Monday, March 16, 2015

What is "Up North"?


Introduction:

            The Tourism Board of Wisconsin as asked me to conduct some research regarding the concept of “up North”.  I have been provided a large data set of variables from the State of Wisconsin. They asked that I choose three variables and conduct a Chi-Square test on each of them as well as create some maps. We have broken up the State of Wisconsin into two parts, northern and southern counties, and we are dividing them along Highway 29.

            The three variables that I have selected are Resident Gun Deer License Sales, Nonresident Gun Deer License Sales, and Nonresident Archery Deer Licenses Sales. I choose these three variables because as a hunter I think I could provide some helpful insight as to why we are seeing any patterns within the maps. I also think that hunting is a fundamental part of Wisconsin, culturally and economically.

            My Null Hypothesis is that there is no difference between Northern and Southern Wisconsin. My Alternative Hypothesis is that there is a difference between Northern and Southern Wisconsin.

Methodology:


            First issue that needed to be addressed was where the boundary of Northern and Southern Wisconsin was. To do this I went and downloaded data from the Census Bureau (for the County Shapefile), ESRI and the Wisconsin DNR for the major roads in Wisconsin. From there I selected highway 29 through a variety of Select by Attribute tools and found that there was some gaps in each datasets version of Highway 29 but when you combine both of them together the create the entire stretch of Highway 29. Then I selected which counties belonged in what part of the state. Most of them feel completely within the Northern or Southern zones but some border counties created some problems. To fix this I put the county in whatever zone that occupied most of the area along the Highway 29 border. Now that I Labeled which counties are in the North or South I added a field in the attribute table and gave the county an attribute of 1 for North and 2 for South. I ended up with a map that looked something like this:
Map One
 

 The next step was to join the data from the State of Wisconsin to my map of Wisconsin counties and Highway 29. We joined the data based off of the counties. I then created more fields within the table for my three variables to set up a Chi Square Test. This was a confusing concept for most but I found it relatively easy.  I went to symbology in Arc, then quantities, then classify, and told it to give me four groups of counties all based off of an equal interval. This gave me the four breaks that I would base my test off of. In the new field in the attribute table I selected field calculator and entered four as the attribute for all the counties. I then went to select by attributes and selected everything that was less than my highest break. Then through the field calculator I gave those attributes a 3. I did this for the rest of the breaks and that gave me a field in the table that grouped the counties into four subsets that I could use for my Chi Square Test.  Once I did this for all three variables I exported the table as a dBAse so I could open it in SPSS.

Now I opened SPSS and my table that I exported from Arc. To get to the Chi Square test I had to go to Analyze, then Descriptive Statistics, the crosstabs. I selected Chi Square and it was rather simple after this as SPSS did all the work for me! Now all I have to do is analyze the data.

Results:

            After conducting the Chi Square tests and creating my maps these are my results.

            Map two below shows the Sale of Resident Gun Deer Licenses. We can see a higher number of licenses in the southern region of Wisconsin with a concentration of sales in Southeastern Wisconsin. This makes sense due to the higher population of Southern Wisconsin. The only Northern County that was in the fourth group (Dark Red) was Marathon County and that could simply be because of the size of the county. This Variable had a Chi Square value of .295 which shows that there is very little similarity between the North and South.
Map Two


            Map three shows the Sale of Nonresident Gun Deer Licenses. In this map we have seen a complete flip of where the highest numbers are. They have moved from the highly populated southeastern portion of Wisconsin to the North/ Northwestern part of Wisconsin. In my opinion most people would think of Northern Wisconsin as where everybody goes to hunt deer. So why would the first map of Residents show the opposite and the map on of Nonresidents show what we see below? The Chi Square Value of this variable was .190 which shows even a further dissimilarity between North and South.


