the data that form the cancer database have been changed into ‘dbase’ format to be used in the ArcGIS software. ArcGIS software was used in transferring the cancer data into the base map. In this process, the previously arranged graphical data of administrative boundaries of Trabzon province together with district boundaries in the city centre of Trabzon were used as the base graphical data. Each case was marked on the map with a point with the guidance of its address information, generating a new cancer distribution layer (Colak, H.E.,2005).

With the production of distribution map of cancer cases for Trabzon province, geographical distributions of cancer cases within Trabzon province for the year 2004 were able to be observed. However, in order to be able to perform some statistical analysis and comparisons, calculation of cancer incidence values for each administrative unit was needed. A cancer incidence rate is the number of new cancers of a specific type occurring in a specified population during a year, usually expressed as the number of cancers per 100,000 populations at risk (URL-3). For this purpose, the number of cases for each administrative unit was determined. With using case numbers and census data of the year 2000, incidence values for each unit was calculated as in the equation below. In this equation the coefficient “k” is 100.000.

Incidence rate = (Number of New Cancer Cases / Population) × k [k=100.000]

Statistical Maps for Geographical Analysis
Cancer density of study area is determined and distribution of cancer cases is observed visually is required for geographical analysis. This research can be realized with statistical map regarding cancer cases. Statistical map presentations are required to examine the distribution of cancer data geographically. ESRI ArcGIS 9.x software was used at map production phase. Cancer cases were pointed with point symbol on the map. The geographic distribution of each case can be seen with point symbol on Density Map of Cancer Cases in Trabzon Province (Figure 2). Also, the distribution of cancer cases in view of cancer types can be seen on this produced map. In addition to this, the prevalence of cancer cases based on villages for each county was presented visually.

In the study, the incidence values have been used for statistical examination as the comparison criteria. Calculated incidence values present cancer prevalence in allocation areas. The maps presenting cancer prevalence in Trabzon was produced for province and city center separately. Cancer Density Map of Trabzon Province was formed for each allocation unit as to calculate incidence values (Figure 3).

According to WHO, it is expected that 150-300 people are taken cancer illness for 100.000 population (URL-4). In the produced map, allocation units having incidence value higher than 300 were determined as risky districts in view of cancer density. These figure outs were presented on the map. In the allocation units having incidence value between 150 and 300 have expected results in view of cancer density, according to world standards.

Figure 2: Distribution of Cancer Cases in Trabzon Province of Turkey

Based on the aspect map of Trabzon, skin cancer cases were also examined. Existing skin cancer cases were determined and distribution of these was presented on the map with point symbol. The reason for skin cancer cases is indicated to be exposed sunlight too much (Bingöl, 1978). In this way, the aspect map was used as base map to present skin cancer cases. An aspect map shows solar orientations of slopes with different orientations and districts affected by sunlight so much can be seen on this map. Relation between skin cancer cases and districts affected by sunlight so much can be examined on this map (Figure 4).

Figure 3: Cancer Incidences Map of Trabzon, Turkey

Figure 4: Skin Cancer Cases overlaid on Aspect layer in Trabzon province

In addition, when cancer cases data are available on city center as a district based, cancer incidence values can also be calculated and related maps also produced for a more focused areas. An example applied for the city center of Trabzon. In Figure 5, calculated cancer incidence values are shown for Trabzon city centre. For the city center it was figured out that incidence value is 143 which can be acceptable incidence values in accordance to the WHO standards.

Figure 5: Cancer Density Map for Trabzon City Centre in district bases.

5. Conclusions

• When the prevalence of cancer cases with point symbol is examined, it is determined that cancer risky regions have more population density than other regions. It can be perceived on the map that allocations areas in coastal regions, across valley, and city center have more cancer cases.
• It was determined that totally 138 allocation units out of 596 in Trabzon exceeded the expected number of cancer cases in 100.000 population. 23% of all allocation units exceeded the value accepted as top limit, 300 incidences.
• The incidence value in Trabzon province was calculated about 118. This value is under expected cancer risk that is accepted between 150-300 regards 100.000 populations.
• When the distributions of cancer are observed in terms of their types, the first most frequently occurring cancer types observed are lung (19%), skin (12%), breast (10%), stomach (9%) and urinary bladder cancer.
• This study can be considered as a pilot application for presenting the distribution of cancer densities on the maps, producing control programs against cancer, and examining environmental factors causing cancer spatially.
• This study proved that cancer data should be collected regularly and quite a few researches about biostatistics and epidemiology can be made. It is firstly emphasized that existing cancer cases in Turkey can be recorded completely.

As a final conclusion, the ability of GIS for comprehensive cancer control, however, comes from the flexibility and extensibility of the digital environment. A GIS-based map has the potential to the capability of classical data to prompt insight about spatial distributions and relationships with the ability of the digital environment to support exploratory analysis, statistical and computational testing of hypotheses, policy decision making, and dissemination of information in a variety of forms. Such products can be integrated to new data continuously and produce new outputs to meet particular cancer researches. It will also provide a framework for extending the GIS functionality over time.

H. Runghen¹, M. Bhuruth² and S.D.D.V. Rughooputh^3
1Department of Mathematics
²Department of Mathematics
³Department of Physics
Faculty of Science, University of Mauritius, Réduit, Mauritius

Abstract
One of the main challenges faced by countries, especially small island states, during an oil spill combat is the identification of vulnerable coastal locations. The lack of precise information of this nature has often led to the inappropriate use of combat materials and response strategies.

In this study we present the application of GIS in the organization of information that will determine the degree of vulnerability in standard formats. Information on relevant factors such as shoreline sensitivity, biological resources, exposure to wave and tidal energy, and human-use resources are systematically presented using the Environmental Sensitivity Index technique. This paper presents an application taking Mauritius as a case study for oil spill preparedness. Significant factors affecting rescue efforts are investigated, as a result of which priorities are established and cleanup strategies identified. The paper shows the applicability of GIS tools and technology in governing actions taken during oil spill accidents, thus ensuring that the response is not only prompt but also appropriate. The methodology is demonstrated on a map of the North-West of Mauritius.

1. Introduction
Oil spill disasters have been a major concern due to increasing number of accidents that have occurred in recent years, for example, Sea Empress (1996), Pallas (1998), Erika (1999) and Prestige (2002). Since direct impact of oil spills in the marine environment are generally wide spread and of long-term, they can have devastating consequences on wildlife, fisheries, coastal and marine habitats, human health, economy as well as recreational resources of immediate coastal communities. Mauritius and its outer islands are located along a very dense maritime route for oil transportation. Tankers along this route carry around 750 million Metric tonnes of petroleum products annually. Every year 36 vessels of 6000 Metric tonnes and 23 vessels of 22000 Metric tonnes offload petroleum products in Port-Louis harbour. As a result, the Government of Mauritius through the Environmental Protection Act of 2002 mandated prescriptions of procedures for cleanup and removal operations. In this context, oil spill combat authorities have set up atlases that provide a means of determining marine and coastal areas of sensitivity that might be impacted should such a pollution incident occur. Many countries, such the United States of America, Australia, Greenland and Mauritius, have included oil spill sensitivity atlases as an integral part of their contingency plan.

The basic requirements for an understandable and usable oil spill sensitivity atlas have been discussed in the IMO/IPIECA report series [1]. Shoreline types, sub-tidal habitats, wildlife and protected areas, fishing activities and other socio-economic features as well as oil-spill response features are important factors to consider when setting up such an atlas. Another determining factor is the seasonal aspect which may alter the sensitivity of some resources. The inability of a hard-copy map to convey these complex logistics has significantly increased the use of Geographical Information Systems (GIS). Halls et al. [2], Fisher et al. [3] and Muskat [4] explain how GIS technology can be applied as a more efficient tool for oil spill preparedness, during an emergency response, and an aid for quantifying natural resource damage. GIS has proven to be an excellent data management, organizational and analysis tool. As the system becomes more widely used, the possibilities of linking different GIS systems and data required for oil spill response and contingency planning are increasing.

