Ranking nodes in complex networks : a case study of the Gaubus
- Authors: Moyo, T. , Musakwa, W.
- Date: 2016
- Subjects: Mobility , Centrality , Strava Data
- Language: English
- Type: Conference proceedings
- Identifier: http://hdl.handle.net/10210/400872 , uj:33478 , Citation: Moyo, T. & Musakwa, W. 2019. Ranking nodes in complex networks : a case study of the Gaubus.
- Description: Abstract: Connecting points of interest through a well-planned, inter-connected network provides manifold benefits to commuters and service providers. In the South African context, traffic congestion has become of great concern. Given how the South Africa community is slowly developing towards the use of multi-modes of mobility, the Gautrain network can be used to promote the use of multi-modes of mobility, as the Gautrain has been identified as the backbone of mobility within the Gauteng province. Currently commuters have the option to board the Gaubus (a form of Bus Rapid Transit) at their origin points which will take them to the Gautrain station to board the Gautrain. The problem to be solved arises when a commuter wishes to traverse from any bus stop to the Gautrain station, currently he/she only has one option and if the bus network has a shutdown at any point in the network the commuter’s journey will not be possible. In solving this problem, we consider the problem of graph robustness (that is creating new alternative routes to increase node/bus stop connectivity). We initial use Strava data, to identify locations were cyclist prefer to cycle and at what time of day. In graph theory, the nodes with most spreading ability are called influential nodes. Identification of most influential nodes and ranking them based on their spreading ability is of vital importance. Closeness centrality and betweenness are one of the most commonly used methods to identify influential nodes in complex networks. Using the Gaubus network we identify the influential nodes/ bus stops, using the betweenness centrality measure. The results reveal the influential nodes with the highest connectivity as these have cross-connections in the network. Identification of the influential nodes presents an important implication for future planning, accessibility, and, more generally, quality of life.
- Full Text:
- Authors: Moyo, T. , Musakwa, W.
- Date: 2016
- Subjects: Mobility , Centrality , Strava Data
- Language: English
- Type: Conference proceedings
- Identifier: http://hdl.handle.net/10210/400872 , uj:33478 , Citation: Moyo, T. & Musakwa, W. 2019. Ranking nodes in complex networks : a case study of the Gaubus.
- Description: Abstract: Connecting points of interest through a well-planned, inter-connected network provides manifold benefits to commuters and service providers. In the South African context, traffic congestion has become of great concern. Given how the South Africa community is slowly developing towards the use of multi-modes of mobility, the Gautrain network can be used to promote the use of multi-modes of mobility, as the Gautrain has been identified as the backbone of mobility within the Gauteng province. Currently commuters have the option to board the Gaubus (a form of Bus Rapid Transit) at their origin points which will take them to the Gautrain station to board the Gautrain. The problem to be solved arises when a commuter wishes to traverse from any bus stop to the Gautrain station, currently he/she only has one option and if the bus network has a shutdown at any point in the network the commuter’s journey will not be possible. In solving this problem, we consider the problem of graph robustness (that is creating new alternative routes to increase node/bus stop connectivity). We initial use Strava data, to identify locations were cyclist prefer to cycle and at what time of day. In graph theory, the nodes with most spreading ability are called influential nodes. Identification of most influential nodes and ranking them based on their spreading ability is of vital importance. Closeness centrality and betweenness are one of the most commonly used methods to identify influential nodes in complex networks. Using the Gaubus network we identify the influential nodes/ bus stops, using the betweenness centrality measure. The results reveal the influential nodes with the highest connectivity as these have cross-connections in the network. Identification of the influential nodes presents an important implication for future planning, accessibility, and, more generally, quality of life.
- Full Text:
An analysis to investigate spatial cognitive factors which influence cycling patterns in Johannesburg
- Moyo, T., Musakwa, W., Mokoena, B. T.
- Authors: Moyo, T. , Musakwa, W. , Mokoena, B. T.
