Browsing by Author "Hamann, Christian"

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    An analysis of microscale segregation and socio-economic sorting in Gauteng
    (Gauteng City Region Observatory, 2024-04-24) Hamann, Christian
    The negative social and spatial effects of apartheid are prominent legacies that shape urban development. This Occasional Paper analyses racial segregation and socio-economic sorting in Gauteng. In this research, a specific microscale analysis is added to the existing knowledge of segregation and socio-economic sorting patterns in Gauteng. A microscale representation of segregation is an important lens through which to view progress toward spatial transformation; it reveals how factors, such as residential expansion, the property market, and the character of neighbourhoods, influence racial segregation and socio-economic sorting. The research includes three points of inquiry about racial-residential segregation and socio-economic sorting in Gauteng. The first considers the relationship between racial diversity and residential expansion. Between 1990 and 2020, the residential footprint of Gauteng increased by roughly 905 km2. The study investigated whether residential growth contributes to desegregation or perpetuates segregation. The analysis shows that areas of residential expansion tend to reproduce the racial composition of the areas from which they expanded. However, public housing programmes and inclusionary housing policies hold significant potential for desegregation at multiple scales. The second inquiry of this study analyses the extent to which racial mixing contributes to class mixing and income equality in desegregated neighbourhoods. In South African cities, middle-class neighbourhoods have been celebrated for becoming racially integrated. However, behind this undoubtedly important transformation, this study finds a largely unrecognised feature: in racially mixed wards, the mean household income of the white residents is significantly higher than the mean household income of black African residents. Racially-inflected income inequality in neighbourhoods therefore remains discernable even in the context of considerable racial desegregation. The third inquiry is concerned with patterns of microscale socio-economic sorting in desegregated neighbourhoods, and specifically how this is associated with the housing characteristics that shape neighbourhoods. The analysis can illustrate how the affordability of housing and the social character of neighbourhoods influences socio-economic sorting. Together, the three inquiries highlight continued segregation, but also nuances in the nature of desegregation in the Gauteng province at various macro- and microscales. Macroscale analysis in Gauteng shows that racial-residential segregation continues to happen in and around townships and is associated with low-cost housing developments. Desegregation is evident in the central suburban areas and is associated with mostly middle- to high-income housing. Although significant racial-residential desegregation has taken place in former whites-only neighbourhoods, the association between space and class in Gauteng has not changed significantly and spatial transformation is slow. The local-level, data-driven analysis reveals that desegregation is uneven in some neighbourhoods and the socio-economic sorting happens based on the characteristics (including quality, quantity and affordability) of the available housing stock. The research argues that a multiscalar view of segregation and socio-economic sorting is essential to understand urban form and function. Microscale analysis reveals both barriers and opportunities for future spatial transformation. Residential expansion, whether by the public or private sectors, should be strategically driven with diversified housing at different affordability levels, while neighbourhood-level developments should foster socio-economic inclusion. In this way, desegregation and socio-economic integration are facilitated at different geographic scales, and more equitable access to opportunities in the city is enabled.
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    Gauteng’s urban land cover growth: 1990-2020
    (Gauteng City-Region Observatory, 2022-03-31) Ballard, Richard; Hamann, Christian
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    Mapping vulnerability to COVID-19 in Gauteng
    (Gauteng City-Region Observatory: Map of the Month, March 2020, 2020-03-20) de Kadt, Julia; Gotz, Graeme; Hamann, Christian; Maree, Gillian; Parker, Alexandra
    The world is reeling as COVID-19 infections spread. This Map of the Month aids an understanding of the localised risk factors that might contribute to transmission of COVID-19, or amplify its health and socio-economic impacts in Gauteng communities. It explores two key themes: (1) the multiple risk factors to maintaining social distance and preventative hygiene; and (2) the multiple risk factors for health and socio-economic vulnerability during an outbreak or broader shutdown.