The aim of this paper is to present the different steps to build an oil spill sensitivity atlas using GIS technologies. The ESRI’s ArcGISTM 9.0 platform is chosen as it guarantees an efficient and effective means of managing geospatial data such as enabling easy alterations and updates. The Environmental Sensitivity Index (ESI) technique developed by the National Oceanic and Atmospheric Agency (NOAA) [5] is used to organize the information in standard formats for shoreline sensitivity, biological resources, exposure to wave and tidal energy and human-use resources. An oil spill sensitivity atlas for Mauritius was developed by Gunlach et al. [6]. In this paper the technique used to set up an updated and accurate oil spill sensitivity map is described. Based on all these information, appropriate methods to respond to oil spills in the different areas of Mauritius have also been assessed. Digital maps of Mauritius with scale 1:25,000 were used as base map for the thematic layers and listings of each processed data. Spatial and non-spatial data were analyzed through various functions of GIS techniques, such as geoprocessing, data analysis and overlaying, and modelling to yield the risk management system as thematic layers.

2. Method of study
Gunlach et al. [7], Mosbech et al. [8] and Anderson et al. [9] describe various techniques for building sensitivity maps for oil spill response. Using the Environmental Sensitivity Index (ESI), shoreline ranking, biological resources and human-use resources were delineated on ArcMAP workspace by colour coding, symbols and other markings. The ESI method compiles the data on shoreline sensitivity, biological resources, exposure to wave and tidal energy and human-use resources into standard and comprehensible formats. The shoreline habitats of Mauritius are delineated and presented in order of increasing sensitivity to spilled oil as listed in Table 1. Factors such as their vulnerability to shoreline type, exposures to wave and tidal energy, biological productivity and weakness and ease of cleanup of an intertidal habitat have determined their relative sensitivity. A ranking of “1″ represents shorelines least susceptible to be damaged by oiling, and “10″ represents the locations most likely to be damaged. Examples of shorelines ranked as “1″ include steep, exposed rocky cliffs, where oil cannot penetrate into the rock and will quickly be washed off by the action of waves and tides. Shorelines ranked as “10″ include protected, vegetated wetlands, such as mangrove swamps and saltwater marshes. Oil in these areas will remain for a long period of time, penetrate deeply into the substrate, and inflict damage to plants and animals.

Table 1: Sensitivity Index Ranking for Shorelines of Mauritius

The intertidal habitats of Mauritius, which cover a shoreline of about 173 km, were identified and mapped during ground surveys conducted from June 2003 to January 2004. The readings were taken starting from 09 hr 00 min to 14 hr 30 min daily. These intertidal habitats were delineated directly onto 1:25000 scale Mauritian geological topographic maps (CAD format). Data were collected using a handheld Global Position System (GPS) receiver which has an accuracy of 3 meters. The geodetic reference (datum) used for GPS is the World Geodetic System 1984 (WGS84). Since the base map is on National Grid Coordinates with origin Le Pouce (20011’42.25 S, 57031’18.58 E), false coordinates (1000000.000 mE, 1000000.000 mN), conversions from WGS84 to Lambert Conical (-one parallel) projection were processed. Additional information through the use of historical sources [6 and 10], maps and aerial photo interpretation were applied. Accuracy of all descriptive and spatial attributes were tested by visual comparison of hard copy check plots to the source materials and verifying the location of the data on screen relative to other data layers in the same geographic area.

The ArcToolbox application in ArcGISTM 9.0 provides a powerful set of geoprocessing functions, one of which is used to import the CAD layers into ArcMap application. Each of the different shoreline types is imported as different layers. In the geographic data view, geographic layers representing these shoreline types are compiled into GIS data sets. A table of contents interface organizes and controls the drawing properties of the GIS data layers in the data frame. The shoreline types are color coded in a ranked format on a scale from 1 to 10 as described before. ArcMap also enables the use different inbuilt symbols to represent some important environmental and human resources that could be affected by an oil spill. These map elements include birds (sea birds, shore birds and herons), public beaches, hotels and special areas designated by legislation such as fishing resources and nature reserves. These areas are indicated specifically to aid and direct the response effort. An example of the legend showing the different color codes is shown in Fig 2. In the page layout view, we can modify the layout to improve the design and visual balance of the composition by adding new map elements and changing the properties of the existing map elements. ArcMap also allows the inclusion of several key components when producing a map. These include the title, scale bar, legend and north arrow. ArcMap also integrates attributes tables, text files, digital photographs and video imagery in the digital maps.

3. Test case: Description of Map 18
Although a greater number of maps with greater accuracy can be produced, we have restricted ourselves to 19 maps as shown in Fig 1. As a result, rapid search will be possible and implementation on the model will be more structured. With all the necessary information gathered, the sensitivity maps can now be produced. Oil spill countermeasure considerations are described for each of the 19 operational maps. In this section, we give an overview of their basis and content. As an example, we present below the case of Map 18 (North-West part of Mauritius) in Fig 2.

Fig 1: Map index for oil spill sensitivity atlas

Fig 2: Oil spill sensitivity Map 18
Map 18 covers the coastal area from Albion to Baie du Tombeau. The shoreline consists mainly of structures and coastal developments at Port-Louis. The coastline of the harbour comprises mostly of metal or concrete walls and sheltered rocky shores. Natural shorelines to the north and west are composed of sand or sand mixed with coarser material. Mangroves and marshes are not very common; they are found only along the sheltered areas of upper Grand-Rivière Bay. The reef platform is several hundred meters wide to the north and west of Port-Louis, and nonexistent within the port area.

Resources at risk
The waters off Port-Louis are designated as a fishing reserve. The shoreline environment needing protection is the marsh and mangrove area in upper Grand-Rivère Bay. A small public beach is present at mare Samson and there are two coastal hotels in Baie du Tombeau area (Corotel and Hotel Les Cocotiers). Precautions need to be taken during the application of dispersant or other chemical because of the fishing reserve present in the area. An electricity-generating facility, which uses seawater for cooling, is located at Bain des Dames. A number of small boats are moored in Grande Rivière Bay.

Response strategies
The port facilities are a possible source of an oil spill. In such an event, the response strategy is to contain the oil within the industrialized port area and not allow it to impact adjacent, more sensitive environments. The calm waters of the port area enable a full response using booms, skimmers and sorbents to be undertaken. The electical station at Bain des Dames must be notified of a spill to determine if intakes should be protected, monitored or closed. Booms and sorbents should also be used to protect the mangrove and marsh area in Grand-Rivière Bay. Oil that impacts adjacent shorelines should be cleared up with the minimal removal of sand to avoid potential erosion problems.

4. Discussion
The application of GIS technology has resulted in the development of an oil spill sensitivity atlas that is no longer a static product of limited usage. One of the major differences between the atlas produced by Gunlach et al. [6] and the new one is that it is now an automated information system that is capable of recording and maintaining data, readily producing relevant maps. The use of digital maps facilitates updating of available data, thus allowing spatial queries to be performed at any time. Along with a digital map, pictures and short movies have been inserted into the map to provide the responder with an idea of the shore under study. The primary motivation for making a digital oil sensitivity atlas was to identify the shoreline at risk during oil spill scenarios in conjunction with building an oil spill model for Mauritius. However, the use of the atlas is not only restricted to oil spill response and planning, but may also be applied to coastal management in a broader context. In order to facilitate the understanding of and access to the different factors at play in the oil spill combat, the atlas compiled during this study will be distributed on free to all stakeholders and easy-to-use software such as print published map files (PMF) and portable document files (PDF) which can be accessed using ESRI ArcReader and Adobe Acrobat, respectively. In addition, the facilities offered by the software mentioned above will help safeguard the accuracy of the original information thereby preventing tampering.

