Get the GIS of it

This week I learned how scale and resolution can affect the interpretation and analysis of data and how to utilize the Polsby-Popper score to determine which voting districts are less than ideal when looking at their compactness and the dividing of counties. Scale effects on vector data involve the level of detail in spatial features: at large scales (high detail), features like rivers and city boundaries are depicted with more precision, while at small scales (low detail), features are simplified, leading to potential data generalization.  Resolution effects on raster data refer to the size of cells: higher resolution provides finer detail, while lower resolution leads to loss of detail, affecting analyses like land cover or terrain modeling. When resampling LiDAR data, you need to consider what analysis you will perform to determine which technique is best to use. I chose bilinear interpolation since we were using the data for a DEM of a watershed area. The lowest and highest-res...

 This week  I learned that different interpolation techniques can produce varied results depending on the frequency of the data points and the contents of that data. A  single instance of coinciding data points can throw off the results of the analysis greatly when using spline interpolation. W hen working with continuous data with gradual changes spline generates a smooth spatial pattern that more accurately represents the data. Where preserving exact values is necessary IDW shows sharp transitions between data points. Thiessen represents the data as various zones using TINs.  Spline (regulated) Spline (tension) IDW Thiessen