In the next phase of my research, I used geospatial data and relevant libraries to create a visual representation of police shooting incidents across the US. In order to do this, I took the dataset’s “latitude” and “longitude” characteristics and painstakingly filtered out any null values in these columns. In order to create a Geospatial Scatter Plot, an accurate geographic map of the United States was created and then enhanced with the addition of recognizable red markers. The resulting visualization provides important insights on the distribution of these episodes across the nation by presenting a spatially accurate depiction of the locations where they occurred. Plotting these occurrences on a map makes it easy to identify areas where there are clusters of police shootings, which aids in a better understanding of local patterns.
Policymakers, scholars, and the general public may now see the scatter plot for each state in the US, providing them with important new information about the geographic scope of police shootings. This could therefore lead to better-informed conversations and motivate activities meant to address this important problem. For the state of Massachusetts, as an example, I created a comparable Geospatial Scatter Plot, which is seen visually below.
Following our professor’s advice from the previous session, I plan to continue my analysis by investigating the use of clustering methods and diving into the world of GeoHistograms. In particular, K-Means and DBSCAN are two different clustering techniques that our professor presented. It’s important to remember that K-Means requires predefining the value of K, which may present a constraint. My objective is to apply both of these methods to the geographic locations of the gunshot data in Python and assess if they produce meaningful clusters. This stage is expected to reveal more levels of information and trends in the dataset, which will advance our understanding of this important problem.