Lab 5 - Regression Analysis

Part 1

A local news outlet is making a claim saying that the greater number of kids who receive free school lunch the greater the crime rate is for the area. To check the accuracy of the claim a regression analysis can be done. After the regression analysis is complete (Figure 1-3) the results show that there is a significance level of .005. This shows there is significance. With that being said, the hypothesis fails to be rejected, there is a small relationship between free lunches and crime. The regression equation is y=1.685x+21.819, looking at the data with crime at 79.7 over 34% are getting free lunch. The news station is making a correct claim except the correlation between the two is not very strong. 





Figure 1 - Summary



Figure 2




Figure 3 - Results
 

Part 2 
Introduction:

The UW - School system is attempting to look at all the Universities in it's network and make some conclusions based on the results after analyzing. Analyzing data from the universities might help the UW System understand how Wisconsin students choose various UW schools. For this lab the data for UW - Eau Claire and UW - Milwaukee were looked at. 

Methods:
Using spatial regression many conclusions can be made after analyzing the data. To do so, the program SPSS and ArcMap compliment each other heavily for analyzing and presenting the results. A series of variables can all be analyzed using the data provided by the UW - School System. In this case there are two hypothesis's possible, the null hypothesis states there is no relationship between the two variables and the alternate hypothesis states there is a relationship. The data looked at for this lab was the number of students attending both Eau Claire and Milwaukee against Population/Distance, % Population with a Bachelor's Degree and Median Household Income at a county level. The regression analysis was done using SPSS and once ran the results showed what sort of relationships existed between the data and variables. After getting the results, data that was statistically significant was mapped. Residual data could be exported as a .dbf and used in ArcMap to make the data more visually appealing and easier to determine patterns. 
 Results:
Three tests were ran comparing Eau Claire and Milwaukee focusing on Population/Distance, % Population with a Bachelor's Degree and Median Household Income at the county level. Of these three tests only two were significant. Median Household Income was not significant. For both Eau Claire (Figure 4) and Milwaukee (Figure 5) the significance value for the number of students attending to Median Household Income (HHI) had significance levels greater than .005 (.104 and .027). With that being said, both fail to reject the null hypothesis.

Figure 4 - HHI Results for Eau Claire

Figure 5 - HHI Results for Milwaukee

    



The other two tests on the other hand did show a level of significance. Both population/distance and percent of the counties population with a Bachelor's degree bad a significance level lower than .005. With that being said the tests show you have to reject the null hypothesis. The pop/dist test for Eau Claire (Figure 6) had a significance of .000 and had a high r^2 (.945) this signifies a strong level of regression. The pop/dist test for Milwaukee county was very similar (Figure 7), it had a significance of .000 also and had a strongly correlated r^2 value of (.922).

Figure 6 - Pop/Dist results for Eau Claire

Figure 7 - Pop/Dist results for Milwaukee

The last test regarding Bachelor degrees was also significant. For Eau Claire (Figure 8) the test produced a significance level of (.003) but had an r^2 value of (.121) this shows a weak level of regression. Milwaukee (Figure 9) had a  similar result with a significance of (.001) and an r^2 value of (.160) showing a weekly correlated regression.

Figure 8 - Bachelor Degree results for Eau Claire

Figure 9 - Bachelor Degree results for Milwaukee

Because two of the three tests came back significant these results could be mapped to give a visual representation. After being exported from SPSS the data was joined to a WI .shp file. (Figure 10) represents the results from the pop/dist test related to UW - Eau Claire. For the most part the counties followed the regression except for Milwaukee county, the other higher population counties have higher amounts of students attending Eau Claire than the model predicted, Milwaukee was lower. (Figure 11) represents the results from the bachelor degree, the counties closest to Eau Claire deviate more than counties further away. (Figure 12) shows the results of the pop/dist test for Milwaukee. The map shows higher deviation in counties with higher populations except for Milwaukee. Closer, smaller populated counties do not deviate as much. The last map (Figure 13) shows the results of the bachelor degree test. The lower population counties with more rural communities deviate higher than the regression and lower the further you get from Milwaukee.


