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Integrated GIS for the studies of prehistoric archaeological assemblages: Insights from Olorgesailie an early Acheulean op...
Project Motivation<br />Despite increasing importance of Geographic Information System (GIS) in different fields of the ar...
Overall Project Objectives<br />Develop Archival Procedures<br />-For applying spatial analysis on tens of thousands of st...
Research Question<br />Intra-site<br />(A.) With a focus on few selected sites within the Olorgesailie basin, explore if t...
Research Question<br />Intra-site<br />2.  (A.): Investigate relations between distance from source of raw material and st...
6<br />Study Area<br />Localities and site distributions within the Olorgesailie Basin<br />
Data Description<br />Paleolandscape Approach: A procedure of sampling archaeological sites over broad lateral exposures, ...
Data Description<br />Sites within the Olorgesailie Basin, selected for more detailed investigations<br />
Methods<br />Data Gathering and Compilation <br />-Conversion of paper archives to digital format<br />(georeferencing, re...
Methods<br />-Replicating orientation of excavated materials in polylines<br />Given (X,Y)<br />Beg(X,Y)<br />Given bearin...
Method<br />A reference for interpretation of the orientations of excavated materials<br />11<br />Random = undisturbed<br...
12<br />Methods<br />-Building data for distance from sources of raw material<br />Location of the archaeological sites<br...
13<br />Methods<br />-Statistical tests employed in different areas of the spatial analyses<br />Statistical Test<br />App...
ArcGIS – Average Neighbor Analysis
ArcGIS – Spatial autocorrelation
GEOrient – Rose Diagram
Xtools Pro – ArcGIS addin
Average Nearest Neighbor</li></ul> Analysis<br />1A. Overall faunal and<br /> stone tool distribution<br /><ul><li>Moran’s...
Circular Variance</li></ul>1C. Site Formation:<br />Hydraulic forces<br /><ul><li>Rose Diagram</li></ul>2. Inter-Site<br /...
Measuring geographic distribution
Analyzing patterns
Mapping Clusters
Analysis tools (proximity)</li></ul>2A. Raw material distribution<br /><ul><li>K - function
Anselin Local Moran’s I</li></ul>2B. Spatial autocorrelation:<br />Flaked pieces and flake fragments<br /><ul><li>Getis-Or...
Ordinary Least Square
Geographically Weighted</li></ul>   Regression (GWR)<br />2C. Bivariate Analysis<br />
Results - Archiving<br />Geocoding of the excavated samples<br />Excavated sites showing on Quickbird Sat. (60cm resolutio...
Results - Archiving<br />Faunal samples and stone tools from site 102 and site 2<br />tools<br />bones<br />Site 102:  Two...
Results – Intra-site (1A) <br />Summary Statistics of faunal and lithics collections from a few selected sites<br />Z-Scor...
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Integrated GIS for the studies of prehistoric archaeological assemblages: Insights from Olorgesailie an early Acheulean open-air site in Kenya

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Integrated GIS for the studies of prehistoric archaeological assemblages: Insights from Olorgesailie an early Acheulean open-air site in Kenya

  1. 1. Integrated GIS for the studies of prehistoric archaeological assemblages: Insights from Olorgesailie an early Acheulean open-air site in Kenya<br />Zelalem Assefa<br />Olorgesailie Prehistoric Site<br />Acheulean Tools (Oval shaped, and longest used technology in human prehistory)<br />1<br />Geog 797 Final Project – Spring 2011<br />
  2. 2. Project Motivation<br />Despite increasing importance of Geographic Information System (GIS) in different fields of the archaeological investigations, its application in Paleolithic collections of the distant past is still very limited. Main objective of this project is to explore and introduce different approaches of GIS based analyses that can be applied for studying spatial distributions of the Paleolithic collections.<br />As a case study this analysis targets the excavated collections from the very well known Acheulean site, the Olorgesailie Basin in southern Kenya<br />
