I used the gender feature to show the dataset in order to better understand its demographics. With almost 2,000 male victims compared to fewer than 250 female victims victims, the visualization showed a considerable gap in fatalities. I started my investigation by figuring out which state had the most instances. According to my research, California accounted for 344 of the total instances.
According to my reseaarch, California had the highest number of instances (344), followed by Texas (192 incidents) and Florida (126 incidents).
I also tried to do a temporal analysis on the dataset, and at first it seemed that the most shootings occurred in 2017.
Racial Breakdown: In this section, I employ the sns.countplot() function from the Seaborn library to generate a countplot. This plot offers a visual representation of the distribution of fatal shootings across different years, and I utilize the ‘race’ parameter as the hue. The resulting visualization provides valuable insights into the racial breakdown of these incidents.
Presence of Body Cameras: Similar to the preceding section, in this segment, I use a countplot to depict the presence of body cameras over the years. By setting the hue parameter to ‘body_camera,’ I can assess the changes in the utilization of body cameras during fatal shootings.
Signs of Mental Illness: This section maintains the same structure as the previous two sections, but it centers its focus on the signs of mental illness. The countplot displays the distribution of fatal shootings across different years, with the ‘signs_of_mental_illness’ parameter as the hue.
I found a difference between my Python code and the data that needs more research. In this instance, even though the dataset has data for the year 2023, it records no gunshots for that year.
I also looked into whih agencies were engaged in the most incidents, and I found that agency ID 38 had the most incidents overall. I used the ‘armed’ feature to visualize the dataset in order to have a deeper understanding of how many victims were armed and what kinds of weapons they carried. The graphic showed that more than 1,200 victims had firearms, almost 400 had knives, and about 200 had no weapons at all.
In order to gain understanding of incident location patterns, I also carried out an experiment to examine the relationship between latitude and longitude. Plotting this association allowed for the creation of a scatter plot that clearly showed the locations of the incidents and provided insight into their relationship. I intend to investigate more features as I go along in order to have a deeper comprehension of the dataset. A snapshot of this analysis is provided below: