Humankind’s survival is dependant on the sustainable availability and subsequent management of natural resources. Humans are central to ensuring the sustainable use of the natural resources and the administration of future development. South Africa is one of the signatories of the 1992 Rio Declaration on Environment and Development. This Declaration stipulates that development should occur in a way that it will fulfil the developmental and environmental needs of current as well as future generations (Aventis CropScience 2000:2). South Africa is continuously exposed to extremes, not only from an environmental perspective, but also from a land use and management perspective. The country is exposed to a wide range of climatic conditions as well as varied soil and terrain characteristics. Limited high potential agricultural land is available for cultivation purposes and the management and protection of this land is crucial for continued food security. The wide variety of animal and plant species unique to South Africa should all be protected. Continued pressure has negatively impacted on the natural resources and in several instances resulted in their loss. The management of these resources has received much attention over the years and many research projects have focused on a better understanding of the resources and how they should be managed. One of the primary requirements in the effective management of natural resources is accurate information. The development of Geographical Information Systems (GIS) as well as spatial data sets and information systems focusing on natural resources have contributed to improved monitoring and management thereof. Through the use of GIS, improved, modelled data sets and decision support systems could be developed that assisted in the decision making, management and monitoring of the resources. The optimal utilization of South Africa’s resources has, to a large extent, contributed to the country becoming independent in terms of food production and therefore currently regarded as food secure. The commercial agricultural sector has adapted to risks through effective cultivation practices and the optimal utilization of the land’s resources. At the same time, however, the country’s land reform programme has given new farmers access to land, but through this process exposed them to challenges they are not equipped to deal with. Moreover, in some instances, agricultural production has also impacted negatively on the environment. For South Africa as a developing country, there is great emphasis on residential, industrial and economic development and the advancement of citizens through job creation and the alleviation of poverty. These developments are mostly financially driven without proper consideration as to their impact on the environment. Legislation, strategic objectives and policies have raised awareness of ensuring that environmental assessments should form part of any proposed development. Nevertheless, these directives are not fully implemented. The Gauteng Province, of special reference to this study, is regarded as the economic hub of the country and is therefore continuously faced with development pressures. However, this province also has a disproportional amount of high potential agricultural land. Against this background, the study focused on current legislation, policies and decision support systems in the protection and management of agricultural land for future production. Copyright / Dissertation (MSc)--University of Pretoria, 2009. / Plant Science / unrestricted

Thesis (Ph.D. (Statistics)) -- University of Limpopo, 2021 / In spatial statistics, several methods have been developed to measure the extent of local and global spatial dependence (clustering) in measured data across areas in a region of research interest. These methods are now routinely implemented in most Geographical Information Systems (GIS) and statistical computer packages. However, spatial statistics for measuring joint spatial dependence of multiple spatial measurement and outcome data have not been well developed. A naive analysis would simply apply univariate spatial dependence methods to each data separately. Though this is simple and straightforward, it ignores possible relationships between multiple spatial data because they may be measuring the same phenomena. Limited work has been done on extending the Moran’s index, a commonly used and applied univariate measure of spatial clustering, to bivariate Moran’s index in order to assess spatial dependence for two spatial data. The overall aim of this PhD was to develop multivariate spatial clustering methods for multiple spatial data, especially in the health sciences. Our proposed multivariate spatial clustering statistic is based on the fundamental theory regarding canonical correlations. We firstly reviewed and applied univariate and bivariate Moran’s indexes to spatial analyses of multiple non-communicable diseases and related risk factors in South Africa. Then we derived our proposed multivariate spatial clustering method, which was evaluated by simulation studies and applied to a spatial analysis of multiple non-communicable diseases and related risk factors in South Africa. Simulation studies showed that our proposed multivariate spatial statistic was able to identify correctly clusters of areas with high risks as well as clusters with low risk.

The Jackson Prairie Region represents a rare, poorly understood and threatened ecosystem. A review of the literature concerning the ecology, physiography and geology of the Region was undertaken, along with a search of General Land Office (GLO) records for locations of historic prairie patches. The prairie patch location data was transcribed, digitized and inserted as a base map in a Geographical Information System (GIS). A set of current, local conditions indicating suitability for conservation or restoration, along with importance weights for each condition, was derived from stakeholder input. A simple additive weighting technique was used to rank the locations in terms of suitability for conservation or restoration. Historic patch locations were visited and the presence of prairie indicator species noted. The use of GLO records and GIS in this way improves the ability of landscape architects to enhance and preserve an imperiled habitat as they design across multiple scales.

