Research Outputs (Geography, Archaeology and Environmental Studies)

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Browsing Research Outputs (Geography, Archaeology and Environmental Studies) by Type "Thesis"

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    Frequency distributions of stone artefacts from Holkrans, North West Province, South Africa
    (2013-04-18) Banhegyi, Stephen
    Excavations at Holkrans rock shelter, located in the Vredefort Dome, Southern Africa, revealed archaeological deposits dating back some 2000 years, to both the ceramic and pre-ceramic Later Stone Age. The ceramic phase, placed within the last 500 years by radiocarbon dates, was likely contemporary with the Late Iron Age stone-walled structures in the nearby area. The pre-ceramic phase dates from the early first millennium BC to about 1000 AD. This pilot study examined a sample of lithics from the base of the shelter mouth using a standard typology and frequency distribution as a first step to a more extensive study to be conducted in the near future. Analysis revealed a substantial temporal gap in shelter occupation between the ceramic and pre-ceramic levels, largely in agreement with the observations of Bradfield and Sadr (2011) who noted raw material and possible technological differences between these layers. New radiocarbon dates suggest a series of punctuated occupations during the pre-ceramic levels and more regular occupation during the ceramic phase. How did contact with early farmers influence the archaeology of Holkrans? With few other shelters known in the area, research at Holkrans has the potential to fill a physical gap among known Later Stone Age sites in the southern African interior.
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    Investigation of Inter-analyst Variability in Stone-walled Structure Classification
    (2013-07-01) Hunt, Tamsin
    For many years stone-walled structures have been classified into different groups using aerial photography. The development of technology such as Google Earth and Geographic Information Systems has resulted in an increase in such classifications. What must be considered, however, is to what extent the results differ depending on the person analysing the structures. A study has been conducted by three different analysts, classifying the stone-walled structures south of the Suikerbosrand Game Reserve. A statistical and visual comparison of the three sets of analyses using Google Earth, QGIS and CrimeStat. These methods showed that variability is obvious between the sets of classifications. It is then important to consider what causes the variability in the classifications and how it can be remedied. This is important as variability in the classifications of stone-walled structures will have an effect on the larger interpretations of the sites and the people affiliated with them.
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    A Phosphate Analysis of Stone-Walled Structures in the Suikerbosrand Nature Reserve
    (2013-04-03) Ensor-Smith, Jesse
    With the purpose of confirming the “kraal index” created by Sadr and Rodier (2012), a group of stone-walled structures in the Suikerbosrand Nature Reserve were selected for study. Confirming this involved testing for livestock presence in the inner enclosures of Group III stone-walled complexes. Phosphate testing of the inner enclosures revealed the absence of evidence showing the presence of livestock occupation. This may be because of the phosphate testing method used. It may also be because of different culture factors involving the recycling of dung as fuel and kraal maintenance. The probability of each hypothesis is weighed up against the supporting data captured by the phosphate analyses.
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    A study of inter-analyst variability in the classification of stone-walled structures in southern Gauteng, South Africa
    (2013-04-18) MacRoberts, Rebecca
    The study of stone-walled structures within the last 1000 years in southern Africa can help archaeologists to understand how the landscape was peopled. Google Earth and GIS can make data capture easier, but when diferent analysts are involved, there can be significant variability in their results. By comparing data classified by three researchers in the same study area, it was possible to quantify inter-analyst variability and to query where and why it occured. The quality of Google Earth imagery had much influence in introducing inter-analyst variability. Subjective decisions on classification also introduced high amounts of variability. With more intensive training on classification and better imagery, inter-analyst variability can be reduced.