Analyzing Crime in STL by Neighborhood: Final Project

The credited creators for this data set are the NIBRS or The National Incident Based Reporting System. The NIBRS is an incident-based reporting system for crimes known to the police. For each crime incident coming to the attention of law enforcement, a variety of data is collected about the incident. These data include the nature and types of specific offenses in the incident, characteristics of the victim(s) and offender(s), types and value of property stolen and recovered, and characteristics of persons arrested in connection with a crime incident. The source of their data is the St. Louis Metropolitan Police Department. They compiled these sets of data for  law enforcement agencies, researchers, policymakers, and the public to analyze crime trends, assess the effectiveness of crime prevention strategies, and inform public safety policies. Its use has been increasing over the years as agencies recognize its benefits for improving crime data collection and analysis. 

The key fields included in NIBRS data for each crime include various categories. They first distinguish the type of crime which lets us know if the crime was committed against a person or property. Crimes committed against a person include things such as murder, assault, rape, and kidnapping while crimes against property can include robbery, arson, and motor vehicle theft. Structuring crime data in this way allows for a more nuanced understanding of crime patterns and trends at the neighborhood level. It can help identify hotspots of crime, understand which types of crimes are more prevalent in certain neighborhoods, and assess the effectiveness of crime prevention strategies in different areas. However, there are potential limitations to consider. If crime data is not reported consistently or if there are discrepancies in how different neighborhoods report crime, it can affect the reliability and accuracy of the analysis. Additionally, the interpretation of crime data should consider factors such as population density, socioeconomic status, and other demographic characteristics of each neighborhood, as these can influence crime rates and patterns. 

They don’t mention a specific methodology or data cleaning choices at all and I assume this is because they are a national database. When creators of a data set do not mention a methodology for data cleaning, it raises some concerns and limitations regarding the reliability and quality of the data. It makes me assume the presence of things such as potential bias, inconsistencies, and transparency issues. All of these things could inadvertently affect the data.  

I could use this data as an argument for spatial injustice as the crime data identifies neighborhoods with disproportionately high crime rates. I could look at both the frequency and severity of crimes in each neighborhood. I could consider the historical context of each neighborhood including factors like redlining, segregation, disinvestment, and systemic discrimination. Exploring how historical injustices have contributed to the current spatial distribution of crime and socioeconomic disparities is a way to argue with data.  

This data set may be providing valuable insight into the complexity of MOBOT’s surrounding neighborhoods by inviting in the notion that accurately keeping track of a communities innerworkings may not be as simple as we thought. There are a lot of discrepancies to be aware of thus making it a very time-consuming process.