Crime, DMV.
Crime in the D.C., Maryland, and Virginia region.
by Kritika Agarwal, Ramsay Farah and Shwetha Sanjeev
The D.C., Maryland, and Virginia region is notorious for crime — at least historically. In this project, we wanted to use a data-driven approach to see if this is still true in 2022. To do this, we created 6 different visualizations. In this article, we present these visualizations and our findings from them.
The video demo can be found here:
Single variable data: Locations of Crimes
We are able to draw a few fundamental conclusions from this visualization. Streets, shops, and restaurants are the places where crimes occur most frequently. This graph served as a proof-of-concept for our other visualizations, demonstrating to us how well we could draw conclusions from the data we had gathered. We observed that certain fields had the labels “other” and “unknown.” Following further investigation into the missing data, we came to the conclusion that, given the nature of crimes, certain data points might be overlooked or lost.
Multi-variable data: Top 3 Crimes by Year
Understanding the links between two or more variables can be aided by visualizing them. When exploring data, it is beneficial to build your knowledge systematically and step by step so that interactions between plotted variables are less obscured and the true impacts of the data are more apparent.
Even the most basic of our multivariate graphs provided us with some insightful data. It is evident from our statistics that larceny has experienced the highest number of incidents over the seven years for which we have information. Interestingly, towards the beginning of the pandemic, larceny offenses stayed at a somewhat typical level while the number of drug and assault crimes dropped significantly.
Comparison of Yearly and Monthly Crime Rate Across DMV
We learned something intriguing from this visualization that we would not have learned from a cursory glance alone. The dataset that is being utilized for all of the visualizations’ data collection began in July 2016. According to the data in the graph, all years are included besides 2016, the year when collection started. Crime has begun to gradually decline. The drop was significantly more pronounced during the beginning of COVID, but otherwise, the rate of drop is standard and roughly equal.
Crimes by the Hour
This visualization was especially helpful and interesting to us. It was helpful because it showed just how granular we were able to be when it came to visualizing the data. Two obvious conclusions can be drawn using the visualization. Firstly, at about midnight, crime is at its peak across all 7 years. Secondly, there is another spike in crime from 12–1pm. The data alone does not give enough context for the reasons for these various spikes in crimes. More research needs to be done to be able to come to a meaningful conclusion.
Advanced Visualizations
There are a variety of factors that can affect how people perceive a visualization. The perception of the visual is impacted not just by which plot type is chosen but also by the order in which variables are plotted, different aesthetic choices like color and symbols, and the range of values shown on each axis. Additionally, Advanced Data Visualizations allow for interactive data visualizations with multiple-dimensional views, animation, and auto-focus to make information easier to understand. This fills the gap when 2D graphics alone do not effectively display data or take longer for viewers to comprehend it.
Number of Crimes per Year
Most notably, some areas have a higher incidence of crime than others. Silver Spring specifically has the highest level of crime by a wide margin. This is most likely due to its proximity to Washington, DC, and other high-risk areas. Additionally, as can be seen through some of our other visualizations, it is interesting to see that during the COVID pandemic, crime had fallen. Another noteworthy observation is how the area encompassing Silver Spring is so small in comparison to the zip codes around it, yet it has significantly more crime than those zip codes do. In our design process, engaging the user with interesting interactions was the goal. Being able to see a granular view into the anatomy of Maryland crime allowed the findings from this choropleth to shine through.
Number of Marijuana Crimes per Year
Originally, the idea for this choropleth was to examine the most common drug-related crimes within Maryland; however, Marijuana was by far the highest in each area. Because of this, a pivot was performed to focus specifically on marijuana-related crimes. The most marijuana-related crimes are concentrated in a few areas. Silver Spring has the highest number of incidents within Maryland. Unsurprisingly, during the COVID pandemic, the number of crimes went down. We also thought that, with the increasing number of states both decriminalizing and legalizing marijuana, this would be a factor in the lessening of these crimes. This is not the case; after some further research into the subject, we found that marijuana will only be legal as of June 1, 2023.
