GIS 5935 - Scale Effect and Spatial Data Aggregation

As seen in the title, this week's module and lab covered two topics. The first topic was scale effect. In a world where we have various interactive maps at the palm of our hand, scale effect may not have the most blatantly obvious issue with viewing data. However, scale effect can have a wide-ranging impact on data we are using and collecting. Vector data, for example, created from imagery or raster data is only as good as the finest detail that can be seen. This means at a small scale the resolution is considered low and small features and intricacies are not visible. Larger features are the only items detectable or discernable. As such, fewer vertices and segments are used to represent a feature, which can lead to less accurate calculations that tend to have a smaller numerical account of the feature. The opposite is true for large scales. 

The second topic covered this week was spatial data aggregation. This refers to the combining of data into a boundary, which in turn generalizes the data to an extent. This is often done with census data to remove the possibility of identifying a single person or group and to make the data more manageable when it comes to processing that data for decision making. Another instance of spatial data aggregation is the setting of political boundaries. This can often be problematic due to gerrymandering, where the boundaries are manipulated to include or exclude certain groups. The image below is an example of gerrymandering, determined during this week's lab assignment, when using the Polsby Popper Score to test for compactness. 

Example of Gerrymandering.


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