6. Conclusion
The new sensitivity atlas has been incorporated into ArcMap GIS format to help the oil spill responder to immediately identify the shore types and possible combat techniques. The various shore types have been visually presented using colour schemes to differentiate between them on the digital map. In this way the decision taking process during incidents requiring immediate action such as during oil spill is facilitated. In addition, some techniques for developing an oil spill sensitivity atlas utilizing GIS technologies have been discussed. The shore types were described and used to update existing sensitivity maps for Mauritius. ESI was used to outline the different types of shoreline identified so far. From thereon a sensitivity ranking has been established and indicated on the map. Methods were discussed and their high degree of accuracy confirmed.

Madhurima Katiyar, Mitika Garg, Akansha Srivastava
pursuing M.Sc in GEOINFORMATICS
Symbiosis Institute of Geoinformatics, Pune

INTRODUCTION:-
Rivers of Asian continent are the chief transporting agents of continental weathering products as they supply about 30% of the global sediment input to the world’s ocean. The Indus and Ganges- Brahmaputra rivers contribute as much as 20% of the global sediment input Ganges River is placed as the third largest transporting river in the world. The investigation of sediments from the hydrosphere has recently become a major subject of interest in research as they reflect the current quality of the system and provide information on the impact of man. Human activities (urbanization, industrialization, mining, etc.) promote the accumulation of polluted sediments in the nearby river system, which is considered be safe disposal site for contaminated sediments. Contaminants in river system can be investigated by analyzing either the water and the suspended material or the sediments. The study of sediments play key role, as they have long residence time. River sediments, therefore, are important sources for the assessment of man-made contamination in rivers. Our main focus is how one can go for easy going analysis through GIS softwares in detecting heavy metal contamination in rivers.

When any element is added to our environment beyond certain limit, it becomes hazardous to our environmental system and this is called POLLUTION. Those elements which are responsible for causing pollution, called POLLUTANTS. There are number of pollutants which are released directly or indirectly by our ANTHROPOGENIC activities into our environment like HEAVY METALS. Our interest is to understand the occurrence, distribution of heavy metal in river sediments of the Ganges say Gomati River (tributary of the Ganga River) as Ganga river system is one of the largest river systems of India

As Computers are now able to process maps -both individually and along with tabular data and “crunch” them together to provide a new perception – the spatial visualisation of information specifically the Geographical Information System (GIS) which is a tool which allows synergism of map data and tabular .So the advent of computers has changed the scope of information processing -whether as an end-user application or for technology support data in the most efficient manner on. Ganges River serves as a lifeline for about 400 million people living in its alluvial and delta plains, one of the most densely populated and highly agricultural regions of the world. Heavy metals in water and sediments of the Ganges River may have a substantial adverse effect on the environment of alluvial plain and delta regions due to their toxicity and accumulation in microorganisms, plants, animals and humans. Hence, knowledge of heavy metal concentration and distribution in sediments is of fundamental importance in an environmental study of the GOMATI RIVER.

STUDY AREA:
The Gomti, Gumti or Gomati River is a tributary of the Ganges River The Gomti originates near Madho Tada, Pilibhit, India. It extends 900 km (560 miles) through Uttar Pradesh and meets the Ganges River near Saidpur After 240 km the Gomti enters Lucknow, through which it meanders for about 12 km. At the entrance point water is lifted from the river for the city’s water supply. The major sources of pollution in the Gomti are:

• Industrial waste and effluent from sugar factories and distillaries.
• Domestic waste water and sewage from habitations.

The river collects large amounts of human and industrial pollutants as it flows through the highly populous areas of Uttar Pradesh. High pollution levels in the river have negative effects on the ecosystem of the Gomti, threatening its aquatic life.. The river flows in the great alluvial plain, which is of Pleistocene-Holocene origin, and redistributes the primary weathered sediments of the Gangetic alluvial plain derived from the Himalaya. It flows over 750km in a SE direction and joins the Ganga River near Varanasi. In the middle of its course the passes through the Lucknow urban area. Lucknow is a capital city in the Gangetic plain, with an urban population of more than 1.6million and some industrial units .The urban effluents, including industrial and municipal waste, flow into the freshwater river through small open drainages. These drainages are Kadar Nala, Daliganj Nala, New Hyderabad Nala, and Haidergarh canal. The river is characterized by sluggish flow throughout the year, except during the monsoon season, when heavy rainfall causes a manifold increase in the runoff. The post monsoon season is associated with the depositional phase of the river due to low water discharge.

IMPACTS ON LIVING BEINGS:-
The fish had died as dissolved oxygen level of the river water had dipped too low .The dissolved oxygen levels dipped to as low as one milligram per litre (mg/l). Over the years, Gomati has become the most polluted river in Uttar Pradesh. Monitoring by the state pollution control board reveals the water is unfit for consumption.

GOMATI RIVER SEDIMENTS:-
The river sediments are made up of fine sand, silt, and clay. The fine silt and clay(<20 micrometer fraction) content in the sediment samples ranges from 6 to 40% with an average of 20%. The XRD analysis shows the dominance of Quartz, followed by feldspar and mica.

HEAVY METAL IN SEDIMENTS:-
Metal concentration in the Gomati river sediments at Lucknow area show varying and different behavior influenced by urbanization. The results of the chemical analysis of the samples are presented below-

1. cadmium (fraction in micrometer)
   Average—1.30, medium—0.61, minimum—0.26 and maximum—3.62

1. iron (fraction in micrometer)
   Average—5.16, medium—5.12, minimum—4.32 and maximum—6.11
2. Copper (fraction in micrometer)
   Average—70, medium—55, minimum—33 and maximum—147
3. Chromium (fraction in micrometer)
   Average—160, medium—155, minimum—115 and maximum—204 etc.

Downstream profiles of these heavy metals show interesting features at sampling stations G5, G6, and G9, where the river receives waste water effluents from urban drains. Total metal concentration in the fraction <20 micrometer of the sediments vary in the range of 115-204 for Cr, 440-845 for Mn, 432000-61100 for Fe, 18.6-25.9 for Co, 45-86 for Ni, 33-147 for Cu, 90-389 for Zn, 25-77 for Pb, and 0.26-3.62 mg/kg for Cd. At station G5, Co, Cr, and Ni concentrations decreases, while Cd, Cu, Pb, Zn increases downstream, at station G9, the river receives urban effluents from Haidergarh canal. The variations in the metal concentrations at the G5, G6 and G9 stations are possibly due to variations in the water chemistry of the river at the urban Nala confluence.

Cd, Cu, Cr, Pb, and Zn concentrations of urban effluents indicate important anthropogenic inputs to the river. This causes the increase of metal concentration in the downstream sediments. While Fe and Co levels remain constant, Mn concentration continuously drop in the downstream section.Ni concentration drop rapidly after the confluence of Khadar Nala and subsequently increase again to reach the previous level within the urban limits.

GIS :-
Geographical Information System (GIS) which is a tool which allows synergism of map data and tabular data in the most efficient manner. Now-a-days GIS has been playing a great role in carrying out an easy going analysis. It has number of applications which force us to be a part of GIS, it involves some important tasks like

• Organizing integrated spatial and non-spatial databases using the GIS tools in a systematic manner. The spatial data -consisting of maps from remotely sensed data and also conventional sources
• Integration or the synthesis of the spatial and non-spatial information within the framework of a coherent data model and linkages between the different datasets
• Generation of spatial outputs, supported by tables/charts, to help the developmental planning and decision-making

APPLICATIONS OF GIS:-
1.)when we keep our focus on such a long stretch of river and we carry out our analysis on such a large scale then for such a large scale analysis GIS softwares are the keyplayers.We gather different types of data like-

• Data about concentration of heavy metal in water.
• Data about concentration of heavy metal in sediments.
• Data about concentration of heavy metal in suspended particles.

