Module 4 - Data Classification

     This module had us create four map frames using ArcGIS, each with a different data classification type. The objective was for us to learn the differences between the Equal Interval, Quantile, Standard Deviation and Natural Break classification methods. 

    The Equal Interval classification method makes sure that the classes are split into equal ranges by using equal intervals. This method’s equal intervals allows for easier relative comparisons. The drawback is that the number of observations in each range will differ depending on the dataset, making it sensitive to outliers, which could be misleading.

    The Quantile classification method distributes the number of observations into ranges containing an equal number of each. This allows for each range to be equally represented on the map. This method is good at displaying ordinal data and it is less sensitive to outliers. The drawback of this method is that observations with widely different values could be placed into the same range.

     The Standard Deviation classification method is centered around the mean of the dataset, helping display data values that are close or far from the mean. This method is good at displaying variation and identifying outliers. The drawback of this method is that if the data is not nominal, it may not be as effective. 

    The Natural Break classification method helps to minimize the differences between observations and maximizes differences between ranges. This method is good for data with outliers since it considers how observations are clustered. The drawback of this method is that if the data is quite evenly distributed, then it may not represent it accurately.