Evaluation of aerial photographs, LiDAR imagery and GPS survey points was conducted to characterise water vole habitat at Grains in the Water and Swains Greave, in the Peak District. Justification for the study is to explore an affective way to monitor water vole habitat in relation to water vole signs utilising GIS. The water vole is a rapidly declining native species (Strachan and Strachan, 2003). The geospatial data was evaluated in terms of integration and extraction. The aerial photography provided a basis for vegetation mapping after visual interpretation. The aerial photograph required geometric correction and an average control point RMSE of 4.17m for the Grains in the Water site, using a 2nd order polynomial model was achieved. Extraction of slope, aspect, stream proximity and elevation were achieved using LiDAR imagery. Pearson’s product moment correlation coefficient highlighted a significant relationship between water vole latrine density with slope at the 0.01 significance level for 4m and 6m resolution data (Grains in the Water). The Swains Greave site supported this result with a 0.01 significance level for 6m resolution slope data. Elevation and aspect did not show a significant correlation with latrine density at Grains in the Water. The main conclusion is that water vole habitat cannot be solely characterised by aerial photography and LiDAR data, as other habitat variables could affect water vole distributions, which cannot be extracted from these geospatial data e.g. pH, bank exposure and stream depth.

Student Number : 0063608J - MSc(Eng) dissertation - School of Electrical and Information Engineering - Faculty of Engineering and the Built Environment / “You can see the computer age everywhere except in the productivity statistics”. This offhand remark by Robert Solow, the Nobel prize-winning economist [1] has stimulated many other economists to conduct more rigorous analyses on the impact of information technology on productivity. The research presented in this dissertation has been conducted on a large telecommunications utility. An important business process of the utility, that of collecting information on its geographically dispersed network assets, was automated using mobile computing and wireless technologies. The research compared this newly developed automated process with the current, manual, process of sourcing the field network asset data using paper-based templates, and capturing the data manually from the templates. The results of the pilot for this automated business process were encouraging and demonstrated an improvement of over 50% in the productivity of the data collection process, and its integrity. An important aspect of the research outlined in this dissertation was to design and implement the mobile computer-based electronic data collection prototype to minimise user obstruction to the technology deployed. The prototype was tested for technology acceptance by the targeted field workers. This test also proved successful. The research demonstrated that an improvement in productivity of over 50% was achievable from a well-considered investment in information technology. The results from the research also pointed the way for the deployment of this data collection solution in other utilities, e.g. electricity distribution, water reticulation, and municipalities. Through user prototype tests and a cultural intervention process on the targeted users (field workers), the research also demonstrated how the automated business process can be geared for use by low-skilled field workers, so important to improve productivity in developing economies such as those in Africa.

