The dataset from the city of Los Angeles that I chose, is Arrest Data from 2010 to Present. The ontology of this dataset is designed in such a way that allows the viewer to search for an arrest in seventeen different ways. One can search by the report ID, arrest date, time, area ID, area name, reporting district, age, sex code, descent code, charge group code, charge group description, arrest type code, charge, charge description, address, cross street, and location.
For the most part, this ontology is understood best by those within the law enforcement system who are aware of the codes used. A member of the general public may be able to self-determine what some of the codes mean, but without reading the descriptions or gathering conclusions from their own information, understanding some types of this categorizing may be difficult. For example, the general public may be able to understand that “M” and “F” in the “sex code” category stands for “male” and “female,” but when it comes to the codes within the “arrest type,” many are clueless as to what “D,” “F,” “I,” “M,” and “O” represent. Several of the categorizes used within this dataset are also largely irrelevant to those who are not part of the authoritative system. The “report ID,” “area ID,” “charge group code,” and “arrest type code” mean very little to the general viewer, but may be crucial aspects of identifying cases to members of law enforcement.
Albeit not a completely straight-forward system of organization for all viewers, the dataset as a whole is very important to many groups of people and individuals inside and outside of the city of Los Angeles. For anyone living within Los Angeles, this dataset could be a useful key for understanding the safety of themselves as well as their children and families. By looking at the locations and times of arrests, one could determine it best to keep away from certain areas during specific times. Additionally, this dataset gives a heads-up to the safety of a particular community that someone outside of the area may be moving to. This dataset could provide answers to research questions, as well. Correlations regarding race and arrests and locations could be made. All in all, this dataset is illuminating to anyone who is curious about current and past trends in Los Angeles arrests. It could uncover possible target areas, target populations, and may even uncover biases within police department sectors.
Although this dataset may prove to be very useful and enlightening, it is not without exclusions. At the most basic level, we know that arrests which happened prior to 2010 are not included. This dataset also does not include instances of police presence without arrests. It does not take into account events where the suspect escaped and was unable to be arrested. Arrests that were not recorded for one reason or another would also not be in this dataset. Additionally, since there are only options for recording male and female gendered arrests, proper documentation for all genders is missing.
From another point of view, a different ontology could be applied. Arrests could be categorized by more than just the binary genders. It also would be interesting to find out who reported the crime which led to the arrest. Was it due to police patrolling, a community watch program, bystanders, or family members? Was violence used during the arrest? Having this information could help researchers look deeper into the dataset and assist with determining new theories of social injustice and inequalities.
Hi!
I like how you analyze the ontology by its search ability aspect. I think that is a good way to combine the digital with the humanities. I also agree with you, that the data set is organized for a specially educated person. I took a class on the school to prison pipeline and we learned that the majority of people in prison did not receive a good education. It is interesting that the people who make up this dataset may not be able to understand it and what it is saying about them.
Hi! I think you did a great job at describing this dataset. I think you really dissected this dataset and dug deep to find this sold information. I also think it would be interesting to find out who reported the crime. Keep up the good work.
Hi.
I thought this data set was super interesting as it is very practical for real-life. I love the way you stated that although this data is very important for those in law-enforcement it has a completely different meaning to those who aren’t.
Along with this, it would be interesting to see this data set from a different point of view regarding police patrolling. This is because during the war on drugs within America, it was very apparent that a lot of arrests were due to police targeting and patrolling particular neighbourhoods.
Great post. Well done.
Hi! I thought you did an awesome job analyzing the data set. I agree that the data set is probably more useful for officials due to the short hand in the labeling. Since the data spans multiple years, I think it would be interesting to see if/how arrest trends change in regards to age, sex, arrest type, etc. with times.