Assignment 1

Assignment 1    

Goals of Assignment 1

Differentiate Between Levels of Measurement

Differentiate Between Classification Methods

Retrieving Data from the U.S. Census and Joining Data

Enhance Cartographic Knowledge

Part I

Nominal Data

Nominal Data is not quantitative, it is basically a label. Nominal data has one specific symbol/ color in a map to represent one value only. For example, bedrock maps which show different types of bedrock by using different colors where one color represents only one type of bedrock.

Map Citation:

"Bedrock geology." Wisconsin Geological Natural History Survey. Accessed September 26, 2017. 
https://wgnhs.uwex.edu/wisconsin-geology/bedrock-geology/.Educational material (publications, maps, rock kits)

Ordinal Data

Ordinal data is data which has a ranked value. This means that the data is assigned a value based on the median or mode, and the data is compared by deciding weather one value is greater or less than another. For example, the map below shows ordinal data of the Average population per square mile. The data is ordinal because the population could be any number within the range that it is given by the different colors.

Map Citation:

Fox, Martin. Comment on "Is there a correlation between the population density of a place and how people vote (Republican v Democrat)?" Quora (web log), December 9, 2013. Accessed September 26, 2017. https://www.quora.com/Is-there-a-correlation-between-the-population-density-of-a-place-and-how-people-vote-Republican-v-Democrat

Interval Data

Interval data is data that has a specific range in the data, for example an elevation map where colors symbolize different elevations. The map below shows elevation in meters in the southeastern united states. each color represents a different range of elevations. 

Map Citation:

"Digital elevation Map of the Southeastern United States." Map. Clemson. Accessed September 27, 2017. http://www.clemson.edu/ces/geolk12/semaps/seregional/screen/digielmap.jpg

Ratio Data

Ratio data is data that has a meaningful zero or different value that data cannot go below or above. for instance a population density map because population can not go below zero. Below shows a population density map of the world, this shows ratio data because you cant have negative people, and you cant have more people than what are on the earth at the time the map was made.

Map Citation:

"Population Density." Map. Wikipedia. Accessed September 27, 2017. https://en.wikipedia.org/wiki/Population_density.

Part II

You have recently been hired by an agriculture consulting/marketing company and have been asked by your boss to provide a number of maps to be presented to potential clients.  Specifically, your company is interested in increasing the number of USDA Certified Organic Farms.  Where do you think your company should concentrate the message to increase farming USDA Certified Farms?  All maps are at the county level and will use the same data, but need to represent three different classification methods.  The three maps to be made are: Equal Interval based on Range, Quantile, and Natural Breaks.  Due to the fact you do not have time to make a presentation, your boss has asked that you write this up in your blog.  Please define each of the methods, linked to your maps, and then choose which map you think would be the best for clients to see.  Please provide a sound argument for the map that you picked.  Think, how does this map link back to the study question as to where should marketing/resources be concentrated to attract more organic based farms.  Make sure in your discussion you are connecting your arguments back to the patterns on the map.  

Map 1 below shows USDA certified organic farms per county in Wisconsin using equal interval classification method. This method sets the intervals of the data in equal sections with 58.25 being added to each new class. This classification is not the best to use because as the map shows, there are only two counties that are not in class one (0-58.25). when looking at this map someone would assume that every county in Wisconsin except for the two that are different could use more USDA certified organic farms. 

Map 1

Map two shows USDA certified organic farms per county using the quantile method, this method classifies data  into groups putting the same amount of values into each group. This causes groups to be to close to where you cant tell the difference in how many organic farms are in a specific county. And also some groups like the darkest color in this map have way to large of a range.

Map 2 

Map three shows the number of USDA certified organic farms per county using the natural breaks method. The natural breaks classification divides the classes by placing breaks where there is low valleys in the data. By separating the data by using low points in the data it groups together more similar values. 

Map 3

Based on the three maps that were produced, the natural breaks method is the best option because it divides the number of farms in each county with values that are more closely related rather than pairing really high values with low values like in Map 1 where the first class contains all values from 0 to 58 which a majority of the data is between those two values. With a majority of the data being between 0 and 58 the entire map is yellow except for the two counties that have a greater number of organic farms than 58. Map two is not the best either because it has an equal number of counties in each color, by using the quantile method it is difficult to tell what the number of organic farms in each county is because there are the same amount of counties in each classification group. When looking at the third map it is easiest to tell that there are less organic farms in northern Wisconsin and central Wisconsin, therefore when deciding where to place another USDA certified farm, central and northern Wisconsin would be the best options based on the way the data is portrayed using the natural breaks method.