In my exploration of police shootings in the US, I took a closer look at the geographical aspects of these incidents. Geospatial analysis helps you recognize patterns and trends in data that are related to geographic locations and can provide valuable insights.
To start, I imported essential libraries: Pandas for data manipulation, Matplotlib for visualization, and Scikit-Learn for clustering. Read the police shooting data from a CSV file, extracting latitude and longitude information for analysis.
Visualized the police shooting locations using scatter plots and 2D histograms. These visualizations provide a clear overview of the geographical distribution of incidents across the continental US.
I explored DBSCAN clustering. Unlike traditional clustering algorithms, DBSCAN does not require specifying the number of clusters beforehand. Instead, it identifies dense regions of data points and labels them as clusters. Points that are isolated from these dense regions are marked as noise, making DBSCAN particularly adept at handling irregularly shaped clusters and noise within the data.
Unlike traditional clustering algorithms, DBSCAN identified densely populated clusters of police shooting locations without any prior assumptions about the number of clusters. This adaptive nature allowed me to uncover spatial groupings that would have been challenging to capture using fixed-cluster methods.
DBSCAN’s ability to classify points as noise or outliers provided insights into isolated incidents. By separating these outliers from the main clusters, I gained a clearer understanding of singular events versus recurrent patterns. This differentiation is crucial in understanding the context of police shootings, distinguishing between random incidents and recurring issues in specific areas.
