Assignment 1

Part 1: Data Types

Nominal Data

• Nominal Data is data that is classified by categories defined by characteristics/names rather than numeric quantities. An example of nominal data could include the geographic regions defined by the different Native American languages as shown in the map below (Figure 1). Other examples could include geological regions of an area or even physical characteristics of a population of a group of people, such as race or gender.   

Figure 1

(http://www.emersonkent.com/images/indian_tribes.jpg)

Ordinal

• Ordinal data is data that can be placed in an order an each values rank is based upon the value previous or ahead creating a ranked order. An example of this could be the productivity of different soils or water penetration index of an area in the map below (Figure 2).

Figure 2

(https://www.toddklassy.com/montana-blog/soil-productivity-index-map)

Interval

• Interval data is data that can be classified by a common interval, such as meters, so that difference between valves can accurately calculated however, there is no true definable zero in the data meaning that there can be negative values. Two very common examples of interval data are that of temperature and elevation (Figure 3).

Figure 3

(https://upload.wikimedia.org/wikipedia/commons/thumb/e/e1/California_Topography-MEDIUM.png/1200px-California_Topography-MEDIUM.png)

Ratio

·        Ratio data is similar to interval data and shares many of the same characteristics of interval data but ratio data has a no negative numbers and clear and defined zero point to were all the other data can be accurately compared to. An example of ratio data would be a percentage of a population that identifies with a particular race or nationality (Figure 4).

Figure 4 

(https://upload.wikimedia.org/wikipedia/en/3/36/Census_Bureau_2000%2C_Hispanics_in_the_United_States.png)

Part 2: Data Classification Types

Equal Interval

Figure 5

Quantile

Figure 6

Natural Breaks

Figure 7

The three maps above display the distribution of organic farms in the state of Wisconsin using three different data classification methods equal interval (Figure 5), quantile (Figure 6) and natural breaks (Figure 7). Equal interval is done by subtracting the range of the data and then dividing  that number by the total number of desired classes. The second method is the quantile method that divides the data so that there is equal number of data points within each classification. The third classification method is the natural breaks method that divides the data at points where there are larger discrepancies between data points showing were data is clustered.

Based on the maps above the map that most accurately depicts the data is Figure 7 which uses the natural breaks method. The equal interval method (Figure 5) is much too distorted due to the outlier in the Vernon County which has 168 more organic farms than the next closet county. Because of that outlier the other counties are all grouped into the first classification leading the map to show very little information. The Quantile method is more accurate than the equal interval method but also fails to show the distribution of organic farms, especially in the western sections of the state where there is a greater variability between counties. It does this by grouping counties that have a large difference in their total number of farms into the same class. The most effective classification method for this data is set the Natural Breaks method because it more accurately displays how the data is distributed amongst the counties.

The most effective place to concentrate resources to attract more organic farms would be in the eastern portions of the state. This is because of the larger population centers such as the Fox Cities and Green Bay in the northeastern region of the state along with Milwaukee and Racine  being in the southeastern section of the state. Along with having higher populations these counties lack large numbers of organic farms making them an ideal place to create a market for more organic farms. While the northern section of the state lacks organic farms it also lacks good soils and a lack of any major population centers to buy these organic goods.