Proceedings of the Academic Track of AfricaGEO 2018
- Adjusting Errors in Global Elevation Data in the Milder Terrains
The Shuttle Radar Topographic Mission (SRTM) Digital Elevation Model (DEM) and the Advanced Spaceborne Thermal Emission and Reflection (ASTER) Global DEM (GDEM) are continuously valuable dated topographical data. The reported Root Mean Square Error (RMSE) ranges of 3.97m to 6.14m for the SRTM DEM and 7.29m to 8.08m for the GDEM limit their use in environmental phenomena analyses and modelling. The errors of the SRTM and ASTER DEMs have been shown to be dependent on terrain specific factors that correspond to the nature of the continuous surface model, including the land surface parameters and land cover types with their associated surface gentleness and roughness characteristics. In this study, error values of the SRTM and ASTER DEMs of Owerri South East Nigeria were determined at 77 Global Positioning Systems (GPS) survey points. The datasets were refined by applying an average error value for each DEM and also by a correction matrix which was created by interpolating for the DEM errors over the project area. The RMSE of the SRTM DEM improved in terms of percentage of error values less than absolute 3m from 10% to 76% and 86% with correction implemented by the error adjustment matrix and by the average error value respectively. The ASTER GDEM also improved as the percentage of error values below absolute 4.6m increased from 47% to 56% and 60% for GDEM corrected by the error adjustment matrix and that by error average value respectively. Thus the method of correcting the DEM using the average error value achieved a better result.
Keywords: Digital Elevation Model (DEM), Root Mean Square Error (RMSE), SRTM, ASTER
- A Geospatial modelling approach to simulating the impact of future planning policies on the City of Tshwane
Many researchers believe that no matter what path a country takes towards economic development the end result will lead to urbanisation. Rapid urban growth will have a major influence on the social, economic and political dialogue over the next few decades. The planning policies that are implemented in cities influence where urban growth will take place. In order for a government to implement effective policies they first need to establish what the possible effects of these policies could be. Land use change models have proven to be effective decision support tools when it comes to determining the effects of planning policies. The aim of this paper is to simulate urban growth for the City of Tshwane over the next 30 years under the Compaction and Densification Strategy policy currently in the process of implementation. This policy aims to reduce the effect of urban sprawl by promoting urban densification. It became clear from the results that the implementation of the Compaction and Densification Strategy could lead to the development of a more compact City of Tshwane. Under the Compaction and Densification Strategy, most of the development will take place within a 25km radius of the Central Business District and around major routes, metropolitan- and urban cores. This leads to most of the development taking place around the existing infrastructure instead of taking place where there is no established infrastructure.
- Comparison of Support Vector Machine and Maximum Likelihood Classification in and around Granite Quarries
Monitoring land cover changes is of prime importance for the effective planning and management of natural and man-made resources. Historically, mapping and monitoring of land cover changes was achieved through field surveys which was time-consuming and costly. Remote Sensing provides an alternative method of land cover monitoring which is less costly and more practical. Capabilities of covering large spatial areas–coupled with the ease of availability of historic and high spatial, spectral and temporal resolution data–remote sensing technology has been widely applied in mapping and monitoring land use land cover changes. This study compared the Maximum Likelihood Classifier (MLC) and Support Vector Machines (SVM) classification algorithms for land cover classification in granite quarries. The two classification algorithms were used to investigate the capability of differentiating granite quarries from other land cover types with similar spectral properties such as platinum and chrome mining areas, built-up land, bare land and the rock formations where quarrying takes place. A Landsat surface reflectance image acquired in November 2016 was used in the study. The image was classified using MLC and SVM and accuracy of classification was evaluated using 532 random points on Google EarthTM. The results of the study revealed that SVM was more accurate (overall accuracy: 78% and kappa: 76%) than the MLC (overall accuracy: 73% and kappa: 68%).
