Assessment of Poverty in Coastal Zone of Tirunelveli District, Southern Tamilnadu Using Geoinformatics

www.ijemr.net ISSN (ONLINE): 2250-0758, ISSN (PRINT): 2394-6962

Volume-5, Issue-4, August-2015 International Journal of Engineering and Management Research Page Number: 400-404

Assessment of Poverty in Coastal Zone of Tirunelveli District, Southern Tamilnadu using Geoinformatics

Mahendran.M1, Chandra Prasath.V.T.S2, Anand.M3, Rajamanickam.M4, Jose Ravindra Raj.B5 1,2,3Post Graduate Student, Department of Civil Engineering, Prist University, Trichy-Thanjavur Highway, Vallam, Thanjavur, INDIA 4Assistant Professor,Center for Geoinformatics, Prist University, Trichy-Thanjavur Highway, Vallam, Thanjavur, INDIA 5Assistant Professor, Department of Civil Engineering, Prist University, Trichy-Thanjavur Highway, Vallam, Thanjavur, INDIA

ABSTRACT objective.The main source for spatial statistics on global The study carriedout during 2007 major Tsunami episode poverty is the World Bank,which has gathered,analysed occurred,its affects damaged to coastal property as well as statistical survey and distributed national level spatial data economically damaged to people’s property. In this study has on coastal Poverty level records since 1990.Their carried out to find out the coastal poverty location along the methodology are based on the analysis of real time household coast of Tirunelveli District of Tamilnadu. The parameters like surveys completed in almost 100 countries.Survey questions gender, religion, family, occupation, house type, electricity, family members, education, total income, food expentiture, cloth cover sources of income, consumption,expenditures,and expentiture, education expenditure, saving and debt numbers of individuals making up the household. Most information. The thematic GIS overlays of different layers were surveys are conducted by government employees.Two types carried out to find out the coastal poverty location.The final of poverty data are produced coastal poverty line data and results show the level and magnitude of poverty level is noticed. international poverty line data. The GIS based analysis of socioeconomic data is significant to Individual nation set up their individual poverty line for understand society as well as country economic development. the national data. Conflicting philosophy in defining poverty make pooling the national poverty line data problematic. Keywords---- Tsunami, Property, Coast, Expenditure, GIS, Inaddition to that more in current times,purchasing power Poverty parity has been introduced into the formulation of international poverty line data.There are a number of problems well documented by the World Bank.Not all countries carry out the poverty surveys,the currently available I. INTRODUCTION data were derived from surveys spanning 1988 through 2010 and the survey repeat cycle is uncertain.The Poverty has emerged as one of the major problem facing intercomparability of the estimates is uncertain due to society during the 21st century.Based on statistical survey difficulties in reconciling consumption and income data,plus from the World Development Indicators[ ] roughly 42% or discrepancies in the purchasing power parity estimate for 2.6 billion people exist in poverty. Poverty is the general term individual countries[2].It is also possible for governments to describing living conditions that are hazardous to influence the outcome of the surveys since they design the health,comfort,trade and industry development.In locations questionaries,chose the site for poverty survey and carry out where poverty levels are high there is typically a convergence the interviews.In recent year Geographical information of inadequacies across several of these areas widely noted System (GIS) plays a vital role in social economical study. consequences of poverty include higher infant death,shorter Now a day, GIS based Poverty maps have emerged as life spans and worse literacy rates.Poverty is also closely mostly used for assessing the poverty level estimation and associated with environmental degradation.The United analysis [3].Poverty maps habitually symbolize a single Nations Millenium Development Goals include a 50% measure or multiple catalog value for study region,for reduction in extreme poverty by the end of 2015.Economic example coastal zone or coastal villages.Spatially intergrated analyses[1]point out that eliminating poverty is a main 400 Copyright © 2011-15. Vandana Publications. All Rights Reserved. www.ijemr.net ISSN (ONLINE): 2250-0758, ISSN (PRINT): 2394-6962 international maps of the numbers of individuals living in Statement of Research Problem different poverty level ,based on a reliable categorization of In many coastal villages of tamilnadu today,most of the the poverty line would be tremendously supportive for development programmes/plans targeted at poverty targeting of efforts to decrease poverty[4] measurement of the alleviation have failed to achieve desired results because value of spatially different data is that they be able to be help successive governments have over the years been planning to understand multiple levels of planning national level,state and executing such programmes/plans without relevant level or district level planning.If spatially dissimilar poverty maps,especially those viewing the distribution of the poor maps can be updated regularly,on an annual or semi-annual people all along the coast.coastal poverty is on the subject of basis,they could be find to pathway of poverty line.The access and consumption of state provided commodities effectiveness of poverty reduction efforts in specific localities namely transportation,water availability,electricity and the cost of natural disasters event,epidemical disorder or facility,health care and sanitation, education and conflicts.Satellite images can able to provide synoptic view marketplace.Coastal Poverty in several communities is and repeatable sources of observations.In the earth and openly related to the lack or poor conditions of these environmental sciences,satellite data have proven vital role in infrastructures/services.In the appearance of decreasing global mapping and global assessment of processes such as public resources therefore,a feasible approach neededto coastal vulnerability assessment.There are numerous assign finanvial esources for poverty alleviation program. applications for satellite images have been developed in the Coastal Poverty mapping social and economic sciences.In part,this to be credited to the Poverty mapping defined here as the spatial information that most earth observation satellite sensors are representation and analysis of indicators of human wellbeing optimized for observation of natural phenomenon (rainfall) and poverty [5] [6]. is becoming an increasingly significant that are not directly related to socioeconomical activities such tool for more integrated investigation and discussion of as total population density,moneyflow,basic amendies and social,economic, and environmental problems.Maps are economic status of the region.In this paper discussed the powerful tools for presenting information to non- application of GIS for coatal poverty assessment using spatial specialists,who are able to examine mapped data to identify analysis. clusters,patterns of distribution,and trends of poverty in the community. II. STUDY AREA Why Use Coastal Poverty Maps? Poverty maps also permit simple comparison of The study area is lies between 77.040’-780.20’E and indicators of poverty or well being with data from new 080.10’-080.50’N.The coastal stretch comprises of small impact assessments, such as the right to use the transportation hamlets, numerous population concentration all along the facility, or services, availability and condition of natural shore.The study area map is shown in fig.1. resources, and distribution of communications facilities. Specifically; 1. Coastal Poverty maps can help to get information on the spatial distribution of Poverty maps that in turn around the targeting of interference or development projects. 2. GIS based coastal poverty analysis makes it easier to incorporate poverty data from diverse sources 3. Geo-referenced spatial information can free analysis of the restrictions of fixed geographical boundaries. For example data can be converted from administrative to ecological boundaries which are often more significant in a natural resource management context. 4. Mapped information on the levels and distribution of poverty make the results of analysis more easily understandable to a non-specialist audience. OBJECTIVES 1. To understand the coastal poverty on village level of the selected coastal Sector 2. To identify the places where vulnerable human populations are dependent on vulnerable fisheries, species, or marine or coastal habitats. 3. To examine economic and social issues of sustainable utilization of coastal and marine resources such as Fig.1.Study Area globalization.

