Scale, Spatial Data Aggregation, Dasymetric Mapping
This week I completed the final lab for GIS 5935 which focused on scale, spatial data aggregation, and dasymetric mapping. Here I learned about the effects of scale on vector data and resolution on raster data. In addition, we covered the effect of the Modifiable Area Unit Problem (MAUP) while using regression analysis, also known as ordinary least squares (OLS) analysis. I also learned how to identify multipart features and measure compactness. For the first part of the lab I examined the effect of scale on vector data, in this case hydrographic polyline and polygon features from Wake County, North Carolina. Understanding the meaning of scale is important in GIS, especially how this effects resolution and extent (Goodchild 2011). I found in my analysis that t he larger the scale, the increased length of polylines, perimeter lengths, area, and number of polygons. Regarding vector data, resolution can be difficult to define and if possible it is best to use raster data for ...