- Date: 2018
- Subjects: Multi-modal , Cycling , Spatial Cognition
- Language: English
- Type: Conference proceedings
- Identifier: http://hdl.handle.net/10210/288492 , uj:31279 , Citation: Moyo, T., Musakwa, W. & Mokoena, B.T. 2018. An analysis to investigate spatial cognitive factors which influence cycling patterns in Johannesburg.
- Description: Abstract: Cycling in most African cities is done as either a mode of commuting or for recreational purposes. Apart from Smart cities encouraging a shift from cars to public transport by providing efficient last-mile connections, commuter cycling can take a significant share of end-to-end short distance trips. The ultimate realization of cycling merits by urban dwellers, (such as in Johannesburg, South Africa) is hindered by a lack of appropriate data to aid in understanding the dynamics of cycling behaviour. This paper seeks to be the first step in building a multi-model to govern the use of multi-modes of mobility in the city by initial focusing on promoting NMT usage as a mode of commuting in the city. Identification of these factors would go a long way in improving cycling uptake as well as inform policy strategies for non-motorized transportation in the city. Using an analytical approach, the authors conducted a survey along pre-known locations were cyclist choose to cycle. One route with newly developed cycling infrastructure and another without cycling infrastructure. A self-reported travel behaviour form, was used for the collection of spatial cognitive and attitudinal data on participants’ travel environment, attitude, behaviour, norm, intention, and habit was utilized to gather data to understand cyclist cognitive reasoning for choosing one path over another. The data collected from the survey was then overlaid with Strava Metro cycling data showing locations were cyclist prefer to cycle in the city. Findings from the analysis suggest perceived safe routes and routes that maximize health benefits are preferred. Based on the findings it is recommended that planners need to use crowd sourced data before developing infrastructure for cycling the city.
- Full Text:
- Authors: Moyo, T. , Musakwa, W. , Mokoena, B. T.
- Date: 2018
- Subjects: Multi-modal , Cycling , Spatial Cognition
- Language: English
- Type: Conference proceedings
- Identifier: http://hdl.handle.net/10210/288492 , uj:31279 , Citation: Moyo, T., Musakwa, W. & Mokoena, B.T. 2018. An analysis to investigate spatial cognitive factors which influence cycling patterns in Johannesburg.
- Description: Abstract: Cycling in most African cities is done as either a mode of commuting or for recreational purposes. Apart from Smart cities encouraging a shift from cars to public transport by providing efficient last-mile connections, commuter cycling can take a significant share of end-to-end short distance trips. The ultimate realization of cycling merits by urban dwellers, (such as in Johannesburg, South Africa) is hindered by a lack of appropriate data to aid in understanding the dynamics of cycling behaviour. This paper seeks to be the first step in building a multi-model to govern the use of multi-modes of mobility in the city by initial focusing on promoting NMT usage as a mode of commuting in the city. Identification of these factors would go a long way in improving cycling uptake as well as inform policy strategies for non-motorized transportation in the city. Using an analytical approach, the authors conducted a survey along pre-known locations were cyclist choose to cycle. One route with newly developed cycling infrastructure and another without cycling infrastructure. A self-reported travel behaviour form, was used for the collection of spatial cognitive and attitudinal data on participants’ travel environment, attitude, behaviour, norm, intention, and habit was utilized to gather data to understand cyclist cognitive reasoning for choosing one path over another. The data collected from the survey was then overlaid with Strava Metro cycling data showing locations were cyclist prefer to cycle in the city. Findings from the analysis suggest perceived safe routes and routes that maximize health benefits are preferred. Based on the findings it is recommended that planners need to use crowd sourced data before developing infrastructure for cycling the city.
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Exploring the potential of crowd sourced data to map commuter points of interest : a case study of Johannesburg
- Authors: Moyo, T. , Musakwa, W.
- Date: 2019
- Subjects: Commuter , Johannesburg , Mobility
- Language: English
- Type: Conference proceedings
- Identifier: http://hdl.handle.net/10210/400878 , uj:33477 , Citation: Moyo, T. & Musakwa, W. 2019. Exploring the potential of crowd sourced data to map commuter points of interest : a case study of Johannesburg.