  3. 3. Overall Project Objectives<br />Develop Archival Procedures<br />-For applying spatial analysis on tens of thousands of stone tools and fauna collected from the site over the years.<br />Undertake Intra-site spatial investigations<br /> -Tracing differences and similarities in patterns of the spatial distributions of different archaeological samples (bones, tools) at site level; reconstruct some aspects of site formation processes<br />Undertake Inter-site spatial investigations<br /> -With a focus on certain attributes of the stone tools, such as the raw material and specific types of stone tools, investigation their patterns of clustering and spatial distribution across the site.<br />
  4. 4. Research Question<br />Intra-site<br />(A.) With a focus on few selected sites within the Olorgesailie basin, explore if there is a difference in the spatial distribution of bones versus stone tools.<br /> -Faunal remains and stone tools will be evaluated for Complete Spatial Randomness (CSR). The null hypothesis will be:<br />H0– the distribution of bones and stone tools at each of the sites included in the analysis is not different from a random pattern.<br />(B.) Explore clustering characteristics of bones and tool at local and higher scales:<br />-Depending on composition and density of the stone tools and fauna at particular sites the distribution of either bones or fauna should show a more localized or extended distribution.<br />(C.) Evaluate possible impacts of hydraulic forces on redistribution and accumulation of the findings from a few selected sites:<br /> -Prior researches indicate possible impact on formation of the archaeological assemblages due to impacts by moving water <br />4<br />
  5. 5. Research Question<br />Intra-site<br />2. (A.): Investigate relations between distance from source of raw material and stone tool distribution across the site<br />-According to the theory of optimal resource exploitation strategy, stone tools made out of raw materials from sources closer to the location of the site tend to be more common than stone tools made out of exotic raw materials from distant sources. Do the stone tools from Olorgesailie reflect this patter? <br /> (B): Is there a specific clustering pattern that the distribution of certain artifact types exhibit across the site.<br />-With a focus on flaked pieces and flake fragments, evaluate the spatial autocorrelation that these groups of stone tools show at few selected sites.<br />(C): Bivariate Analysis to evaluate if the spatial distribution of flake pieces can be explained by the flake fragments.<br />-Such relations between the two types of tools is assumed because the flake pieces are end-products of tool manufacturing activity which often results in accumulation of high concentration of flake fragments as residues or debris.<br />
  6. 6. 6<br />Study Area<br />Localities and site distributions within the Olorgesailie Basin<br />
  7. 7. Data Description<br />Paleolandscape Approach: A procedure of sampling archaeological sites over broad lateral exposures, targeting continuous and narrow natural layers.<br />Interval Layers<br />UM1p = Upper Member 1 paleosol<br />M6/7s = Member 6 and Lower Member 7 sands<br />LM7ds = Lower Member 7 diatomaceous silts<br />Du<br />Total excavated materials 12, 781<br />(stone tools 6,781, bone 6,000)<br />Duration<br />Age<br />746ka<br />780ka<br />~ 500 yrs<br />900ka<br />Excavated sites<br />(following lateral extension of single layer)<br />≤ 1000 yrs<br />974ka<br />0.900<br />16<br />18<br />≤ 1000 yrs<br />990ka<br />85<br />992ka<br />85<br />t<br />7<br />Composite Natural Layers<br />
  8. 8. Data Description<br />Sites within the Olorgesailie Basin, selected for more detailed investigations<br />
  9. 9. Methods<br />Data Gathering and Compilation <br />-Conversion of paper archives to digital format<br />(georeferencing, rectifying, digitizing)<br />-With the excavated materials, extrapolation of <br />the Total Station readings (XYZ ) to true<br />Geographic coordinates<br />-Joining and linking the excavation data and the <br />stone tool attribute data<br />Code<br />D = Dip (plunge)<br />O = Orientation (bearing)<br />ArtType = Artifact Type<br />WGT = Weight<br />rawmat = Raw material<br />= behavioral and ecological <br />= site formation process<br />9<br />
