Spatial analysis of the impact of human activities on the marine environment in Algoa Bay, South Africa
- Authors: Maphoto, Tidimalo Mary Anne
- Date: 2021-10-29
- Subjects: Marine resources conservation Algoa Bay South Africa , Spatial analysis (Statistics) , Human ecology , Nature Effect of human beings on , Marine ecology Algoa Bay South Africa , Integrated coastal zone management Algoa Bay South Africa , Marine spatial planning Algoa Bay South Africa
- Language: English
- Type: Master's theses , text
- Identifier: http://hdl.handle.net/10962/192086 , vital:45194
- Description: Background: Humans have a long history of using the marine environment in multiple ways and continued use has led to a decline in the ecosystem services provided by marine systems in many places. In addition, human activities have steadily increased with time and advances in technology, further increasing impacts on marine systems. To understand and manage these impacts, we need to assess the spatial distribution and intensity of human activities in the marine environment, and quantify, where possible, their cumulative impacts on marine ecosystems. The spatial consideration of human activities and their associated impacts is important for conservation planning, Integrated Ocean Management and Marine Spatial Planning (MSP) initiatives. The main deliverable of this research study was to develop a cumulative impacts layer of human activities in Algoa Bay, South Africa, to support the Algoa Bay Marine Spatial Planning Project. Objective and Relevance: This research analyses the spatial impacts of human activities on the Algoa Bay marine environment (excluding the seashore). Algoa Bay is located on the south coast of South Africa in the Eastern Cape. The research explores stakeholders' perceptions of their knowledge of the human activities that take place in the bay. This research is informed by an expert-based geographical information systems (GIS) approach and cumulative impact assessment in order to map the spatial impacts of the activities as part of marine spatial planning. "Experts" were defined as stakeholders that contributed valuable knowledge of the human activities and their impacts; this definition of expert included "professional" and "non-professional" contributors to knowledge. The spatial aspect of the research is a significant contribution to the field as it will help inform decision-making in the Algoa Bay Marine Spatial Planning Project. Design and Methods: A mixed-method approach was used to generate data. A snowball sampling approach was used to identify research participants from key informants. Primary data were collected through questionnaire surveys, interviews and a focus group. Secondary data sources consisted of GIS data and reports from scientific organizations. Findings and Conclusion: The research findings indicate that the top three pressures that cause the greatest impact on the Algoa Bay marine environment are fishing, pollution and shipping. The cumulative impact of these activities was highest near harbours in Algoa Bay. The marine ecosystems that were most impacted by pressures were the Agulhas Island and the Agulhas Mixed Shore. The Warm Temperate marine ecosystems had fairly low cumulative impacts. The research findings indicate that there is a complex mix of human activities that impact the marine environment. This research supports the findings of other researchers that reveal that the highest cumulative impact is in areas closer to the coast and harbours owing to high population densities. Value of Study: This study builds onto the existing data by expanding the knowledge base and including more stakeholders to integrate as many human activities as possible and bring a holistic picture of the ocean's uses to inform MSP in Algoa Bay. , Thesis (MSc) -- Faculty of Science, Geography, 2021
- Full Text:
- Authors: Maphoto, Tidimalo Mary Anne
- Date: 2021-10-29
- Subjects: Marine resources conservation Algoa Bay South Africa , Spatial analysis (Statistics) , Human ecology , Nature Effect of human beings on , Marine ecology Algoa Bay South Africa , Integrated coastal zone management Algoa Bay South Africa , Marine spatial planning Algoa Bay South Africa
- Language: English
- Type: Master's theses , text
- Identifier: http://hdl.handle.net/10962/192086 , vital:45194
- Description: Background: Humans have a long history of using the marine environment in multiple ways and continued use has led to a decline in the ecosystem services provided by marine systems in many places. In addition, human activities have steadily increased with time and advances in technology, further increasing impacts on marine systems. To understand and manage these impacts, we need to assess the spatial distribution and intensity of human activities in the marine environment, and quantify, where possible, their cumulative impacts on marine ecosystems. The spatial consideration of human activities and their associated impacts is important for conservation planning, Integrated Ocean Management and Marine Spatial Planning (MSP) initiatives. The main deliverable of this research study was to develop a cumulative impacts layer of human activities in Algoa Bay, South Africa, to support the Algoa Bay Marine Spatial Planning Project. Objective and Relevance: This research analyses the spatial impacts of human activities on the Algoa Bay marine environment (excluding the seashore). Algoa Bay is located on the south coast of South Africa in the Eastern Cape. The research explores stakeholders' perceptions of their knowledge of the human activities that take place in the bay. This research is informed by an expert-based geographical information systems (GIS) approach and cumulative impact assessment in order to map the spatial impacts of the activities as part of marine spatial planning. "Experts" were defined as stakeholders that contributed valuable knowledge of the human activities and their impacts; this definition of expert included "professional" and "non-professional" contributors to knowledge. The spatial aspect of the research is a significant contribution to the field as it will help inform decision-making in the Algoa Bay Marine Spatial Planning Project. Design and Methods: A mixed-method approach was used to generate data. A snowball sampling approach was used to identify research participants from key informants. Primary data were collected through questionnaire surveys, interviews and a focus group. Secondary data sources consisted of GIS data and reports from scientific organizations. Findings and Conclusion: The research findings indicate that the top three pressures that cause the greatest impact on the Algoa Bay marine environment are fishing, pollution and shipping. The cumulative impact of these activities was highest near harbours in Algoa Bay. The marine ecosystems that were most impacted by pressures were the Agulhas Island and the Agulhas Mixed Shore. The Warm Temperate marine ecosystems had fairly low cumulative impacts. The research findings indicate that there is a complex mix of human activities that impact the marine environment. This research supports the findings of other researchers that reveal that the highest cumulative impact is in areas closer to the coast and harbours owing to high population densities. Value of Study: This study builds onto the existing data by expanding the knowledge base and including more stakeholders to integrate as many human activities as possible and bring a holistic picture of the ocean's uses to inform MSP in Algoa Bay. , Thesis (MSc) -- Faculty of Science, Geography, 2021
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Bayesian hierarchical modelling with application in spatial epidemiology
- Authors: Southey, Richard Robert
- Date: 2018
- Subjects: Bayesian statistical decision theory , Spatial analysis (Statistics) , Medical mapping , Pericarditis , Mortality Statistics
- Language: English
- Type: text , Thesis , Masters , MSc
- Identifier: http://hdl.handle.net/10962/59489 , vital:27617
- Description: Disease mapping and spatial statistics have become an important part of modern day statistics and have increased in popularity as the methods and techniques have evolved. The application of disease mapping is not only confined to the analysis of diseases as other applications of disease mapping can be found in Econometric and financial disciplines. This thesis will consider two data sets. These are the Georgia oral cancer 2004 data set and the South African acute pericarditis 2014 data set. The Georgia data set will be used to assess the hyperprior sensitivity of the precision for the uncorrelated heterogeneity and correlated heterogeneity components in a convolution model. The correlated heterogeneity will be modelled by a conditional autoregressive prior distribution and the uncorrelated heterogeneity will be modelled with a zero mean Gaussian prior distribution. The sensitivity analysis will be performed using three models with conjugate, Jeffreys' and a fixed parameter prior for the hyperprior distribution of the precision for the uncorrelated heterogeneity component. A simulation study will be done to compare four prior distributions which will be the conjugate, Jeffreys', probability matching and divergence priors. The three models will be fitted in WinBUGS® using a Bayesian approach. The results of the three models will be in the form of disease maps, figures and tables. The results show that the hyperprior of the precision for the uncorrelated heterogeneity and correlated heterogeneity components are sensitive to changes and will result in different results depending on the specification of the hyperprior distribution of the precision for the two components in the model. The South African data set will be used to examine whether there is a difference between the proper conditional autoregressive prior and intrinsic conditional autoregressive prior for the correlated heterogeneity component in a convolution model. Two models will be fitted in WinBUGS® for this comparison. Both the hyperpriors of the precision for the uncorrelated heterogeneity and correlated heterogeneity components will be modelled using a Jeffreys' prior distribution. The results show that there is no significant difference between the results of the model with a proper conditional autoregressive prior and intrinsic conditional autoregressive prior for the South African data, although there are a few disadvantages of using a proper conditional autoregressive prior for the correlated heterogeneity which will be stated in the conclusion.
