This week we went over Spatial Data Aggregation and MAUP (Modifiable Areal Unit Problems). MAUP is most affected by scale effect and zonation effect.
MAUP, a Modified Areal Unit Problem, arises from imposition of a spatial recording's artificial units when working with a continuous geographical event. This leads to the creation of artificial spatial patterns, skewing data and causing for erroneous analysis. MAUP arises from issues with scale and zoning in GIS. The most common example of this is Gerrymandering, which is manipulating boundaries to favor certain results, political party, or class. Gerrymandering is a major issue in regards to politics, districts often times separated across multiple counties.
To deal with Scale MAUP, the best thing to do is to pick the correct scale for what you are interested in measuring and analyzing. If possible, and dependent on your research question, it is better to view your map/data at a finer scale, which allows for it to be aggregated and give a better result. Coarser maps (due to lower resolution) does not allow for you to aggregate the data as well and lead to erroneous assumptions. This was observed in Part A of our lab, where we had to compare the Percent of the Non-White Population with the Percent Below Poverty. This was compared over four different scaling methods: by original block groups, by zip code, by house (voting), and by counties.
Figure 1: Table displaying the changes regression results by changing the scale.
To deal with Zone MAUP, map out the zones in your map in a simple, but critical method. You want to avoid things such as gerrymandering by making the zones easy to understand and able to be duplicated. One way to simplify the zones is to keep the same zonal shape among all zones. Make sure to incorporate factors in what you are studying to make the zone shape that has the best fit for your data. This practice was performed in Part B of the lab. We had to determine the Top 10 Worst Districts in Regards to 'Compactness' and 'Community'. To measure Compactness, a ratio was calculated based on the area and perimeter of each district. Through this I was able to calculate the Top Worst in 'Compactness'.
Figure 2: Screenshot showing how spaced out one voting district can be. In this situation, GEOID 26ZZ is spaced out across 4 different counties.
Figure 3: Screenshot displaying GEOID 3712, which was ranked 1st out of the 10 Worst in 'Compactness'
Figure 1: Table showing the Top 10 Worst Districts in 'Compactness'
To find 'Community', I intersected the districts from the counties, determining which counties were divided among different districts and how many districts they were spread across. By joining these two analysis together I was able to list the top 10. Overall the 'Compactness' calculations in Part B was not difficult. I had a lot of difficulty in determining the 'Community' Top 10. I also noticed some issues with running OLS Analysis on Counties in Part A, having to finagle the data to even run the analysis. I learned a lot in regards to MAUP, and it was nice to apply it to a real situation: gerrymandering in regards to voter populations.