- Description: Abstract: Modern African cities are faced with various mobility and transportation challenges. In developing smart sustainable cities, city planners need to create a balance between supply and demand for public transportation. Development of multi-mobility mode models has contemporarily received a special interest in smart cities development. Globally, the use of bike sharing services to complete the first kilometre or last kilometre of the trip has been highly received, with commuters using either rail or road mobility modes for the middle section of their trip. Within the developing world context, the use of multi-mobility modes in daily commuting is still new, and little research has been done to guide this. Notwithstanding the influence of uncertainties and fragmentation over demand and supply in public transportation provision. In the South Africa context, various modes of public transportation have been developed which seek to be smart, sustainable and efficient such as the fast train (Gautrain), Bus rapid transport (Rea Vaya and Gaubus) and Bikes sharing platforms (Upcycles), however most of these modes are currently not spatially connected. Hence the researcher sought to develop a stepping stone in planning for future mobility demand. Using an explorative methodology, the authors collected quantitative and spatial data in the form of land-use data and crowd sourced data (from twitter) to map commuter points of interest in and around the city of Johannesburg. The results reveal hot and cold spots in the city. The hot spots reveal areas where commuters frequently travel to, and when overlaid with transportation data, we are able to identify potential locations to develop new transportation hubs as these will overtime become key points of interest.
- Full Text:
- Authors: Moyo, T. , Musakwa, W.
- Date: 2019
- Subjects: Commuter , Johannesburg , Mobility
- Language: English
- Type: Conference proceedings
- Identifier: http://hdl.handle.net/10210/400878 , uj:33477 , Citation: Moyo, T. & Musakwa, W. 2019. Exploring the potential of crowd sourced data to map commuter points of interest : a case study of Johannesburg.
- Description: Abstract: Modern African cities are faced with various mobility and transportation challenges. In developing smart sustainable cities, city planners need to create a balance between supply and demand for public transportation. Development of multi-mobility mode models has contemporarily received a special interest in smart cities development. Globally, the use of bike sharing services to complete the first kilometre or last kilometre of the trip has been highly received, with commuters using either rail or road mobility modes for the middle section of their trip. Within the developing world context, the use of multi-mobility modes in daily commuting is still new, and little research has been done to guide this. Notwithstanding the influence of uncertainties and fragmentation over demand and supply in public transportation provision. In the South Africa context, various modes of public transportation have been developed which seek to be smart, sustainable and efficient such as the fast train (Gautrain), Bus rapid transport (Rea Vaya and Gaubus) and Bikes sharing platforms (Upcycles), however most of these modes are currently not spatially connected. Hence the researcher sought to develop a stepping stone in planning for future mobility demand. Using an explorative methodology, the authors collected quantitative and spatial data in the form of land-use data and crowd sourced data (from twitter) to map commuter points of interest in and around the city of Johannesburg. The results reveal hot and cold spots in the city. The hot spots reveal areas where commuters frequently travel to, and when overlaid with transportation data, we are able to identify potential locations to develop new transportation hubs as these will overtime become key points of interest.
- Full Text:
Exploring the potential of crowd sourced data to map commuter points of interest : a case study of Johannesburg
- Authors: Moyo, T. , Musakwa, W.
- Date: 2019
- Subjects: Commuter , Mobility , Crowd sourced data
- Language: English
- Type: Conference proceedings
- Identifier: http://hdl.handle.net/10210/401833 , uj:33602 , Citation: Moyo, Thembani & Musakwa, Walter. (2019). EXPLORING THE POTENTIAL OF CROWD SOURCED DATA TO MAP COMMUTER POINTS OF INTEREST: A CASE STUDY OF JOHANNESBURG. 10.5194/isprs-archives-XLII-2-W13-1587-2019.
