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.
