Final business project

Final business project

• 1.
  Crime In Denver FinalGroup project
• 2.
  Topic Our motivation forpicking this topic was to be more aware of the occurrence and location of crime in Denver. As Denver residents, we chose to analyze crime in Denver to understand its nature and raise awareness for the benefit of all residents in Denver county.
• 3.
  Data Source We gotour data from the “Open Data Catalog” on the City and County of Denver’s official website. As this data is based on the National Incident Based Reporting System (NIBRS), there was no funding for this data. It seems more of a public good than anything else that this information was provided.
• 4.
  Team members Cole Rister Jakebehnken Alyssa montano
• 5.
  Challenges It is importantto note that crime data become more accurate over time, as new incidents are reported and more information comes to light during investigations. Furthermore, crimes that occurred at least 30 days ago tend to be the most accurate, although records are returned for incidents that happened yesterday. For the sake of completing our project, we will use the data retrieved on December 5, 2021. Regardless, this data is still accurate and the majority (if not all) of the information we will provide seeks to accurately represent crime activity in the Denver metropolitan area.
• 6.
  Challenges Some questions andtopics we hope to answer through this project include discovering which neighborhoods and districts experience the least and the most crime, how many traffic occurrences take place in each neighborhood, how has crime changed throughout the years, and lastly, how much crime takes place within each month.
• 7.
  Challenges This dataset coversseveral key variables-- “Is Crime, Is Traffic, Neighborhood, Geo Longitude, Geo Latitude, Offense Category ID, Offense Type ID, First Occurrence, Reported Date, and District ID”-- that shed light on the overarching narrative of this project: the awareness of crime in Denver. While most of these variables are self-explanatory, there are criteria to variables such as “Offense Category ID” and “Offense Type ID” that need to be distinguished. The main difference between “Offense Category ID” and “Offense Type ID” is that “Category ID” designates the general nature of the crime, whereas the “Type ID” specifies the case and what occurred. For example, based on the “Offense code dataset” (as shown below), when law enforcement reported “damaged-prop-bus,” it was then categorized as “public-disorder.”
• 8.
  Challenges Another variable thatour team found somewhat confusing was the “Is Traffic” measurement. Although the formerly provided “Official definitions for NIBRS crime types” document provided definitions for nearly every crime, they didn’t specify the criteria for what is considered traffic. After looking at the “Offense code dataset,” however, our team understood that the “Is traffic” variable meant any incident OR crime that was committed in a car. For example, according to the dataset, “Traffic accident- DUI-DUID” fell solely under the “Is Traffic” parameter.
• 9.
  Conclusion violent crime hasrisen drastically in Colorado. After interviewing several politicians, crime experts, and law enforcement officials, Burness concluded that this growing statistic is due to “underinvestment in areas proven to help prevent crime in the first place — living wages, behavioral health supports, education, [and] community-building programs.”