Tableau // Hate Crimes in New York State

Background:

I made this project for the Data Analysis course I was taking at the time. The task was to find a dataset, analyze it, gain insights and visualize them.

The data:

I found on Kaggle a dataset which contained data of hate crimes committed in New York State between 2010-2019; here's a glimpse of it:

The process:

I started out by exploring the data, creating various visualizations and looking for meaningful insights.

Once I compared the anti-Jewish hate crimes to other anti-religion crimes, it became easily apparent that anti-Jewish crimes are extremely high:

Next, I compared anti-Jewish crimes to crimes committed against other minorities (Black, Hispanic, White, Asian, Arab), and again, the difference is huge. Anti-black crimes are also much higher than the rest, but not nearly as high as anti-Jewish crimes:

I could stop here, but I wanted to take into account the size of each group. This was not included in the original dataset, so I had to find it elsewhere. I managed to find data of the religious affiliation of NY residents in 2014. Since the hate crime data is from 2010-2019, it was perfect. I added this data to Tableau in a seperate table, and created the visualization below, which compares side-by-side the religious affiliation of NY residents and the anti-religion crimes committed. This shows the terrible fact that although only 7% of NY population are Jewish, they suffer 84% (!) of all anti-religion crimes.

The dashboard:

I combined the three visualizations above to one final dashboard. Even though I had created a few more visualizations, I decided not to include those, since they didn't contribute as much to the final conclusions.

On visual clutter:

One of the most important things I learned while working on this project, is that often, less is more. In order to focus on the main insights and make them as clear as possible, it is often best to include less visualizations, less color, less labels, less detail. Initially it was difficult for me to remove so many of the colors and labels, because it meant losing data. But once I did, I saw how much clearer the dashboard became - which is not only nice visually, but also makes it much easier to understand. Now I recognize the importance of correct use of color - and neutrals! Removing the visual clutter created by excess color, labels, numbers, lines and legends, is now one of my favorite parts of creating a visualization - I just love how it makes the message stand out.