Scythian Burial Mounds: Report

The process of modeling the spatial distribution of Scythian burial mounds in Tuekta, Siberia, focuses on the relationship of the mounds to their environment.  First, a DEM of the region was clipped to the study area, and three secondary surfaces were created from it – slope, aspect, and elevation.  Slope was reclassified to weigh more heavily in favor of flat or gently sloping terrain.  Aspect was reclassified to favor southern facing areas.  Elevation was reclassified to emphasize areas with a similar elevation range.  The Tuekta mounds sites were digitized into a point shapefile to be used in the OLS regression analysis, and then an additional 100 random points were created.  These were merged, creating a new shapefile for the dependent variable – the presence or absence of sites.  The data was edited to reflect the slope, aspect, and elevation of each point, and to populate each point with XY data.  The data could now be used in the OLS Regression.

The OLS Regression was done on the model to identify trends and to see relationships between the dependent and explanatory variables. The results showed that all three explanatory variables were contributing to the model (Aspect = 0.088971, Slope = 0.130735, Elevation = 0.581643).  These positive values are expected with models using reclassified values that weight the higher numbers to aspects of the landscape that are favorable for site location. The Adjusted R-squared value was .718372, indicating a high percentage of site presence or absence would be predicted using a model with these variables. The Spatial Autocorrelation showed the clustering of sites.  The p-value of 0.000 indicates a 100% confidence level that the patterning is completely non-random, and that there is a spatial variable influencing the data.  The z-score of 13.3590348071 indicates that there is a less than 1% likelihood that this clustered pattern could be the result of random chance.  There are areas where the model has under-predicted the presence or absence of sites.  These include areas on the edges of the valleys in areas of transition to higher elevation and increasing slope, and along the valley floor in the eastern part of the study area. Other variables could be included to further refine the model, such as geological features, soil type, and vegetation.  Proximity to rivers could also be included as a variable.  This model is limited because of its use of only three variables, so the addition of these others would make it more precise, but clearly it shows the significance of these variables.  A Geographically Weighted Regression model is another option, because it is suited to a regional scale and clustered data, while the OLS regression model is better for non-clustered data.  However, the dataset used here was not large enough for GWR Regression.