Lab 3: Significance Testing

Introduction

For years the question of what is “Up-North” has rattled around with everyone giving a truly different answer. The Wisconsin Tourism Board wants to put an end to this debate and create an accurate map showing what “Up-North” actually means. Before conducting this assignment, I did a little google search to find some different ideas of what people think of “Up-North” as.

Some results found “Up-North” to be...

 -Above the 45th Parallel 

-Most of Wisconsin

-There were also several images found portraying what "Up North" was:

Above we have three separate images found doing a quick Google image search, three images, three different interpretations of what North is. 

Other online searches came up with results as...

Webster: Any section of land above the Mason Dixon Line, or North of the Ohio River

Urban Dictionary: The upstate portion of New York where most of the states prisons are (not really relevant to this project, but still quite interesting)

Objectives

This assignment has three main objectives,

1.) Understand how to calculate Chi-Square and relate it to Hypothesis Testing

2.) Connect spatial output to you calculated Chi-Square statistic

3.) Utilize real-world data connecting stats and geography

“Where is Up-North...?”

Clearly it is impossible to just try and Google search something this broad and hope to find one definite answer. So instead of using Google to try and solve this problem that the tourism board of Wisconsin has come to us with, we are going to use our Geospatial and Statistical Backgrounds to try and solve this problem of where Up-North truly is.

We will be using 4 different data sets, along with our state and county data. Those four data sets are, the number of deer hunting licenses sold per country, number of campground per county, miles of snowmobile trails per county and forest acreage per county. With these four data sets, and the State of Wisconsin split by State Highway 29, runs across the state (east/west), we are ready to try and find where Up-North actually is.

Methods

Using the State of Wisconsin with its 72 counties mapped on it, I brought in a shapefile containing the State Highway 29, found an ArcGIS online page, and used that to decide which counties were north of the highway, and which counties were south of it (figure 1).

Figure 1: Wisconsin divided by Highway 29. Several counties were split by the highway. Which ever part of the county had more land north or south of the highway would determine which category it would belong to.

I found there were 28 counties north of the highway and 44 south of the highway. On the map the counties in blue are considered North by this divide, and the counties in red are considered south by this type of divide.  Later in this lab we are going to use this data to try and find our Chi-Squared data. When that section comes Chi-Squared will be further explained, but until then the basic reason for running that test is to compare your observed outcome with the theoretical expected distribution. Every different category will be ranked by a numerical bases, every category except for North/South counties will be composed of four different categories. The ranking will go from 1-4 where 4 is the least number of tested attributes and 1 is the most. This data will be calculated by finding the range (maximum value – minimum value) and dividing by 4.

Using SCORP DATA provided, we have to join it with our newly created North/South Wisconsin shapefile. If we do not join the two, we will not be able to perform analysis on the data. The four features which will be tested to try and determine where Up-North is listed below once more along with some statistics and a map depicting the 1-4 ranking. This ranking system will use a blue to red color spectrum where dark blue = 1, light blue = 2, light red = 3, dark red = 4. I chose this color spectrum because my north/south map uses dark blue to show the north and dark red to show the south. 

Results

Number of Campgrounds per County in Wisconsin (map 1)

Max Value = 49 Campgrounds
Minimum Value = 0 Campgrounds
Range = 49
Categorical Gap = 12
                4 = 0 – 12
                3 = 13 – 24
                2 = 25 – 37
                1 = 38 – 49 

Map 1: Campgrounds in Wisconsin Counties. Dark blue received a score of 1 while dark red received a score of 4

This map shows several northern counties in Wisconsin to be in the blue, indicating they fall into categories 1 or 2 (above). This is the first of four categories we will be testing to determine what is "Up North". Just looking at this map and this one category of campgrounds, we can assume the northwestern corner of Wisconsin is the most "Up North" and the rest of the state is not considered part of the "Up North" category. 

Geography Reasoning: In Wisconsin we have a very wooded northern region, and because of this we can expect to see more campsites in those locations as oppose to a campground in the middle of the City of Milwaukee. 

