Using CAC and GIS to map the prevalence of Poverty and access to basic services across the Greater Sekhukhune District Council, Limpopo Province

R. Pillay
University of Limopo: Turfloop Campus
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 is 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 (e.g. the Sekhukhune inhabitants) face 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 Greater Sekhukhune District Council (SDC) using social indicators as measured by their HDI values.

The poverty disease knows no boundaries, but spatial poverty index mapping through cartographical analysis may yield vital clues as to poverty distribution 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 this Council.

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 (attribute data) from five local municipalities within the district of Sekhukhune are cartographically mapped at 2001/02 levels to show the trends in the spread of social poverty. Further, social poverty estimates of the five local municipalities would be statistical analyzed and ranked according to household infrastructure and household circumstances indices.

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 techniques poverty maps can serve as dynamic tools to help identify and locate poor areas and populations, especially at a micro-meso scale. Moreover, the emerging patterns and geo-spread of poverty across different economic areas within the 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 province and, in particular, across SDC by 2006.