Suchergebnisse

This study analyses the joint effects of the two transmission routes of cholera on the space‐time diffusion dynamics. Statistical models are developed and presented to investigate the transmission network routes of cholera diffusion. A hierarchical Bayesian modelling approach is employed for a joint analysis of nonlinear effects of continuous covariates, spatially structured variation, and unstructured heterogeneity. Proximity to primary case locations and population density serve as continuous covariates. Reference to communities is modelled as a spatial effect. The study applied to the Kumasi area in Ghana shows that communities proximal to primary case locations are infected relatively early during the epidemics, with more remote communities infected at later dates. Similarly, more populous communities are infected relatively early and less populous communities at later dates. The rate of infection increases almost linearly with population density. A non systematic relation occurs between the rate of infection and proximity to primary case locations. It is discussed how these findings could serve as significant information to help health planners and policy makers in making effective decisions to limit cholera epidemics.

The introduction of apartheid in 1948 resulted in racial residential segregation that has influenced the spatial distribution of the population in South Africa. Apartheid laws, which were mainly based on race, brought about the exclusion of the non-white population from urban areas and the mainstream economy of South Africa, as well as the benefits that come with it. In the early 1990's, apartheid was abolished and the South African government set to bring about social and spatial justice, address inequalities and promote social cohesion. This also meant doing away with racial residential segregation that had been entrenched into the urban morphology of the country. Despite this, in the post-apartheid era, racial-residential segregation still exists (Parry and Van Eeden 2015). Figure 1 shows the density (kernel) distribution of each of the four population groups in Gauteng in 2011: Indian/Asian (IA), white (W), black African (BA) and coloured (C). It is a reflection of the legacy of apartheid town planning which isolated non-whites to the peripheral areas of urban economic centers. Densely populated areas are coloured in red while less populated areas are coloured in green. As it can be seen in the map (Figure 1), the white population group densely occupies areas close to the business centers of the province in places such as Pretoria and northern parts of Johannesburg, while non-whites densely occupy peripheral areas in former townships designated to non-whites, such as Soweto (black African), Mabopane (black African) and Lenasia (Indian/Asians). This observed pattern is more pronounced for black Africans. To study the pattern of racial residential segregation in South Africa, non-spatial indices of segregation are widely employed despite their shortcomings. Parry and Van Eeden (2015) are among the few authors who have acknowledged the importance of employing spatial indices of segregation, even though they did not explicitly use one due to the lack of ready to use GIS software. Massey and Denton (1988) define residential segregation as "the degree to which two or more groups live separately from one another, in different parts of the urban environment", i.e. racial residential segregation manifests itself across space. Hence, in order to assess the extent to which the levels of racial residential segregation have subsided, adequate empirical studies that employ spatial segregation indices on socio-economic data are necessary. The purpose of this research is to study the pattern of racial residential segregation by employing a spatial index of segregation namely the 'spatial information theory index (H)' for Gauteng province, the economic hub and most populated province of South Africa. Some of the shortcomings of existing non-spatial indices of segregation (and also of some of the spatial ones) include the Modifiable Areal Unit Problem (MAUP) which refers to how such indices are sensitive to the size of the areal units (i.e. administrative or political boundaries) of analysis that might be arbitrarily chosen or might not accurately reflect the actual racial composition of the local neighbourhoods. This introduces possibilities of obtaining inaccurate measures of racial residential segregation and also being unable to compare the results at various scales of analysis (Reardon et al. 2004; Weir-Smith 2016). One of the major challenges that impedes the use of spatial segregation indices is the lack of ready to use software that has implemented spatial segregation indices which have attempted to address the MAUP. To address such a challenge, Hong et al. 2014 implemented a series of spatial equivalences of existing segregation measures in R under the package 'seg' based on Reardon et al. (2004)'s formulation of spatial segregation indices. Reardon et al. (2004) emphasise the computation of spatial indices of segregation based on the racial composition of the population as reflected by their immediate local environment instead of relying on arbitrary or fixed administrative boundaries. The 'spatial information theory index (H)' as implemented by Reardon et al. (2004) is experimented in this study. This study explores existing literature related to racial residential segregation in order to further complement and supplement existing theories on segregation in South Africa by adopting a spatial analytical approach. The authors take advantage of the R implementation of spatial measures of segregation (Hong et al. 2014), namely the spatial information theory index (H), to study the patterns of residential segregation in Gauteng province (South Africa).

