Lab 14: Gerrymandering

In this week's lab we had an assignment on Gerrymandering. Gerrymandering is when voting districts are drawn in a way that favours one party over another. In this lab we had to look at compactness and community as factors for determining if a district is gerrymandered or not.

Most heavily gerrymandered districts have huge perimeter to area ratio. They will end up long and snakey. This can be detected by looking at the district's compactness. I used the Polsby-Popper method to evaluate compactness. This compares the area to perimeter as if the district was a circle the higher the score the more compact the district. I have included a screenshot of what I found to be the least compact district below. It is Congressional District 12 of North Carolina.

A lot of heavily gerrymandered districts also chop up counties that should be contiguous and will end up being made up of a lot of counties. Congressional District 1 also of North Carolina (shown below) is a district made up of bits of counties that don't even neighbor each other. It is necessary for some districts to contain multiple counties due to population sizes or even portion of counties. LA county for example is very densely populated. It needs to be split up because a single district couldn't contain it. District 1 of NC on the other hand contains little bits of many counties and few counties in their entirety.

Lab 13: A Tale of Two DEMs

I had to compared to two DEMs of a coastal Californian water basin for lab 13. I had one that was created using LIDAR and I had a SRTM one. I created two maps to show some of the differences between them. I had one in which I subtracted them from each other to show the differences in elevation. I also made a similar map for slope.

Lab 12: OLS vs GWR

The biggest difference between Geographically Weighted Regression (GWR) and Ordinary Least Sqaures (OLS) is that GWR takes into account geographic distribution and explores local relationships while OLS models the data globally and is a bit more rigid. GWR improved the model in this lab greatly because all the data was organized spatially and affected things closer more than things at a distance. OLS is better for things that don't have a big spatial component.

Lab 11: Regression using a GIS

In this lab I used the lessons in regression we learned last week and used them in ArcMap. This allowed me to familiarize myself with how ArcMap processes and reports regressions. The performance of a model can be evaluated using the six Ordinary Least Squares (OLS) checks. They are as follows:

1. Are the explanatory variables helping your model? This can be checked by examining the p-value of the variables and confirming that they are statistically significant.
2. Are the relationship what you expected? This is checked by looking at the signs of the coefficients and making sure they make sense. If a variable has a negative coefficient when you expect it to be positive something is wrong with your model.
3.  Are any of the explanatory variables redundant? This is easily checked by examining the Variance Inflation Factors (VIF). If the VIF shows that any of the variables are redundant you may have to remove one.
4. Is the model biased? The Jarque-Bera test looks for bias or skewed residuals. If it is significant you may have an issue with your model.
5. Do you have all key explanatory variables? If you run the Spatial Correlation (Global Moran's I) tool it will provide you with information about whether or not your residuals are clustered, dispersed, or random. If your distribution isn't random you may need more key variables in your model
6.  How well are you explaining your dependent variable? This is easily evaluated by looking at the adjusted r-squared value. The higher the number the bigger the percent of variation is explained by your model. If you are trying to compare this model to others you can also look at the Akaike's Information Criterion (AIC). The AIC means nothing by itself but it is useful to compare it to other models. The model with the lowest AIC is usually the better one.