ABSTRACT
This chapter aims to compare two methods of classifying districts in terms of the skewed spatial and racial distribution of services and socio-economic status. The first method creates an unweighted composite index value for each of the 372 districts using five social criteria and ten service criteria. In the second method neural networks utilize the same variables to group the South African districts into ten classes. The urban areas of South Africa have residential areas that are divided along racial lines, with under-serviced ‘Black’ areas where economic opportunities are few. In order to clarify the access-to-services and social status of the neural network categories, criteria means in each category are standardized and summed to give an indication of the extent to which each category deviated from average levels of socio-economic status and access-to-services in South Africa. The Growth, Employment and Redistribution macro-economic strategy that guides South Africa’s future development has several core elements that impact on socio-economic status and service provision.
