Search

The physical separation of people from economic activities creates spatial disparities, fragmentation, and unequal access to opportunities. Contemporary South Africa displays spatially divided communities. Affluent locations are socio-economically integrated with fewer crime activities, while poor communities are disintegrated from economic activities with high crime occurrences. The study explores the relationship between spatial mismatch and violent crime in South African metropolitan areas. The study used regression modelling and choropleth mapping to analyse and present the relation. Data used were acquired from Stats SA, CSIR, and SAPS National Crime Statistics. The findings revealed that areas located closer to jobs are associated with low levels of violent crime. In contrast, poor areas are located away from job opportunities and have high levels of violent crime. The study concludes that spatial mismatch determines the level of violent crime in South African metros. [ABSTRACT FROM AUTHOR]

The spatial location of job opportunities contributes to people’s livelihood outcomes. Privileged communities are well located and have access to advanced educational systems, while poor communities are marginalized with deprived education outcomes. Understanding the influence of spatial mismatch on educational attainment requires information about job locations and education levels. Using a regression model, the study examined the role of spatial mismatch in explaining education attainment in South African metropolitan areas. Data were acquired per sub-place from Stats SA, CSIR, and GeoTerraImage. The regression models in all metros confirmed the relationship between spatial mismatch and educational attainment, which is valuable for area-based and people-based development approaches. The findings presented a significant relationship between spatial mismatch and educational attainment. Through the results, the study concluded that spatial mismatch is one of the significant determinants of educational attainment in South African metro areas. [ABSTRACT FROM AUTHOR]

Unequal spatial concentration is at the heart of economic imbalance in South Africa. This research aimed at spatially characterizing labor absorption patterns and exploring the link to spatial development. Labor absorption is often used as an indicator of labor market well-being and for many countries, the indicator is often more insightful than the unemployment rate. The research used secondary data to create a longitudinal data set of labor absorption, functional literacy, the ratio of urban population and gross domestic product growth rate, among others, at a municipal level. Exploratory spatial data analysis and thematic mapping were undertaken to illustrate the characteristics of the labor market, while spatial grouping created clusters of municipalities based on labor absorption. A positive upward trend was observed between labor absorption and the share of people in an urban area; that is, the more urban an area, the higher the labor absorption rate. Furthermore, the spatial clustering of municipalities highlighted linear patterns and when this was overlayed with roads and development corridors, clearly indicated the impact of regional spillovers. The findings suggest a more integrated focus on regional innovation policy to reduce spatial disparities in the economic landscape. [ABSTRACT FROM AUTHOR]

Increased HIV/AIDS testing is of paramount importance in controlling the HIV/AIDS pandemic and subsequently saving lives. Despite progress in HIV/AIDS testing programmes, most people are still reluctant to test and thus are still unaware of their status. Understanding the factors associated with uptake levels of HIV/AIDS self-testing requires knowledge of people's perceptions and attitudes, thus informing evidence-based decision making. Using the South African National HIV Prevalence, HIV Incidence, Behaviour and Communication Survey of 2017 (SABSSM V), this study assessed the efficacy of Generalised Linear Poisson Regression (GLPR) and Geographically Weighted Poisson Regression (GWPR) in modelling the spatial dependence and non-stationary relationships of HIV/AIDS self-testing uptake and covariates. The models were calibrated at the district level across South Africa. Results showed a slightly better performance of GWPR (pseudo R2 = 0.91 and AICc = 390) compared to GLPR (pseudo R2 = 0.88 and AICc = 2552). Estimates of local intercepts derived from GWPR exhibited differences in HIV/AIDS self-testing uptake. Overall, the output of this study displays interesting findings on the levels of spatial heterogeneity of factors associated with HIV/AIDS self-testing uptake across South Africa, which calls for district-specific policies to increase awareness of the need for HIV/AIDS self-testing. [ABSTRACT FROM AUTHOR]

South Africa is set to implement a universal health care system, called National Health Insurance (NHI). The country is plagued by inequality at various levels, and the NHI will attempt to provide more equitable access to health. A national online COVID-19 survey in 2020, measured respondents' favour or not of such a system. Multivariate logistic regression identified unique determinants of perception of NHI among urban residents (total respondents 12,682) in South Africa. Data were benchmarked using 2019 mid-year population estimates. Results showed that the majority (77.5%) of urban residents were in favour of NHI in South Africa. Furthermore, township (aOR = 1.36, 95% CI [1.10–1.69], p = 0.005) and informal settlement (aOR = 1.55, 95% CI [1.09–2.20], p = 0.016) residents were significantly more likely to be in favour of NHI than those residing in inner cities. This research sheds light on urban people's perception of the NHI system for South Africa. The findings showed, respondents were mostly in favour of the NHI. It is recommended that the question of NHI be repeated in future research, during a time when knowledge of the COVID-19 pandemic has broadened and to a population with more representative education levels. [ABSTRACT FROM AUTHOR]