So for such a voluminous data it’s difficult to handle them in paper sheets so to have proper arrangement and proper record we require the assistance of GIS softwares as they help not only in the developmental planning but also in decision-making. So not only we have proper record of information but also one can easily update the information i.e. not possible in hand sheets as it become a tedious job. For easy going analysis GIS softwares play key role. As we know that anthropogenic activities are basically responsible for bringing heavy metals into our river system. In analysis part, geochemical maps of metals(Cd,Cr,Cu,Ni,Pb and,Zn) are digitized through GIS software and the layers of drainage pattern, land use map, heavy metal concentration of surveyed area are also added to this analysis.

This will tell us the sources of heavy metal concentration present in water bodies passing through industrial area, which in turn leads to their adverse effect on different land units, like agriculture, urban areas, etc so on the basis of above mentioned fact assume number of industry in an area and some of them are present in the adjacent region of a river system. So by going through some OVERLAY OPERATIONS one can easily come to know that which area is more prone to contamination

STEP I—
Suppose we have two layers, one layer is of landuse say agriculture and other layer is of water body say river. So if we go for operations in ARC VIEW 3.2 we will come to know that which area is having more proximity to river. Such operations are need to be performed between different layers of the same region because then we come to know the interrelationship.

As here landuse unit and river system, both layers belong to the same region. When we do the overlay operation then we come to know the relation of both these layers with each other.

STEP II:-
After getting the final overlaid layer of agriculture and river, we again perform this operation in taking into account this layer with another layer say layer of industry and urban area so this final is telling us that which industry is adjacent to river so that river will be enriched in that particular industrial effluent. As a result of this knowledge one should focus his study on that flood plain which is adjacent to particular industry as most of the industrial effluents are dumped into the river system which affect the near by portion of flood plain. On the basis of above mentioned overlay operation one can easily make the prediction about urban areas. As those urban areas which are adjacent to industries have more exposure to diseases.

EXAMPLE OF SUCH OPERATION IN GOMATI RIVER STUDY:-
If the above mentioned figure of Gomati River is considered and some operations are performed on different layers of region then we will have result like this.

This all is possible, when we go for ARC VIEW 3.2 software. First digitize the Gomati River of Lucknow region and make a new theme as a line. Afterwards locate the different sampling stations in an area along with urban effluent sites of a region and make a new theme as a point layer. Now join these two layers (line and point layer) the final layer will be formed. Then one of the tools of ARC VIEW i.e. geoprocessing is used for further operations. According to above mentioned figure the final layer is explicitly showing the location of different sampling stations. There are ten Sampling stations, let’s consider content of different sample stations. The following content of heavy metal is present—

Now on the basis of above data, the G9 sampling station having highest content of heavy metal. So with the help of GIS SOFTWARE esp. ARC VIEW 3.2 one can create BUFFERS. First we can build query for highlighting the highest content of heavy metal of a sampling station. When we build this query the G9 station is highlighted, afterwards we can create buffer say 200m around G9 station and we can focus our study only to that zone.

ADVANTAGE OF CREATING BUFFER AROUND G9 STATION:-
As a Government body we can confine the construction or establishment of new things. Like restricting some private or other bodies for any urban settlement or any other type of construction in the buffer zone of G9 SAMPLING STATION as it is highly prone to heavy metal contamination so hazardous to everyone.

2) Remote Sensing data is a classic source of data on natural resources for a region and provides a record of the continuum of resource status because of its repetitive coverage. Remotely sensed data in the form of satellite imageries can be used to study and monitor land features, natural resources and dynamic aspects of human activities. Now-a-days satellite imageries have been playing key role in assessing polluted water. As different range of EMR (electromagnetic radiation) are used and their properties easily get changed as per the quality of water is concerned.

This factor is of fundamental importance in understanding the quality of water, and for that we take assistance from different satellites like LANDSAT, IRS. But it has some drawback, like water is highly dynamic in nature so we can’t totally rely on this factor for the analysis of heavy metal. Therefore river sediments are important source for the assessment of heavy metal contamination in river. For easy analysis of river sediments one can go for DEM (Digital elevation model), recording a topographic representation of terrain of the earth or another surface in digital format. It implies the attitude/elevation of the point contained in the data. Steps involve in the preparation of DEM are mentioned below-

STEP A: – Topographical Maps
STEP B: – Georegistration of images of maps in ERDAS IMAGINE 8.5.
STEP C: – Preparation of vector layers of spot heights and drainage in ARC VIEW 3.2.
STEP D: – Preparation of DEM (Digital elevation model).

Utility of DEM in analysis of polluted water of GOMATI RIVER:–
After making the DEM of flood plain of GOMATI RIVER, we keep our focus on the sediment coverage of flood plain and their varying thickness. The varying elevation or thickness of sediments help in their assessment as the contamination is supposed to be higher where the accumulation of sediment is high. So directly or indirectly DEM help us to make assumptions about the content of heavy metals. If we keep our focus on above mentioned urban effluent sites (different nalas) and we prepare contour maps of 2m or 5m depending upon the scale in ARC VIEW 3.2.

We will come to know that the concentration of contours along GOMATI RIVER is highly variable, which in turn tells the varying surface morphology that ranges from circular to elliptical and many more. During monsoon season large part of river basins are inundated by water due to low relief, which migrate into major rivers of the GANAG PLAIN say GOMATI RIVER, in summers and winter seasons, this source of water make easily infiltration into the groundwater of the alluvium. The major fluvial processes of rivers are thus principally controlled by monsoon rainfall.

CONCLUSION:-
Like other known pollution episodes around urban regions where rivers are affected by human activities, the GOMATI RIVER in LUCKNOW presents a good example that can be used to compare and contrast the effect of Urbanization .on the distribution of heavy metal in river sediments. Recently deposited river sediments show increased heavy metal concentrations due to urban waste effluent discharged into the river. Compared with background values, the average enrichment factors are around 2 for Pb and Cr, 4 for Zn and Cu, and 11 for Cd, indicating the anthropogenic source of these heavy metals in the river sediments. It is evident from the present study that the urbanization process has a great influence on transportation and accumulation of these toxic heavy metals in the GOMATI RIVER and all this information is processed, managed, manipulated, analyzed and modelled through GIS SOFTWARES. As it has ability to handle multiple-layers of information in the spatial domain and allow for the integration and modeling of these parameters to arrive at inputs to decision-making.

The present data can provide useful information for pollution control strategies and urban centers located along the Ganga River system. The best example for explaining the utility of GIS in decision making about the settlements of different industries is ATLAS ZONING. Zoning is a legalized and institutionalized form of land use management

• To identify locations for siting of industries.
• To identify industries suitable to the identified sites.

This methodology involves preparation of maps of various themes and uses of overlay procedure & GIS (Geographical Information System).As it identify locations for siting of industries which in itself minimize the exposure of hazardous elements to urban areas.

Ed Shaffer, RS
Supervising Sanitarian
Abstract:
Sewage treatment systems are utilized extensively in rural areas of Muskingum County, Ohio to manage wastewater from households that do not have the option of connecting to a municipal system. Muskingum County has over 10,500 existing systems and over 300 new systems are installed each year. To better manage alterations to existing systems and construction of new ones, a GIS was implemented.

Utilizing an existing Mircosoft Access data set of sewage treatment system information, and spatial data generated by the Muskingum County GIS department, all sewage treatment system locations were geocoded using ArcGIS 9.0. Now, utilizing parcel information, soil composition, and land contours, sanitarians are able to perform preliminary sewage treatment system reviews in minutes and eliminate the need to review printed parcel maps located in another building, and usually at least one site visit. The resulting map was made accessible to all sanitarians on the computer network using ArcPublisher.

