Data Types and Classification

Part one: Data types

Data is a big part of research and geography, especially quantitative geography research. Data comes in four different types; nominal, ordinal, interval, and ratio. they type of data that is needed changes based on what information is being shown and how it is being shown.

Nominal data: This data type sorts the information into different unique categories that have no assumed relationship between them. This data type is normally for information like names or street numbers that no meaningful math can be applied. An example of this is that "346 Water St" and " 792 Clairemont Ave". The text cannot be added or subtracted and the numbers can be added or subtracted but the result does not mean anything. The map below is an example of nominal data because it maps the counties in the US and separates them into different unique categories and there is not assumed relationship between the categories. The categories in this map are republican and democrat won counties during the 2016 US presidential election.

        "2016 US Presidential Election Map By County & Vote Share." Brilliant Maps. November 30, 2016. Accessed February 01, 2017. http://brilliantmaps.com/2016-county-election-map/.

Ordinal data: This data type sorts information into an arbitrary scale whose categories are related to each other by a rank. There are two different types of ordinal data. The first one is strong ordered data, which is ranked data where the information is given a specific place in the order. An example of this is if the 10 coldest cities were mapped. The next one is  weakly ordered data, which the data is put into differently ranked categories. An example of this is if the percentages of a certain ethnic group was mapped and put into different parentage categories. The map below is an example of ordinal data because the information is split up into categories that have a specific order, are on an arbitrary scale, and are related to each other. In this case that categories go from soft to hard and show that hardness of the ground over the US. 

     "Aggregate Hardness Map of the United States." ForConstructionPros.com. Accessed February 01, 2017. http://www.forconstructionpros.com/article/10745911/aggregate-hardness-map-of-the-united-states.

Interval Data: This data type sorts information into a scale that has no meaningful zero point. This scale may have a zero but it is just a reference point and not a starting point. This data can be mapped in different units such as temperature (Fahrenheit, Celsius) or elevation (meters, feet). The map below is an example of interval data because the information does not have a meaningful zero point and can be mapped using different units. This map shows elevation in the US by meters. 

     "US Elevation and Elevation Maps of Cities, Topographic Map Contour." US Elevation and Elevation Maps of Cities, Topographic Map Contour. Accessed February 01, 2017. http://www.floodmap.net/Elevation/CountryElevationMap/?ct=US.

Ratio data: This data type sorts information to a scale that has a meaningful zero point that serves as a starting point. This data is used for counting a certain amount of something in an area such as people per county, rates of something in an area such as how many pets there are per person in an area, or densities such as how many cows there are per square mile. The map below is an example of ratio data because it is a rate that has a meaningful zero point which is also the starting point.

     "CensusScope -- Demographic Maps: African-American Population." CensusScope -- Demographic Maps: African-American Population. Accessed February 01, 2017. http://www.censusscope.org/us/map_nhblack.html.

Part two: Data classification

Data can be classified in many different ways and that was that the information is classified can change how the map looks and can be misleading or skewed if the wrong one is chosen. Three of the most common types of data classification are equal interval, quantile, and natural breaks.

Equal interval: This data classification method takes the range of the values in the information and divides the range equally into the number of classes that are desired. This makes each class cover the same amount of the range as the other classes in increasing or decreasing order. The map below is an example of equal interval data classification.

Quantile: This data classification method divides the number of entries by the number of classes that are desired. This makes each class cover the same amount of entries as the other classes. The map below is an example of quantile data classification.

Natural breaks: This data classification  method looks at the data and divides it where the data has notable separations or gaps.  The map below is an example of the natural breaks method.

The data classification method that should be used is the equal interval method. This method was chosen because it makes it look like there is not a lot of women that operate farms compared to the other two. If the company wants to increase the number of women operated farms they need to show that there is a shortage of them and that is what the equal interval data makes it seem like. The other maps have far more darker counties and the equal interval has a lot more light counties so the equal interval map makes it look like there are not a lot of women running farms in Wisconsin.