GIS 5007 - Computer Cartography: Module 4 - Data Classification

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Our module this week covers diverse types of data classification and introduces the basic procedures used to classify data. Provided with census data for Dade County, Miami, Florida students created (4) maps using four different classification methods: Natural Breaks, Equal Interval, Quantile, and Standard Deviation.

The deliverables included two separate map compilations. One asks for the results of the percentage of the population above the age of 65 (Map A) and the population count above 65 per square mile (Map B). 

Map A


Map B

 Four different classification methods:

 

  • Natural Breaks: The natural break classification is based on the natural grouping of the features from the data. Also known as the manual method. This method groups together values that are similar, and features created by divided classes where boundaries are set due to significant differences in the data value. 
  • Equal Interval: The equal interval classification method divides the attribute values into equal parts or ranges. This method would allow you to specify the number of intervals and breaks based on the value range. So, for example, if we have a range of 500 and we break it down into 5 separate classes we would have      0 – 100, 101 – 200, 201 – 300, 301 – 400, 401 – 500. This method would be most ideal for percentages. 
  • Quantile: The quantile classification each class has an equal number of features. Due to the nature of grouping for this method the result of this type of map can be misleading for the viewer. Since each class contains an equal number of features, some features with widely different values can be placed into the same class – causing confusion. If you increase the number of classes, then you can spread out the number of features more accurately. 
  • Standard Deviation: The standard deviation classification is a result of how the features attribute value(s) varies from the mean. All calculations are automatically computed for this method. The class breaks for this method are divided into equal ranges that are a portion of the standard deviation. For example, as we saw on our map the class breaks would be 0.5 – 1.0, 1.0 – 1.5, etc.


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