Quantitative Methods in Geography, Assignment 1

The goals of assignment one are as follows: to differentiate between levels of measurement, to differentiate between classification methods, retrieving data from the U.S. Census and Joining Data, and to enhance cartographic knowledge. The first part of this assignment was to define the difference between nominal, ordinal, interval, and ratio data.  Nominal data is data that names objects, such as state names. Nominal data is usually shown as a label of some kind. With nominal data, each feature has its own value. Single symbol maps are used when representing nominal data. Shown below is an example of nominal data scales. Nominal data also does not include any quantitative values. Below is an example map that shows nominal data.
 
Source: https://web.natur.cuni.cz/~langhamr/lectures/vtfg1/mapinfo_2/barvy/colors.html

Ordinal data is a categorical data type. It may consist of some type of ranking from low to high, it could range from village, town, to city, or it could be variations in symbol color and size, all in order to indicate an increase in value. Ordinal data scales usually measure non-numeric concepts. Ordinal is represented with unique values maps. Below is an example map that shows ordinal data.

Source: https://www.e-education.psu.edu/natureofgeoinfo/book/export/html/1553

An interval scale is a regular numeric scale. In this case, the order of the values is known along with the exact differences between the values, unlike with ordinal data. Some good examples of interval data include the Celsius temperature scale, the pH scale, and time, because in both of these cases the increments between each value is known and measurable (as well as consistent). One thing to remember about interval data is that they do not have a “true zero” or absolute zero. Without an absolute zero, ratios cannot be computed. Interval data is represented with quantities maps. With interval data addition and subtraction can be done. Below is an example map that shows interval data.

Source: http://support2.dundas.com/OnlineDocumentation/RSMap/DesigningMaps.html

Ratio data tell the order of the values, the exact number between each value, and they also have an absolute zero. Some examples of ratio data include height, weight, population, and rainfall. Ratio data having an absolute zero allows for a wide range of descriptive and inferential statistics to be applied to it. Addition, subtraction, multiplication, and division can be done with ratio data. Quantities maps are used to represent ratio data. Below is an example map showing ratio data.

Source: http://sites.uci.edu/randersonlab/available-data-2/

The goal of part two of this assignment is to provide maps that will presented to potential clients as a new hire to an agriculture consulting/marketing company. The company is interested in increasing the number of women as the principle operator of the farm. The company should concentrate their message in areas that females tend to visit a lot, and areas where farmers may go in their leisure time. Bringing this message to places where females and farmers commonly spend leisure time would be an effective way to draw them in to look at the message. Three maps will be created for this project. The three maps will be equal interval based on range, quantile, and natural breaks. Equal interval based on range is a classification method where each class has an equal range of values. This can be used when data is distributed evenly. The quantile classification method is when each class has about the same number of features. The natural breaks method is when data values that cluster are placed into a single class. Class breaks are when there is a gap between the clusters. This method can be used when data is distributed unevenly. Once these maps are completed, the next step will be to decide which map would be best for the potential clients to see and explain why this is the best choice.

The first step in this process was to gather data from the census fact finder website, the dataset chosen was: 2010 SF1 100% Data, and then the geography was set to all counties in Wisconsin. After the data was located, the next step was to download the shapefiles from this page into my folder for this assignment in order to begin the project. The next step is to open up ArcMap and prepare the Excel document for this assignment to be used in ArcGIS as well as add the shapefile of Wisconsin that was just downloaded to the map. To do this, it is necessary to add the data to the GIS platform. To do this, the “add data” button in ArcMap is used to select “Sheet 1” to be added to the GIS so the data can then be joined. To join the Wisconsin shapefile to Sheet 1, the field that the join will be based on was set to Geo_ID, the table to join to this layer was set to Sheet 1, and the field in the table to base the join on was set to Geo_ID. The next step in this project was to change the coordinate system to “USA Contiguous Albers Equal Area Conic” projection. After creating these three maps based on female farm operation in Wisconsin counties, the final project can be seen below.

 I think that the map that should be shown to potential clients should be the quantile classification method map. This map shows the most range of values, which can be seen from the large area of maroon that covers this map, compared to the maps from the other two classification methods. With more of a range of colors, which represents different values of female operators in each county, this map will be easier for clients to read and understand. The quantile classification map also gives the reader a good idea of where female farm operators are most concentrated in Wisconsin counties. When looking at the quantile map, it can be seen that female farmers are most concentrated in central and southern Wisconsin. Because of this, it makes sense that potential clients should direct their marketing to these general areas in order to reach the largest number of female farm operators. The strong variations in the value colors for the quantile classification map was the greatest of the three maps created in this project, and this is why it is the best choice to present to a potential client.