There is limited consensus among researchers on whether the spatial distribution of environmental perceptions and place attachment is influenced by socio-economic factors. This study aimed to determine if environmental perceptions and place attachment concepts in our study area (within Gauteng province, South Africa) showed specific spatial patterns. Hot spot, cluster-outlier analysis, and geographically weighted regression (GWR) were used to depict the spatial patterns of environmental perceptions and place attachment. Results showed a pattern where the central, generally affluent wards of the province hold more positive environmental perceptions and place attachment than those in the periphery. This is in line with dependency and other models that associate lower socioeconomic status with lower levels of environmental awareness but is incongruent with other findings that have associated lower socio-economic status with pro-environmental behaviour being more prominent. Geographically weighted regression results revealed the combined importance of numbers of people with above-average income, college-level education, age below 50 years, female proportion, formal dwelling residence and African race in explaining the numbers of people with positive views on place attachment and environmental perceptions. Further, the GWR modelling allowed for the spatial dependence of the relationship between place attachment and environmental perceptions on the one hand and socio-economic factors on the other. These results have significant implications for environmental sustainability, planning and policy formulation in the province. [ABSTRACT FROM AUTHOR]

The need to understand the spatiotemporal change in South African unemployment trends underlies this research. The country has undergone significant socioeconomic change since the fall of apartheid in 1994. Not only has the labor market expanded, but unemployment has also increased. This necessitates a detailed spatial understanding to ensure effective policies at the municipal level to address unemployment. A database of unemployment statistics at the municipal level between 1991 and 2007 was created and used to spatially group municipalities. The results showed that metropolitan municipalities had unique unemployment characteristics and, contrary to findings elsewhere, an overall decrease in the dissimilarity between the unemployed and employed took place after 1991. Therefore, policies should promote economic growth and skills development in municipalities where education levels are low. [ABSTRACT FROM AUTHOR]

The persistence of regional unemployment disparities in South Africa has led to geographically imbalanced development. The advent of 4IR has initiated new ways in which big data can be analysed in a spatial context. Since the 2030 Agenda for Sustainable Development aims at reducing unemployment, this paper will use Twitter data in conjunction with traditional statistics to provide spatial analysis on the topic. Structural unemployment refers to the inability of the economy, due to structural imbalances, to provide employment for the total labour force. In an attempt to quantify this tendency spatially, investigations will be done at a local municipality level (N=213). To supplement the statistics, social media sentiment analysis from Twitter will be used to provide valuable insights into public opinions and perceptions on poverty, unemployment and related themes, in South Africa. By analysing social media content pertaining to unemployment and job prospects, the study will identify spatial patterns in sentiment that reflect the prevailing attitudes and emotions surrounding structural unemployment in various regions of South Africa. Notably, the sentiment analysis will classify the sentiment of each post as positive, negative, or neutral, enabling the identification of prevailing sentiments. This mixed-method approach will enhance the research output. The quantitative data originate from Global Insight and will include variables like unemployment, employment sector, education level and poverty, among others. A correlation coefficient analysis will be undertaken to identify the spatial concentration of unemployment and poverty, if any. Furthermore, regional trends of structural breaks will be identified by considering the percentage of people employed in the nine official sectors (agriculture, mining, manufacturing, electricity, construction, finance, trade, transport and community services) between 1997 and 2020. Case studies of six municipalities will be undertaken to get a more detailed understanding of temporal change and the selection of municipalities is based on the results of spatial factor analysis. The persistence of regional unemployment disparities usually has two origins: the first is that it is based on slow or weak labour market equilibrating mechanisms and the second option is due to the structural equilibrium phenomenon. It is expected that the results will show patterns of structural breaks in municipalities where employment patterns deviated from the national trend over time. Employment in agriculture has been on a downward trend, while employment in the finance sector slightly increased and then decreased again following global economic effects. The research will highlight policy implications for local economic growth interventions. [ABSTRACT FROM AUTHOR]