Assignment 1

Part I: Data Types

Nominal Data

Nominal data is all based on the name. Things such as state name, FID, or any other unique identifier is what the data classification method is based off of. 

Figure 1: Map of all of the Counties Within Wisconsin.

Figure 1 (1) above shows all of the counties in Wisconsin. The data is based on the county names, and separates the data based off of each county's name.

Ordinal Data

Ordinal data is any data that is ranked in any certain way. Things such as grade level (1-12), hurricanes (1-5), and many others all follow this method. If the data can be ranked, it is ordinal.

Figure 2: Ordinal data displayed across the World shows the rank of concern due to acts that violate various norms.

Figure 2 (2) shows ordinal data in the form of varying degrees of violations of political, economical, and use force norms across the countries of the world. It is ordered based on the counts of violations, displayed in darker shades of red as the number of violations gets higher.

Interval Data

Interval data is all the relationship of distance from one variable to another with no true set zero. It does not rely on a set scale, and can be used for multiple variables. Things such as temperature have no set scale of measurement. Some countries used Celsius, some use Fahrenheit. It is all about the relationship of the distance of measurement in interval data.

Figure 3: The map above displays temperature values for different regions across the U.S. in Fahrenheit. 

Figure 3 (3) shows the varying temperatures across the conterminous United States. The data scale is all set on the Fahrenheit temperature scale, which measures things differently than Celsius.

Ratio Data

Ratio data is very similar to interval data, except that it has a set zero for measurement. Some examples of ratio data weight and height. They both have a set zero starting point, and then can only get bigger from there. A common example is percentages. Things can't get less than 0%, or larger than 100%. Figure 4 shows this clearly (4).

Figure 4: The map above shows the influence humans have had on the natural land in the United States. It measures things in terms of how much natural land is left untouched.

Part II: 

    An important facet of a well functioning society is gender equality. With gender equality comes more opportunities for every member of the society, and thus create more economic stimulus for the economy as a whole. In Wisconsin, there are over 7,100 farms where the primary operator is a female. This is a good step, but there is much work to be done to ensure the state as a whole becomes much more balanced in terms of women's rights, and educating women to help them become principle operators of a farm themselves if they wish.

    The first map in the series is created using equal interval breaks. This creates evenly spaced groups and allows the viewer to see a general overview of the spread of the data throughout the entire range. When looking at this map, one can see that both the central sands region of the state and the northern portion of the state have the fewest number of principle female farmers. This makes sense, because of the sandy soil, and forested areas; however, the central portion of the state holds massive potential for principle female farmers.

Figure 5: Map of principle female farmers in the state of Wisconsin by county.

The red outline displays the developing study area.

    The next map is done using the natural breaks method. This creates the five most "natural" breaks throughout the data to create groups that are inherently displayed within the data already, counties for this map. This map shows that the central region of Wisconsin shows tremendous opportunity for growth among principle women farmers. The counties selected have surrounding counties that have high populations of principle female farmers, so as soon as the program would be initiated, the communities would rally around it and become stronger in the process.

Figure 6: Map of principle female farmers in the state of Wisconsin using the natural breaks method. The red outline displays the developing study area.

    The final map further illustrates the point made by the last map. It was created by classifying the data so that each grouping had the same amount of features. This further narrows the search for an area to begin our work to the smaller 6 county region in central Wisconsin. It shows that there's an "island" of counties that don't have the same amount of principle female farmers as the ones around them. 

    These 6 counties should be the base of our work, and if successful we can move west and try to increase awareness from the study area to the Mississippi River too.

Figure 7: The map above displays the principle female farmers with a quantitative classification. The red outline displays the starting study area.

Citations

1) https://www.presentationmall.com/wp-content/uploads/wi-multicolor.jpg

2) http://vmrhudson.org/SOCIC07color.jpg

3) http://www.smu.edu/-/media/Site/Dedman/Academics/Programs/Geothermal-Lab/Graphics/TemperatureMaps/surfacetemp.ashx?la=en

Data: https://www.agcensus.usda.gov/Publications/2012/Full_Report/Volume_1,_Chapter_2_County_Level/Wisconsin/st55_2_047_047.pdf