- Full Text:
- Authors: Southey, Richard Robert
- Date: 2018
- Subjects: Bayesian statistical decision theory , Spatial analysis (Statistics) , Medical mapping , Pericarditis , Mortality Statistics
- Language: English
- Type: text , Thesis , Masters , MSc
- Identifier: http://hdl.handle.net/10962/59489 , vital:27617
- Description: Disease mapping and spatial statistics have become an important part of modern day statistics and have increased in popularity as the methods and techniques have evolved. The application of disease mapping is not only confined to the analysis of diseases as other applications of disease mapping can be found in Econometric and financial disciplines. This thesis will consider two data sets. These are the Georgia oral cancer 2004 data set and the South African acute pericarditis 2014 data set. The Georgia data set will be used to assess the hyperprior sensitivity of the precision for the uncorrelated heterogeneity and correlated heterogeneity components in a convolution model. The correlated heterogeneity will be modelled by a conditional autoregressive prior distribution and the uncorrelated heterogeneity will be modelled with a zero mean Gaussian prior distribution. The sensitivity analysis will be performed using three models with conjugate, Jeffreys' and a fixed parameter prior for the hyperprior distribution of the precision for the uncorrelated heterogeneity component. A simulation study will be done to compare four prior distributions which will be the conjugate, Jeffreys', probability matching and divergence priors. The three models will be fitted in WinBUGS® using a Bayesian approach. The results of the three models will be in the form of disease maps, figures and tables. The results show that the hyperprior of the precision for the uncorrelated heterogeneity and correlated heterogeneity components are sensitive to changes and will result in different results depending on the specification of the hyperprior distribution of the precision for the two components in the model. The South African data set will be used to examine whether there is a difference between the proper conditional autoregressive prior and intrinsic conditional autoregressive prior for the correlated heterogeneity component in a convolution model. Two models will be fitted in WinBUGS® for this comparison. Both the hyperpriors of the precision for the uncorrelated heterogeneity and correlated heterogeneity components will be modelled using a Jeffreys' prior distribution. The results show that there is no significant difference between the results of the model with a proper conditional autoregressive prior and intrinsic conditional autoregressive prior for the South African data, although there are a few disadvantages of using a proper conditional autoregressive prior for the correlated heterogeneity which will be stated in the conclusion.
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A review of generalized linear models for count data with emphasis on current geospatial procedures
- Authors: Michell, Justin Walter
- Date: 2016
- Subjects: Spatial analysis (Statistics) , Bayesian statistical decision theory , Geospatial data , Malaria -- Botswana -- Statistics , Malaria -- Botswana -- Research -- Statistical methods
- Language: English
- Type: Thesis , Masters , MCom
- Identifier: vital:5582 , http://hdl.handle.net/10962/d1019989
- Description: Analytical problems caused by over-fitting, confounding and non-independence in the data is a major challenge for variable selection. As more variables are tested against a certain data set, there is a greater risk that some will explain the data merely by chance, but will fail to explain new data. The main aim of this study is to employ a systematic and practicable variable selection process for the spatial analysis and mapping of historical malaria risk in Botswana using data collected from the MARA (Mapping Malaria Risk in Africa) project and environmental and climatic datasets from various sources. Details of how a spatial database is compiled for a statistical analysis to proceed is provided. The automation of the entire process is also explored. The final bayesian spatial model derived from the non-spatial variable selection procedure using Markov Chain Monte Carlo simulation was fitted to the data. Winter temperature had the greatest effect of malaria prevalence in Botswana. Summer rainfall, maximum temperature of the warmest month, annual range of temperature, altitude and distance to closest water source were also significantly associated with malaria prevalence in the final spatial model after accounting for spatial correlation. Using this spatial model malaria prevalence at unobserved locations was predicted, producing a smooth risk map covering Botswana. The automation of both compiling the spatial database and the variable selection procedure proved challenging and could only be achieved in parts of the process. The non-spatial selection procedure proved practical and was able to identify stable explanatory variables and provide an objective means for selecting one variable over another, however ultimately it was not entirely successful due to the fact that a unique set of spatial variables could not be selected.
