Author: tagilbe

CODE 123: FINAL PROJECT

Data set profile: City of St. Louis Metropolitan Police Department Hate Crime Statistics for 2020

The Metropolitan Police Department acts as the contributor to this data set. There is no specified data set creator or contributor designated to create or contribute to the Metropolitan Police Department Hate Crime Data Set of 2020. I do believe it could be possible that the Department may have a statistical crime analyzer or some sort who may curate this data. Although the sources of their data aren’t extremely clear, the sources of their data could be any crime cases that have a motivation based strictly on a specified identity of the victim. They possibly could also have a predetermined case file dedicated to hate crimes. Once again, a clear reason for creating or compiling this data set is uncertain. I believe that they created this data set because they wanted to reflect on the hate crimes within the St. Louis Region in 2020. The data set doesn’t describe any specific uses, I believe that it possibly has been used to address and document crime cases that have a motivation based strictly on a specified identity of the victim.

The structure of the data set is in an Excel file formatted in CSV. The reference guide provided on the website states 23 columns include; complaint number, occurrence, both the victim(s) and the offender(s) age(18 years of age or older), total number of both victims and offenders, offender race code, location, motivation, and victim type. The effect that it may have on pertaining on how the data can be used may be to address the common motivations behind hate crimes or even the more common areas where hate crimes occur. I believe that without the reference guide it would have been difficult to understand the structure and the fields that are within the file. Unfortunately, there isn’t a creator’s description of the data set. To find any possible specific ways in which the creator described their choices would be through the reference guide. A question I would ask the creator based on the data set and the reference guide: Why their duplicated columns with similar codes? For example, columns S through W all are based on motivation that have a motivation code and description that mean the same things. Why would that be beneficial? Without knowing the creator, I could imagine that their goal in creating this data set would be to provide a statistical analysis of hate crime data. This may intentionally shape how the City of St. Louis Metropolitan Department views hate crimes. I would use this data to bring about awareness of what exactly a hate crime is and educate the public on how to notify the police when a hate crime incident occurs so that more data can be contributed. This data helps me understand the complexity of MOBOT and its surroundings because it demonstrates how important justice truly is not only to victims of crime but within the community. As we help MOBOT work toward reparative justice one of the biggest ways I believe we can do this is by educating the public and getting accessible learning resources available.

The person I interviewed happened to be a friend of mine, at first it was kind of awkward being so formal with them when our relationship consists of more casual conversations. Once the interview had a nice flow, I began to feel more comfortable and less awkward. My group formulated two questions that focused on how much time the interviewee devotes their time to studying “science-based subjects” over “history-based subjects”, I believe this question could’ve been asked differently. Making this question less focused on particular subjects could have allowed the interviewee to feel more pressured to think only about history-based and or science-based subjects they study. Depending on the interviewee’s major and schedule they may or may not take or study for either of these subjects. When crafting questions I thoroughly analyzed our quantitative data to come up with questions that I thought provided complementing qualitative data that created a deeper understanding of our numerical data. Previously, in my Human Communications and Disorders class, I interviewed a family member whose son has a speech disorder. For that interview, I just had an audio recording of their responses along with the questions I asked and referred back to them in the essay I wrote. With my partial experience in transcription, this experience wasn’t that much different. I often replayed or paused the audio recording after so many words and or sentences for better accuracy of what my interviewee was saying. I did notice that during the transcribing experience, I began to formulate further questions about their responses that are both related and non-related to studying habits.

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