- Description: Abstract: Modern African cities are faced with various mobility and transportation challenges. In developing smart sustainable cities, city planners need to create a balance between supply and demand for public transportation. Development of multi-mobility mode models has contemporarily received a special interest in smart cities development. Globally, the use of bike sharing services to complete the first kilometre or last kilometre of the trip has been highly received, with commuters using either rail or road mobility modes for the middle section of their trip. Within the developing world context, the use of multi-mobility modes in daily commuting is still new, and little research has been done to guide this. Notwithstanding the influence of uncertainties and fragmentation over demand and supply in public transportation provision. In the South Africa context, various modes of public transportation have been developed which seek to be smart, sustainable and efficient such as the fast train (Gautrain), Bus rapid transport (Rea Vaya and Gaubus) and Bikes sharing platforms (Upcycles), however most of these modes are currently not spatially connected. Hence the researcher sought to develop a stepping stone in planning for future mobility demand. Using an explorative methodology, the authors collected quantitative and spatial data in the form of land-use data and crowd sourced data (from twitter) to map commuter points of interest in and around the city of Johannesburg. The results reveal hot and cold spots in the city. The hot spots reveal areas where commuters frequently travel to, and when overlaid with transportation data, we are able to identify potential locations to develop new transportation hubs as these will overtime become key points of interest.
- Full Text:
- Authors: Moyo, T. , Musakwa, W.
- Date: 2019
- Subjects: Commuter , Mobility , Crowd sourced data
- Language: English
- Type: Conference proceedings
- Identifier: http://hdl.handle.net/10210/401833 , uj:33602 , Citation: Moyo, Thembani & Musakwa, Walter. (2019). EXPLORING THE POTENTIAL OF CROWD SOURCED DATA TO MAP COMMUTER POINTS OF INTEREST: A CASE STUDY OF JOHANNESBURG. 10.5194/isprs-archives-XLII-2-W13-1587-2019.
- Description: Abstract: Modern African cities are faced with various mobility and transportation challenges. In developing smart sustainable cities, city planners need to create a balance between supply and demand for public transportation. Development of multi-mobility mode models has contemporarily received a special interest in smart cities development. Globally, the use of bike sharing services to complete the first kilometre or last kilometre of the trip has been highly received, with commuters using either rail or road mobility modes for the middle section of their trip. Within the developing world context, the use of multi-mobility modes in daily commuting is still new, and little research has been done to guide this. Notwithstanding the influence of uncertainties and fragmentation over demand and supply in public transportation provision. In the South Africa context, various modes of public transportation have been developed which seek to be smart, sustainable and efficient such as the fast train (Gautrain), Bus rapid transport (Rea Vaya and Gaubus) and Bikes sharing platforms (Upcycles), however most of these modes are currently not spatially connected. Hence the researcher sought to develop a stepping stone in planning for future mobility demand. Using an explorative methodology, the authors collected quantitative and spatial data in the form of land-use data and crowd sourced data (from twitter) to map commuter points of interest in and around the city of Johannesburg. The results reveal hot and cold spots in the city. The hot spots reveal areas where commuters frequently travel to, and when overlaid with transportation data, we are able to identify potential locations to develop new transportation hubs as these will overtime become key points of interest.
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Spatio-temporal modelling & the new urban agenda in post-apartheid South Africa
- Mokoena, B. T., Moyo, T., Makoni, E. N., Musakwa, W.
- Authors: Mokoena, B. T. , Moyo, T. , Makoni, E. N. , Musakwa, W.
- Date: 2019
- Subjects: Spatio Temporal Modelling , Integrated Urban Development Framework , Planning Support Systems
- Language: English
- Type: Conference proceedings
- Identifier: http://hdl.handle.net/10210/400018 , uj:33366 , Citation: Mokoena, B.T. et al. 2019. Spatio-temporal modelling & the new urban agenda in post-apartheid South Africa.
- Description: Abstract: This paper presents the potentialities of spatio-temporal modelling in transforming South Africa’s previously marginalised townships. Using the Katlehong township in Ekurhuleni as a case study, the paper argues that the hitherto marginalised townships can benefit from a localised implementation of smart-city concepts as articulated in the Integrated Urban Development Framework. Instead of viewing townships as spaces of perpetual despair and hopelessness, the paper appreciates these areas as having the potential to benefit from new smart innovative planning approaches that form part of the Fourth Industrial Revolution. So, the discussion identifies smart transportation modes such as bicycle-sharing, as well as Bus Rapid Transit Networks as critical in promoting mobility in and beyond townships, while contributing to spatial integration and transformation. Using geolocation data, the paper concludes that formerly marginalised townships such as Katlehong can and must form part of the emergent smart cities in South Africa.