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    Quality of Life Survey V (2017/18): The quality of life of students in Gauteng
    (Gauteng City-Region Observatory, 2020-07) Hamann, Christian; Joseph, Kate
    In the Quality of Life (QoL) V (2017/18) survey, respondents from all population groups were represented in the student sample. However, a higher percentage of all Indian/Asian respondents (17%) and white respondents (13%) were registered as students compared to the proportion of all African respondents (10%) and coloured respondents (11%). The differences were larger among younger respondents from each population group. • However, racialised socio-economic inequality is evident in the fact that the average monthly household income of African students was around R11 755 while the average monthly household income of white students was around R38 541. • Similarly, a lower percentage of African students reported having access to assets which are likely to assist learning (like a laptop or internet at home) when compared to the access of coloured, Indian/Asian or white students. But African and white students had higher levels of access to these assets than African and white non-students. • The majority of all students in the sample would have qualified for National Student Financial Aid Scheme (NSFAS) funding (69%) based on their household income. A further 26% of students were considered part of the ‘missing middle’, and only about 5% of students could be categorised as upper class. Racialised socio-economic inequality is evident in students’ average monthly income • There were important lifestyle and class differences between full time and part time students. On average, students had a higher socio-economic status than non-students, but part time students had a higher socio-economic status than full time students. • The mean age of full time and part time students was 24 and 31 years, respectively. Further, part time students were more likely to be household heads, while in the households of full time students it was more likely for the mother or father of the student to be the head of the household. • On average, students were 6% more likely to be satisfied with a range of services, facilities and spheres of government than non-students, but higher satisfaction with services did not translate into higher satisfaction with spheres of government. • Although the differences remain relatively small, students were more likely to respond positively on various measures of physical wellbeing (like general health status) and mental well-being (like having emotional support) than non-students. • Despite a significant degree of racial inequality in the student sample (in terms of income and access to assets), students score higher on the overall quality of life index than non-students. • While respondents born in Gauteng were the most likely to be students (12%), migrants from other provinces were nearly as likely to be students (11%). By contrast, only 6% of respondents who had migrated from another country were students. • Across QoL surveys, students predominantly made use of taxis (44% on average) or private motorised transport (31% on average) for their trips to the places where they study. • A slightly smaller percentage of students (7%) participated in protest action compared to non-student respondents (9%).
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    What are participants telling us as we collect data for the next Quality of Life survey?
    (Gauteng City-Region Obervatory, 2021-02-26) de Kadt, Julia; Hamann, Christian; Mkhize, Sthembiso Pollen
    Data collection for our forthcoming Quality of Life 2020/21 Survey (QoL 2020/21) is now over two thirds complete. Along with the regular difficulties of data collection, such as ensuring everyone’s safety and negotiating access to conduct the survey, data collection has also had to navigate the challenges of COVID-19, and more recently, heavy rains and flooding due to Cyclone Eloise. We hope to complete data collection by the end of May 2021, and share preliminary results in July 2021. While data collection is still underway, our February Map of the Month shares some of the comments and feedback we’ve received from survey participants since we started conducting interviews in October 2020. As part of our regular scrutiny of incoming survey data, we review comments shared by participants at the end of the survey interview. While the comments do not constitute representative survey data, and do not tell us about overall or localised levels of concern regarding particular issues, they do shed some light on the people behind the data we are collecting, and what is on their minds. We have identified some of the most prominent themes in these comments, and shared various colour-coded maps of the selected comments. We were struck that one of the strongest themes, shaded in green, was one of gratitude for the opportunity the survey provides for people to share their experiences and challenges. Many of these participants also expressed hope that their voices and concerns will be heard by the government, and will inform positive change. Other comments highlight issues or challenges that participants want to emphasize - key amongst these are crime and safety (yellow), unemployment (purple), governance (orange) and gender-based violence (red). These comments remind us of why we conduct the Quality of Life survey, and motivate us to do our utmost to ensure survey findings will influence decision making and action to improve living conditions in the Gauteng City-Region. GCRO thanks the fieldworkers from GeoSpace International for their enormous contributions to making this survey possible. We also thank all the participants across Gauteng who have generously given their time to participate in the survey. Without their willingness to share information about their lives and challenges, we would not be able to conduct this survey. As we are still collecting data all across Gauteng province, there is a chance that a GeoSpace International fieldworker may still knock on your door. If they do, we hope you’ll be willing to allow them to interview you!