Our inspiration comes from looking at how poorly the drug crisis is handled in the United States. Though we have implemented educational programs in schools, deaths caused by drug poisoning from opioid consumption in America has continued to increase. We wanted to analyze these trends and patterns in our data and put them on a single dashboard in order tell a data story on the drug crisis in America.

What it does

The visualization sheds light on how the Opioid Crisis is growing in the United States through three different visuals. The line graph points out deaths caused by overdose over time for the top 10 states with the highest rates. The color coded map shows the most consumed drug in each state. The stacked bar chart looks at drug deaths per race category per state. The deaths can be sorted from highest to lowest. These 3 visuals can help doctors and scientists find common trends among deaths in certain regions or races and can help steer important steps to more robust legislation.

How I built it

The dataset used to create the data visualizations was manipulated using SAS. By using SAS, we were able to take a dataset provided by the CDC and manipulate the data to observe different explanatory variables needed for each trend. We were also able to convert the data into proper formats needed, such as csv, tsv, and json, when needed using SAS.

After we manipulated the data, we then used the D3.JS package along with JavaScript and HTML on VS Code to construct the visualizations by binding svg element with the CDC data. We then coded in interactive features to make the charts more customizable such as zoom, sorting, mouseover, and color coding.

Challenges I ran into

Challenges we ran into were finding more various data sets. With more access to different data sets regarding the issue, our team would have been able to break down other trends based on different variables.

Another challenge was coding some part of the graphs and adding zoom and mouseover elements. Because you can't really take shortcuts when using D3.JS, we had break up the code more and figure out the errors through trial and error.

Accomplishments that I'm proud of

We are proud to have built this project through a culmination of past experiences. Because this was our first hackathon, and first virtual one at that, we are proud to that we were able to quickly adapt to the situation and bring together our different knowledge and skill sets to build this project. This hackathon proved that knowledge gained can always be applicable in the future.

What I learned

We learned a lot regarding the opioid crisis in America– for example, we learned that natural and synthetic opioids are most commonly used nationwide, whilst heroin and methadone are not. Aside from many riveting trends we have uncovered, we also learned a lot about the compatibility of computer science and statistical knowledge. With all team members having knowledge in both fields, we were clearly able to see how the duality aided the process of this project and further understand the importance of both subject matters.

What's next for Analyzing the Rising Opioid Crisis in America

Data visualizations are not only used to make information easier to interpret, but they can also be used to move an audience. In the future, our team would like to add more visualizations of data that could clarify possible underlying issues that correlate with the increase in drug poisoning mortality or show how the increase in mortality rates can effect other nationwide aspects. By doing so, perhaps we can uncover a solution to this nationwide problem or further understand how necessary it is to bring forth attention to this issue.

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