- Full Text:
- Authors: Mokoena, B. T. , Moyo, T. , Makoni, E. N. , Musakwa, W.
- Date: 2019
- Subjects: Spatio Temporal Modelling , Integrated Urban Development Framework , Planning Support Systems
- Language: English
- Type: Conference proceedings
- Identifier: http://hdl.handle.net/10210/400018 , uj:33366 , Citation: Mokoena, B.T. et al. 2019. Spatio-temporal modelling & the new urban agenda in post-apartheid South Africa.
- Description: Abstract: This paper presents the potentialities of spatio-temporal modelling in transforming South Africa’s previously marginalised townships. Using the Katlehong township in Ekurhuleni as a case study, the paper argues that the hitherto marginalised townships can benefit from a localised implementation of smart-city concepts as articulated in the Integrated Urban Development Framework. Instead of viewing townships as spaces of perpetual despair and hopelessness, the paper appreciates these areas as having the potential to benefit from new smart innovative planning approaches that form part of the Fourth Industrial Revolution. So, the discussion identifies smart transportation modes such as bicycle-sharing, as well as Bus Rapid Transit Networks as critical in promoting mobility in and beyond townships, while contributing to spatial integration and transformation. Using geolocation data, the paper concludes that formerly marginalised townships such as Katlehong can and must form part of the emergent smart cities in South Africa.
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Exploring the potential of open source data to generate congestion and emission trends in developing cities
- Moyo, T., Kibangou, A., Musakwa, W.
- Authors: Moyo, T. , Kibangou, A. , Musakwa, W.
- Date: 2020
- Subjects: Emission , Congestion , COVID-19
- Language: English
- Type: Conference proceedings
- Identifier: http://hdl.handle.net/10210/437133 , uj:37949 , Citation: Moyo, T., Kibangou, A. & Musakwa, W. 2020. Exploring the potential of open source data to generate congestion and emission trends in developing cities.
- Description: Abstract: The growth in Intelligent Transportation Systems (ITS) has enhanced the way mobility in contemporary cities is managed. Given the growth in availability of traffic data that contains location-aware datasets, congestion and pollution indexes can be developed. Metropolitan cities such Johannesburg due to their economic activities, attract commuters into the city on a daily basis seeking greener pastures. This has led to major freeways and roads experiencing high levels of congestion. In 2020, due to a global pandemic of an outbreak of Corona Virus (COVID-19), the national government declared a national shutdown with only essential traffic being allowed to operate. Given the scenario of the national lock-down this allows for the statistical analysis of the impact of essential traffic on the overall transportation system. Consequently the aim of the paper was to explore the congestion and C02 emission impact of essential traffic for the City of Johannesburg. Using an exploratory approach, we monitored and collected traffic congestion data from the Tomtom traffic index for the metropolitan city of Johannesburg, South Africa. Using a mathematic model, we develop a relationship between congestion and pollution to visualise the variations in pollution and congestion levels during varies scenarios. We demonstrate this by comparing datasets for variations in congestion levels in two epochs, viz the period without movement restrictions and the period whereby movement is restricted. The results reveal essential traffic on the congestion index to be below 22 percent for both weekends and weekdays. A scenario common only during weekends in 2019. Whilst for the emission index, C02 levels are approximately less than 45 percent throughout the week. The paper concludes the investment into mining and analysing traffic data has a significantly role for future mobility planning in both the developed and developing world and, more generally, improving the quality of commuting trips in the city.
- Full Text:
- Authors: Moyo, T. , Kibangou, A. , Musakwa, W.
- Date: 2020
- Subjects: Emission , Congestion , COVID-19
- Language: English
- Type: Conference proceedings
- Identifier: http://hdl.handle.net/10210/437133 , uj:37949 , Citation: Moyo, T., Kibangou, A. & Musakwa, W. 2020. Exploring the potential of open source data to generate congestion and emission trends in developing cities.