Figure 10 - Map results from Pop/Dist test for Eau Claire



Figure 11 - Map results from Pop/Dist test for Milwaukee

Figure 12 - Map results from Bachelor test for Eau Claire

Figure 13 - Map results from Bachelor test for Milwaukee

Conclusion:
   Out of all the three tests the biggest indicator is population normalized by distance. The results showing a high r^2 value highlights that. Although there was significance regarding bachelor degrees it happened to be rather week overall. People generally enjoy going to school nearest to where they live and in this case this is no different. The higher populated counties send more kids to schools and this can be assumed without the use of tests. Areas with more money are likely to send kids to a more well known school such as Milwaukee or Madison. Going off of that, areas with a high amount of the population holding degrees will often times produce more kids going to school to seek a degree. Although pop/dist and bachelor degrees per population are good variables to look at, there are endless variables that can be explored and focused on additionally when marketing a certain school to an area.

Lab 4 - Correlation and Spatial Autocorrelation

Part 1: Correlation
1.

Correlation Chart

When looking at a set of data that focuses on distance and sound level one might get curious as to how they correlate to one another. Using tools such as Excel and SPSS calculating and visualizing the correlation between the two becomes much easier.

The null hypothesis would be that there is no linear association between distance (ft) and sound level (db). The alternate hypothesis is there is a linear association between distance and sound level.

Looking at the Correlation chart you can see the correlation for the two is -.896. Being that .896 is close to 1 tells us the variables have a strong correlation and the fact it is a - tells us that as the scatter plot shows us as distance increases the sound level decreases.

The null hypothesis is rejected.

2. Correlation matrices can also be created using SPSS. A correlation matrix is all the variables compared to one another. The following matrix is based on Detroit Census data.
Looking at the matrix, you can see that a strong correlation exists.
White and having a Bachelor Degree (+)
White and Black residents (-)
 Median Household Income and Median Home Value (+)
White and Retail Employee (+)

3. Introduction:
     The Texas Election Commission or (TEC) wants to evaluate the elections in Texas through analyzing patterns and voter turnout in counties. The TEC has provided voter data from the 1980 and 2012 Presidential Elections. Through the examination of spatial auto-correlation and correlation the TEC will have a better idea of what the voter breakdown and turnout is across the state.

Methodology:
     Unfortunately the TEC did not provide enough data alone to analyze and come to any conclusions. Luckily, the U.S. Census Bureau website has a database including demographic data needed.  Once downloaded the data can be joined together using the 'join' tool in ArcMap and exported as a shape file. Once exported the data could be opened in GeoDa, a freeware program and analyzed.

     GeoDa is able to run spatial auto-correlation tests and this is essential in determining information for the TEC. Because the tests are weighted the settings remained default when looking at the Poly ID.
     Spatial Auto-correlation is relevant because it looks at each individual counties and evaluates them based on counties touching each other and in each direction, (side to side, below and above).
A Moran's I test was done as well as a LISA map created to represent the results on a per county basis.
     This was done 5 times covering, Hispanic Population percent in 2010, Voter Turnout in both 1980 and 2012 and also Democratic Vote % in 1980 and again in 2012.
The Moran's I test is done to measure the degree of spatial auto-correlation. This is represented by a value between -1 and +1. A value close to -1 indicates a strong negative pattern, a value close to 0 represents a lack of spatial pattern and a +1 indicates a strong positive pattern.
The LISA maps help create a visually friendly representation the significance difference at a county level.

Results:

Figure 1 - 2012 Democratic Vote Percent

Figure 2 - Moran's I chart for 2012 Democratic Vote Percent

     Looking at the map (Figure 1) and Moran's I chart (Figure 2) regarding the 2012 Democratic Vote Percent in Texas, we can see a significant number of High (red) counties in the south/west part of the state and Low (blue) counties in the North. Looking at the Moran's I chart you can see see it has a value of .6959 which is a positive correlation. The data is grouped in the lower area and spread widely in the high area. 

Figure 3 - 1980 Democratic Vote Percent

Figure 4 - Moran's I chart for 1980 Democratic Vote Percent

      This LISA map (Figure 3) shows a larger variation in voting pattern for the 1980 election. The Moran's I chart (Figure 4) shows a spread as well and has a value of .5752 which equates to medium, positive correlation.

Figure 5 - 2012 voter turnout

Figure 6 - 2012 voter turnout Moran's I chart

     The LISA map (Figure 5) shows the 2012 voter turnout was high in only a few counties while low in the southern counties nearest to the border. The Moran's I chart (Figure 6) represents a weak trend with much variation but still is slightly positive with a value of .3359.