Background:
As the population in southeast Ohio continues a steady movement from cities to rural areas, the extension of public sewers has not kept pace. In many cases extending public sewer to remote or sparsely populated areas is not economically feasible. As a result, the individual sewage treatment system has taken a prominent place in the overall practice of sewage treatment, and is utilized extensively throughout Muskingum County, Ohio.

The first step in the design of a home sewage treatment system is to determine the suitability of soils. Most home treatment systems depend on soils to both treat and absorb wastewater. Factors such as soil permeability, depth to seasonal ground water, bedrock or other limiting layers, surface topography, and the flow of runoff water all must be evaluated in determining the suitability for an on lot sewage treatment system.

The most common, and economical, type of system consists of a septic tank followed by a series of leaching lines. The tank serves as primary treatment. It allows for the settling and storing of most of the solids and starts the anaerobic breakdown of the wastewater. The leaching lines disperse the wastewater over a large area allowing further treatment and absorption by the soil. Other types of systems have been developed to overcome limitations of the soil, or to operate on smaller home sites. These systems include aeration, filtering water through sand, peat or other material, and chemical disinfection.

Muskingum County, Ohio has over 10,000 existing systems of record and over 300 new systems are installed each year. To better manage alterations to existing systems and new installations, a GIS was implemented which enables ZMCHD sanitarians to quickly assess a property’s suitability for a sewage treatment system.

Methods:
Since the health department does not generate and maintain base geographic files of the county, the Zanesville/Muskingum County Health Department (ZMCHD) has worked closely with the Muskingum County GIS department to obtain current geographic files to be used in GIS projects. An accurate centerline file with address ranges provides the most use since most health department data sets contain an address. In the past, ZMCHD relied on TIGER road files for most GIS projects. Recently, Muskingum County was one of the first counties in Ohio to take advantage of the Location Based Response System (LBRS) initiative. LBRS is a state effort to share the county’s cost (determined by the road miles and addressable structures in the county) of obtaining accurate, field verified, road centerline data with address ranges. Additionally, every residence’s road access (i.e. driveway) was also collected so the data includes a field verified address point for every residence in the county. Now the health department is able to accurately geocode its data using these point and centerline files. Other local geographic data such as land parcels, contours, spot elevations, orthophotography, flood plains, and soil composition were used to implement the sewage treatment system GIS.

Utilizing a Mircosoft Access data set of sewage treatment system information, existing system locations were geocoded using ArcGIS 9.0. The goal was to locate a point as close as possible to the actual sewage treatment system using existing data. To accomplish this, two geocoding services (address locaters in ArcGIS 9.1) were created. The first geocoding service was created using the “Single Field (file)” style. The reference data used was the LBRS address point file, and the “Key Field” was the field containing the address consisting of house number and street name, labeled LSN in our data set. System defaults for matching options of 80 for spelling sensitivity, 10 for minimum candidate score, and 60 for minimum match score were used. Output fields of “X and Y coordinates”, and “Standardized address” were also selected. The geocoding service was run matching the sewage treatment system address, labeled Address in our data set, and saved as a point shapefile. The second geocoding service was created using the “US Streets with Zone (file)” style with reference data being the LBRS centerline file. Fields were matched to the LBRS centerline file for “House from Left”, “House to Left”, “House from Right”, “House to Right”, “Street Name”, “Street Type”, “Left Zone”, and “Right Zone”. System defaults for matching and output fields were the same as in the first geocoding service, so that the two resulting shapefiles could be easily merged.

The first geocoding service was run enabling a point to be placed on the property where the sewage treatment system is actually located. It is understood that it is not the actual location of the system on the property, only that it is matched to the property. The systems that could not be matched to the address point were then matched using the second geocoding service to approximate the system’s location on the road centerline. Unmatched records were reviewed to determine why geocoding by either method was unsuccessful.

Included in the sewage treatment system database is a scanned sketch of the system layout, as documented by the licensing sanitarian. Being able to locate the sewage treatment system with a point, enabled ArcView’s hyperlink feature to be utilized (Figure 1). This now allows the sanitarian reviewing the sewage treatment system to view the sketch without having to retrieve the paper record. NOTE: The scanned image was included in the Access data set as an embedded OLE object. Extracting the path location of the sketch required the use of a Visual Basic program in order for the hyperlink feature to be utilized.

Figure 1: Image of scanned sketch of sewage system accessed from the map using a hyperlink.
Results:
Of the 10,426 existing sewage treatment systems records, 8,386 (80.4%) were successfully geocoded to the address point file. Manual geocode matching was performed to place as many as possible to the address point. This was done to compensate for typographical inconsistencies between the LBRS centerline file and address information in the health department’s sewage treatment system database. The remaining 2,040 records were geocoded to the address verified centerline file to approximate the system location. 1,801 (17.3%) systems were successfully geocoded to the centerline file leaving only 239 (2.3%) that were unable to be geocoded. Upon review of the unmatched records, most were older records that lacked an address, or only designated by a lot number or street name.

While detailed timesaving data is not available, the sewage treatment system GIS has reduced the number of man-hours needed to manage the program. Using the GIS has eliminated the sanitarian’s requirement to review printed parcel maps only available in the auditor’s office located in another building. It has reduced time needed to leaf through paper files to review system configuration. Some site visits have been eliminated due to the ability to assess the property information using GIS.

Discussion:
The GIS allows sanitarians to perform sewage treatment system reviews of existing systems in minutes. These reviews are necessary if there is a complaint or if a homeowner wishes to modify the system or request a variance. Some homeowners request a variance for special circumstances, which must be approved by the health board. The GIS allows for a detailed visual presentation to be made to the board improving their decision-making ability.

The GIS is especially useful as new sewage treatment systems are installed. Since current land parcel information is available, initial evaluations as to the size of the lot, and location relative to flood plains can be made quickly. With soil composition, orthophotography, land contour, and spot elevation information, initial assessments as to system location can be also be made (Figure 2). By having the GIS as a visual tool, sanitarians can now have detailed phone consultations with property owners and contractors regarding the placement of homes and sewage treatment systems. These preliminary conversations would be difficult without visiting the site first. Additionally, many of the property owners do not reside in the county and are building retirement or weekend homes, so being able to discuss issues with them and not having to arrange a meeting at the site saves time and improves customer service. Additionally, the GIS will allow the sanitarian to quickly reference systems in close proximity, which can aid in the design of the system.

Figure 2: Parcel information with land contours, roads, and soil types displayed.
Conclusions:
GIS is a valuable tool for sewage treatment system management. It allows sanitarians to quickly utilize geographic information critical to decision making, and eliminates the need to refer to cumbersome printed maps. While it does not eliminate all field visits, it does make field visits more productive since the sanitarian already has a feel for the property (size, contours, soil) prior to visiting the site.

Making the map accessible using ArcPublisher allows for more staff to be exposed to GIS. As staff members become more familiar with GIS, it is anticipated that future GIS projects will be undertaken. Ideas will be generated on what programs might benefit from GIS analysis, and since most public health data contains an address, geographic references are available.

Challenges still remain. This project utilized an existing database of sewage treatment system information that is maintained separately from the GIS. Therefore, new systems are not incorporated into the GIS unless a separate geocoding project is undertaken. Integrating the sewage treatment system database into the GIS using a geodatabase is a logical future step. Efforts to keep day-to-day operations for clerical staff responsible for inputting sewage treatment system data the same is a priority. As a result, this must be accounted for in the integration process. Ideally, an automatic geocoding process could be incorporated so new systems are immediately available in the GIS once they are entered into the database. This will require some outside expertise to fully implement.