- Description: Abstract: The growth in Intelligent Transportation Systems (ITS) has enhanced the way mobility in contemporary cities is managed. Given the growth in availability of traffic data that contains location-aware datasets, congestion and pollution indexes can be developed. Metropolitan cities such Johannesburg due to their economic activities, attract commuters into the city on a daily basis seeking greener pastures. This has led to major freeways and roads experiencing high levels of congestion. In 2020, due to a global pandemic of an outbreak of Corona Virus (COVID-19), the national government declared a national shutdown with only essential traffic being allowed to operate. Given the scenario of the national lock-down this allows for the statistical analysis of the impact of essential traffic on the overall transportation system. Consequently the aim of the paper was to explore the congestion and C02 emission impact of essential traffic for the City of Johannesburg. Using an exploratory approach, we monitored and collected traffic congestion data from the Tomtom traffic index for the metropolitan city of Johannesburg, South Africa. Using a mathematic model, we develop a relationship between congestion and pollution to visualise the variations in pollution and congestion levels during varies scenarios. We demonstrate this by comparing datasets for variations in congestion levels in two epochs, viz the period without movement restrictions and the period whereby movement is restricted. The results reveal essential traffic on the congestion index to be below 22 percent for both weekends and weekdays. A scenario common only during weekends in 2019. Whilst for the emission index, C02 levels are approximately less than 45 percent throughout the week. The paper concludes the investment into mining and analysing traffic data has a significantly role for future mobility planning in both the developed and developing world and, more generally, improving the quality of commuting trips in the city.
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Internationalisation of a South African insurance broking firm
- Moyo, T.
- Authors: Moyo, T.
- Date: 2020
- Subjects: Insurance - South Africa , Insurance policies - South Africa , Insurance companies - South Africa
- Language: English
- Type: Masters (Thesis)
- Identifier: http://hdl.handle.net/10210/478877 , uj:43294
- Description: Abstract: Africa Rising is a narrative which suggests that Africa’s economies are opening up to create a conducive environment for increasing Foreign Direct Investment (FDI) activities in the most attractive countries, offering the highest opportunities. As a result, there has been growing interest in African markets and in the last decade alone. South African firms have played a leading role in FDI into other African countries. Like other promising sectors, such as energy and mining, the financial services sector is among key sectors that anticipate to be a key pull factor for investors into the continent’s economies. However, there is limited research on the internationalisation of insurance providers into other African markets. A qualitative study was undertaken to gain a deeper understanding of the opportunities and challenges experienced and the actions taken to overcome the challenges in internationalising a South African insurance broking firm. The study sought to gather data from views held by executive and senior managers as well as technical staff employed to manage and service multinational insurance accounts in Africa where The insurance broking firm in the study is operating. A sample of 20 participants employed by The insurance broking firm in the study was selected through a purposive non-probability sampling technique.These participants were well positioned to provide insights and perspectives in relation to the research questions of the study. Data was collected from each participant using semi-structured, face-toface interviews. Each interview was recorded and transcribed as permitted by the participants. Qualitative content analysis was subsequently used to analyse the data.The study identified that The insurance broking firm in the study is using networks as a platform to gain entry into markets but a major finding was that networks have over-exposed The insurance broking firm in the study to commercial risks, thereby having a negative impact on the optimum potential of its internationalisation strategy. Recommendations informing future strategy were highlighted and recommendations for future studies were proposed. , M.Com. (Business Management)
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- Authors: Moyo, T.