Um modelo dedicado ao planejamento da conservação e restauração de habitats deve incluir informações estratégicas para assegurar a eficácia e de fácil obtenção, para assegurar a agilidade necessária. Planos e estratégias para conservação usualmente são complexos e demandam informações detalhadas, difíceis de se obter, como inventários biológicos e certos tipos de mapeamento, o que traz limitações em termos de disponibilidade, qualidade e custo das informações. Assim, procurou-se testar a eficiência de métodos simples para a seleção, em escala local, de áreas prioritárias para conservação de habitats fortemente fragmentados e reduzidos, uma situação comum no sul do Brasil, usando dados de imagens LANDSAT e planos de informações disponíveis em mapeamentos comuns, e trabalho de campo. Inicialmente analisou-se a estrutura da paisagem e o padrão de fragmentação em parte da região norte do estado do Paraná, e testar se tamanho e forma são adequados para selecionar os fragmentos florestais mais importantes para a conservação, ou seja, as que contribuem para manter maior quantidade e melhor qualidade de habitats, bem como tenham maior impacto (positivo) na conectividade e em outras variáveis da paisagem. Os resultados mostram que a floresta madura cobre cerca de 3% da paisagem, e a cobertura florestal total atinge perto de 8%, consistindo principalmente de pequenos fragmentos (82% tem entre 1 e 10 ha). Fragmentos grandes (>100 ha) são apenas 1,4% dos remanescentes, mas somam 34% da área de floresta. Apesar de estarem sujeitos a efeitos de borda em toda ou quase toda a sua área, fragmentos pequenos mostraram ter um papel importante na conectividade da paisagem. Numa área maior, foi feita uma pré-seleção de áreas com potencial para estabelecimento de redes de conservação. A pré-seleção procurou responder às seguintes perguntas: 1-Quais são os sítios com maior potencial para a conservação da biodiversidade? 2-Quais são os sítios sob maior risco para objetivos de conservação? e 3-Quais sítios têm melhores oportunidades para o estabelecimento de zonas de conservação de uso múltiplo? Foi identificado um conjunto de 11 fragmentos pertencendo a 5 sub-regiões, sofrendo variados graus de pressão antrópica. Adicionalmente, usando medidas simples de estrutura da paisagem, relacionadas com tamanho, forma e conectividade dos fragmentos, procurou-se identificar tipos estruturais de fragmentos, como uma forma alternativa para auxiliar o estabelecimento de prioridades para conservação a partir do seu papel, efetivo ou potencial, na paisagem. Foram identificados 5 tipos de fragmentos, pequenos (ilhotas isoladas, trampolins), médios (núcleos auxiliares e corredores) e grandes (núcleos principais), que podem ser usados para subsidiar estratégias de conservação. Utilizando informações sobre a estrutura da paisagem, hidrografia e legislação ambiental, propõe-se aqui uma estratégia de conservação para o complexo das bacias dos ribeirões Apertados-Três Bocas (CATB), ordenando atividades de restauração e conservação de fragmentos florestais, criação e expansão de unidades de conservação, além da proposição de formas de uso do solo compatíveis com o entorno de unidades de conservação. / Models dedicated to habitat conservation, management and restoration planning must include information both strategic, to ensure effectiveness, and of easy collection, to ensure agility. Plans and strategies for conservation usually are complex and demand detailed data, such as biological inventories and fine scale mapping, which limits work due to information availability, quality and cost. Hence, as a first approach, we tested the efficacy of a simple methods for selection, in local scale, of conservation sites in strongly fragmented landscapes, a common situation in southern Brazil, using LANDSAT data, common mapping data layers, and fieldwork. First, we analyzed landscape structure and fragmentation patterns in the northern portion of Paraná state, looking for the suitability of using size and shape of forest fragments for selection of most important sites for conservation, i.e. fragments that contribute to encompass large and of best quality forest areas, as well have higher positive impact on connectivity and overall landscape structure. Results show that mature forest cover near 3% of landscape area, with total forest cover reaching near 8%, mostly small fragments (82% between 1 and 10 ha). Large fragments (>100 ha) are only 1,4% of fragment number, but encompass 34% of forest area. In spite of suffering of edge effects in almost or all their area, small fragments showed an important role in landscape connectivity. In a larger area we did a pre-selection of suitable areas for establishing conservation networks. Such pre-selection were intended to answer the following questions: 1-What sites have higher biodiversity conservation potential? 2-What sites have higher threats to conservation goals? and 3-What sites have better opportunity to establish multiple-use conservation zones? A set of 11 fragments over 500 ha were selected, being part of 5 conservation sites, of varying degree of development pressure. Using simple landscape structure measures, such as size, shape and connectivity, we tried to identify structural types of habitat fragments, as an alternative way of fragment prioritizing. We identified 5 fragment types, small (stepping-stones and small isolated islands), mid-sized (auxiliary nuclei and corridors) and large (main nuclei), that can be used for landscape conservation strategies. Finally, using information about landscape structure, hidrography, human impacts and environmental legislation, we proposed a integrated conservation strategy for the Apertados-Três Bocas river basin complex (CATB), near Londrina city, encompassing ordination of restoration activities, establishment of new conservation units and expansion of existing ones, as well as proposing land uses compatible with conservation goals.