4. To prepare an integrated coastal poverty mapping and Therefore, this paper concentrates on the above six select a method to calculate, estimate, or display poverty variables and discussed the problems encountered in using Indicator for a geographic area. them in a socioeconomic index. 3.3.GIS integration III. METHDOLOGY Some poverty mapping techniques use composite indexes as the poverty measure and rely on the direct Depending on the chosen poverty indicator,input aggregation of census data to display the poverty indicator for data,and method of assessment and calculation,researchers the chosen geographic area. Please refer to [8]. For detailed will have diverse options for calculating or estimating the descriptions and examples of other coastal poverty mapping poverty indicator across a geographic area case in point,if approaches that do not rely on small area estimation. GIS map maker are using census-level data made available at the based Composite indexes were used for poverty mapping. household level,then simple aggregation of the data for the selected geographic unit may suffice.However,researchers IV. RESULTS AND DISCUSSIONS often need techniques that are more sophisticated.Coastal Poverty maps often combine census data(featuring complete The results shows Human Development Index (HDI) is country coverage)with household survey data(encompassing prepared based on six variables namely,population,house a representative sample of the selected population).This is type,food expenditure,educational expenditure,cloth skillful by means of advanced statistical methods based on expenditure,conservation status variables:house socioeconomic analysis,sometimes referred to as small area holds,education (literacy) and income.All components are estimation.Combining data from divorce sources,enables a weighted equally.Basic needs indexes typically have included poverty mapping study to benefit from both the complete more than six variables for example,literacy,access to spatial coverage of the census and from a relevant poverty water,access to sanitation,access to health services,and indicator in the household survey.Such statistical techniques quality of housing shown in Figures 2,3,4,5 and 6.Many of help overcome the survey’s insufficient sample size,which the existing basic needs indexes have equal weighting could not be aggregated to small administrative units, and the schemes similar to the HDI.Others have relied on expert census’ lack of an appropriate poverty measure. opinion or multivariate statistical techniques to provide 3.1. Decide on number of units for final map (resolution) to weightings for each variable,Adapted from [9].[10]. Present poverty data House type categorised into thatched house, tailed house For many poverty-mapping methods,this step is often and concret house the tailed house is predominantly present combined with the earlier one.In the case of small area all along the coastline.Very few concret house was noticed assessment based on household-unit data, researchers cannot 400m from coastline Fig.3.TIN MODEL showing food map an individual household; they must aggregate expenditure level,peoples are spending money minimum 150 household-level data to larger units to reduce the statistical Rs to maximum 12,000Rs.From the generated TIN model error in their prediction model.Sensitivity tests conducted by clearly shows the maximum pink colour spread all over the researchers suggest that a minimum of 5,000 households is study area,it indicated that maximum number of peoples required to reduce statistical error to an acceptable spending money range from 1842 Rs to 3535 Rs.In addition level[7].The number of households requirement may be to that from the fig.4.clearly shows the educational significantly higher in other cases,especially if the statistical expenditure cost range from 8% to 26%.It was clearly model is not as strong in its predictive power. depicts the only 30% of peoples interested in education rest 3.2. Selection of coastal poverty variables of 70% of peoples occupied with fishing activities,they are There are numerous possible indicators of coastal not aware of importance of education.From the poverty. In deciding which features consisting of the map.6.showing cloth expenditure cost per year. attractiveness must be balanced against the availability of up- The cloth expenditure cost range from 1,000 Rs to to-date data that is in a useable format. Therefore the 45,000 Rs / annually. The maximum number of people variables chosen for inclusion in the index were those, for spending money 2500 to 5000 Rs yearly noticed. Occasional which data could be simply obtained, and which were also festival time cloth expenditure cost, increasing compared to deemed to be of relevance to coastal areas. The coastal other season. The light expenditure map was shown in Fig.6. poverty variables selected for inclusion in the coastal poverty It clearly depicts the light expenditure cost range from 40 to variables sub-index are listed below: 190 Rs / month. • Population • House type • Food Expenditure • Educational Expenditure • Cloth Expenditure • Conservation status