- Date: 2020
- Subjects: Insurance - South Africa , Insurance policies - South Africa , Insurance companies - South Africa
- Language: English
- Type: Masters (Thesis)
- Identifier: http://hdl.handle.net/10210/478877 , uj:43294
- Description: Abstract: Africa Rising is a narrative which suggests that Africa’s economies are opening up to create a conducive environment for increasing Foreign Direct Investment (FDI) activities in the most attractive countries, offering the highest opportunities. As a result, there has been growing interest in African markets and in the last decade alone. South African firms have played a leading role in FDI into other African countries. Like other promising sectors, such as energy and mining, the financial services sector is among key sectors that anticipate to be a key pull factor for investors into the continent’s economies. However, there is limited research on the internationalisation of insurance providers into other African markets. A qualitative study was undertaken to gain a deeper understanding of the opportunities and challenges experienced and the actions taken to overcome the challenges in internationalising a South African insurance broking firm. The study sought to gather data from views held by executive and senior managers as well as technical staff employed to manage and service multinational insurance accounts in Africa where The insurance broking firm in the study is operating. A sample of 20 participants employed by The insurance broking firm in the study was selected through a purposive non-probability sampling technique.These participants were well positioned to provide insights and perspectives in relation to the research questions of the study. Data was collected from each participant using semi-structured, face-toface interviews. Each interview was recorded and transcribed as permitted by the participants. Qualitative content analysis was subsequently used to analyse the data.The study identified that The insurance broking firm in the study is using networks as a platform to gain entry into markets but a major finding was that networks have over-exposed The insurance broking firm in the study to commercial risks, thereby having a negative impact on the optimum potential of its internationalisation strategy. Recommendations informing future strategy were highlighted and recommendations for future studies were proposed. , M.Com. (Business Management)
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Modelling of natural fire occurrences : a case of South Africa
- Moyo, T., Musakwa, W., Nyathi, N. A., Mpofu, E., Gumbo, T.
- Authors: Moyo, T. , Musakwa, W. , Nyathi, N. A. , Mpofu, E. , Gumbo, T.
- Date: 2020
- Subjects: Natural fire , Global warming , Local Moran
- Language: English
- Type: Conference proceedings
- Identifier: http://hdl.handle.net/10210/437165 , uj:37952 , Citation: Moyo, T. et al. 2020. Modelling of natural fire occurrences : a case of South Africa.
- Description: Abstract: In contemporary literature there have been growing concerns regarding preservations of natural ecosystems. Given the global growth in awareness of global warming, the need for natural fire prediction models has grown rapidly. Using South Africa as a case study, we evaluate the potential of integrating several natural fire prediction models and geographical information system (GIS) platforms. Initially, natural fire prone regions in South Africa were spatially demarcated basing on municipal historical data records. Thereafter, the natural fire prediction models were applied/tested in parallel to identify the best prediction models that give optimum results in predicting natural fires. The models were assessed for accuracy using historical data. Preliminary results reveal locations in the North West, Mpumalanga and Limpopo province had the highest recorded potential for natural fires. In conclusion, the work demonstrates huge potential of prediction models in informing the likelihood of natural fire outbreaks. Lastly, the work recommends the adoption of natural fire prediction models and the subsequent formulation and use of relevant future natural fire mitigation policies and techniques to avert disasters in time.
- Full Text:
- Authors: Moyo, T. , Musakwa, W. , Nyathi, N. A. , Mpofu, E. , Gumbo, T.
- Date: 2020
- Subjects: Natural fire , Global warming , Local Moran
- Language: English
- Type: Conference proceedings
- Identifier: http://hdl.handle.net/10210/437165 , uj:37952 , Citation: Moyo, T. et al. 2020. Modelling of natural fire occurrences : a case of South Africa.
- Description: Abstract: In contemporary literature there have been growing concerns regarding preservations of natural ecosystems. Given the global growth in awareness of global warming, the need for natural fire prediction models has grown rapidly. Using South Africa as a case study, we evaluate the potential of integrating several natural fire prediction models and geographical information system (GIS) platforms. Initially, natural fire prone regions in South Africa were spatially demarcated basing on municipal historical data records. Thereafter, the natural fire prediction models were applied/tested in parallel to identify the best prediction models that give optimum results in predicting natural fires. The models were assessed for accuracy using historical data. Preliminary results reveal locations in the North West, Mpumalanga and Limpopo province had the highest recorded potential for natural fires. In conclusion, the work demonstrates huge potential of prediction models in informing the likelihood of natural fire outbreaks. Lastly, the work recommends the adoption of natural fire prediction models and the subsequent formulation and use of relevant future natural fire mitigation policies and techniques to avert disasters in time.
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