  10. 10. Methods<br />-Replicating orientation of excavated materials in polylines<br />Given (X,Y)<br />Beg(X,Y)<br />Given bearing<br />Excavated object<br />End(X,Y)<br />Given dip<br />distance<br />Back Azimuth<br />- If O is ≤ 1800 add 180<br /> - If O is > 1800 subtract 180<br />To find the XY at one tip of the bone:<br />Excel worksheet for calculating necesary data for replicating the polylines<br />(X + d*sin(o), Y + d*cos(o)) <br />Trigonometric function = <br />Distance = given (changed to 10cm)<br />0rientation angle = converted to radian<br />
  11. 11. Method<br />A reference for interpretation of the orientations of excavated materials<br />11<br />Random = undisturbed<br />Aligned = disturbed by hydraulic force (single event)<br />Criss-cross = disturbed by hydraulic force (multiple event)<br />Modified from McPherron (2005).<br />
  12. 12. 12<br />Methods<br />-Building data for distance from sources of raw material<br />Location of the archaeological sites<br />Sources of raw materials used <br /> for making stone tools<br />-Distance from raw material (created using<br />the ArcGIS’s proximity tool – ‘Near’)<br />Raw material sources<br />
  13. 13. 13<br />Methods<br />-Statistical tests employed in different areas of the spatial analyses<br />Statistical Test<br />Applicability<br />Application/tool <br /><ul><li>Point Pattern Analysis</li></ul>1. Intra-Site<br /><ul><li>SDA4PP – GIS plugin
  14. 14. ArcGIS – Average Neighbor Analysis
  15. 15. ArcGIS – Spatial autocorrelation
  16. 16. GEOrient – Rose Diagram
  17. 17. Xtools Pro – ArcGIS addin
  18. 18. Average Nearest Neighbor</li></ul> Analysis<br />1A. Overall faunal and<br /> stone tool distribution<br /><ul><li>Moran’s I ?</li></ul>1B. Extent of distribution:<br />Local or higher<br /><ul><li>Nearest Neighbor hierarchical clustering
  19. 19. Circular Variance</li></ul>1C. Site Formation:<br />Hydraulic forces<br /><ul><li>Rose Diagram</li></ul>2. Inter-Site<br /><ul><li>Weighted Standard </li></ul>deviational ellipses<br /><ul><li>ArcGIS tool box
  20. 20. Measuring geographic distribution
  21. 21. Analyzing patterns
  22. 22. Mapping Clusters
  23. 23. Analysis tools (proximity)</li></ul>2A. Raw material distribution<br /><ul><li>K - function
  24. 24. Anselin Local Moran’s I</li></ul>2B. Spatial autocorrelation:<br />Flaked pieces and flake fragments<br /><ul><li>Getis-OrdGi*
  25. 25. Ordinary Least Square
  26. 26. Geographically Weighted</li></ul> Regression (GWR)<br />2C. Bivariate Analysis<br />
  27. 27. Results - Archiving<br />Geocoding of the excavated samples<br />Excavated sites showing on Quickbird Sat. (60cm resolution) imagery <br />Geocoded excavated samples<br />overlaid on the Quickbird sat. image <br />
  28. 28. Results - Archiving<br />Faunal samples and stone tools from site 102 and site 2<br />tools<br />bones<br />Site 102: Two-dimensional and three-dimensional <br />Views of excavated materials<br />15<br />Site 2: Two-dimensional and three- dimensional <br />Views of excavated materials<br />
  29. 29. Results – Intra-site (1A) <br />Summary Statistics of faunal and lithics collections from a few selected sites<br />Z-Score results for the <br />most part reject the <br />CSR null hypothesis<br />for randomness <br />
  30. 30. Results – Intra-site (1B)<br />-Strong local and well extended clustering<br />(Site 102, Site 15 Main, C7-Trench 1 and 3)<br />-More localized clustering<br />(Site 2, Site 15 Ext. 2) <br />-Marked difference between the patterns<br />Faunal and stone tool clustering at Site 102<br />
  31. 31. Results – Intra-site (1C)<br />Orientations of excavated materials from a few selected sites<br />Close-ups<br />A more random pattern of orientations<br />
  32. 32. Results – Intra-site (1C)<br />Rose diagrams plotting orientations of the same material shown in the earlier slide <br />Randomly scattered <br />orientations<br />
  33. 33. Results – Intra-site (1C)<br />Quantitative results from the ‘directional mean’ computations in ArcGIS and the rose-diagram in GEOrient<br />Circular Values<br />Suggest a more <br />Random pattern of <br />orientation<br />1 = The mean for compass angle (clockwise from due north)<br />(only for one angle, the reverse (360 – 94.29 = 265.71) is also true for orientation)<br />2= Directional mean counterclockwise from due east<br />3 = Circular variance – indicates how much line directions/orientations deviate from the directional mean<br />-If all input vectors have exact or very similar direction CirVar is near 0<br />-If the input vectors span the entire compass, the CirVar is near 1<br />
