Assignment 4

Part one:

Section 1:

           The first exercise in this assignment has us looking at the correlation between Distance (in Feet) and Sound Level (in Decibels). My null hypothesis is that there is no linear correlation between Distance and Sound Level. My alternative hypothesis is that there is a linear correlation between Distance and Sound Level. The Pearson Correlation test that I ran shows that there IS a correlation between the two and it is a very high negative correlation (-.896). Therefore I reject the Null hypothesis and fail to reject the alternative hypothesis.

Table 1: Graph showing a very high negative correlation between Sound Level and Distance. Sound level is on the X-Axis while Distance is on the Y-Axis.
 

Section 2:

 

           For the second section of Part One we created a Correlation Matrix of Milwaukee County in Wisconsin. There are some patterns that we can gather from this matrix. One variable we can look at is Percent White. When looking at Percent White we can see that when it increases, other races decrease. This is rather obvious as when one percent of race goes up then the other must go down, however in Milwaukee County it is especially true between white and black. Milwaukee is one of the most racially segregated cities in America and the high negative correlation (-.887) shows that through statistical values.

Some more analytical variables we can look at are the correlation between people who are below the poverty line to people that walk to work. There is a low correlation between the two which means that we can see that when proportions of people below the poverty line increase, proportions of people that walk to work increase as well.

Part Two:

 

Introduction:

                I have been hired by the Texas Election Commission (TEC) to run some statistics on data from the 1980 and 2008 elections. They have given me the percent democratic and voter turnout for each election. I wanted to add another variable to maybe shed some light on why we might be seeing the patterns that we see after running the statistics tests, so I have also downloaded a Hispanic population dataset.

The reason why they want me to analyze this data is to determine if there is a clustering of voting patterns anywhere in state along with if there is a clustering of voting turnout patterns. The TEC wants to give this information to the governor to see if over a 30 year period the patterns have changed throughout the state. To run this data analysis I will be mostly using GeoDa and SPSS to determine if any patterns take place. The TEC specifically wants me to determine if there is any special autocorrelation within the state.

Methods:

                To start the data analysis I first had to gain access to all of the required data. Luckily for me the wonderful TEC commissioner has provided me with the Texas election data. It is up to me to get the Hispanic data, so I choose to go through the Census Bureau. I got the Percent Hispanic Population for 2010 and while I was there, I also downloaded the county shapefile for Texas. Now I have all the files that I need to run the statistics, all I need now is the software to do all the complicated stuff that I cant do. No need to download any software as it is already on the computers so I jump right into GeoDa. Within GeoDa I imported the Texas County shapefile and created a new spatial weight since I would be running a spatial autocorrelation test. While creating the weight I selected ROOK as the contiguity weight.

                Now since I determined the weight I am able to make Moran’s I and LISA Cluster Maps. To create the Moran’s I was very self-explanatory as I simply clicked on the Moran’s I icon and selected the variable I wanted and it instantly made the graph.  I then did that for the rest of the variables and then moved onto the LISA Cluster Maps. This was just as simple as I selected its own icon and then cluster map and WALA, I had myself a LISA Cluster Map.

 

Results:

                After running all the tests I was left with 5 Moran’s I’s and 5 LISA Cluster Maps. I can instantly see from the Moran’s I that over time (from 1980-2008) that the counties that vote democratic have become more clustered. This is not the case with the voter turnout as it seems to have gotten less clustered over time. When looking at the percent Hispanic population we can see that it is very concentrated and by looking at the beautiful LISA Cluster Maps that Geoda created we can see where that is. As you see by Figure 1 we can see that there is high clustering of counties that all have high Hispanic population in the south along the US-Mexico border. There is also a cluster in the northeast of counties that all don’t have high populations of Hispanics. We can see from the other Cluster Maps that the same area that is occupied by a high number of Hispanics also has a high number of counties that all vote democratic both in 1980 and 2008. One other pattern we see in the state (especially in south Texas) is that those areas with high numbers of Hispanics and a high number of democratic voters also have a low percentage of voting turnout.

Figure 1: LISA Cluster Map showing percent Hispanic Population.

 

Legend for LISA MAPS

                One interesting thing that goes against the pattern that we see through most the state is that the Dallas-Fort Worth area (Figure 2) has a high voter turnout but with a higher Hispanic population as well. It doesn’t show up on the map in Figure 1 because all the counties around it have a much lower Hispanic population and due to the Rook Contiguity Weight that I put on before, those high Hispanic counties are affected by the lower Hispanic counties to the top, bottom, left, and right of them.

Figure 2: LISA Cluster Map showing voter turnout in 2008.

 

Here are the other Moran’s I and LISA Cluster Maps:

Figure 3: Moran's I for Percent Hispanic Population

Figure 4: Moran's I for Percent Democratic 1980

Figure 5: Moran's I for Percent Voter Turnout 2008

Figure 6: Moran's I for Percent Voter Turnout 1980

Figure 7: Moran's I for Percent Democratic 2008

Figure 8: LISA Cluster Map showing Percent Democratic 1980

Figure 9: LISA Cluster Map showing Percent Democratic 2008

Figure 10: LISA Cluster Map showing Percent Voter Turnout 1980

 

 

 

Conclusion:

                 As far as if election patterns have changed over time I would say they have but only slightly. There doesn’t seem to be any mass migration of voters but rather individual little pockets of change that pop up over the state. Those pockets of change seem to be around urban areas with regard to voter turnout and rural areas with regard to democratic voters. I would think that this could be due to the huge turnover of rural to urban populations that we have experienced over the last 30 years with a majority of our population living in urban areas now.  I do not believe that any of these patterns will affect elections in an astronomical way nor does anything need to done to redistrict Texas in order to make up for these changes.

Sources:

                Texas Election Committee

                U.S. Census Bureau