It really is hard to believe, but here we are at the end of Special Topics in GIS, it has bee a great course and I have learned so much in the past 8 weeks. From data accuracy to spatial data assessment, we have covered so much this semester. For this final lab, we covered a huge topic that is very relevant in GIS today and that is the effect of scale on various types of data in addition to the Modifiable Areal Unit Problem and how it pertains to congressional district gerrymandering. I'd like to break down the effect of scale and resolution on the two types of spatial data: Vector and Raster.
When vector data is created at different scales there will obviously be a difference in detail of the data. For the lab, we were given multiple hydrographic features taken at three scales:
1:1200
1:24000
1:100000
At the 1:1200 scale, hydrographic lines and polygons can be expected to reflect the nature of the real world in the data. Polylines at this level will be very detailed and have a high number of vertexes. Polygons will be more inclusive in features.
At the 1:24000 scale, feature detail begins to drop. Polylines have less detail and shorter total lengths to account for the less number of features illustrated. Polygons drop off at this level of scale, and polygons compared to the 1:1200 scale are not accurate reflections of the features.
At the 1:100000 scale, data detail becomes even less than at the 1:24000 scale. Polylines are as minimal as possible with very little detail and polygons are missing even more since features are too small to draw by eye. The data very minimally reflects the real world.
For raster surface data, the difference in resolution can heavily impact the data. For this lab, I resampled a raster surface (1-meter cell size) DEM of a watershed surface feature at five different resolutions:
2-meter cell size
5-meter cell size
10-meter cell size
30-meter cell size
90-meter cell size
Using a bilinear sampling method designed specifically for this type of continuous data, with each resample to the next resolution size up, the quality of the raster surface decreased. Larger cell values did not reflect the values accurately of the initial 1-meter cell size values when calculating the average slope of each raster.
These two typed of spatial data with there issues point to a problem well known in the GIS world known as the Modifiable Areal Unit Problem. This 'problem' essentially arises when you create new boundaries to aggregate smaller features together into new areas. These 'new boundaries' are usually always arbitrary and in no way reflect the data of the smaller pieces within them and can be deceiving. A great modern GIS issue of this is Gerrymandering. Gerrymandering is a political strategy to redraw boundaries of districts so that they favor one particular political party or class. In the state of Florida, gerrymandering has been under the spotlight for some years. While gerrymandering is tough to measure by the human eye (with some exceptions), it can statistically be measured. Through a method called the Polsby-Popper Score, congressional district compactness can be measured. This value is calculated by multiplying the area of the district by 4pi and then dividing that number by the perimeter squared of the district. The values returned can range from a value of 0 to 1. Values closer to 1 reflect districts that are more compact while values closer to 0 reflect districts that are very 'loose'. The looser the district, the higher the likelihood it has been gerrymandered.
Below is the district (in red) that received the lowest Polsby-Popper Score value of 0.029
~Map On!
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