Module 4

Module 4 Blog 

    This module focused on getting us into the mindset of what do we do with data if we want to make a map based on it. We learned a bit about MAUP (Modified areal unit problem). A short explanation of this idea is that raw data should be normalized, but not normalized in just any method. The methods are just as important because it is very easy to mistakenly misrepresent data. 

    In this module we were given one data set to work with that came from the US Census bureau back in 2010. I found this information by sifting through the meta-data. 

    After the data was brought into the map, we did this 3 more times. creating four separate maps. 

    The reason for Doing this was because in each map I re-classified the data using four different methods. This is done in the symbology pane. Those classification methods were Quantile, Equal Interval, Natural breaks, and standard-deviation. 

One of the question towards the end of the process summary was what classification method seemed to be the best representation of the data visually. My initial thought was that the quantile classification method would most accurately depict the information I wanted to convey due to the fact I was working with a Ratio data set and that I wanted the ages not to be skewed within the data ranges. 

However when this classification method was applied to the map with the default method that Esri provides, the Equal Interval classification actually appears to represent the data correctly at at a visual level.