The fourth part of the survey determined how the participant used his or her GIS software The fifth part of the survey asked for the impact of GIS on the participant’s research. Finally, the sixth part of the survey sought to establish the participant’s familiarity with GIS issues that potentially skew the results. This part is beyond the scope of this article. For details, visit http://research.hunter.cuny.edu/arch/survey.html

About the Participants
The project ultimately accepted 140 entries. The list of the geographic location of the participants is given in Table 1.

Seventy seven per cent had higher degrees. 91% were involved in GIS projects when they completed the survey. More than 72% had more than two years of experience using the tool, which is a reasonable amount of time to familiarise oneself with the technology. It was interesting that 33% of the participants had not attended any formal GIS classes, workshops, or seminars. It is important to note that the length of the classes and their level were not emphasized. The question was intended to establish who had taught themselves GIS and who had not. Frequency of GIS use was high, with 41% of the participants using the tool everyday.

Support groups are a way of sharing news, suggestions and problems with people that share a common interest. Being part of an online support group is now one of the best ways to find out about common applications of GIS use as well as common mistakes. Thirty two per cent of the participants were members of GIS support groups. Only 6% of the participants attended GIS related conferences on a frequent basis. Going to a conference about a topic is a good indicator of the level of involvement of the participant in that topic.

GIS Archaeology survey

Sixty three per cent of the participants were involved in choosing their system, which makes sense in a discipline like archaeology, where projects usually involve an intimate number of decision-makers, and a substantial number of projects are one-person operations. Figure 1 shows the type of software used by archaeologists and the platforms they run on. ESRI’s Arcview and ArcInfo were the leaders in software used by archaeologists. PCs used more than any platforms, indicating, again, the small nature of GIS operations in archaeology.

Types of Applications
Answers to the first question in this category emphasized the fact that site-based analyses using GIS are in their infancy. Region-based analyses have dominated the use of GIS. Determining the type of applications used was rather important because it showed whether the participants were making the most of the tool. While GIS is undoubtedly a powerful tool and has functions that cater to very complex modelling needs, it is still a tool that is driven by market demands , often of a non-archaeological nature. GIS is fundamentally a set of incomplete modules that can only be expanded upon and effectively utilised by adding one’s own algorithms through programming languages. Without this functionality, the user is left with the default capabilities of the software, which leaves the software makers in control of the shape of future research. Only 22 of the 140 participants used their own algorithms, and, of these 22, only 14 listed them as one of their most successful applications.

The scope of the software analyses used was wide, and showed that the participants made use of most of the features available in their GIS packages. Problematic operations were by far data collection, data conversion, and data compatibility. Data sources showed a high frequency of manual digitizing and database sources. This could be a result of directly imputing data in a computer database in the field, or exporting an existing database into a GIS readable format. Internet downloads were among the least frequent source of data.

Impact on Research
Four per cent of the participants thought that the simplicity of GIS software limited their ability to apply their models. Seven per cent stated that GIS complexity reduced their ability to apply their models. Fifty per cent believed that GIS opened their mind to more expressive models, while 15% expressed that GIS did not change the way their models were designed. Twenty three per cent decided that none of the above answers applied to them and finally 1% did not answer the question. The results reinforced the idea that GIS is more than a tool. It is certainly not a simple tool because it is not limiting anyone’s imagination in terms of analysis. If it is restrictive in a way, it is the software’s complexity that keeps people from implementing what they have in mind. Nonetheless, it is clear that GIS has significantly impacted users spatial thinking as half of the participants thought GIS opened their mind to more ideas.

Sampath Thiruvengadachari
Software Engineer
Infosys Technologies Ltd

Pradeep Kishore
Project Manager
Infosys Technologies Ltd

Abstract
GIS has played a significant role in the development of Network Support Systems through its ability to offer mapping solutions across different networks in Telecom industry. It helps in maintaining the various inventories involved in a telecommunication network like routers, Center Office terminals, cables, etc effectively. The users could see the locations of these inventories spatially in a map together with their attribute information. These attribute details are available either as separate reports or as a summary table just below the map. Furthermore they can also be viewed as notes on the map, when the user hovers around a particular area. These features will facilitate the users in analyzing, mapping and querying the network data relatively faster, thereby helping the network engineers to plan efficiently. This paper deals with one such application developed by Infosys for a particular Network Expansion scenario for one of its Telecom customers.

The paper starts describing about the general business benefits that can be achieved by implementing the GIS technologies in a Telecom industry. It then continues elucidating the application in detail and the business values provided by the application. Conceptually this application, Network Path Indicator (NPI), can be used as a decision making tool for querying and tracing the spatial field data. It could also help the plant engineers to create, plan, save and publish their designs. It facilitates the display of network features in a map view along with associated cable capacity information, the network trouble locations and bonded pair data of the cables. This allows the Network Engineers to plan the provisioning for the subscribers effectively. The users will be able to view the effects of placing hypothetical terminals and equipments depending on the service availability at distribution terminals and service sites (subscriber locations).

Introduction:

GIS is a powerful technology that can be used to design any decision-making and planning tools to work on the geographical features. It also helps in managing the processes and actions that take place on these features in telecom affairs like customer relationships, workforce management, expanding the network services and other location based services. Earlier the focus was on individual projects where individual departments/users created and maintained their data sets on their own desktop computers. Later as the Telecom industry evolved and expanded its networks, there has been extensive interaction and work-flow between departments. To manage such operations smoothly it was imperative to get the details about the customer locations and network assets deployed at various locations. To meet these challenges, the organizations had switched from stand alone desktop GIS applications to more integrated GIS applications.

One such Enterprise GIS application was needed for a particular network expansion scenario, for which Infosys had built the NPI. The NPI can use and display spatial data from different data sources, helping the Plant Engineers to view and analyze the network data. The planners can create hypothetical designs to install the necessary Telecom equipments at the right places and enhance their services to all subscriber locations. Some of the potential benefits that the NPI has provided as an Enterprise GIS solution to the Telecom industry are:

• Significantly reduced redundancy of data across the system.
• Improved accuracy and integrity of geographic information.
• More efficient use and sharing of data.
• Spatial Data can be integrated and used in decision making processes across the whole organization.
• Reduces the data acquisition costs and maintains the data quality across organizations and departments.

A Network Expansion Scenario:

A Telecom Company was trying to expand its service capabilities by providing high-speed broadband and IP Voice based services over an advanced IP based network. They were planning to do this by deploying fiber-to-the-network (FTTN*) and fiber-to-the-premises (FTTP*). In order to achieve this, they were looking for a system which would provide the following information on a user friendly map interface.

• Total number of the terminals available in its network.
• Subscribers served by each terminal.
• Possibilities of increasing the subscribers served by a particular terminal.
• The nearest terminal to a subscriber location from which a new connection can be provided.
• The number of incoming cables and pairs to a particular terminal and those that branch out from the same terminal.
• Identify the subscribers as per their reach from the Service Providing Terminals.
• Finding out whether any pair bonding solution is required in a particular distribution area.

As we could clearly see that all these information deal with the locations on the earth and hence had to be supported only with the spatial data. Thus it is very much essential for a Telecom service provider to locate its inventories, network elements and its subscriber locations spatially. NPI is an extremely useful tool to display all the above mentioned information geographically using a GIS interface. It also facilitates in displaying these information as a Report, to trace them and view on a map, to plan the designs and to make decisions. Simulation of such real-time scenarios in the GIS environment can enable an easy and effective decision making with less manual effort.

Advantages of a Map Interface and Platform Independent Architecture:

The advanced data acquisition techniques and the concepts of spatial data representation as Interactive Maps have become a revolution these days. Also Java and Struts, being a platform independent language and a flexible J2EE distributed framework respectively, they help to build robust applications. Such applications could be used for network planning and decision making and also in many other telecom affairs. Thus combining both GIS and J2EE framework, NPI has been built as a powerful application for serving spatial data over a Map Interface, across the different telecom systems in an enterprise.