- Data-related challenges towards analysing the spatial economic attributes of airport-centric developments in South Africa
Various airports in South Africa are the subject of airport-led development initiatives, epitomised by the models of airport city and aerotropolis. Despite the growing popularity of this form of development, there is a lack of literature discussing factors that might hinder its planning and implementation in South Africa. To partially fill that gap, the paper shows how the lack of appropriate spatial economic data can hinder the empirical analysis and thus the comprehensive understanding of the spatial economic characteristics of airports and their environs. The paper is based upon the empirical research conducted in 2013 and 2014 on the spatial economic attributes of the environs of Cape Town and OR Tambo international airports. The following main findings are drawn from the research: one, there is a dire lack of data that are suitable for detailed spatial economic analyses of airports and surrounds; and two, municipalities tend to aggregate data at the level of land use per property (instead of compiling firm-level data), which particularly hinders the comprehensive analysis of airports and surrounds. To circumvent the continuation of such problems in future, it is proposed that further research be undertaken towards establishing an appropriate form of spatial economic data that are associated with airports and surrounds.
- Development and morphological classification of service catchments to support social facility distribution in South Africa
South Africa, as a developing country, is faced with a number of challenges, one of which is the provision of social facilities in an equitable and sustainable manner. The problem is compounded by uneven development arising from geographical variations in respect to resource availability and dualistic development arising from the apartheid era. This has resulted in a wide variety of development patterns and resultant settlement types ranging from well-developed neighbourhoods usually found within city limits to under-developed settlements in deep rural areas. Development patterns impact on the provision of social services as geographical dispersion and low density sprawl are major factors influencing the efficiency of service delivery. Thus, an understanding of morphology is crucial to promote equitable access of services within limited resources. Using service area analysis approach, a set of service catchments for social facilities provision were created around South African towns and settlements identified in the CSIR and SACN settlement typology. Using a range of datasets, these catchments were profiled and then classified according to their settlement morphology. This paper outlines the approach used arrive at the catchments and following this, it discusses the process used to analyse and classify these catchments according to their morphology. It highlights the nine main identified types and then provides some detail on the most common environments where these morphology types occur.
Key words: Settlement Morphology, GIS, Catchment, Service Delivery, Social Facilities
- Fortunate Index and Food Security: City of Tshwane Case Study using GIS
This article investigates the possible spatial relationship between being fortunate and food insecurity to establish whether the one could be a proxy for the other in the absence of data. Food security according the World Food Program (WFP) is the availability and adequate access to sufficient, safe and nutritious food to maintain an active and healthy life. The fortunate index is loosely based on the Gross National Happiness index of Bhutan. The fortunate index is an index based on interviews using 2011 Census data on what people think makes them being fortunate. For comparative purposes both food security and the fortunate index has been calculated at Stats SA’s Small Area Layer for the City of Tshwane to determine a possible spatial relationship between the two. Three steps were followed to determine the relationship namely to determine whether there are any clusters for each to enable comparisons using GIS, followed by the Pearson’s correlation coefficient to determine what type of relationship there is and the Getis-Ord Gi* hot and cold spots function in ArcGIS to determine the locality of these relationships, meaning if the cold and hot spots between the two indexes are geographically close. The overall finding is that there is an inverse relationship between the two but not strong enough that one or the other can be used as proxy for the other.
- Geospatial Accessibility Analysis of Primary Health Care Clinics in the Mbizana Local Municipality in the Eastern Cape
A key challenge in rural South Africa is providing social facilities such as clinics. This is difficult due to the nature of settlement distributions, especially in traditional authority areas. Geospatial accessibility analysis can help find suitable locations for facilities to serve the inhabitants. Settlements in South Africa’s traditional authority areas often do not meet the minimum thresholds required for specific social facilities, therefore determining the best location can be daunting. This study applied a geospatial accessibility approach to primary health care clinics in the Mbizana Local Municipality in the Eastern Cape Province. The results of this accessibility analysis identified suitable locations to build the eight clinics that were proposed in the municipality’s Integrated Development Plan for 2015-2016. We calculated facility catchments using approximate road-based travel distances to the nearest facility within the municipality. Two types of catchment area analysis were performed: one included distance and capacity constraints and the other excluded them. The unconstrained analysis showed that only 36% of the population is situated within the service reach of 5km. This decreased to 33% when distance and capacity constraints were considered. The remaining demand of 67% is significant, indicating high unsatisfied demand for additional clinics. Although the proposed eight clinics increased the overall accessibility to primary health care clinics, a large proportion of the population remains unserved, suggesting that more than eight clinics would be needed. The results of this analysis therefore also aim to support health care facility planning and distribution within the Mbizana Local Municipality.