Figure 7 - 1980 voter turnout

Figure 8 - Moran's I chart for 1980 voter turnout

     In 1980 the LISA map (Figure 7) shows a few more high turnout counties while the south even then had a very low turnout. The Moran's I chart  (Figure 8) presents a low yet positive trend at .4681.

Figure 9 - 2010 Hispanic Population Percent

Figure 10 - Moran's I chart for 2010 Hispanic Population Percent

     The last map (Figure 9) represents the Hispanic % throughout Texas. The South/Western counties have a very high percent while the North Eastern counties are generally low except for one county which sits at High-Low. The Moran's I chart (Figure 10) shows a significant positive trend and has a value of .7787 which happens to be the closest to +1 of all of our values. 

Figure 11 - Correlation Matrix

This Correlation matrix (Figure 11) shows there is a very high confidence level. At 99% it shows that each variable relates to one another. 

Conclusion:

     Looking at the maps and charts one can see there is an apparent relationship between the percent of Hispanic Population and percent of Democratic votes. It appears that in the counties with the highest percent of Hispanic population there is also a very low voter turnout. It makes sense there is a higher Hispanic population percentage in the southern part of the state as it is closest to the border of Mexico. Using this information and visuals the TEC and the Governor can see that voter turnout has ever so slightly changed the last 30+ years. Key areas to be considered are counties with higher Hispanic populations as these counties are less likely to turnout and vote but perhaps with the right campaigning these results could be changed depending on the messaged projected to those populations. 

Lab 3 - Significance Testing

1.

2. Ground Nuts
Probability: 35.24
z Value: -.9346

Fail to reject null hypothesis because it lies in the range of normal distribution.

Cassava
Probability: 0.0348
z Value: -2.1127

Reject the null hypothesis. -2.1127 falls too far away from the normal distribution of -1.96

Beans
Probability: 0.324
z Value: 2.1429

Reject null hypothesis. The samples are too different.

There are a few similarities amongst the data including 2/3 data sets fall outside of the significant range. As far as differences go each data set had a completely different Z-Score. This shows that among the three different crops there are some very different amounts.

3. Null Hypothesis: With a 95% confidence interval it shows there is not a difference between the average number of people per party comparing 1960 and 1985.

Probability:
1960: 2.8
1985: 3.7 (Sample size: 25)

T Test
(3.7 – 2.8) / (1.45/sqrt 25) = (0.9)/(0.29) = 3.1034
df=25-1=24

1.711

There is a significant difference between the 1985 sample and the 1960 party size meaning the null hypothesis is rejected.

Assignment 2

Introduction:

     Local residents of Eau Claire have begun to complain about the amount of disruption college age residents have been causing especially in the Water Street Area. There are a lot of factors that may contribute to the issue but most believe it could be related to the amount of alcohol consumed in the area.
     Using crime data provided by the Eau Claire Police Department once can start making correlations with the proximity of the arrests to certain block groups and even more so their proximity to establishments that serve alcohol. Having multiple years of data is important because it can be used to see if patterns can be associated with this behavior. In the data there aren't specifics as to why the crimes occurred but using spatial tools they can be analyzed nonetheless.

Methods:

     To analyze the data I used a variety of spatial tools to dig deeper in to the data I was presented with. Because this data all revolves around the location of crimes committed it was important that the distribution of crime locations be looked it. The first facet I looked in to was the mean centers of the crimes. (Figure 1) (Figure 2) Mean centers are important because it takes all the points inputted and finds the mean location or geometric center. Weighted mean centers are also important because they are similar to mean centers but unlike mean centers it takes in to account the number of instances at one certain point as well.

Figure 1 - Mean Centers for Disorderly Conducts in Eau Claire - 2003



Figure 2 - Mean Centers for Disorderly Conducts in Eau Claire - 2009



     The data above is from two different years although if you quickly glance at them there are some glaring similarities, especially with the various mean centers. To compare the two years I also created a map that includes all the data from the above two maps. (Figure 3) The mean centers show the average location of where all the crimes were committed.


Figure 3 - Combined Data for 2003 & 2009

     The next tool I used to analyze the data was standard distance. Standard distance is perfect for the data we have because it shows us the area with the highest occurrences.  The standard distance ring covers 68% of the points so more than half the points can be found inside of the ring. Standard distance is a tool used to show the highest concentration of points, the results can be found below in (Figure 4) (Figure 5).

