Bryana Nichelson
CODE 123
Prof. Margaret Smith
May 1, 2024
The Metropolitan Police Department of the city of St. Louis is credited as the creator and contributor to the crime data dataset. The Metropolitan Police Department is accredited through the Commission on Accreditation for Law Enforcement Agencies, Inc (CLAEA). The CLAEA was established in 1979 to assist law enforcement agencies in establishing and maintaining high standards of excellence, as well as a commitment to public transparency. This dataset includes crime statistics by neighborhood, district, city, and by patrol. I chose to analyze the dataset specific to crime comparison in St.Louis by patrol. This dataset covers crimes reported in April 2021 and April 2022 in Central St. Louis, North St. Louis, South St. Louis, and a section titled “Unknown”. This dataset was interesting to me because I wanted to see the differences between crimes in different parts of St. Louis based solely on officers on patrol. The source of this data is crime reports from police on patrol.
The data is structured in charts. Each “area” of St.Louis has a chart including the type of crimes committed, how many times that crime occurred, and the difference between those crimes in 2021 and 2022. Each chart has a list of person crimes, property crimes, society crimes, and unspecified crimes. The way the data is structured is effective because you don’t have to guess where anything is. All the information is organized in a way that is easily findable and straight to the point.
In the description of the dataset, the creator(s) expressed that the police department transitioned from the uniform crime reporting summary reporting system to the National Incident Based Reporting System. They made this change to ensure that the dataset would accurately reflect crime reports in St. Louis versus the previous hierarchical format that reports were made in. The change was made in 2021 and caused the numbers in the dataset to increase, not because crime increased, but because crime is being reported with more detail and counting each individual crime.
This data could be used to examine crime rates in the St.Louis area. The data can determine which neighborhoods seem to be the safest versus the most dangerous as well as give St.Louis insight on what work needs to be done amongst communities to lower the numbers of certain crimes. This data is helpful to understand the complex dynamics of MOBOT and the surrounding communities because crimes have been reported in these surrounding areas and part of MOBOT’s problem is their relationship with the communities. MOBOT could potentially be a positive light and influence on the communities that they’ve neglected to give attention to and possibly decrease crime rates in those areas.