Map Three

            In map four we see the sale of Nonresident Archery Deer Licenses. This map compliments map two and shows relatively the same pattern. Low numbers in the south and high numbers in the north. The Chi Square was even smaller than the previous variables at .085! This would imply a great difference between the Northern Wisconsin and Southern Wisconsin. Even with map two we have seen a difference and with every map after that, the difference keeps getting greater and greater.
Map Four
 

Conclusion:

The data has been complied and my interpretation of the data has ended. This is what I have found. There IS a difference between Northern and Southern Wisconsin with regard to Deer License Sales. Therefore I reject the Null Hypothesis and fail to reject the Alternative Hypothesis. I have come to this conclusion through analyzing the maps I created and the Chi Square Tests. The concept of “Up North” is a cultural concept and I think that is shown by these maps. It’s not only residents that hunt deer in Wisconsin, Thousands of people migrate to Wisconsin to hunt deer and where do they go? Northern Wisconsin. Why you may ask? Well where else would you go? From tales of the Turdy Point Buck to stories from the Wisconsin Wilderness, Hunting in Northern Wisconsin has become a staple for this state. I’m sure that other variables show similar results but I believe that these variables really speak to what “Up North” is. It says that although the majority of the population may live in the Southern region as shown by map 1, that when getting rid of where you live and trying to locate where you hunt, the Nonresidents point us right in the right direction. Therefore I can say that statistically there is absolutely a difference between Northern and Southern Wisconsin and that definitely plays a role in people’s perception of what “Up North” is.

Thank you to the Wisconsin Board of Tourism for letting me conduct this study and to the State of Wisconsin, Wisconsin DNR, and ESRI for their data.

My data concerns are small. I do question the validity of the license sales locations as I imagine it shows just where the license was bought. Most people will buy them when they are near their deer camp and this number doesn’t count for those nonresidents that buy their license in their state. I don’t think this would change much but it is worth noting.

 

Assignment 3 Part 1






Interval Type
Confidence Level
n
a
z or t?
z or t Value
A
Two Tailed
90
45
.1
z
1.28
B
Two Tailed
95
12
.05
t
2.179
C
One Tailed
95
36
.05
z
1.64
D
Two Tailed
99
180
.01
z
2.32
E
One Tailed
80
60
.2
z
.84
F
One Tailed
99
23
.01
t
2.807
G
Two Tailed
99
15
.01
t
2.947



1.       A Department of the interior in Washington D.C. estimates that the number of particular invasive species in a certain county (Bucks County) should number as follows (averages based on data from the whole state of Pennsylvania) per acre: Asian-Long Horned Beetle, 4; Emerald Ash Borer Beetle, 10; and Golden Nematode, 75.  A survey of 50 fields had the following results:

μ               σ
                Asian-Long Horned Beetle           3.2          0.73
                Emerald Ash Borer Beetle            11.7        1.3
                Golden Nematode                          77           5.71
               
a.       Test the hypothesis for each of these products.  Assume that each are 2 tailed with a Confidence Level of 95% *Use the appropriate test
a.       Asian Long Horned Beetle- Reject the Null Hypothesis
b.      Emerald Ash Borer Beetle- Reject the Null Hypothesis
c.       Golden Nematode- Reject the Null Hypothesis
b.      Be sure to present the null and alternative hypotheses for each as well as conclusions
a.       Null: There is no difference between the number of a particular invasive species in the state of Pennsylvania to the number of the same species in Bucks County.
b.      Alternative: There is a difference between the number of a particular invasive species in the state of Pennsylvania to the number of the same species in Bucks County.
c.       What can ascertained pertaining to the findings about these invasive species in Buck County?
a.       Something is going on in Buck County.  Both the Emerald Ash Borer and Golden Nematode have higher numbers than the state average. One could imply that the county government is not doing enough to help the spread and growth of these invasive species, however, you could also imply without even leaving the computer that the habitat could be very different in Bucks County versus the whole state which would allow for more growth of those species in Bucks County. The same could be said for the Asian Long Horned Beetle except on the other end of the spectrum. Bucks County has a lower average than the state so you could imply that they have little habitat in the county or that the county is doing an exceptional job of eliminating that species.

2.       An exhaustive survey of all users of a wilderness park taken in 1960 revealed that the average number of persons per party was 2.1.  In a random sample of 25 parties in 1985, the average was 3.4 persons with a standard deviation of 1.32 (one tailed test, 95% Con. Level)

a.       Test the hypothesis that the number of people per party has changed in the intervening years.  (State null and alternative hypotheses)
a.       Null: There is no difference between the number of parties in 1960 versus 1985.
b.      Alternative: There is a difference between the number of parties in 1960 versus 1985.
b.      What is the corresponding probability value
a.       I got a T Value of 4.92 after running the numbers and for a one tailed test at 95% confidence level the probability value is 1.711.