After cleaning the dataset, I focused on choosing features for clustering. Latitude, longitude, year, and event type stood out as the most meaningful. I started mapping event locations using scatter plots to get a visual sense of clustering patterns. Early patterns showed high densities in specific areas like Washington D.C., Portland, and parts of California. This confirmed that location-based grouping would be effective. I also calculated event frequency per state, which revealed states like California and New York had higher counts of protest-related activity. These initial visuals were important for narrowing down clustering methods and identifying possible biases.