How does socioeconomic status affect crime in Chicago?

Redesigning a dashboard to learn about the relationship between crime and socioeconomically disadvantaged areas.

The original dashboard aimed to highlight the geographical distribution of the crimes in Chicago and was trying to see if more crimes were committed in regions that are socioeconomically disadvantaged. It highlighted the worst neighborhoods as well as some basic metrics about the different types of crimes.

The old dashboard annotated with graph numbers

Critique

• Chart 2 doesn’t show any information that is particularly valuable or provides more information about the story because we have chart 3.
• Chart 5 is mainly highlighting the top 7 crime types which chart 4 does as well.
• The information in chart 5 is not important for learning about how the crime impacts Chicago especially because it shows that each type has stayed at relatively similar levels and stayed the same relative to each other.
• Chart 1 highlights useful information that is mentioned in the writing and supports the argument, but the bars are an ineffective mark for the purpose of this graph which is more to highlight the worst 5 neighborhoods which are then marked on the graph.
• Chart 3 shows that there is not really a correlation and results in the conclusion being very basic and lacking nuance, telling an incomplete story.
• The text block is also very large although it does provide valuable information.

The Redesign

Final redesign with interactive scatter+bar chart

The redesign focuses on conveying a clear message with simple text support and an interactive graph to improve user engagement.

• The arrest to non arrest ratio chart for the top 7 crimes was carried over from the old design with some adjustments.
  - Changed color scheme to suit new design.
  - Removed Y axis title to simplify.
  - Adjusted title to be more readable and understandable.
• The crimes scatter plot was simplified and given interactivity with the bar graph below.
  - Removed the choropleth which only highlighted the three worst neighborhoods because it did not contribute to the story.
  - The scatterplot uses gold for the Theft crimes to distinguish them on the map as the most prevalent crime type.
  - The chart can be brushed to show the distribution of crime types in the selected region to highlight the regionality of crimes on a bigger scale.

Painfully low resolution gif that shows the interactivity of the main visualization on the dashboard, enabling users to see the distribution of crime types in selected areas to provide a better understanding of crime in the Chicago area.

• Saved space on the right for text to allow it to be readable and fit on the page nicely.
• Used a multi shade pink/purple for the different sections to make the main graphic pop.

My Takeaways

My main three takeaways from INFO 4602 — Information Visualization have been simplicity, having a clear story/message and prototyping.

When coming into this course I had been excited to create elaborate visualizations which would look cool and be exciting to view. While I still recognize the importance of having a strong emotional appeal in the visualization, the most important thing I have learnt is that simplicity is essential when trying to actually communicate information. Despite how cool many of the animated visualizations look, they might not convey the message as powerfully simply because it pulls the users attention away from the main idea. When building these dashboards and visualizations, simplicity is essential to not overwhelm the user and that was a big part of my redesign. I removed the redundant/unnecessary charts from the original design to draw more attention to the main message of the dashboard. I want the user to interact with the chart to discover the distribution of violent vs non violent crimes and not be distracted by other charts which show irrelevant supplementary data. There is too much data in the world and we need to be conscious about what we are choosing to highlight to ensure that we can convey our message.

Going along with the simplicity, I found out the importance of having a clear message for a dashboard in mind in order to help keep them readable and simple. Because of how much data is available to us, it is too easy to get carried away with wanting to visualize and including each and every graph that is produced. However, having a clear message that is trying to be conveyed for a single chart or a dashboard makes the project more cohesive and easy to follow. This made doing things like changing the color of the theft plots as well as reducing the opacity of the others good choices to really highlight how theft thrives outside of those areas. I think that this also allows users that are engaging well to consider how battery, which is not THAT far behind Theft, is distributed and would find it primarily in the disadvantaged regions.

The final takeaway I had for this course was implementing the process of prototpying in my design projects. I was able to find much greater amounts of nuance in my designs by prototyping and iteratively working through my designs to improve them. I feel like the process of prototyping and getting the input from users is super important because ultimately even if I can understand the visualizations I make, it means nothing if the rest of the world cannot parse the message in the charts. For this redesign, I made sure to show my friends the charts/dashboard at various points and asked them to tell me what they thought and where they were struggling to follow the message which allowed me to improve the design and create a better final product. In the real world where lots of people (hopefully) will be looking at my work, prototyping is a key way to ensure that I can maintain quality work that people appreciate.