ETD Collection

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    Parallel algorithms for querying spatial properties in the protein data bank
    (2019) Selvan, Joshua
    Searching large protein databases for proteins with certain structural properties is expensive. This research explored the use of GPGPUs (General Purpose Graphical Processing Units) in speeding up such structural queries. Brute force and kd-tree spatial data structure algorithms were compared and benchmarked against non-GPU parallel algorithms to assess the e ectiveness of using GPGPUs. This was done with the aim of increasing the speed at which queries against large protein databases can be completed to help mitigate the e ect of increasing data set sizes of current protein databases [56]. A set of parallel variations of range search algorithms were developed and imple- mented in the GPU programming language CUDA and their performances times in completing batch range search jobs were compared against other parallel approach types such as multi-threading and message passing to see if the GPU approaches completed notably faster or slower than more traditional parallelised approaches. The results showed GPGPUs can construct kd-trees far faster than other parallelised implementations can achieve and that in most scenarios (excluding speci c cases such as very low or zero result searches) the GPGPU approaches either matched or performed far better than the other parallelised approaches. While comparing di erent GPU algorithms, the complex GPU based kd-tree algo- rithm performed similarly to a simple GPU brute force range search. This high- lighted the bene ts of writing code which made the most of the GPU's parallel architecture as opposed to modifying e cient (recursive) algorithms to adequately t into those same GPU architectures. This implied that even though spatial data structures are e ective ways of dealing with protein data, there are better returns on e ort in writing code speci cally for the GPU's inherently parallel architecture for initiatives which require algorithms to be developed from scratch.
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    Spatial and temporal patterns of conflict events in Africa: an exploratory analysis
    (2018) Maswanganye, Menelisi Lungelo
    Conflict events across the world have usually been studied from a qualitative perspective. The current abundance of data pertaining to conflicts now allows for a more statistical approach. This research report examined the spatial distribution of three conflict types (battles, riots and protests, and violence against civilians) throughout mainland Africa. It also considered the evolution of the observed spatial patterns through time by comparing the patterns before 2007 with those from 2007 and onwards. The results show that although Somalia and Sudan are unusually afflicted with conflict, this is only statistically discernible at the more granular areal levels. It was also found that when conflict variables are dichotomised, loss of information occurs, but is somewhat mitigated by considering the analysis at more granular areal levels. Finally, although the number of conflicts has increased, there were mostly no significant differences between spatial patterns before and after 2007.
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    An assessment of the physical drivers for farm dam distribution in Midlands KwaZulu Natal, using GIS and remote sensing
    (2018) Tsoka, Jonathan
    The interest in farm dams emanates mainly from their use for livestock watering, irrigation and fisheries enhancement on a sustainable basis. While management information on large dams in South Africa is largely available, it is lacking for farm dams which cumulatively store large volumes of water. As a result they are barely considered as part of the water resources of a river basin. Data acquisition methods for obtaining information about farm dams are costly, time consuming and labour intensive. This study was an attempt to map farm dams and establish the factors driving their spatial distribution pattern in the Midlands, KwaZulu-Natal, South Africa, using cost effective, time saving and less laborious GIS and remote sensing techniques. A classified April 2017 Landsat 8 satellite image was used to identify all water bodies in the Umgeni River basin U2 quaternary catchment (U2) while Google Earth was subsequently employed for differentiating farm dams from other water bodies. There were approximately 2000 water bodies that were identified by the classification. These included large national dams, pools in golf courses, ponds and disused mine dumps. A total of 864 farm dams in the U2 region quaternary catchment was observed. Six physical factors, namely slope, aspect, elevation, land use, soil type and geology were assessed to establish to what extent they influenced the siting of farm dams. The results indicated the importance of soils and land use as farm dams were mainly found in clusters in areas where agricultural farm land is also found since water is required for crop and livestock production. The influence of other factors such as slope, geology and elevation were observed in the spatial distribution maps. They all gave significant p-values in their univariate analysis. Of the six variables only aspect gave non-significant results while the rest were significant. A binary multivariate logistic regression was created for forecasting future farm dam sites and to establish which sites are poorly sited. The other four factors were fitted to the model except aspect and geology type which had no significant p-values in the model. The model had an Akaike information criterion (AIC) score of 293.42 and had the best combination of variables relative to other models. It was validated using 500 farm dam sites and it predicted 86% correctly.
