The City of LA’s Crime Data from 2010 to Present draws information from crime reports of the Los Angeles Police Department to form this dataset. The ontology of this dataset is made up of 26 categories of detailed information for each incident.
The dataset starts with the “DR Number”: the Division of Records Number that assigns a unique number to each case for reference. The next three categories contain the date reported, the date occurred, and time occurred. After organizing the time of the occurrence, the dataset details the location with the Area ID, Area Name, and Reporting District. Next follows the Crime Code and Crime Code Description that detail the event taking place along with monetary value if applicable. Next follows a MO Code(Modus Operandi), a number that details crimes associated with the suspect. The following categories detail the chilling information of the victim including their Victim Age, Victim Sex, and Victim Descent. The Premise Code assigns a number to the crime’s location or vehicle along with a Premise Description detailing the location. Next the Weapon Code and Weapon Description detail the type of weapon used. The next few categories include the Status Code and Status Description that describe the status of the case as well as the seriousness of the Crime in Crime 1,2,3,4. Address, Cross Street, and Location columns give a more specific location and coordinates of the crime.
This dataset is most useful to the police and legal officials in Los Angeles to make sense of the crime reports that occur over the city. They can use this information to spot trends in heavy crime areas or certain targeted areas and act accordingly. This dataset includes very detailed reports of every crime which calms me knowing that these records are being kept in the city that I live in. The information shows exact locations of incidents as well as details of the crime and the seriousness of each. Although this data is very detailed, one aspect that is left out is the background of the criminal. Details of the crime including motivation, physical appearance, public status, previous crime history could create a completely new dataset and spark even more humanities questions.
If I started this dataset over, instead of viewing this information from the point of view of the police, I could reorganize the information in the point of view of an economist. In this view, I could focus specifically on the damage done by every crime and see how these crimes affect the revenue of these businesses, and if they reflect on the value of property in the areas.
Great start! So if you were an economist, what specific content types would you need?
Your report was really helpful and explanatory in breaking down the meanings of the categories rather than just listing them. Interesting to think about the data from an economists point of view also!