The main focus of this week's module was to experiment with a variety of surface interpolation methods in interpreting artifact and population density. Surface interpolation produces a continuous surface from point data. This could be useful if, as in our readings, one were attempting to identify large-scale communities or social interaction over distances that may not be obvious from point data. In the above maps of Volcan Baru, for example, the surface interpolations were derived from shovel test point data, specifically the number of artifacts from each shovel test. These interpolations may give us some idea of the social interaction of what may at first appear to be separate sites.
In addition to surface interpolation, we also were required to convert text and AutoCAD data into more GIS friendly formats. This is something GIS specialists deal with quite often, and I was glad to get some practice working with them.
Graduate students were also required to do some more experimentation with the inverse distance weighted interpolation method. By doing so, we saw first hand that tweaking the parameters of interpolation methods can have a significant effect on the outcome. By changing the power parameter, the surface interpolation goes from extremely smooth with little detail (power of 0.5) to being little better than the original point data (power of 10). The default power of 2 seemed the right balance to see potential community interactions without stretching the data too far.
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