  34. 34. Results – Intra-site (2A)<br />Elliptical Polygons showing directional distribution of stone tools sorted by types of raw material<br />At least two different<br />patterns of distributions<br />were observed<br />
  35. 35. Results – Intra-site (2A)<br />K – function plots showing differences in patterns of stone tool distribution (sorted by types of raw material) with distance<br />All, but the stone tools made<br />from Otp raw material, show<br />a similar clustered distribution <br />across the basin<br />
  36. 36. Results – Intra-site (2B)<br />Hot spot analysis (Getis-OrdGi*) on flaked pieces<br />Cluster/Outlier analysis on flaked pieces using local Moran’s I statistic<br />
  37. 37. Results – Intra-site (2B)<br />Hot spot analysis (Getis-OrdGi*) on flaked pieces<br />Cluster/Outlier analysis on flaked pieces using local Moran’s I statistic<br />-Overall a patchy distribution of flaked pieces<br />-More clustered distribution and better concentrations<br /> of high-high values at Site 1, Site 2, and Site 15 Main<br />-Mixed clustering of high and low values at Site 102<br />-A cold spot (low values) at Site 15 Ext. 2<br />-At Site 126, strong clustering (hot spot) with high-high and low-high values<br />
  38. 38. Results – Intra-site (2B)<br />Hot spot analysis (Getis-OrdGi*) on flake fragments<br />Cluster/Outlier analysis on flake fragments using local Moran’s I statistic<br />
  39. 39. Results – Intra-site (2B)<br />Hot spot analysis (Getis-OrdGi*) on flake fragments (Site 126)<br />Cluster/Outlier analysis on flake fragments using local Moran’s I statistic<br />(Site 126)<br />-Strong clustering of flake fragments and high concentrations<br /> of high-high values at Site 1, Site 2, and Site 15 Main<br />-Hot spots of flake fragments extended to hyena hill areas <br />-Significant clustering of low values at Site 104 and Site 15 Ext. 2. <br />-At Site 126 still strong clustering (hot spot) marked by<br />high concentration of high-high values in southern portion of the site<br />
  40. 40. Results – Intra-site (2C)<br />Bavariate Analysis – Flake pieces and flake fragments<br />Unreliable OLS result<br />-Problem of nonstationarity<br />-Misspecification <br />GWR Result<br />-More effective with multiple exploratory variable<br />-Indicate only 13% of the flake pieces<br />Variance can be related with the flake fragments<br />
  41. 41. Conclusion<br />Different analytical approaches employed in this project can be used as a model to apply GIS as effective research tool in the study of Paleolithic collections of the distant past. <br />Specific to Olorgesailie, this project has managed to:<br />Develop efficient archival system for studying spatial distribution of all excavated findings.<br />Look at variations in the spatial distributions of stone tools and fauna at several sites in the basin. The average nearest neighbor result rejects the CSR based null hypothesis. <br />Develop a new approach of investigating possible impact of hydraulic forces in site formation process. The result from the analysis show lack of any major impact from hydraulic forces on distribution and accumulation of the archaeological remains<br />Investigate the relation between distance from source of material and patterns of spatial distributions with a focus on particular group of stone tools.<br />Analyze spatial autocorrelations of particular types of stone tools (flake pieces and flake fragments) across the basin. For the most part, the observed clustering noted at many of the sites found to be consistent with predicted patterns. Observations at a few sites, such as Site 126 reveal some important features shared by other major sites in the basin.<br />Conduct bivariate analyses, which in spite of some limitations in applicability, provided important highlights that the spatial distribution of the flake pieces has no strong relations with distribution of the flake fragments<br />
  42. 42. Future Improvements<br />Integrate observations from multiple paleosols or intervals for better understanding changes in land-use patterns through time<br />In the analysis of spatial data, integrate three-dimensional analyses for better control of spatial variations both horizontally and vertically.<br />Incorporate multiple explanatory variables in future bivariate analyses.<br />

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