Figure 4 - Weighted Standard Distance around the Weighed Mean Center - 2003

Figure 5 - Weighted Standard Distance around the Weighted Mean Center - 2009

  
     Once again the two maps are very similar comparing 2003 to 2009. Here is a map comparing the two (Figure 6).


Figure 6 -  Combined results of Figure 4 and 5.
 

     The standard distances are overlapping almost entirely so between the two years of data there is almost no change in weighted means.

     Many of the residents believe the occurrences of disorderly conducts can be related to the number of bars in an area as there are often high concentrations of college students. To show this information I used the block data for Eau Claire and included the bar locations within each individual block. For comparison I also included the standard deviation of disorderly conducts within each block (Figure 7). This data was from 2009. Standard deviations are good indicators of how much variation there was from block to block and can highlight where the high volume of arrests occurred.

Figure 7 - Standard deviation representation of arrests per block group with bar locations - 2009.

Results:

     After analyzing the data there are some interesting trends that jump out. As (Figure 1) and (Figure 2) show you can see that the weighted means pull away slightly southwest indicating that there are higher occurrences at specific addresses in that direction. These points are also closer to Water St. If you compare 2003 to 2009 using (Figure 3) you may notice that although similar both the mean centers and weighted mean centers are trending slightly more north in 2009 compared to 2003. In order to see if this trend has been occurring since 2003 we would need more data.
     Upon examination of the standard distances in (Figures 3-6) we can see that the size of ellipse in 2009 is smaller than 2003 meaning there is more of a concentration in arrests surrounding the mean.
     (Figure 7) is very telling of the current situation in Eau Claire. The block group containing Water St. has as standard deviation of over 2.5 meaning it has a high variation compared to the other block groups. Coincidentally there is a higher amount of bars in this block as well along with a high student population although that does not necessarily guarantee the two are related.

I was also tasked with finding the Z scores of three certain block groups.

The first block group was 41, in this block group notable structures are Oakwood Mall and some residential neighborhoods.
Disorderly Conducts: 10
Mean: 5.3593
Standard Deviation: 7.8149

Z-Score = (10 – 5.3593)/7.8149

Z-Score = 0.5938

 

The second block group was 46, which could be considered "Old Downtown."

Disorderly Conducts: 40

Mean: 5.3593
Standard Deviation: 7.8149

 

Z-Score = (40-5.3593)/7.8149

Z-Score = 4.4326

 

The last block group was 57 which can be found north west of block 46.

Disorderly Conducts: 1
Mean: 5.3593
Standard Deviation: 7.8149

 

Z-Score = (1-5.3593)/7.8149

Z-Score = -0.5578

 

Additionally, I was asked to figure out some probabilities for the Eau Claire block groups. With the current trends, we can figure out the probability of a certain amount of disorderly conducts to occur in a block group.

Z-Score = -0.52

-0.52 = (X – 5.3593) / 7.8149

-4.0637 = X – 5.3593

X = 1.2956

This tells us that 70% of the time the amount of disorderly conducts per block group will exceed 1.2956. 

 

On the opposite side of the spectrum:

Z-Score = 0.84

0.84 = (X – 5.3593) / 7.8149

6.5645 = X – 5.3593

X = 11.9238

Meaning 20% of the time the amount of disorderly conducts per block group will exceed 11.9238. 

 

Conclusion: 

     By looking at the results you can see that Water St. has a large impact on the results as the mean centers pull towards it. The standard distance ellipses also contain much of which is student housing around Water St. near the campus. It is reasonable for the complaining citizens to be argue that college students are the primary cause of the "ruckus." Looking at the standard deviation for that block group you can see that it is over 2.5 standard deviations away from the mean.

 

     However, Water St. was not the only area with a significant amount of disorderly conducts. Many were filed near the police station on Lake St. It is hard to tell if the actual crime was committed there or if they were just brought to the station to be processed. There was also a significant amount of disorderly conducts near the confluence, an area that also contains a large amount of bars looking at (Figure 7).  

 

     It is easy to make assumptions just by looking at the results but unfortunately disorderly conducts is a very broad and varying charge. You can have anything from public drunkenness too as simple as loitering in a certain area. Not all disorderly conducts have to involve alcohol assumption.  

