M3.1: Scale Effect and Spatial Data Aggregation

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-resolution images from the lab are shown below.





Gerrymandering is the manipulation of electoral boundaries to favor a political party. It is typically measured using the Polsby-Popper score (compactness) or the efficiency gap (vote waste) to identify unfair advantages. The worst offender I found was District 1.





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