LA Crime Dataset on Safety in Schools

Members:

Kira Arell, Emma Callahan, Madison Michlanski, Tayven Hoffman, Neema Kariuki

Introduction

This data was designed to create more transparency between the public and the LAPD. Upon exploring the data, it is clear that there is a lack of information provided about the perpetrator in comparison to the victims. 

Using the information from the dataset, as a group, we asked three main questions: How do variables such as age, sex, and race contribute in relation to the perpetrator’s connection to the victim? How can schools become aware of issues regarding safety for children in schools? And what missing information could be reported that would help to further promote safety practices within school settings?

Work Performed

Due to the extremely large volume of the dataset, we decided to narrow the amount of data we would be looking at to focus primarily on victims under the age of 10 and crimes committed in schools by filtering through the data. This narrowed our dataset down to 270 entries, which made our data much easier to interpret and analyze trends and patterns in the data. 

Methods

We used a denotative reading of our dataset to understand the definitions encoded within it, as well as an analysis of LAPD definitions of key variables within the crime dataset. Along with that, we utilized a deconstructive reading so we could understand what data is not included in this dataset and how this relates to whether the crimes were committed near schools or inside school grounds. These methods helped us determine whether the dataset meaningfully represents school safety for children. 

Findings 

We found that there was information listed about the victim, but a lack of information about the perpetrator, such as age, race, and sex.  When looking at the dataset, we determined that there was a consistent pattern between the crime that was committed and a victim’s age, as well as finding that boys and girls are affected at similar rates.

Although we were able to determine some patterns within our narrowed dataset, the lack of important information regarding the perpetrator prevented us from being able to conclude an actual connection for each crime committed.  Information about motivation and the relationship between a victim and the perpetrator could help to determine ways to prevent future crimes committed within a school setting. 

With this information, schools can put in place preventative measures such as more thorough background checks and increased supervision.  

Overall, greater transparency could help lead to a safer learning environment for young children.  In addition to our proposed questions, to consider this dataset to be fully transparent to the public, the National Incident-Based Reporting System (NIBRS) still needs to provide a good balance between privacy measures and important data for both victims and perpetrators.