Masters of Science / Over the years, South Africa has produced over 468 million tons of mine waste yearly, in which gold mining waste accounted for 221 million tons (47%) of all mine waste produced, making it the largest, single source of waste and pollution. The exposure of these mine wastes such as tailings dams, waste rocks to oxidation and leaching has been the source of heavy metal release into the environment. This study assessed the magnitude of possibly leachable metals, its distribution and associations and predicted the AMD load discharge over time, from Mogale’s tailings dam into the environs in Randfontein area, Witwatersrand Basin, South Africa. Fifty-one tailings dam samples were analysed for their mineral and multi-elements contents. Petrography studies was done by X-Ray Diffraction (XRD) technique to determine the mineralogical composition. X-ray fluorescence (XRF) and Inductively Coupled Plasma-Mass Spectrometry (ICP-MS) techniques were used to determine the multi-elements content in the tailings dam samples. The dataset were evaluated using multivariate statistics, Geographical Information Systems (GIS) and geochemical mass balance techniques. From the results, the tailings dam lithology was grouped into four distinct layers. The uppermost oxidized layer is siliceous and contains the highest SiO2 (87.32%) contents, which is with the lowest contents in Tot/S, U, As, Zn, Ni, Co, and Cu. A downward decrease in SiO2 (76.39%) contents occurs, coupled by an increase in Fe2O3, Tot/S, U, As, Zn, Ni, Co, and Cu, reaching maximum contents in layer 3. Layer 4 is the least weathered horizon. The cluster analysis grouped the samples into four sub-clusters based on the variation in SiO2 and Al2O3 contents. Factor analysis (83.542% total data variance) related the four controlling factors of element distribution to the occurrence in ore elements (sulphides), silicates, mining additives and refractory minerals. Elements of the same origin show a similar concentration trend down hole in the GIS interpolation analysis. The geochemical mass balance showed variable gain and loss of oxides and trace elements within each layer. Based on the variation patterns of the Tot/S contents and other mobile elements, about 0.164kg/tonne/yr(±0.02) of the tailings materials are leached yearly. Layer 1 is the most altered. This assessment and prediction study therefore gives an insight to the geochemical behaviour of an abandoned tailings dam, highlighting its extent of oxidation. However, the interaction between the oxidized zone and transition zone should be given more attention, to determine the actual extent of damage.

The research presented in this Thesis is primarily concerned with the field of Geographical Information Systems (GIS) - specifically, the business applications of the technology. The empirical problem addressed is the selection of branch banks as candidates for closure using the network of branch banks of the Commonwealth Bank of Australia in the Sydney metropolitan region as the case study. Decisions to close branches are made by the Bank on the basis of performance indicators that are essentially financial. In this research, however, an alternative approach is adopted: the problem is addressed using a set of spatial criteria. Following the deregulation of the finance industry in the 1980's and the rapid introduction of new electronic channels for delivering financial services, the major banking institutions have been engaged in a process of reorganising their networks of branch banks. The most visible manifestation of this has been the ongoing and widespread closure of branches. Selecting branch banks for closure is a typical example of a complex semi-structured multi-dimensional, multi-criteria, decision-making problem. It has been well documented in previous research that Multi-Criteria Decision-Making (MCDM) models are the most appropriate ones for solving problems in this particular domain. The identification of branches for closure is also characterised by a significant spatial dimension. Decisions are based on a consideration of a number of geographical criteria and various forms of spatial analysis may be involved. An appropriate technology to assist with solving decision-making problems with a significant spatial dimension is a Spatial Decision Support System (SDSS). Most SDSS have been based on the integration of Geographical Information Systems (GIS) technology with analytical models that are proven to be best suited to specific decision-making problems and this is the approach adopted in this research. The prototype MCBC-SDSS (Multi-Criteria Branch Closure SDSS) developed here is based on the integration through the loose coupling of the ArcView GIS software with the Criterium DecisionPlus (CDP) software, which contains the suite of non-spatial analytical models that provide the analytical capability for solving multi-criteria problems. ArcView GIS is used as the engine that drives the system and to provide the analytical and display facilities to support the spatial data involved. Two MCDM models from the CDP software are used to support the decision-making analysis - the Analytical Hierarchy Process (AHP) and Simple Multi-Attribute Rating Technique (SMART). The integration of GIS with the MCDM models is based on a considerable amount of software enhancement, interface development, and computer programming. The development of the integrated system is designed to create an intelligent and user-friendly SDSS, the application of which, from the user's perspective, is a seamless operation. The success of the MCBC-SDSS is demonstrated by its application to identify candidates for closure among the 197 branches of the CBA in the Sydney metropolitan area in 2000 - the year when the building of the database for the research had been completed. The analysis is based purely on spatial considerations that have been gleaned from a major review of the literature that previous researchers have identified as affecting branch viability and performance. A set of 17 spatial variables was used as the criteria in the MCDM models. The criteria are organised in two blocks: the first includes 9 criteria relating to the characteristics of demand for branch service in the branch trade areas ('catchment area' specific criteria) while the second includes 8 criteria relating to aspects of supply provided by the existing branches in their location ('location specific' criteria). Using the developed approach, the MCBC-SDSS has been used directly to compare alternatives against criteria, not only spatial based but also financial ones, thus providing a basis for identifying the best choices regarding branch closure. The steps in the preparation of the data and the iterative procedure for implementing the MCDM models are explained and illustrated. This involves building the initial evaluation matrix, normalising the raw criteria scores, assigning weights to the criteria, and calculating priorities. Based on these, the AHP and SMART models then calculate a decision score for each branch that is used as the basis for creating the preference ranking of the branches. In this, branches with a high rank score based on the combined weighted contribution of the 17 criteria are considered to be operationally viable. On the other hand, branches with the lowest rank scores are considered as potential candidates for closure. The preference rankings generated by the models have been tested to examine their robustness in terms of the validity of criteria and their weights used in the decision analysis. Sensitivity analysis has been conducted, the results of which show that the preference rankings are stable. Different approaches have been used to validate the initial criteria, and analyse their contribution to the ranking of branch banks for closure. These help identify critical spatial variables among the 17 initial criteria selected, and suggest that some of the criteria initially selected could be deleted from the criteria list used to generate the preference rankings without substantially affecting the results. The reasonableness of the resulting preference ranking has been further demonstrated from analyses based on changing criteria weights and alternatives. The research successfully demonstrates one of the ways of enhancing the functionality of a GIS through its integration with non-spatial analytical models to develop a SDSS to aid solving decision-making problems in the selected domain. Given that to date there has been relatively few applications of SDSS similar to that developed in this research to real world decision-making problems, the procedure adopted makes it suitable for decision-making in a range of other service business applications characterised by a significant spatial dimension and multiple outlets including shopping centres, motor car dealerships, restaurant and supermarket chains. Instead of just providing solutions, however, the SDSS-based analysis in this research can better be thought of as adding value to spatial data that forms an important source of information required by decision-makers, providing insight about the situation, uncertainty, objectives, and trade-offs involved in reaching decisions, and being capable of generating alternative scenarios based on different inputs to the models that may be used to identify recommended courses of action. It can lead to better and more effective decision-making in institutions involving multi-outlet retail and service businesses and hence enables both integrated data analysis and modelling while taking multiple criteria and decision-makers' preferences into consideration.

