My Digital Data Diary

This week I was able to develop more questions that would help me draw more out of my main question but I was unable to figure out how to make the visuals again. I am attempting to make a linear graph or a histogram of the correlation between police shootings and race and how they have increased or decreased over the years. I will find this answer by using the dates in the data set and race.

My main issue right now is that there is no other data set from past or future years of fatal polio shooting so I am unable to look at past year patterns so I am trying my best with what I have right now.

This week I was pondering my question from last week of “Are fatal police shootings more common in southern states since their gun laws are less strict”. I took this question and broke it up into 3 smaller questions that would help me get the get the answer I needed.

1. Which state has to most common police shootings?
2. What city has the most common police shootings?
3. What are the gun laws for this state?

Using python I was able to solve my questions and found out that California was the state with the most police shootings and Los Angeles specifically was the city with the most shootings as well. I was under the impression that Texas would be first but it turned out to be the second state with the most shootings and Phoenix, AZ was the second city with the most shootings. I used matplotlib in python to develop a visual for this as well (shown below). I also asked chatgbt to tell me the gun laws of California to see if the reasoning as to why they had the most shootings was because their gun laws were not as strict but turns out they were more strict then I thought.

 

You must be 21 years old to posses a gun, permits are only given out on strict requirements, they are prohibited from schools, government buildings, airports etc. Those are just a few of the responses that Chat gave me but it was enough to tell me that California laws were not as bad as I thought.

 

During week 2 I was trying to think of another question that would fit my semester long question of ” How have fatal police shootings in the United Staes changed over time? What role does race, age,armed status, and gender play in these trends?”

I think my next few weeks of research are going to be more simplified and specific questions regarding each factor listed and others and I will later bring them together and see if there are pattern changes over time. One big question I have is if police shootings are more common in southern states because they have less strict gun laws and also their laws are beginning to change of recently,but before they started to change were polish shootings more common?

On September 10th, I was looking at a data set of fatal police shootings and 3 beginning questions came across my mind. The first one being how many of each victim was there, what are the percentages of armed versus unarmed victims and what is the median age of victims in these police shootings. Using Python I was able to analyze the data and answer my questions but also had other questions raised. Firstly, there were 9,935 male victims, 462 female victims and 5 non-binary victims throughout this data set. This tells me that the primary target of police shootings are males but females, although the difference is large, do not fall entirely behind. Next, I asked python the percentage of named versus unarmed victims and I found that 5.42% of victims were unarmed, 57.91% were armed with a gun and 17.03% were armed with a knife. I thought that the drastic difference between unarmed and armed victims seemed inaccurate and these values raised the question if majority of the victims who were supposedly armed were ACTUALLY armed with some sort of weapon or if the police lied order to cover themselves. Lastly, I was curious about the median age of victims and found that the median was around 35 years old, which is still pretty young considering that is when most young professionals tend to really excel and step into their careers which makes this all the more sad.

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