Number of Deer Hunting (Gun) Licenses Issued Per County in Wisconsin (map 2)

Max Value = 21575 Licenses
Minimum Value = 0 Licenses
Range = 21575
Categorical Gap = 5394
                4 = 0 – 5393
                3 = 5394 – 10787
                2 = 10788 – 16180
                1 = 16181 – 21575

Map 2: This map is showing which counties have purchased the most Deer Hunting Licenses where blue is most and red is least

This map is one that can throw off the thought of what truly is up north. Many residents of Wisconsin own land in the northern area of the state, but live in other regions of the state. These numbers are based on how many licenses were purchased in total, not per capita, which gives areas like Milwaukee, Madison and Green Bay areas the advantage (all three locations appear blue on map) due to their large populations. A more accurate map to use instead of this would be where are Deer being shot instead of where the licenses are purchased since in Wisconsin a license is good for the entire state. 

Geography Reasoning: As mentioned above three of the largest cities in Wisconsin; Milwaukee, Madison and Green Bay are all found in the blue and this is because a large number of people live in these cities who may travel else where to go hunting. The northern regions of Wisconsin are not home to that many people, which is why most counties are dark red and a few are light red.

Miles of Snowmobile Trails Per County in Wisconsin (map 3)

Max Value = 641 Miles
Minimum Value = 0 Miles
Range = 641
Categorical Gap = 161
                4 = 0 – 159
                3 = 160 – 319

                2 = 320 – 480

                1 = 481 – 641

Map 3: Northeastern Wisconsin seems to be the hot spot for snowmobile tracks measured in miles per county

In map 3 we see which counties have the most or least snowmobile trails in measurement of miles in the State of Wisconsin. Snowmobiling is often thought of as an "Up-North" activity done through the woods or on the frozen lake surfaces. This map shows the strong presence of those trails being located in the northeastern region. And very view in the southern region of the state.

Geography Reasoning: The northeastern portion of Wisconsin, where we see much of the dark and light blue, is made mostly of forests and lakes. Very view city establishments are located in that region, making them prime real estate for these trails. Also that portion of Wisconsin is home to many cottages and cabins; one popular activity to do at those home away from homes is to go snowmobiling. 

              

Acres of Forest Per County is Wisconsin (map 4) 

Max Value 963,865 Acres
Minimum Value = 8,100 Acres
Range = 955,765
Categorical Gap = 238,942

                4 = 8100 - 247,043

                3 = 247,042 - 485982

                2 = 485,983 - 724,923

                1 = 724,924 – 963865

Map 4: Where are the forests in Wisconsin? Looks like they are all in the North.

This map best shows where the north is located based on popular sayings. Most of Northern Wisconsin is composed of dense forests, while Southern Wisconsin is mostly farm land. This map makes it look like there are not any forests in the southern portion of the state, but that is not true. The map is skewed because of the large Northern Wisconsin counties that are made up entirely of forests. Nearly 1 million acres of forest are found in Bayfield County, that is about 75% of the entire county and 400 square miles more forest then the size of the entire Milwaukee County. 

Geography Reasoning: As mentioned above the northern part of this state is home to dense forests and multiple state and nationally recognized forested parks such as the Chequamegon National Forest and the Nicolet National Forest. The southern portion of this state is composed of large cities, urban buildup and vast farm lands. 

Where is up north really located then?

Taking our four factors we used plus the north/south divide, I used the field calculator in ArcMap to create a map showing the scores each county received. One more breakdown on how the points were awarded to different counties:
north of highway 29 = 1
south of highway 29 = 2
Of each of the 4 variables used:
Within 25% of the max = 1
Between 25-50% of the max = 2
Between 50-75% of the max = 3
Less then 75% of the max = 4

Using this data I created a map which shows where up north actually is, based on the criteria listed above.

Map 5: Blue is considered the north, while red is considered not up north

Taking a look at this map we see there are several blue counties located in the northern portion of the state, along with a few red ones up there. The general estimate of the up north area though can be classified as the dark and light blue counties and some of the red ones. The breakdown of points was 7-12 was considered up north and 13-16 was not considered up north. The reason several counties that are believed to be up north but show up light or dark red can most likely be traced back to the Deer License set of data, since not many people live in those counties, not many people are going to buy Deer Hunting Licenses.

Conclusion 

The features I picked for this lab were picked for a specific reason. When I think of "Up North" I think of all my trips up to my Grandmother's house in the U.P. (Upper Peninsula of Michigan). Whenever we would drive there we would pass through the northern section of Wisconsin, and I would constantly be seeing Snowmobile trails along the roadways, forest after forest after forest, and once I learned how to drive, I began seeing a lot of deer. So naturally when asked what features I would pick when starting this assignment, I instantly thought of all the trips up to Michigan.

Just looking at a map cannot give you an affirmative answer as to which part of the state is "Up North" and which part is not considered that. In order to figure this out more accurately we are going to use a statistical test called Chi-Squared testing. This test will take the observed numbers we received and tell us what the expected count should be, and then give us a corresponding probability and critical value to see if the feature is dependent or independent on location. Dependent or related would suggest that the feature is considered to be a trait of up north (such as forests are dependent on a northern location in the state). Otherwise they could be independent or not related to one another (such as car sales are not dependent upon how much food you can eat).

Discussion 

Study Question = Does X variable mean that something is considered up north      

           

          Null Hypothesis = X variable is same in the north and south

                  meaning: That variable does not relate to the north suggesting it can be anywhere

          

          Alternative Hypothesis =  X variable is different in the north and south

                 meaning: That variable does relate to the north suggesting if it exists it is up north

These tests will be done to a significance level of 0.05 (95%). 

Number of Campgrounds per County in Wisconsin

Above we see the table which was produced using SPSS software. We calculated the Expected 

Outcome based on the observed outcome. Using that data we then could calculate the Chi-Squared 

value. For this variable our hypotheses are;

Null: There is no difference in campgrounds in the north vs campgrounds in the south

Alternative: There is a difference in campgrounds in the north vs campgrounds in the south

In order to find out the degree of freedom (df) we have to use this formula

(column #s - 1)(row #s - 1) = df

df = 3

The critical value at df = 3 at 0.05 level of significance is 7.815

The Chi-Square Value for Campgrounds is equal to 15.812

0______________________7.815___________^15.812^__________Infinity

Because this number falls past the critical value we have to reject the null hypothesis. 

There is a difference in campgrounds in the north vs campgrounds in the south and if we scroll back 

up to map 1 we can see there are many more blue counties in the north then there are blue counties in

the south.

Number of Deer Hunting (Gun) Licenses per County in Wisconsin

Above we see the table which was produced using SPSS software. We calculated the Expected 

Outcome based on the observed outcome. Using that data we then could calculate the Chi-Squared 

value. For this variable our hypotheses are;

Null: There is no difference in deer hunting licenses in the north vs campgrounds in the south

Alternative: There is a difference in deer hunting licenses in the north vs campgrounds in the south

In order to find out the degree of freedom (df) we have to use this formula

(column #s - 1)(row #s - 1) = df

df = 3

The critical value at df = 3 at 0.05 level of significance is 7.815

The Chi-Square Value for Deer Hunting Licenses = 2.968

0________^2.968^______________7.815_____________________Infinity

Because this number falls before the critical value we have to fail to reject the null hypothesis.

There is not a significant difference in deer hunting licenses in the north vs deer hunting licenses in

the south and if we scroll back up to map 2 we can see there are more blue counties in the south then

there are blue counties in the north, but there is a fairly even spread of light red counties throughout 

the state.

Miles of Snowmobile Trails per County in Wisconsin

Above we see the table which was produced using SPSS software. We calculated the Expected 

Outcome based on the observed outcome. Using that data we then could calculate the Chi-Squared

value. For this variable our hypotheses are;

Null: There is no difference in miles of snowmobile trails in the north vs miles of snowmobile trails in the south

Alternative: There is a difference in snowmobile trails in the north vs miles of snowmobile trails in the south

In order to find out the degree of freedom (df) we have to use this formula

(column #s - 1)(row #s - 1) = df

df = 3

The critical value at df = 3 at 0.05 level of significance is 7.815

The Chi-Square Value for Campgrounds is equal to 18.742

0______________________7.815___________^18.742^__________Infinity

Because this number falls past the critical value we have to reject the null hypothesis.

There is a difference in miles of snowmobile trails in the north vs miles of snowmobile trails in the 

south and if we scroll back up to map 3 we can see there are many more blue counties in the north 

then there are blue counties in the south.

Number of Acres of Forest per County in Wisconsin

Above we see the table which was produced using SPSS software. We calculated the Expected 

Outcome based on the observed outcome. Using that data we then could calculate the Chi-Squared 

value. For this variable our hypotheses are;

Null: There is no difference in acres of forest in the north vs acres of forest in the south

Alternative: There is a difference in acres of forest in the north vs acres of forest in the south

In order to find out the degree of freedom (df) we have to use this formula

(column #s - 1)(row #s - 1) = df

df = 3

The critical value at df = 3 at 0.05 level of significance is 7.815

The Chi-Square Value for Campgrounds is equal to 33.962

0______________________7.815___________^33.962^__________Infinity

Because this number falls past the critical value we have to reject the null hypothesis. 

There is a difference in acres of forest in the north vs acres of forest in the south and if we scroll back up to map 4 we can see there are many more blue counties in the north then there are blue counties in the south.

So where is the North?

Using both maps and stats I was able to find which of my tested variables would give an accurate description of what up north really means. Three of the four tested variables showed that there is a difference between the north and south for the given variable. Everything tested but the number of deer hunting licenses issued proved to be above the critical value, meaning there was a difference between the north or south, and the maps helped confirm that theory.

Study Questions

Fill in the missing portions of the table

	Interval Type	Confidence Level	n	Sig Level	z or t	z or t value
A	Two Tailed	90	45	.1 (.05/side)	Z	+1.65
B	Two Tailed	95	12	.05	T	2.201
C	One Tailed	95	36	.05	Z	1.65
D	Two Tailed	99	180	.01(.005/side)	Z	+2.58
E	One Tailed	80	60	.2	Z	2.06
F	One Tailed	99	23	.01	T	2.5
G	Two Tailed	99	15	.01	T	2.997

1.       A Department of Agriculture and Live Stock Development organization in Kenya estimate that yields in a certain district should approach the following amounts in metric tons (averages based on data from the whole country) per hectare: groundnuts. 0.5; cassava, 3.70; and beans, 0.30.  A survey of 100 farmers had the following results:

     μ              σ

                Ground Nuts      0.40        1.07

                Cassava              3.4          1.42

                Beans                 0.33        0.14

               

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

b.      Be sure to present the null and alternative hypotheses for each as well as conclusions

c.       What are the probabilities values for each crop? 

d.      What are the similarities and differences in the results 

A.) 

Z-score = sample mean – country mean/ (SD/sqrt(n))

Ground Nuts = (0.4 – 0.5)/(1.07/sqrt[100]) = (-0.1)/(0.107) =  -0.9346

Cassava = (3.4 – 3.7)/(1.42/sqrt[100]) = (-0.3)/(0.142) = -2.1127

Beans = (0.33 – 0.3)/(0.14/sqrt[100]) = (.03)/(0.014) = 2.1429

2 Tailed Test with 95% CI (0 – 0.25) (0.25 – 97.5) (97.5 – 100)

Z-Score Breakdown (-infinity – -1.96) (-1.96 – 1.96) (1.96 – infinity)

BOLD = FAIL
NOT BOLD = FAIL TO REJECT

Z-Score(Ground Nuts)
-infinity__________-1.96____^-0.9346^______0__________1.96__________infinity

Z-Score (Cassava)
-infinity_____^-2.1127^_____-1.96__________0__________1.96__________infinity

Z-Score (Beans)
-infinity__________-1.96__________0__________1.96_____^2.1429^_____infinity

B.) 

Null Hypothesis: at 95% Confidence intervals there is no difference between Kenya’s average production of crops (Ground Nuts, Cassava, Beans) when compared to the sample of 100 farmers.

Alternative Hypothesis:  at 95% Confidence intervals there is a difference between Kenya’s average production of crops (Ground Nuts, Cassava, Beans) when compared to the sample of 100 farmers.

C.) 

is mixed in with A

Ground Nuts = -0.9346 FAIL TO REJECT THE NULL HYPOTHESIS (NO DIFFERENCE)

Cassava = -2.1127 REJECT THE NULL HYPOTHESIS (DIFFERENCE)

Beans = 2.1429 REJECT THE NULL HYPOTHESIS (DIFFERENCE)

D.) 

The similarities amongst the data are that two of the three data sets fall outside the significant range of having a difference between the 100 farmer sample and the Country average.  The numbers were all across the board in terms of Z-Scores. One was a -2.1127, meaning it was over 2 standard deviations below the Country Average. One was a -0.9346, technically no significant difference at a 95% confidence interval, but still almost an entire standard deviation below the mean. The last one was 2.1429 standard deviations over the mean. So very different numbers amongst the 3 crop types.

 An exhaustive survey of all users of a wilderness park taken in 1960 revealed that the average number of persons per party was 2.8.  In a random sample of 25 parties in 1985, the average was 3.7 persons with a standard deviation of 1.45 (one tailed test, 95% Con. Level) (5 pts)

a.       Test the hypothesis that the number of people per party has changed in the intervening years.  (State null and alternative hypotheses)

b.      What is the corresponding probability value

A.)

Null Hypothesis: at a 95% confidence interval there is no difference in the average number of people per party in 1960 when compared to those of a 1985 sample.

Alternative Hypothesis: at a 95% confidence interval there is a difference in the average number of people per party in 1960 when compared to those of a 1985 sample.

B.)

1960 = 2.8
Sample from 1985 = 3.7 with a standard deviation of 1.45 (sample size = 25)

t – Test = (sample mean – 1960s mean) / (standard deviation / sqrt [sample size])

t – Test = (3.7 – 2.8) / (1.45/sqrt [25]) = (0.9)/(0.29) = 3.1034

t = 3.1034

df = 25 – 1 = 24

= 1.711 

0________________________1.711_________^3.1034^_______infinity

Due to the fact that the sample came up with a t – score of 3.1034 we have to reject the null hypothesis. There is a significant difference between the 1985 sample at the Wilderness Park when compared to the average party size in 1960. 

                

Lab 2: Z-Scores, Mean Center and Standard Distance

INTRODUCTION

A common stereotype of United States colleges is that students are reckless and always causing havoc. In this assignment I was given the task to play the role of an independent researcher and determine if students at the University of Wisconsin- Eau Claire are the exact reason why city residents are constantly complaining. In this assignment I will be using Disorderly Conduct violations from all of the City of Eau Claire during the years of 2003 and 2009. Included with this data is the exact addresses of the crime. Using this data and bar location data, from the year 2009, it is my responsibility to determine if the citizens of Eau Claire are directing their complaints about UWEC students in the right direction.

For this assignment I will be using ESRI ArcMap to perform a series of spatial analysis tools. I will be using mean center, weighted mean center weighted standard distance/deviation (to 1 standard deviation) and I will use graduated symbols for the 2003 and 2009 crime data to emphasis which areas of the city have more disorderly conduct.

TERMS TO KNOW FOR THIS LAB

Mean Center: Takes all of the data points and figures the center point of all the points

Weight Mean Center: Works similar to Mean Center, but you can put more emphasis/weight on a given factor

Standard Distance/Deviation: Measures the degree to which features are concentrated or dispersed around the points (68% of data falls within 1 standard deviation)

Disorderly Conduct Crimes: Can fall into many different criminal charges, most notably is crimes of being drunk in public, disturbing the peace, or loitering in certain areas

METHODOLOGY

The first stage of this assignment is to find the Mean Center and Weighted Mean Center of the crime data. During this stage I created 3 maps, will be discussed and shown in the discussion section, one showing mean center and weighted mean center for the 2003 Disorderly Conduct crimes. The second map will show the mean center and weighted mean center for the 2009 Disorderly Conduct crimes. The third and final map of this stage of the assignment will show both the 2003 and 2009 data on the same map. All maps will show the crime data in graduated symbols, meaning the bigger the circle the more crimes which have been committed in that location.

In order to access the proper tools needed we must open the ArcToolbox and locate the MEAN CENTER tool, which is located under Spatial Statistics tools > Measuring Geographic Distributions > Mean Center. This will open up the tool, select the 2003 or 2009 Crime Data. The tool will be ran for both crime features so order does not matter. After the tool runs it will produce the mean center, see definition above. The next step of this stage is to find the weighted mean center for 2003 and then also 2009. Opening up the same tool as before we can select the crime data from either 2003 or 2009 (both have to be done eventually) and this time under the weighted category select COUNT. Be sure to select the crime data and not the Mean Center, otherwise the tool will not use the crime data but instead use the one point of Mean Center, giving you false data.

After running the tool for a total of 4 times, once for 2003 Mean Center, 2003 Weighted Mean Center, 2009 Mean Center and 2009 Weighted Mean Center, I put all four points on the same map to see how the Disorderly Conduct Crimes appear to be shifting.

The next stage in the assignment is to find the Standard Deviation/Distance of Disorderly Conduct in the City of Eau Claire. For this tool we are only looking for 1 standard deviation, meaning approximately 68% of all crime data should fall within the circle.

Using the same data as the previous stage, we are going to create maps showing the standard distances. This tool is found in the same area as the Mean Center. Spatial Statistics Tools > Measuring Geographic Distributions > Standard Distance. This section again will have 3 maps produced from it. One for 2003, one for 2009 and one showing both 2003 and 2009.

Opening up the Standard Distance Tool we are going to use the 2003 or 2009 Disorderly Conduct crime data, again be sure not to select just the mean center or weighted mean center because that will mess up the data giving you false data. After selecting the 2003 Disorderly Conduct crime data for the input, select COUNT again for the weighted portion of the tool. Once the tool runs it will give you a perfect circle on the map, showing you one standard deviation of crime. Technically, the data is right, but because we are using a Geographic Coordinate System projection it is not completely accurate because on the real world it would appear elliptical.

The next step is to correctly project your data. I used the Wisconsin Central State Planar projection for my maps. This gave my map the elliptical projection which we would actually see on a globe. Once projected, I made my circle 60% transparent, that way I could see the data underneath the circle, and I also made the outline much thicker, thus ensuing best visualization of the data. These steps were once again repeated for the 2009 data.

After both the 2003 and 2009 Standard Deviation/Distance data were calculated and mapped, they were both put on the same map, along with the crime data from both years, the bar data, mean center and weighted mean center.

The final section of this lab involved finding Z-Score and Standard Deviation. Using the Block Group data for the City of Eau Claire, we will find which areas have the highest and lowest Disorderly Conduct Crimes in terms of Standard Deviations. In order to do this step, we will open the symbology of the Block Groups and then assign a Standard Deviation to the Join_COUNT field, this field is the number of Disorderly Conduct Crimes in each Block Group. Once selected the Block Groups will change colors based on the Standard Deviation compared to the mean.

The final part of this assignment is to locate the Mean Center of bars in Eau Claire. This is done the same exact way as it was when finding the Mean Center for Disorderly Conduct Crimes in 2003 and 2009.

RESULTS

After running analysis on the data I found the Citizens of Eau Claire may be on the right trail for blaming the UWEC students for causing the most havoc. The Mean Center and Weighted Mean Centers for both 2003 and 2009 were located about a block or two from a majority of the Water Street Bars. The weighted mean center for both 2003 and 2009 are located on the Chippewa River near the Lake Street Bridge, in my opinion I believe that is because student housing is on both sides of the river. This could mean students who live on the East Side of the Chippewa River are getting Disorderly Conduct charges on their walks home.

Map 1—Mean Center and Weighted Mean Center of Disorderly Conduct: Eau Claire 2003

Map 1

In this map we see the number of Disorderly Conduct Crimes in blue. It shows a large majority of them to be near Water Street and also near the Police Station. There are several bars located on the map and it appears that where ever a red martini glass is located there are several blue dots located in the general vicinity. The Mean Center for the 2003 data is located near the intersection of Lake Street and Graham Avenue. The Weighted Mean Center is located southwest of the Mean Center, over the Chippewa River near the Lake Street Bridge. This is indicating something is causing a shift in crimes. The Weighted portion of the crime data was the Count Field, this means there are more crimes conducted in one area causing the shift. The data shows that Water Street appears to be the reason for this.

Map 2—Mean Center and Weighted Mean Center of Disorderly Conduct: Eau Claire 2009

Map 2

Using the same color scheme as Map 1, blue crimes, red martini glasses representing bars, a yellow dot for Weighted Mean Center and a green dot for Mean Center. This data again shows a large portion of crimes located near Water Street Bars, but also we see large concentrations of crime by Graham Avenue and S. Barstow Street. Other areas of high Disorderly Conduct include; by the police station and by the hospital. The Mean Center of all Disorderly Conduct Crime in Eau Claire is near the intersection of Lake Street and S. Farwell Street. The Weighted Mean Center is again southwest of the Mean Center (Lake Street Bridge). This appears to be caused by the large number of Disorderly Conduct Crimes charged by the Water Street strip of bars.

Map 3—Mean Center and Weighted Mean Center of Disorderly Conduct: Eau Claire 2003 and 2009

Map 3

This map is a combination of the previous two maps. It is showing the crime count from 2003 and 2009 as well as the Mean and Weighted Mean Centers from both 2003 and 2009. The map is showing that over the six year gap the mean center of crime is being shifted north, away from the believed primary cause of Water Street. The Weighted Mean Center is also being shifted north. This is meaning that more crimes are being conducted in 2009 in northern Eau Claire then there was in 2003. And with the Weighted Mean Center also shifting north, it means Water Street is not the most influential location, or at least not as much, of all Disorderly Conduct. Yes there are several Disorderly Conduct Crimes being committed in that area, but there must be other areas in Eau Claire which are pulling the Weighted Mean Center away from the area.

Map 4—Weighted Standard Distances of Disorderly Conduct: Eau Claire 2003

Map 4

This map is using a circle/ellipse  to give a visualization of where approximately 68% or 1 standard deviation of Eau Claire Disorderly Conduct Crimes are being committed. To no surprise, based on previous three maps, the entire “Student Ghetto” falls within this one standard distance. Looking at the crime data, there are lots of Disorderly Conduct Crimes being committed in that area, and there are also a large number of bars which fall inside the circle. Although we cannot directly link Disorderly Conduct Crimes to liquor consumption, the map makes it look like a pretty strong factor.  The Weighted Standard Distance is ran using the Weighted Mean Center as the center point, and it appears ellipse because we projected the data, if we did not project the data it would appear as a perfect circle.

Map 5—Weighted Standard Distances of Disorderly Conduct: Eau Claire 2009

Map 5

Again showing an ellipse of where  approximately 68% or 1 standard deviation of Eau Claire crimes are being committed, we see not much change in location. The “Student Ghetto” again is completely incased in this ellipse of data. Common with map 4, we are seeing large concentrations of crimes being committed in close proximity to bars or places where liquor is available. On the map, inside the one standard distance, if you draw a diagonal line crossing through the Weighted Mean Center, the map shows far more crimes being committed in the northwestern portion then in the southeastern portion. Not being able to link this because we do not have the exact information on the crimes, but it appears that crime and liquor consumption may be related in areas of college housing.

Map 6—Weighted Standard Distances of Disorderly Conduct: Eau Claire 2003 and 2009

Map 6

On this map we are comparing the 2003 Standard Deviation of Disorderly Conduct with the 2009 Standard Deviation of Disorderly Conduct. On the map we see the ellipses are very similar, but have a slight shit to them. The 2009 data is indicating a northern and slight western shift in crime. Looking at the map, yellow circles are 2003 Disorderly Conducts and 2009 Disorderly Conducts are mapped in blue, it looks like more Disorderly Conducts have been charged in northern Eau Claire, such as the area just north of Half Moon Lake, also areas near the confluence of the Eau Claire and Chippewa Rivers. Not being too familiar with those areas in 2003 I would think that is newly developed area or areas where the recession may have hit harder.

