An exploratory study of Middle Stone Age and Later Stone Age site locations in Kenya’s Central Rift Valley using landscape analysis : a GIS approach

Английский. The Kenyan central rift has witnessed dramatic climatic changes over relatively short periods of time in response to global climatic changes, with the water levels of the lakes within the rift rising and falling with these changing conditions. There is considerable evidence showing extreme wet and dry phases throughout their existence. These wet and dry phases also influenced the vegetation cover, and by extension, the resources available to human and animal populations at any one time. The rise and fall of lake levels is reflected in the settlement patterns and subsistence strategies of different populations through time. Previous studies have been carried out to compare how the Middle Stone Age (MSA) and Later Stone Age (LSA) social and territorial systems differed in their adaptations to similar resource structures, since they are generally found in the same areas. However, earlier comparisons are based on the constitution of lithic and faunal assemblages at individual sites, without considering broader spatial scales that include territories and areas of land use that surround sites and settlements, and more ephemeral features that may influence the choice of site settlements. Since archaeological sites are a part of a cultural landscape within which particular systems of activities take place in space and time, landscape analysis is suggested for a broader approach than just tool types and morphology. Settlement patterns are instrumental in explaining subsistence strategies and spatial organization in relation to ecological and physical resources. The main aim of this study is to use geographical information system (GIS) methods to explore patterns in site locations during the MSA and LSA, and to establish differences and similarities between the periods. GIS is ideal for analyzing social and ritual landscapes by testing proxies for visual perception. Mapping archaeological sites using GIS improves our ability to detect settlement patterns that are not otherwise apparent. Visualization of sites makes it possible to compare their locations in relation to geographic features that may have influenced their locations. The methodology employed includes visibility analysis and statistical analyses that include Spatial Autocorrelation, Average Nearest Neighbor, Multi Distance Cluster, and Directional Distribution. Mapping archaeological sites using GIS improves our ability to detect settlement patterns that are not otherwise apparent. Visualization of sites makes it possible to compare their locations in relation to geographic features that may have influenced their locations. Results indicate that there are differences in the locations of MSA and LSA sites, with distinct patterning at specific distances. The clustering shown may be an indication of location preference due to availability of resources and security considerations, but may also have been highly influenced by climatic conditions and existing physical features. The locations of view sheds generated from selected sites indicate different target areas and therefore suggest differing visibility considerations. It is suggested that more intensive surveys and research should be concentrated in areas of site clustering, and in viewshed areas to determine factors that may have influenced this patterning. Site location patterns may give us insights into how sites were chosen and give us an idea on where to look for new sites to explore in future.

Английский. Global climatic changes have caused great environmental variations within the Rift Valley over a long period of time. In the Kenyan central rift valley, these climatic changes are reflected in well documented high and low lake levels, as a result of corresponding high and low amounts of rainfall. Amounts of rainfall influence types of vegetation cover, and by extension, the kind of food resources available for both humans and animals. The choice of human settlement areas is thought to have been determined by environmnental factors, and therefore settlement patterns and behaviour related to subsistence may have varied with lake levels and vegetation cover. Middle Stone Age (MSA) and Later Stone Age (LSA) sites are both found within the central rift, and studies have been carried out to establish how their social and territorial systems differed since they are found in the same general area. Previous studies are based on the differences in tool types and technology at individual sites without considering aspects of land use and physical features that may have influenced their choice of settlement areas on a larger scale. Humans create cultural landscapes through their activities in both space and time; therefore landscape analysis is suggested here for a more comprehensive approach to the study of relationships between individual sites. Settlement patterns are important in explaining subsistence strategies and spatial organization in relation to ecological and physical resources. The main aim of this study is to use geographical information system (GIS) methods to explore patterns in site locations during the MSA and LSA, and to establish any differences and similarities between the periods. GIS is ideal for analyzing spatial data because it has specially developed application for this purpose. These methods aid in summarizing landscape patterns by both visualization and statistical analysis. The methodology suggested includes visibility analysis and several statistical methods such as Spatial Autocorrelation, Average Nearest Neighbor, Multi Distance Cluster, and Directional Distribution. Mapping archaeological sites using GIS improves our ability to detect settlement patterns that are not otherwise apparent. Visualization of sites makes it possible to compare their locations in relation to geographic features that may have influenced their locations. Results indicate that there are differences in the locations of MSA and LSA sites, with distinct patterning at specific distances. The clustering shown may be an indication of location preference due to availability of resources and security considerations, but may also have been highly influenced by climatic conditions and existing physical features. The locations of view sheds generated from selected sites indicate different target areas and therefore suggest differing visibility considerations. It is suggested that more intensive surveys and research should be concentrated in areas of site clustering, and in viewshed areas to determine factors that may have influenced this patterning. Site location patterns may give us insights into how sites were chosen and give us an idea on where to look for new sites to explore in future.