Keywords: Clinic, Catchment area analysis, Geospatial accessibility, GIS, Primary health care
- Implementing a GIS Based Methodology for Determining Highly Vulnerable Rural Access Roads to a Changing Climate in Ethiopia
Climate-related natural disasters have been steadily increasing in both incidence and intensity across the globe over the last century. This is especially true for Ethiopia given the country’s high and recurrent exposure to extreme droughts and floods, the two most notorious disasters that have impacted on the country’s development trajectory and the livelihoods of its citizens. Climate-related risks are the major driver of hunger and food insecurity in Ethiopia, with the majority of poor communities being most vulnerable to their impacts. Due to the high degree of food and water insecurity caused partially by climate variability, it is argued that improved rural accessibility is vital to reducing the number of highly vulnerable communities, and increasing rural resilience. In this paper, a geospatial indicator-based risk and vulnerability assessment method was applied as a tool for determining rural access roads that are highly vulnerable to changing climate in Ethiopia. The assessment is intended to help guide, through prioritisation, the identification of highly vulnerable areas where appropriate climate adaptation measures would be most effective in reducing the impacts of climate variability and change. The research methodology relies on using GIS processes and spatial data to calculate a composite vulnerability index, the combined output of a hazard exposure index as well as a road criticality index, for identifying regions most at risk. It was found that almost half of Ethiopia’s districts, mostly in the Awash River Basin and southern Somali lowlands, are highly vulnerable to a changing climate in terms of the impact on rural accessibility. The paper further elaborates on the processes used to identify major climate hazards affecting roads in Ethiopia as well as open source data sets used in this analysis. The methodology was validated through an elaborate stakeholder engagement process and was found to be an accurate, efficient and effective way of identifying high-risk regions in terms of community dependence on roads for accessibility and the physical impact of climate on road infrastructure in Ethiopia.
Key Words: GIS; Risk; Vulnerability; Composite Indicator; Rural Access; Resilience; Climate Hazard; Climate Change; Climate Adaptation; Risk Management; Ethiopia
- Monitoring land use and land cover change in the Keiskamma Catchment area (South Africa) using Landsat imagery
Countries around the world are faced with land use and land cover (LULC) change due to various factors such as rapid population growth, increased demand for agricultural productivity, and change in climatic characteristics. Land cover change needs to be addressed through monitoring and management. Automated geographical analysis offers a powerful tool for monitoring and detecting LULC change over time and space. This study was conducted to assess LULC in the northern Keiskamma catchment of the Eastern Cape Province of South Africa. The study specifically aimed to quantify LULC dynamics in the area using Landsat imagery between the years 2000 and 2016. Five images were acquired at an interval of approximately four years. Six land cover classes were generated by classifying the multispectral and normalized difference vegetation indices (NDVIs) of each image using an unsupervised classification technique. Accuracy assessment of the classification based on the latest image was evaluated using reference data interpreted from Google Earth image. The multi-temporal classes were subsequently compared in a post-classification change detection analysis. The overall accuracy of the study in comparison to the reference data was 77%. The change detection analysis revealed a 16% decrease in dense forest, while there were increases in grass (9%), bush (5%), bare soil/built-up area (2%) and water body (2%). The cultivated land was the most stable class showing an increase of only 1%. These findings show that bush encroachment and deforestation are the leading causes of forest degradation in the area.
Keywords: Remote sensing; Land use – Land cover; Change detection; Landsat; NDVI; Keiskamma Catchment
- Multi-source remote sensing data for natural habitat mapping in India
Efficient mapping and monitoring of large spatial extents of the natural habitats are essential for nature conservation. The current study aimed to compare three open access remotely sensed data (Landsat-5 Thematic Mapper, Hyper Spectral Imager – HySI and Linear Imaging Self Scanning Sensor-III – LISS-III) to map the Land-Use Land-Cover (LULC) categories in and around Ranthambhore Tiger Reserve (RTR) of northwestern India. The Landsat-5 images were acquired in the pre-monsoon and post-monsoon seasons of 2011, while HySI and LISS-III images represented the pre-monsoon and post-monsoon seasons of 2011, respectively. Five land cover types including water bodies, bare soil / rock / sand, agricultural lands, open scrublands / grasslands and woodlands were created using Support Vector Machine (SVM) algorithm. The overall accuracy obtained from the HySI and LISS-III images were 67.5% and 79.1%. Landsat-5 yielded better accuracies for both the pre-monsoon and post-monsoon classified maps (77.6%). The spatial coverage of woodlands ranged from 13.6% to 20.4% while the spatial extent of the open scrublands / grasslands was estimated from 21.1% to 37.4% out of the total area of 3200 km2. Agricultural lands were the dominant land use type (ranging from 23.3% to 53.7% in spatial extent), whose progression could be a threat to other natural habitats and associated biodiversity. Therefore, future long-term studies are encouraged using such multiple data sources to assess the dynamics of natural habitats and other competing land cover types to understand the trend and level of threat that the natural habitats of this region in India are facing.