The Network Path Indicator (NPI) –:

The NPI is a map interface which simulates all the network inventories as a real time environment and helps the planners to: Make a decision as to how to design the FTTN distribution areas in order to provide enhanced services to all customers within the distribution area.

• Identify areas and customers eligible for pair bonding* solutions.
• Interpret where there may be ‘false red*’ (fault) scenarios that can be corrected.
• Identify cable trouble locations that affect pair bonding to be used in network analysis.
• Enhance the business values and to allow for efficiencies within the network along with operational savings.

Following are the business benefits achieved by using NPI in Telecom firms:

• Decision Support tool for network management.
• Provide efficiency within network along with Operational Savings.
• Increase organization’s overall efficiency.
• Save time and manual effort.
• Provide a competitive edge.
• Ensure a higher level of customer satisfaction

. To cater the Network Expansion Scenario’s objectives explicitly, the NPI was built with the following tools:

• Hypothetical Element/Image placement Tool.
• Extended Identify Tool.
• Drill Down and Network Path Tracing tool.
• Map Notes.

The NPI Architecture:

ArcIMS, one of the ESRI’s Server GIS Product, is designed with flexible open architecture, providing almost unlimited customization possibilities. Several APIs including the Servlet connectors, Java Connectors, HTML Viewer are provided by the ArcIMS, to facilitate the customization. AJAX is also implemented in NPI for a faster searching of input data. Hence NPI Application has incorporated the ArcIMS Architecture to build its Map Interface and its Request/Response Communication with the server. The Map Interface has few more additional map tools along with the basic map tool functionalities like zooming, panning, etc.

The ArcIMS clients had to communicate with ArcIMS Server through a Web server and Connectors using ArcXML requests. NPI has used Servlet Connectors, which has its own API. The developers can use these APIs to build the ArcXML requests and parse ArcXML Responses to communicate with the ArcIMS Server. The ArcIMS Servlet connector is available in both Windows and UNIX. NPI’s

Functional Process Flow:

NPI uses the HTML viewer as its front end for the Map User Interface.
• The application builds its ArcXML requests and embeds in the servlet and sends the same to the Web Server.
• The Web Server compiles the servlets in the Servlet Engine.
• This connector uses the servlet engine to provide a communication link between the Web server and the ArcIMS Application Server. It accepts ArcXML and sends ArcXML only.
• The ArcIMS Spatial Server gets the work done with the services running inside it and sends the response back to the ArcIMS Application Server.
• The ArcIMS Application Server sends back the response in the ArcXML format to the Servlet Connector and it’s passed to the client end.
• Finally the response is displayed as a Map on the Client end.

Fig.1: NPI Architecture

Hypothetical element/image placement

This tool is used to place hypothetical elements or images on the map and store the details of those elements as x and y co-ordinates. This looks similar to placing notes on the map. It can help the planners and engineers in the following ways:

To perform design operations such as locating the places for new cellular towers, business outlets, service terminals etc., with suitable back end data.

• To store the values of these hypothetical elements in the database as a specific design under respective user login ids.
• To classify the network elements and or regions seen on the map based on different criteria such as distance ranges, strength of the signal from terminals, subscriber locations etc.
• To load the designs and view them based on their user ids or the zoomed regions on the map.
• To provide the planners an Interactive Classify Frame where the planners can have options for choosing the classification type, such as color, distance ranges, etc., thus helping the planners to analyze the data seen on the map.

Fig.2: Hypothetical Network Elements Placed on the Map

Extended Identify Tool:

This facility would operate in a similar way like the basic identify map tool. However an additional DHTML layer has been used to display the initial data with links to show the detailed information on a separate page. This helps the users to obtain the first level information of the data initially. This improves the performance substantially as it takes a lot of time to get the detailed information from the database every time when the identity tool is clicked. Thus it helps the planners for an effective analysis of the work force management/ network elements/other location based spatial data such as:

• Cable
• Terminal
• Living Unit
• Central Offices

Fig.3: Extended Identify Tool with links.

Fig.4 Detailed information appearing in a separate page

DrillDown and Network Path Tracing Tool:

This tool helps the planner to drilldown on any features on the Map. For instance, to get a detailed information about a cable running on the field, the planner has to click on the particular cable on the map. The details of the entire cable path would be displayed at the bottom with a hyperlink. The single cable path can have many cable segments. To view the detailed information of each cable segment present in a particular cable path, the planner has to select that cable path link. Meanwhile the selected cable path will be highlighted and seen on the Map. Some of the cable information that can be obtained and viewed are :-

• The cable pair numbers.
• The cable material.
• The Gauge value of the particular cable.
• The status of the cable, if it’s already assigned or if its fault or if it is to be assigned in future.
• Any logical name assigned for that particular cable.
• The spatial data source name that provides these cable information.

Fig 5: Drilldown Information with a Link.

Fig 6: Cable segment details of the particular cable path link selected

Fig 7: The Traced Network (Green Colored) on the Map

Map Notes:
Whenever a planner creates a design and has to attach some notes to it on the map, the Map Notes can be used. These notes can be added anywhere on the map as per the planner’s choice, by simply clicking on that location and entering the required information. Such map notes are generally useful for making notations of any adjustments made on any feature on the field. The planner can also save the whole design along with the attached notes in the database for future analysis.

Fig 9: The Map Notes tool

Reports:
The Plant Engineers can build their query to retrieve specific data for analyzing purposes using the Reports Tool. Such Reports can assist in:

• Collecting details about various network components deployed on the field.
• Retrieving the spatial data from various spatial data sources.
• Displaying the specific data in a user defined format.
• Tracing and viewing the selected data on the map.

These report generating modules were developed using Struts, one of the loosely coupled J2EE architecture. They are platform independent and can be plugged into any other map application, to retrieve the data and display on the map.

Fig.10: Data Trace on the Map

Conclusion:
We have seen that a GIS based telecom application supports various telecom inventory and operational service system related requirements along with standard GIS functionalities. NPI is one such real time application which uses a map interface to display the spatial data and helps the enterprise in:

• Managing the Inventories and network equipment placement at the subscriber locations.
• Planning and designing the network and provide network data.
• Identifying the unreachable and fault locations and to offer service maintenance accordingly.

Thus apart from viewing the spatial data, NPI also serves as a decision making tool, a report generating tool, a classification tool and as an Interactive Planner. The implementation of this enterprise GIS Solution has reduced the overall maintenance costs and manual work. NPI has made an effective use of GIS resources in providing efficiency in service fulfillment, customer relationship, workforce management, network expansion and extended network services.

Surekha Dudhani
Assistant Professor, Electrical Engineering,Bharati Vidyapeeth College of Eng.,A-4, Paschim Vihar, New-Delhi-63

Geological surprises
Especially in the Himalayan region where the water resource potential is high by means of glaciers and intensive rainfall updating of the information is required frequently, which is time consuming by conventional method.

Most of the power projects in these regions demands for underground tunneling which is most difficult and expensive affair. Present means to conduct survey using remote sensing, aerial survey, and GPS based survey have certain limitations

• The topological maps used for walkover survey and preliminary can be very old and recent changes in inhabitation pattern, vegetation coverage and water bodies etc. are not updated frequently.
• The surveying staff does not have bird’s eye view of the present ground condition, which may result in large inaccuracies in estimation of civil works.
• There is every possibility of error in recording the ground data and subsequent transfer on the route map.
• Expensive: Require lot of expertise, specialized equipment and time consuming.

The conventional method of assessing hydro potential could not be directly adopted in the inaccessible areas like Himalayas where the water resource potential is high by means of glaciers and intensive rainfall. Similarly the geological, structural configuration is essential to study and to understand the strength and weakness of the area so that the project will be implemented in the suitable terrain. For geologic mapping, reflectance information of the rocks is very important.

