The detailed data on fatal police shootings compiled by the Washington Post can provide unique insights when explored using unsupervised machine learning techniques like k-means clustering. In this post, I’ll overview how k-means could group and segment this important dataset. K-means algorithm works to partition data points into a predefined k number of clusters based on similarity. Some applications to the police shooting data could include:
– Segmenting victims into clusters based on demographics like age, race, gender, mental health status. This could identify high-risk victim profiles.
– Grouping police departments into clusters based on shooting rates, trends over time, victim characteristics. This reveals patterns across cities.
– Clustering cities based on geographic patterns and density of shootings at the neighborhood level. Can pinpoint areas of concern.
– Discovering clusters of seasons/months that have significantly higher shooting rates compared to others. Informs temporal factors.
The ability of k-means to incorporate many variables provides a more holistic view compared to analyzing dimensions independently. The data itself drives the generation of clusters.Insights from k-means clustering can inform policy and reform efforts by revealing subgroups and patterns not discernible through simple data summaries. Moving beyond predefined categories allows a fresh perspective.Overall, k-means represents a valuable unsupervised learning technique for segmenting the rich Washington Post database into meaningful groups and discovering new insights on police use of lethal force.