- Full Text:
- Authors: Michell, Justin Walter
- Date: 2016
- Subjects: Spatial analysis (Statistics) , Bayesian statistical decision theory , Geospatial data , Malaria -- Botswana -- Statistics , Malaria -- Botswana -- Research -- Statistical methods
- Language: English
- Type: Thesis , Masters , MCom
- Identifier: vital:5582 , http://hdl.handle.net/10962/d1019989
- Description: Analytical problems caused by over-fitting, confounding and non-independence in the data is a major challenge for variable selection. As more variables are tested against a certain data set, there is a greater risk that some will explain the data merely by chance, but will fail to explain new data. The main aim of this study is to employ a systematic and practicable variable selection process for the spatial analysis and mapping of historical malaria risk in Botswana using data collected from the MARA (Mapping Malaria Risk in Africa) project and environmental and climatic datasets from various sources. Details of how a spatial database is compiled for a statistical analysis to proceed is provided. The automation of the entire process is also explored. The final bayesian spatial model derived from the non-spatial variable selection procedure using Markov Chain Monte Carlo simulation was fitted to the data. Winter temperature had the greatest effect of malaria prevalence in Botswana. Summer rainfall, maximum temperature of the warmest month, annual range of temperature, altitude and distance to closest water source were also significantly associated with malaria prevalence in the final spatial model after accounting for spatial correlation. Using this spatial model malaria prevalence at unobserved locations was predicted, producing a smooth risk map covering Botswana. The automation of both compiling the spatial database and the variable selection procedure proved challenging and could only be achieved in parts of the process. The non-spatial selection procedure proved practical and was able to identify stable explanatory variables and provide an objective means for selecting one variable over another, however ultimately it was not entirely successful due to the fact that a unique set of spatial variables could not be selected.
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Spatial autocorrelation and the analysis of patterns resulting from crime occurrence
- Authors: Ward, Gary J
- Date: 1978
- Subjects: Geography -- Statistical methods , Correlation (Statistics) , Spatial analysis (Statistics) , Criminal statistics -- South Africa -- Grahamstown
- Language: English
- Type: Thesis , Masters , MA
- Identifier: vital:4864 , http://hdl.handle.net/10962/d1007244
- Description: From Introduction: In geography during the 1950's there was a definite move away from the study of unique phenomena to the study of generalized phenomena or pattern (Mather and Openshaw, 1974). At the same time interrelationships between phenomena distributed in space and time became the topic of much interest among geographers, as well as members of other disciplines. The changing emphasis initiated acceptance of certain scientific principles (Cole, 1973), and mathematical techniques became the recognized and respected means through which objective analysis of pattern, structure, and interrelationships between a really distributed phenomena could be achieved (Ackerman, 1972; Burton, 1972; Gould, 1973). Geographers, as do members of other disciplines, frequently borrow mathematical techniques developed for problems encountered in the pure sciences and apply these techniques to what are felt to be analogous situations in geography.
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- Authors: Ward, Gary J
- Date: 1978
- Subjects: Geography -- Statistical methods , Correlation (Statistics) , Spatial analysis (Statistics) , Criminal statistics -- South Africa -- Grahamstown
- Language: English
- Type: Thesis , Masters , MA
- Identifier: vital:4864 , http://hdl.handle.net/10962/d1007244
- Description: From Introduction: In geography during the 1950's there was a definite move away from the study of unique phenomena to the study of generalized phenomena or pattern (Mather and Openshaw, 1974). At the same time interrelationships between phenomena distributed in space and time became the topic of much interest among geographers, as well as members of other disciplines. The changing emphasis initiated acceptance of certain scientific principles (Cole, 1973), and mathematical techniques became the recognized and respected means through which objective analysis of pattern, structure, and interrelationships between a really distributed phenomena could be achieved (Ackerman, 1972; Burton, 1972; Gould, 1973). Geographers, as do members of other disciplines, frequently borrow mathematical techniques developed for problems encountered in the pure sciences and apply these techniques to what are felt to be analogous situations in geography.
- Full Text:
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