For this assignment, I decided to take a look at sex offenders in the Chicago area over the past year. Before divulging into my own personal assumptions, I needed to first gather all the information. My curiosity began at the racial divide between offenders. I found that there were 749 registered Black-Hispanic sex offenders in the last year. Which is nearly 50 percent of the total persons registered.
That led me to filter the Black-Hispanic race by gender. Eighty-five percent are male and only 14 percent are female. Overall, offenders are typically between the ages of 38-59 years old. In general, these offenders had a tendency to target a minor above all else. In fact, 899 out of the 1254 victims were minors.
While digesting this information, the first question that pops into my head is “why?”
This data is helpful in understanding the connection between race, gender, age and sex offenses in the city of Chicago. However, it lacks much need explanations. For example, why are there significantly more registered sex offenders that are Black-Hispanic? Why are minors being targeted? Why do men of a certain age tend to be a large statistic in sex offenses?
I researched the demographics of the city to find that 32 percent of the city is Black, according to the US Census Bureau’s lasted demographic research in 2015. I wondered if the largely Black-Hispanic issue is sex offenses was due to a larger demographic in the city or inter-racial problem experienced by minorities.
Eventually, after looking through the information in the dataset, I went on a quest to answer some of these questions. According to the U.S. Department of Justice National Sex Offender Public Website (NSOPW), one is six adult women experience an attempted or completed sexual assault by a male. This information solidifies the significantly higher statistic of male sex offenders compared to women in Chicago’s data. However, the department does note that there is “no typical” profile on a sex offender. An offender can be “anybody.”
In continuation of my research, I found another percentage that was alarming. According to NSOPW, 12 to 24 percent of sex offenders will reoffend. Unfortunately, this is where the database became unhelpful. The data set did not state how many times a person had committed a sex crime. If I were to improve upon this data, I would add an ID number that would help me get better access to exactly how many times a person has been convicted.
Another area I wanted to explore was location. The data set does provide the block address for each offender. The issue is unless your incredibly familiar with every street in Chicago, it is rather difficult to assess the areas where sex crimes are more volatile. Perhaps, in class we could learn how to “map” out data points.
In conclusion, this data set provided a giant stepping stone into many different story ideas. While many questions were left unanswered, the data was a good starting point. For example, I would be very interested in understanding why other racial groups such as Black and Hispanic have such higher sex crime rates than Asians. I’m curious to find the psychological effects that could be happening to different races in the city of Chicago. And even though sex crimes are drastically different that gun crimes or homicides, considering the amount of concerns around gun violence in the city I wonder if there are any connections.
In the end I think in order to do a thorough analysis, I think we have to look at multiple different types of data sets at once. Instead of just exploring one at a time. Unfortunately, I ran into difficulty finding a set that had just enough information to analyze but not so much information that it was “too big of a file” to export.
“Sex Offenders in Chicago Data Set”.
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