Symbolizing data isn't limited to applying graphic marks to a feature; it can refer to any method of representing the data to improve communication. Earlier, we looked at scales of measurement, which influence the type of statistical analysis techniques that can be used when analyzing data, as well as the ways in which we represent them. In general, there are more alternatives for statistical analysis when the data is quantitative, and more types of visual variables that can be applied. Remember that your map is only as good as the data that goes into it, and make sure you understand the limitations and potential error that may be already baked into it. Our job is not to magnify that error through poor representation.
Classifying the data allows us to identify patterns in the data by sorting it into buckets that will be represented in the same way. For example, equal interval divides the range between highest and lowest values into a certain number of equally-sized buckets...