 

     There is no simple fix to this issue, higher police representation may help but the biggest factors are educating students and having those student be personally accountable for themselves and their friends. I would take in to consideration the specific location of where the complaints are coming from and possibly up police representation in that areas in hopes that it lowers the disturbances. As you can see from the maps there are many areas in Eau Claire that do not experience disorderly conducts at all so the majority of police resources should be focused on the problem blocks in hopes of reducing crime and disturbances.

 

     Based on the information provided I would say yes the college students are a large factor in the in the disturbance the citizens are complaining about. However, I would be very cautious for blaming alcohol consumption as there is no conclusive evidence of that, just trends that may or may not be coincidental. No one will argue that fact that a lot of disorderly conducts do happen near Water St. and in an area that houses a lot of students though.

 

 

                                                                                                                                                             

Assignment 1

Part 1

     For this part of the assignment we were tasked with analyzing a sample of test scores from the Eau Claire School District. There was some worry from the public that the test scores were significantly lower at North High School than they were at Eau Claire Memorial and that firing teachers at North might be an appropriate. The results are below.

Eau Claire North Scores                                        Eau Claire Memorial Scores

153	160.9231	<-- Average	145	158.5385	<-- Average
120	164.5	<--Median	189	159.5	<--Median
180	170	<--Mode	198	120	<--Mode
194	83	<--Range	140	91	<--Range
182	23.63544	<-- Standard Deviation	135	27.15766	<-- Standard Deviation
170			175
175			182
165			194
164			107
142			134
130			165
184			167
170			120
135			148
188			189
192			120
120			190
111			154
170			120
162			167
154			175
176			184
145			145
189			149
164			193
149			137

     Using the given scores and Excel I was able to calculate a set of stats for each school including the range, mean, median, mode and standard deviation for each data set. The results show that even though an Eau Claire Memorial got the highest score (198) of the two schools it also achieved the lowest score too (107.) As a collective group the students at North outscored the Memorial students. The public could argue that North did better and the teachers should not have to worry for a couple of reasons. One, the mean or average test scores was two points higher than Memorial. Two, the median score for North is higher meaning the middle value of the given scores was better than Memorials (164 vs 159.) Next, North’s score range was less than Memorial’s showing the difference in the highest score and lowest score for the school was closer than Memorial’s highest and lowest score. Lastly, the standard deviation is lower for North showing their scores are more grouped together showing more group consistency. An argument that could be used against North is that Memorial had a higher top score (198 vs 194) and also had another student tie North’s top score. I think the best stat to illustrate this situation is the average score. The issue shouldn’t necessarily be about having a single student with a higher score but more focused on the overall group. North as a group achieved higher scores and was more consistent with their scores. Based on the information, the teachers at North should not be fired.

Part 2

     For Part 2 of this assignment our job was to help an organic farming firm select where the best place to establish an organic goat farm was in Wisconsin. We were given very general information such as the number of goat and organic farms for each county but using statistics and creating some maps showing these stats, a decent well thought out recommendation should be able to be made.

     The first step was using the data provided and calculate things such as the mean, median, mode, skewness, kurtosis and standard deviation for each data set. These will come in handy when creating the maps and interpreting the data later on.

COUNTY	NAME	Organic Farms	Goat Farms
001	Adams	1	12
003	Ashland	5	3
005	Barron	5	47
007	Bayfield	16	8
009	Brown	3	30
011	Buffalo	25	35
013	Burnett	6	11
015	Calumet	10	20
017	Chippewa	18	47
019	Clark	49	87
021	Columbia	15	49
023	Crawford	24	34
025	Dane	32	72
027	Dodge	20	57
029	Door	15	26
031	Douglas	5	18
033	Dunn	31	58
035	Eau Claire	27	44
037	Florence	0	9
039	Fond Du Lac	13	29
041	Forest	1	8
043	Grant	35	84
045	Green	16	81
047	Green Lake	2	25
049	Iowa	18	47
051	Iron	0	0
053	Jackson	23	41
055	Jefferson	9	43
057	Juneau	7	38
059	Kenosha	1	14
061	Kewaunee	2	17
063	La Crosse	26	22
065	Lafayette	24	35
067	Langlade	5	14
069	Lincoln	9	12
071	Manitowoc	21	36
073	Marathon	38	68
075	Marinette	4	15
077	Marquette	2	16
078	Menominee	0	0
079	Milwaukee	3	2
081	Monroe	59	74
083	Oconto	13	29
085	Oneida	3	7
087	Outagamie	11	35
089	Ozaukee	9	25
091	Pepin	5	9
093	Pierce	15	34
095	Polk	7	50
097	Portage	10	29
099	Price	4	12
101	Racine	5	33
103	Richland	28	34
105	Rock	18	59
107	Rusk	6	16
111	Sauk	38	74
113	Sawyer	4	5
115	Shawano	19	57
117	Sheboygan	6	54
109	St. Croix	18	57
119	Taylor	8	16
121	Trempealeau	32	31
123	Vernon	229	107
125	Vilas	0	5
127	Walworth	16	45
129	Washburn	3	20
131	Washington	12	31
133	Waukesha	1	21
135	Waupaca	5	36
137	Waushara	9	29
139	Winnebago	7	36
141	Wood	14	35