Fig.5.Cloth Expenditure cost (RS)

e Fig.3.Food Expenditure cost (RS)

Fig.2. House Type (No)

Fig.3.Food Expenditure cost (RS)

Fig.5.Cloth Expenditure cost (RS)

Fig.4.Educational Expenditure cost (RS)

[6] W.M., Adams, Aveling. Biodiversity conservation and the eradication of poverty. Science 306 (5699), 1146–1149,2004. [7] . .. . Sabins, Remote Sensing:: principles and interpretation - 3rd editio: :. .. . Freeman and company, New York, NY, 1997. [8] R. B. Footy, On the geology of Madura and Tinnevelly Districts. Memoirs of the Geological Survey of India, 20, pp. 1–103,1883. [9] S. Lawrence, and J. Travis. The new landscape of imprisonment: Mapping America’s prison expansion. Research Report. Urban Institute, Justice, Police Center, 2100 M Street NW, Washington, DC 20037 (202) 833-7200,2004. [10] R. E. Lewis, and A. M. Riddle, Sea Disposal: modeling studies of waste field dilution. Marine Pollution Bulletin.20 (3), 124-129.Pergamon Press.1989. [11] J. Krohn, A. Müller and W. Puls. Pollutant Transport Monitoring and Prediction by Mathematical Modeling: North Sea and adjacent estuaries.Marine Pollution Bulletin.23,699- 702,1991. [12] A.Mates and Y.Scheinberg.A Model for Approving and Controlling Sea Water Pollution for Recreational Activity. Toxicological and Environmental Chemistry. 31- 32,479-487. Gordon and Breach Science Publishers,S.A,1991. [13] K.B.BarnesCartographic Modeling of Nonpoint Pollutant Surfaces for a Coastal Drainage Area,in K.B. Barnes, W.L. Lyke and T.J.Hoban[eds.],Proceedings of the Symposium on Coastal Water Resources.American Water Resources Association,pp.133-146,1988. Fig.6.Light Expenditure

V. CONCLUSIONS

The results shows the application of GIS for poverty mapping along the coastline of Tirunelveli district.The thematic GIS overlays of different layers were carried out to find out the coastal poverty location.The final results show the level and magnitude of povertylevel is monitored.

REFERENCES

[1] World Bank 2000.World Development Report 2000/01: Attacking Poverty. New York: Oxford University Press. [2] Sachs, D. Jeffrey The End of Poverty: Economic Possibilities for Our Time. New York: Penguin Books, 2005 [3] K. Dow. Exploring differences in our common future (s): The meaning of vulnerability to global environmental change. Geoforum 23:417–436,1992. [4] K. Dow, Unpublished literature review on the concept of vulnerability’and the ‘factors contributing to vulnerability’. Worcester: George Perkins Marsh Institute, Clark University, 1993. [5] W. M. Adams, Green Development: Environment and Sustainability in the Third World. Routledge, New York, 2001. 404 Copyright © 2011-15. Vandana Publications. All Rights Reserved.