This thesis is structured around the analysis of a model that describes the Cantonment, the Civil Lines, the Sadr Bazar and part of the Native City of the Western Indian settlement of Poona in the late nineteenth and early twentieth centuries.

<p>Since Dengue fever (DF) and its related forms, Dengue Hemorrhagic fever (DHF) and Dengue Shock Syndrome (DSS) have become important health concerns worldwide, it is also imperative to develop methods which will help in the analysis of the incidences. Dengue fever cases are growing in number as it also invades widely, affecting larger number of countries and crossing climatic boundaries. Considering that the disease as of now has neither an effective vaccine nor a cure, monitoring in order to prevent or control is the resorted alternative. GIS and its related technologies offer a wealth of interesting capabilities towards achieving this goal.</p><p><strong> </strong></p><p>The intention of this study was to develop methods to describe dengue fever outbreaks taking Rio de Janeiro, Brazil as a case study. Careful study of Census data with appropriate attributes was made to find out their potential influence on dengue fever incidence in the various regions or census districts. Dengue incidence data from year 2000 to year 2008 reported by the municipal secretariat of Rio was used to extract the necessary census districts. Base map files in MapInfo format were converted to shape files.  Using ArcGIS it was possible to merge the dengue fever incidence data with the available base map file of the City of Rio according to corresponding census districts. Choropleth maps were then created using different attributes from which patterns and trends could be used to describe the characteristic of the outbreak with respect to the socio-economic conditions. Incidence data were also plotted in Excel to see temporal variations. Cluster analysis were performed with the Moran I technique on critical periods and years of dengue outbreak. Using the square root of dengue incidence from January to April 2002 and 2008, inverse distance was selected as the conceptualised spatial relationship, Euclidean distance as the distance method. More detailed analyses were then done on the selected critical years of dengue outbreak, (years 2002 and 2008), to investigate the influence of socio-economic variables on dengue incidence per census district.</p><p> </p><p>Dengue incidence rate appeared to be higher during the rainy and warmer months between December and May. Outbreaks of dengue occurred in years 2002 and 2008 over the study period of year 2000 to 2008. Some factors included in the census data were influential in the dengue prevalence according to districts. Satisfactory results can be achieved by using this strategy as a quick method for assessing potential dengue attack, spread and possible enabling conditions. The method has the advantage where there is limited access to field work, less financial means for acquisition of data and other vital resources.</p><p>A number of difficulties were encountered during the study however and leaves areas where further work can be done for improvements. More variables would be required in order to make a complete and comprehensive description of influential conditions and factors.  There is still a gap in the analytical tools required for multi-dimensional investigations as the ones encountered in this study.  It is vital to integrate ‘GPS’ and ‘Remote Sensing’ in order to obtain a variety of up-to-date data with higher resolution.</p><p> </p>