Map 7—Standard Deviation of Disorderly Conduct Crimes by Block Group: Eau Claire 2009

Map 7

On this map we see the student housing section of the map to be a dark blue color, and as distance increases from this area it goes from a light yellow to orange. This is indicating a change in standard deviation. For the City of Eau Claire there was a total of 343 Disorderly Conduct Crimes in the 64 Block Groups (2009 Crime Data). There was a max of 42 Disorderly Conduct Crimes in a Block Group, and multiple Block Groups with 0. The City had an average (mean) of 5.3593 Disorderly Conduct Crimes per Block Group, with a standard deviation of 7.8149. Using this data we had to calculate the Z-Score for 3 separate Block Groups. The last part of this map is a green dot centered right in the dark blue Block Groups (2.5+ Standard Deviations). This is the Mean Center for Eau Claire Bars.

FINDING Z-SCORE OF CERTAIN BLOCK GROUPS

The three block groups outlined in black and numbered in Red correspond to the next three problem sets

Block Group 41-Location: Oakwood Mall and Surrounding Residential Areas

Disorderly Conduct Crimes (Xi): 10
Mean: 5.3593
Standard Deviation: 7.8149

Z-Score = (10 – 5.3593)/7.8149

Z-Score = 0.5938

Block Group 46-Location: Old Downtown and Eau Claire/Chippewa River Confluence

Disorderly Conduct Crimes (Xi): 40
Mean: 5.3593
Standard Deviation: 7.8149

Z-Score = (40-5.3593)/7.8149

Z-Score = 4.4326

Block Group 57-Location: Southwestern Eau Claire

Disorderly Conduct Crimes (Xi): 1
Mean: 5.3593
Standard Deviation: 7.8149

Z-Score = (1-5.3593)/7.8149

Z-Score = -0.5578

WHAT IF SCENARIOS…

Using current trends (from previous section) how many Disorderly Conduct Crimes will the City of Eau Claire exceed 70% of the time?

Z-Score = -0.52

-0.52 = (X – 5.3593) / 7.8149

-4.0637 = X – 5.3593

X = 1.2956

The City of Eau Claire will exceed 1.2956 Disorderly Conduct Crimes per Block Group 70% of the time

20% of the time?

Z-Score = 0.84

0.84 = (X – 5.3593) / 7.8149

6.5645 = X – 5.3593

X = 11.9238

The City of Eau Claire will exceed 11.9238 Disorderly Conduct Crimes per Block Group 20% of the time

CONCLUSION

 Overall, the data does show Water Street has had a large number of Disorderly Conducts in both the 2003 and 2009 data sets. The Mean and Weighted Mean Centers do confirm that Water Street does have a large pull in the city. The Standard Distance also confirms that students are responsible for a large majority of these crimes, most of student housing falls within one standard distance of the Weighted Mean Center. When looking at the Standard Deviation of the City of Eau Claire we can see the student area housing is over 2.5 Standard Deviations away from the mean, while other areas are less than 1 Standard Deviation from the mean. The reason they are less than 1 Standard Deviations below the mean, and not greater, is because the average was 5.3593, and a Standard Deviation of 7.8149, which would mean a Block Group would have to have -2.46 crimes, which is not possible.

Looking through the results I did find that Water Street was not the only major area where Disorderly Conduct Crimes were being charged. There were lots filed near the police station on Lake Street, this may be though because they were transported there and then processed, marking the location as the police station. Other areas of alarming concern which I was unaware of was near the confluence of the Chippewa and Eau Claire Rivers.

There may be several implications with the results, such as unfiled charges, misfiled charges or incomplete records. Not knowing the exact reasons why the charges were filed can be a major issue. At the beginning I defined Disorderly Conduct as disturbing the peace, there was also loitering as a disorderly conduct charge. Being a large and broad field of crime, it is uncertain if you could link this to liquor consumption. Also if you were to walk down Water Street on a Friday night you would see easily 10 cop cars within a 4 block stretch, but in other areas of the city you will not see any. That could play a factor into all the charges on Water Street and very few in other parts of the city.

A Possible solution for this would be to inform the citizens and teach students about proper behavior. To answer the question at the beginning of the lab, are the students of UWEC to blame for this? I would say yes and no. Yes there are a lot of crimes on Water Street near the bars, and yes that is primarily dominated by students at UWEC, the Standard Deviation Map (MAP 7) does in deed confirm that students may be to blame, but I cannot give a guaranteed answer to the issue without knowing the exact crimes, and if alcohol was indeed involved.