The introduction of apartheid in 1948 resulted in racial residential segregation that has influenced the spatial distribution of the population in South Africa. Apartheid laws, which were mainly based on race, brought about the exclusion of the non-white population from urban areas and the mainstream economy of South Africa, as well as the benefits that come with it. In the early 1990's, apartheid was abolished and the South African government set to bring about social and spatial justice, address inequalities and promote social cohesion. This also meant doing away with racial residential segregation that had been entrenched into the urban morphology of the country. Despite this, in the post-apartheid era, racial-residential segregation still exists (Parry and Van Eeden 2015). Figure 1 shows the density (kernel) distribution of each of the four population groups in Gauteng in 2011: Indian/Asian (IA), white (W), black African (BA) and coloured (C). It is a reflection of the legacy of apartheid town planning which isolated non-whites to the peripheral areas of urban economic centers. Densely populated areas are coloured in red while less populated areas are coloured in green. As it can be seen in the map (Figure 1), the white population group densely occupies areas close to the business centers of the province in places such as Pretoria and northern parts of Johannesburg, while non-whites densely occupy peripheral areas in former townships designated to non-whites, such as Soweto (black African), Mabopane (black African) and Lenasia (Indian/Asians). This observed pattern is more pronounced for black Africans. To study the pattern of racial residential segregation in South Africa, non-spatial indices of segregation are widely employed despite their shortcomings. Parry and Van Eeden (2015) are among the few authors who have acknowledged the importance of employing spatial indices of segregation, even though they did not explicitly use one due to the lack of ready to use GIS software. Massey and Denton (1988) define residential segregation as "the degree to which two or more groups live separately from one another, in different parts of the urban environment", i.e. racial residential segregation manifests itself across space. Hence, in order to assess the extent to which the levels of racial residential segregation have subsided, adequate empirical studies that employ spatial segregation indices on socio-economic data are necessary. The purpose of this research is to study the pattern of racial residential segregation by employing a spatial index of segregation namely the 'spatial information theory index (H)' for Gauteng province, the economic hub and most populated province of South Africa. Some of the shortcomings of existing non-spatial indices of segregation (and also of some of the spatial ones) include the Modifiable Areal Unit Problem (MAUP) which refers to how such indices are sensitive to the size of the areal units (i.e. administrative or political boundaries) of analysis that might be arbitrarily chosen or might not accurately reflect the actual racial composition of the local neighbourhoods. This introduces possibilities of obtaining inaccurate measures of racial residential segregation and also being unable to compare the results at various scales of analysis (Reardon et al. 2004; Weir-Smith 2016). One of the major challenges that impedes the use of spatial segregation indices is the lack of ready to use software that has implemented spatial segregation indices which have attempted to address the MAUP. To address such a challenge, Hong et al. 2014 implemented a series of spatial equivalences of existing segregation measures in R under the package 'seg' based on Reardon et al. (2004)'s formulation of spatial segregation indices. Reardon et al. (2004) emphasise the computation of spatial indices of segregation based on the racial composition of the population as reflected by their immediate local environment instead of relying on arbitrary or fixed administrative boundaries. The 'spatial information theory index (H)' as implemented by Reardon et al. (2004) is experimented in this study. This study explores existing literature related to racial residential segregation in order to further complement and supplement existing theories on segregation in South Africa by adopting a spatial analytical approach. The authors take advantage of the R implementation of spatial measures of segregation (Hong et al. 2014), namely the spatial information theory index (H), to study the patterns of residential segregation in Gauteng province (South Africa).