Part One:
Nominal Data: this is data that is distinguished by a naming system to label variables. This data is not usually measurable or quantifiable. The map below contains nominal data because it uses names to differentiate the distinct climate zones across the globe and the variables do not contain numbers.
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Source: http://esdac.jrc.ec.europa.eu/projects/RenewableEnergy/Images/Climate_Zone%2011_s.jpg
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Ordinal Data: data that can be put into a ranking order or category based on position on an established scale. The difference between rank can not be measured. The map below shows an example of ordinal data because it is ranked into order in three categories with no way measuring the difference between each ranking.
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Source: http://aldf.org/wp-content/uploads/2015/12/ALD-127-US-Protection-laws-rankings-map-2015-Large2.png
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Interval Data: data that can be measured along a scale with each point being equal distance from one another. Interval data can not have zero as a starting point and can not be multiplied or divided by. The map below shows interval data because elevation does not have a starting point and each increment on the map goes up equally.
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Source: http://topocreator.com/ned-jpg/city_a/600/mn.jpg
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Ratio Data: Data that is measured along a scale with each point being equal distance from one another. Unlike interval data, ratio data have a known starting point of zero. The map below shows ratio data because the starting point is zero and progresses by intervals of 8 million.
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Source: http://support2.dundas.com/OnlineDocumentation/RSMap/Images/DesigningMaps1.bmp
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In an effort to visualize where the numbers of female farm operators in Wisconsin are located and where there needs to be an increase in farming among females I have created the following maps. Each map has a different classification method, therefore changing the look of the map.
The first map, shown below, uses equal interval classification. Equal interval uses the range of data from highest to lowest and divides that by the number of classes desired, in this case 4. Each interval will be the same size but might not have the same number of data in them.
The next map was classified by Jenks Natural Breaks. Natural Breaks finds the minimum variance in data by finding class breaks based off of differences in size of numbers in a set of data.
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Jenks Natural Breaks
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The last map is classified using the Quantile method. This method places an equal amount of units in each class, unlike the equal interval method.
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| Quantile Method |
The company should be targeting counties that are lightest in color, or have the fewest number of female farm operators, since these are the places which would benefit the most for marketing and have the most need to increase their numbers. The map with the best classification method for the job is the Natural breaks map because it groups similar values the best to give a more accurate representation of the numbers of female farmers in each county. It also keeps the intervals of classes with fewer female farmers smaller so it is easier to differentiate sizes and target these counties for marketing. The last class can have a larger grouping because we are not interested in the counties with the highest numbers of female farmers. From this map it is clear to see that counties in the northern portion make up the largest portion of the state lacking female farmers.
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