Study of alternative ranking and taking optimal decision require lot of time in co-ordination of data (topology, hydrology, geology, geographic, meteorology and environment etc.) from various departments and preparation of maps of updated informations. These factors are great deterrants to faster implementation of hydropower projects.

In the proposed methodology the satellite images obtained from IRS-IB/1D is used to develop GIS database for, identification of source, selection of site, environmental planning, digital terrain model data (DTM), transmission line network and ranking of the sites. A rule-based expert system is developed on Prolog platform for decision support at each stage of modeling.

Development of GIS in Knowledge-Base environment:
Geographic Information System (GIS) is a computer based information system used to digitally represent and analyze the geographic features present on the Earth surface and the events that are taking place. It is not restricted to the conventional view of geography, i.e. that of people and places on the Earth’s surface, but GIS is the perfect tool to discover hidden geographies, to explore the hidden facts of World Wide Web, the complex geography of a printed circuit, the architecture of a combat aircraft, or layout of high-tension transmission lines. The planning for the Hydel Power generation can be accomplished in the following steps.

1: Remote sensing and Image processing:
Remote sensing technique has witnessed a wide range of application in natural resources database management in recent years. In satellite-based remote sensing data collected by satellites are processed by digital computer or optical techniques to extract valuable informations for scientists and engineers. One of the most widely used data format for information extraction is the infrared False Color Composite (FCC) image. The extraction of information from such images about ground reality is done by image interpretation for which generally three methods namely photo interpretation, spectral analysis and data integration are used. In this paper our concern is with photo interpretation method and the development of a rule based expert system for image interpretation of the region and planning Hydel Power Generation. Photo interpretation is the visual interpretation of images based on features like tone, pattern, shape, size, shadow, texture and association. Most of the conventional digital images processing techniques are based on color or size or texture or tonal variation of each pixel in the image.

In contrast to digital analysis of the images, a human interpretater exploits the aggregate information related to various basic image-features of unknown object along with his scientific knowledge, general knowledge of the phenomena as well as experience of doing classification rather than analysing the image pixel by pixel. As a consequence, the interpretation result for land use and land cover produced by a well-trained human interpreter is often less crude than the same obtained using digital techniques. For human interpreter it is easy to interpret natural color image but the interpretation of FCC image becomes difficult and requires adequate training and experience. Also different band combinations of satellite data for three primary colors result in different FCC images, which are suitable for different application. Every application of remote sensing deals with a specific subject or integrated process of different subjects. Thus the process of visual interpretation of wide variety of remotely sensed data is a complex intuitive process of combining evidential information from different sources and subjecting such information to an expert’s knowledge, experience and heuristics at each levels namely detection, identification, analysis, recognition and classification of the process. It calls for the analysis of a number of related information by a domain expert. So even with on- going advances in digital image processing technique the importance and role of human photo interpreter can not be ignored and it is required to train human resources to gain this skills .In future the scope of on-screen interpretation of high resolution remote sensing data will increase and image identification system providing way to combine together human interpreter and machine interpretation accuracy achieved using digital image processing technique has been reported up to 70% to 80%. The associated information and logical reasoning that are used by a well-trained human interpreter can be encoded in the form of rules and facts to create knowledge-based systems. The activity of image interpretation has similarity with the nature of explorative and qualitative reasoning in the line of Artificial Intelligence (AI) and expert system. Since the interpretation of each FCC images requires different skill and experience and thus even for a human interpreter it becomes difficult to manage properly application of huge knowledge while making decision in photo interpretation. Also the human interpreter may be absent or not easily available. The knowledge used in photo interpretation can be represented in logical paradigm that makes the logical programming language like Prolog highly suitable for expert development.

1: Source identification:
The remote sensing technology is an effective tool for the identification of suitable sites for locating new hydropower projects especially in the inaccessible areas like Himalayas where the water recourse potential is high. Remote sensing data available in the near infrared region (0.8um – 1.1 um) provides clearly the contrast between land and water features can easily be discernable.

Most of Meteorological data (river flow data, temperature, solarity, relative humidity, wind speed etc.) and Aerial Climatographical data (digital/paper maps of precipitation, evaporation, temperature, snow cover, relative humidity, wind direction etc.) are obtained and interpreted with the help of GIS in knowledge base environment.

2: Site selection:
Building a hydropower station in a rugged terrain such as in the Himalayas requires thorough studies of geological lithologies, water drainage patterns, surface and subsurface structures. The geological, structural configuration is essential to understand the strength and weakness of the area so that the project could be implemented in the suitable terrain. If hydropower is to be generated from dam water then selecting suitable dam sites requires careful consideration of environmental impacts. Satellite imagery is used for the identification of catchment boundary; drainage network, perennial streams, land use and vegetations cover for these projects. Remote sensing and GIS have the potential of relatively accurate analysis of a site location. Digitizing the elevation contours and spot heights from topographic maps and using capabilities of various GIS softwares generate Digital Elevation Model (DEM) of these catchments. The catchment boundary, drainage network and location of major habitation are overlaid on these DEMs for further analysis.

3: Environmental planning:
Basic environmental issues for hydropower project developments, catchments area treatment, aforestation and rehabilitation and resettlement are solved with the help of GIS solutions. 4: Digital Terrain Model Data (DTM): Digital Terrain Models are prepared for computation of slope, channel length, catchment area, head available for power generation and location of suitable sites for civil structures. Repetitive satellite data for these catchments are effectively used to locate the region of deforestation and impact on forest development.

5: Transmission Line Network:
To execute transmission line network, precise planning, costing, scheduling etc are required. To optimize the cost of transmission line lot of survey is required like,

1.Shortest and alternative route from the generating station to load centers
2.Topographical and geographical nature of the terrains
3.Physical constrains (railway crossings, road crossings expansion of villages and towns, etc.)
4.Environmental factors (reserved forests and high tree areas, national parks and wild life sanctuaries etc.)
5.Soil condition
6.Costing etc.

Selection of suitable areas, the optimum path finding, the profile analysis, the engineering design of towers and wires and the cost estimation can be done using GIS and satellite images. The steps involves,

• Planning of master guidelines of route construction including voltages, number of lines, starting and ending substations.
• By interpreting the satellite images of the area depicting it’s geographical features, environmental features, several alternative routes can be designed and compared. In comparing the alternative, technical cost and social cost (environmental impact, rehabilitation, irrigation etc.) of laying of the transmission lines can be included in the model.
• Preparing basic routes
• Preparing detailed route with optimal concept
• Implementation of an actual route.

3.6: Ranking of the sites:
All infrastructure development for hydel projects follow a set procedure of survey and investigation before taking up implementation. These steps involve, Pre-Feasibility Report, Feasibility Report, and Detailed Project Report. Since the balance potential hydropower sites in the country are of the order of 450. At the pre-feasibility report stage a ‘Ranking’ or ‘Order of Priority’ should be evolved so that least socio-economic, environment and infrastructure development costs and best return in hydro-power generation ensuring river basin-wise resource optimization . The technical and non-technical criteria are,

(i). Reconstruction and rehabilitation
(ii). Accessibility to site
(iii). Height of dam
(iv). Length of tunnel/ channel
(v). Hydro-power potential
(vi). Type of scheme (run-of-the river or storage development)
(vii). Status of project
(viii). Status of upstream or down stream hydel development
(ix). Inter-state aspects
(x). International aspects.

Central Electricity Authority has adopted certain maximum and minimum value between number range from 6 to 15 for each of the above 10 criterias. Accordingly sites have been graded from ‘A>80′ to ‘E<20′. Space technology using satellite and aerial remote sensing with GIS technology provide faster result in terrain mapping and scientific assessment of the ground conditions especially for mountainous regions where majority of sites are located. Image analyses of satellite digital data and creating spatial geo-reference information of terrain, using GIS tools, provide basis to address indices in the ranking study faster than the traditional method at comparatively comfortable cost.