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Abstract
Using GIS & CAC to thematically portray social poverty levels and basic services in Great Brak and Little Brak, Mossel Bay Local Municipality.
Ray Pillay
Lecturer/ Cartographer
Iniversity of Limpopo,
Email: pillayr@ul.ac.za
Background: With the onset of the new millennium poverty is still very much, a social ill, that many developing countries across the globe have to grapple with. Of the six billion people in the world, 2.9 billion live on less than two dollars a day and 1.2 billion live on less than one dollar a day. One of the pursuits of the Johannesburg WSSD was to adopt an effective action plan for a global effort in which the fight against poverty has one of the main tasks. Key to poverty reduction is access to services and infrastructure that deny an individual opportunities and choices most basic to his/her human development.
Being multidimensional poverty varies in scale and context with the rural poor facing different challenges to those in urban areas. There are however, various ways of estimating poverty: monetary poverty is expressed in economic terms; human poverty relies on social indicators and social exclusion broadly implies marginalization. This paper will attempt to map poverty levels largely through a case study of the Great Brak and Little Brak local village areas within the Mossel Bay Local Municipality using social indicators as measured by HDI values and statistical methods.
The poverty disease knows no boundaries, but spatial poverty index mapping through cartographical analysis may yield vital clues as to poverty distribution (clustered or random) across communities with different norms and socio-economic status. Cartographic and GIS techniques would also assist in developing measures for monitoring the geographical spread of the poverty line across the southern Cape Region.
Methods: A base map for poverty index mapping would be constructed for the purpose of spatial human poverty portrayal across the study area. Using ArcView GIS, social indicators (data) from two local village towns within the district of Mosssel Bay are cartographically mapped at 2004/06 levels to show the trends in the spread of social poverty. Further, social poverty estimates of one adjoining (white) local municipality would be statistical analyzed and ranked according to household infrastructure and household circumstances indices for comparison.
Conclusion: To draw up poverty reduction policies and redistribute economic benefits local authorities needs to understand who the poor are and where do they live. Using GIS & CAC techniques poverty maps can serve as dynamic tools to help identify and locate poor areas and populations. Moreover, the emerging patterns and geo-spread of poverty across different economic areas within the Western Cape Province may provide some guidelines to the possible trend that poverty line would take over the next four year cycle. This however, must be met with the appropriate clinical, educational and social programs to secure some control or curtailment on the geographical spread of poverty across the South Western Cape region by 2006/2007.
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