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    Temporal and spatial variation in population structure of the African baobab (adansonia digitata) in the Kruger National Park, South Africa
    (2016) Taylor, Robert William
    The African baobab (Adansonia digitata) is a charismatic and iconic tree. This keystone species has highly specific moisture requirements for recruitment, a very slow life history, low dispersal capabilities, and is exposed to heavy, often destructive utilization by baboons, elephants and humans. These characteristics result in the reduced ability for baobabs to accommodate and respond to changing environmental conditions. During periods of unfavourable conditions, baobabs rely on the persistence of established individuals for the survival of the population. The Kruger National Park (KNP), South Africa falls within the southern-most distribution of the baobab. A perceived lack of recruitment together with high numbers of dead large baobabs recorded in aerial surveys prompted concern over local population decline in the early 1990s. Long-term monitoring is necessary in order to quantify changes occurring within the KNP baobab population. For this reason Dr Ian Whyte sampled 424 baobab trees in northern KNP in 1995/1996 to describe population size-class distributions and elephant damage. Nearly all of these baobabs, with the addition of 486 individuals were resampled in 2001 by Michele Hofmeyr. This study resampled the majority of these 910 baobabs plus 126 individuals within 50m of those previously sampled, culminated in a data set spanning 18 years and most of the dominant vegetation and climatic zones within the baobab’s KNP range. Photographs taken in 1995/6 and repeated in 2013 allowed for simultaneous direct visual comparisons of elephant damage to baobab stems. This study aimed to record the temporal and spatial changes in the demographics of, and damage to, this sample of 1036 baobabs in order to define and evaluate the factors threatening KNP baobab persistence. The overall population stem diameter structure of baobabs in the KNP showed a healthy inverse J-shaped distribution, with a high proportion of smaller trees and a decreasing proportion of larger trees at each sampling snapshot. However no seedling recruitment was observed during intensive searches within 50m of sampled trees, accumulating in a total area of 4km2. Growth was only noticeable in sub-adult trees >0.15m and <1m in stem diameter. Annualised mortality increased three-fold between the periods 1995/6-2001 (0.25%) and 2001-2013 (0.79%). It is speculated that this increase in mortality is due to the cumulative effect of increased elephant damage, together with below average rainfall years of 2002 to 2013. Trees that had previously sustained severe and very severe damage contributed the highest proportion (32%) of mortality. When comparing sizeclasses, the majority of mortality (61%) was recorded within the <1m stem diameter size-class. However, a high proportion of very large trees (21% of the 4- 4.5m and 29% of the 5-5.5m stem-diameter baobabs sampled) had died from unknown causes. With no recruitment of seedlings evident and little recruitment between size classes, mortality alone was not enough to significantly change the baobab population structure between 1995/6, 2001 and 2013. This might change should more very large (>4m stem-diameter) trees die, as these size classes make up less than 2% of the population. At a spatial scale, maximum fire return interval, level of elephant damage and mean annual temperature all had significant impacts on the size-class distribution of baobabs. Longer (>30 years) maximum fire return intervals and higher mean annual temperatures (23°C) supported an inverse J-shaped baobab population structure whilst shorter (<30 years) maximum fire return intervals and lower mean annual temperatures (21-22°C) supported a bell-shaped population structure. Increases in bark damage over time correlated with increased elephant density (primarily attributed to mature male elephant density). Smaller baobabs – those less than <1m in stem diameter, generally escaped elephant utilization altogether, or suffered very severe damage by elephants. Despite a 3-fold increase in mortality and no recruitment over the 18 year study period, the KNP baobab population has maintained its inverse J-shaped population structure. The high proportion of mortality in the >4m stem diameter is however concerning as the persistence of these large baobabs is essential for the survival of the population during unfavourable conditions for recruitment.