Clustering is a technique in data analysis and machine learning used to group similar data points or objects together based on certain features or characteristics. The goal of clustering is to identify patterns and structures within the data, such that data points within the same cluster are more similar to each other than to those in other clusters.
The process of clustering police shooting locations in the continental US involves using geodesic distance, which calculates distances between latitude and longitude coordinates. In our case, Mathematica provides a convenient automatic clustering procedure called “Find Clusters.” What sets it apart is that it doesn’t require us to predefine the number of clusters.
To calculate the geodesic distance, which represents the distance along the Earth’s surface, between two locations using latitude and longitude data, we can use the Geo Distance function. This function takes the coordinates of two points and calculates the distance between them, taking into account the curvature of the Earth’s surface.