Using the information above here is the information I calculated using Excel.

	Organic Farms	Goat Farms
Mean	16.38888889	33.59722222
Median	9.5	31
Mode	5	35
Skewness	6.260257766	0.863929631
Kurtosis	46.54725042	0.538343524
Standard Deviation	28.0027556	23.04262941
Sum	1180	2419

Although having this information is wonderful it may be a hard to interpret visually in this format. Making maps using this data allows for a much more visually appealing way of presenting the data.

     Three important stats to pay special attention to are Skewness, Kurtosis and Standard Deviation. Skewness is important because it shows how symmetrical the distribution is. The closer you are to zero the more symmetrical the data is. With this in mind if we look at the skewness of Organic Farms in Wisconsin we can see that at 6.26 there is a substantial difference between 6 and 0 in terms of skewness meaning our data is not very symmetrical. Looking at the Goat Farm data however presents a different story, with a skewness of .86 that is within the 1 to 0 threshold and means our data is distributed relatively symmetrically throughout. Kurtosis is important because it quantifies whether or not the data matches the Gaussian distribution. In order to have Gaussian Distribution the kurtosis has to be 0. Very similarly to skewness we see that the Organic Farm data is nowhere near to a Gaussian Distribution while the Goat Farm data is within the 1 threshold. Lastly standard deviation is important because it shows how closely data is clustered around the mean. By looking at the map above you can see the standard deviation for Goat Farms by county. The lower the standard deviation the more tightly your data is clustered around the mean.

      Whether or not an Organic Goat Farm should be built in the state of Wisconsin really comes down to many variables. Some of the questions that need to be asked in addition to the data we were given include: What plants are you going to grow along with having the goats? Are you dedicating your sales to local markets? How big of farm do you want to have? Where is your primary customer base located? Do you want to be in an area that already has a lot of the same products you will have? Would you rather have the farm in an area with no other farms Organic or Goat related? These and many more questions need to be asked and considered when picking a location for a potential farm. Over 99% of Wisconsin farms are family owned, consumers are aware of this. The idea of an Ag Firm coming in might not sit as well with the consumers as they would hope.

     Looking at the maps we can certainly see some patterns and they make sense knowing Wisconsin's topography and locations of large populations. Both Organic and Goat farms are located in the most popular agricultural counties around the state. Counties that have good soil, easily farmed terrain, climate although varies slightly throughout the state is certainly a factor, markets available in relation to the farms location. Based on the information I have, I would recommend Clark, Monroe, Vernon, Trempealeau, Grant counties all good possible candidates. These are counties with a rich history of agriculture and based on the data these counties all support a blend of Organic and Goat farms so having an Organic Goat Farm would be a perfectly acceptable. These counties are good for the firm if they are interested in taking other farms head on as there is obvious competition already established in these counties. As a wildcard county I would select Brown county. Although there is already a couple of Organic and Goat farms located in the county there is a huge opportunity for sales with Brown county being a huge tourism county. It is the home of Green Bay and also very close to the hugely popular Door county for visitors. If we had more information this area could be explored further but the potential is there simply based on the farm data. I believe the "Percentage of Organic Farms by County" and the "Percentage of Statewide Goat Farms Per County" are both great maps for explaining my reasoning.

  The potential for a successful Organic Goat Farm in Wisconsin is certainly there. When doing a quick Google search for "wisconsin organic goat farm" there was more than 400,000 results. The interest and information is out there. There is pages of variables that need to be considered to make the most informed decision. A relatively safe bet for this farm would be one in a county already rich with agriculture and one to take a chance on would be one away from the most popular counties for farming but counties that really bank on tourism such as Brown or Door County, pending the right farming variables are there.