Keywords: HySI; Landsat; LISS-III; Ranthambhore Tiger Reserve; Remote sensing; Support Vector Machine.
- Perceived supply and demand for GISc knowledge and skills in South Africa
The implementation of the South African spatial data infrastructure (SASDI) commenced in 2010, but indications are that there is a lack of relevant expertise in the country. Much work has been done on understanding and implementing SDIs all over the world, which has resulted in best practices and various training resources. However, there is a need to understand the requirements of the local community. In this paper, results are presented of a survey distributed in South Africa to determine the perceived demand for GISc skills and knowledge. The results are compared to an earlier study on the supply of GISc skills. This comparison is useful for workforce and curriculum planning, not only in South Africa, but worldwide.
- Reflections on geomatics and participation summer/winter schools – Mobile map apps for nature conservation in Germany and for informal settlements in South Africa
To initiate a long term partnership between the Karlsruhe University of Applied Sciences (HsKA) and the University of Pretoria (UP), a summer school was held in Karlsruhe (Germany) in 2016 and a winter school in Pretoria (South Africa) in 2017. The overall theme of the schools was participatory sensing with a focus on engaging citizens in nature conservation in Germany and supporting communities in informal settlements in South Africa. In this paper, we describe the aim, design and programmes of the summer/winter schools and then reflect on achievements and room for improvement. Reflections were collected independently from lecturers involved in hosting the schools, and then integrated into a discussion. We believe that these perspectives will be useful for others who plan to host summer/winter schools, especially those hosted in geomatics and/or to attract students to exchange programmes.
- Towards Conceptualising Building Information Modelling for the Mining Industry
South Africa is a mineral resource-rich country with the largest concentrations of gold and platinum in the world. Yet the South African mining industry is facing an economic crisis. Some of the reasons for this crisis are: Low commodity prices, escalating production costs, depletion of economically ore grades, volatile currency, volatile exchange rates, difficulty to compete in the international markets. Thus, there is a need to develop an information and decision-making system that will cater for modern-era needs. Such a system would need to optimise production cost, while properly linking it to current and expected market conditions to enable synchronised and timely decision-making. This can only be done via a framework that is supported by relevant and timely information. This will need to include the following mine and market data (in both current- and anticipated-forms): Production rates; assessment of what is going on underground; incident reporting; scheduling; costing; market updates; inventory management; life cycle management. Such a system, named Building Information Modelling (BIM), was developed for the construction industry. This indicates that development of a Mining Information Modelling (MIM) may also address above-mentioned aspects, allowing for production improvements, optimal cost, less uncertainty and more efficiency – something that is difficult to attain via existing mining-software.
- Using Accessibility to Investigate the Level of Integration of the Various Public Transport Systems within the City of Johannesburg
The main focus of this study was to investigate the level of integration between various public transport systems operating within the City of Johannesburg. Accessibility was used to evaluate integration, since integration influences accessibility. Easy access to available public transport from departure points, enables movement between locations and easy reach to goods and services. Two methods were used: the first created 400-metre buffers around the public transport stops, stations and ranks to estimate the service areas for each system and the area served by an integrated public transport system. The population within the buffers was calculated to estimate how many people have access to public transport. The second method used a commuter survey to examine travelling experiences. The results obtained show accessibility varying between transport types: the Metrobus and taxis serve larger areas than the Gautrain, Metrorail and Rea Vaya. However, taxis served the largest population in comparison to all other public transport systems. The results also indicated that the primary public transport systems operated on a fragmented level spatially, with only 0,1% of the spatial area served by an integrated public transport system. The significance of the study was to aid in transport planning. Public transport should be easily available and accessible, so that basic services, jobs and education can be accessed with ease.
Keywords: Public transport integration, Accessibility, Commuter survey, Service area