When we are looking for clusters of data or trying to determine the overall geographic distribution of our data, one of the first things many of these tools do is determine a center of mass for the data. From there, it can compare nearby features looking for clusters, determine area concentrations of data, look at directional distribution, and more.
However, finding the center of the geographic distribution of our data can be a powerful analytical tool. This can allow us to strategically locate new facilities, pick a central meeting place, plan a reaction to events, and more. There are three types of centers we can calculate: mean, feature, and median.
The Mean Center is the easiest to calculate. It is simply the average of all the x and y coordinates for all features in the layer you are analyzing. The result is the mean center. This can be useful in tracking movement or shifts over time, such as population shifts from urban to suburban areas...