2. Topic
Our motivation for picking 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 got our 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.
5. Challenges
It is important to 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 and topics 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 covers several 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 that our 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 has risen 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.”
