sparksql2.md

1. What fraction of counties had a Republican majority?

• (About 81.2% of counties had with percentage republican votes larger than 0.5). About 84.35% of counties had a republic majority(per_gop > per_dem).

2. What fraction of counties went Republican by a margin of 10% or more? What about Democratic?

• Based on per_point_diff, about 78.7% of counties went Republican by a margin of 10%. Only 10.8% went Democratic by 10% margin.

3. Plot the election results with an X-axis of the number of votes cast and a Y-axis of the percent Democratic votes minus the percent Republican votes.
4. Using both the election results and the zip code data, plot the election results by county geographically. So X is longitude, Y is latitude, and the color is a based on percent Democratic with 40% or lower being solid red and 60% or higher being solid blue.
5. In this question, I want to look at the impact of some recent recessions on the unemployment distributions for the US. In particular, I want you to look at the following recessions:
   • a. 7/1990 - 3/1991
   • b. 3/2001 - 11/2001
   • c. 12/2007 - 6/2009

For each one, I want you to make a number of histograms for two different months. One is the month before the recession started, and the other is the last month of the recession. You will make a grid of histograms of the unemployment rates for all states combined (so you can use the big combined file I made) with bins of (0.0 to 50.0 by 1.0) for the types of series listed below for those six months. Note that the Plot.histogramGrid method can help you to make the grid. How has the distribution of unemployment rates changed over time?

 a. Metropolitan Areas
 b. Micropolitan Areas
 c. Counties and Equivalents

I suggest that you make a grid with three rows and six columns. The rows are the different types of areas while the columns are the six different months. You might want to use colors to differentiate the month before a recession from the last month of the recession. Perhaps green for the month before and red for the last month. So you would have six columns that alternate green and red histograms. What do you observe in these plots?

• Below is the histogram grid plot with each row representing each type of area and columns representing six different months. We can see from the graph that there are larger variances in micropolitan areas, that some micropolitan areas have high unemployment rate even before recession. We also see that the 2008 financial crisis influences unemployment the most among all three recessions. The unemployment rate skyrocketed during that period and was really high at the end of this recession. Notice that the overall unemployment rate is the lowest before the 2008 recession, which matches the reality that the economy looks pretty good and not many people are worried about danger of subprime mortgages and housing bubbles. The 2001 recession is pretty well recovered, that at the end of it the average unemployment rate basically recovers to the same level as that in May, 2001. The 1990-1991 recession influences micropolitan area more than metropolitan areas.
  

6. I am interested in correlations between employment status and voting tendencies. Let's look at this in a few different ways.
   • a. For all counties, calculate the correlation coefficient between the unemployment rate and percent democratic vote for November 2016. (Note that this will be a single number.) What does that number imply?
   • • The correlation coefficient is 0.16672925713740164, which implies that unemployment rate and percent democratic vote is somewhat positively correlated. The higher the unemployment rate, the more likely voters will vote for democrats. The number is only about 0.167, which means this is not a strong correlation, so the graph of the correlation is sparse.
   • b. Make a scatter plot that you feel effectively shows the three values of population, party vote, and the unemployment rate (again in November 2016). For the population, you can use the labor force or the number of votes cast. Your scatter plot should have one point per county. In addition to X and Y, you can use size and color. What does your plot show about these three values?
   • Below is the plot for correlation between unemployment and percentage democratic vote in 2016, with x-axis unemployment rate, y-axis % democratic vote, and different color representing different population (we used labor force to estimate population, assuming more labor force, more overall population). Green represents 10,000 labor force; blue represents 50,000; yellow and red represents 100,000 and 1,000,000 respectively. We see that unemployment rate and percentage democratic vote is slightly positively correlated, but more importantly, we say that a majority of small counties with less population tend to have low percentage of democratic votes, while large, populated counties, as represented by red and yellow dots, tend to have high percentage of democratic votes. Most counties have unemployment level less than 8%.
7. Look at the relationships between voter turnout (approximate by votes/labor force), unemployment, and political leaning. Are there any significant correlations between these values? If so, what are they? You can use plots or statistical measures to draw and illustrate your conclusions.
   • We calculate turnout by using total votes divided by labor force. We find a significant correlation between turnout and unemployment with a coefficient of 0.1773494983384578, which means that the higher the unemployment, the more motivated voters participate in voting. The correlation coefficient is negative between turnout and percentage democratic votes (-0.034695985794154956) and is positive between turnout and percentage republican vote (0.05403469166804504) as expected, although the result is less significant because the coefficient is basically close to 0.