Are you likely to be attacked by a bear?
By: Vanna Quon
Tags: Data Visualization, Tableau Public, Udacity Nanodegree

My Viz Improvement Journey

The problem statement for the visualization below is “In which park and when, are you most likely to be killed by a bear?”

Data collected, prepared, and distributed by Ali Sanne on data.world | Visualization by Zachary Crockett, Vox

Naturally, I was expecting the visualization to have locations of parks and what time of the year bears are more likely to attack. When I didn’t find that information, I clicked on the source article and read through the story. Here is my process to creating a new dashboard:

Download the source data set

The data set was included on data.world, which was conveniently found next to the source visualization on MakeoverMonday. After looking at the preview to see what data was included, I went ahead and downloaded the file.

2. Explore / Clean up data in Excel

The first thing I did was open the data set in Excel to see the raw data. I first scrolled through to get familiar with the column headers and see what all was included. I was looking for any inconsistencies in the data such as missing/blank fields, cell formats (i.e. was the field set on general, text, or date format), extra spaces, and spelling errors. Right away, I noticed some cells under the ‘Location’ column was written as “city, state” while others were written as “near city, state”. I found all the cells with “near” before the location and hit replace with nothing filled in (essentially deleting all instances of near). Next, I changed the ‘Date’ column to date format instead of leaving it at general. Lastly, I removed any extra spaces I could find and ran spell check. A few things to note:

Some fields were left blank, which I assume was selection bias during data collection. It seems that these fields are missing completely at random.

Some fields under the ‘Age’ column contained decimals such as 0.41666 or 0.83333 which indicate some limitations in processing the data. (Luckily age was not a key factor to answering my problem statement — more details in next section)

Locations format was not standardized — some fields had names of national parks, others had city name and state, or a variation of both.

3. Define problem statement and story outline

After scrolling through the data set, I thought about a few questions the data could indicate trends for. The key takeaways I got from the data was location of fatal bear attack, type of bear, and date of attack . I also referenced the original problem statement as I thought Zachary’s story on Vox was very well written and fitting of the data, but the titles weren’t as descriptive of the data as it could be (the visualization seen above was missing the location of the park). Taking what I learned about the data and my thoughts from Zachary’s story, the problem statement I decided on was “Where are you most likely to be attacked by a bear in North America?”

At this point, I had the problem statement defined and I knew the main pieces of data so all I needed to do was fit it into a story sequence. I laid the foundation of the problem statement by starting out with how many overall fatal bear attacks and in which years. Then I moved into the climax of the story by pinpointing the location in which most of the attacks happened. Lastly I wrapped up the story with what type of bear was involved in the attack.

4. Connect to data in Tableau

I was ready to connect the data in Tableau to start playing around with visualizations. However, Tableau repeatedly gave me an error saying I didn’t have permission to access the file. My quick workaround was to copy all the cells with data in the original file, paste it in a new Excel file, and save it under another name. It worked.

5. Create individual sheets and summary tiles

Because I had the problem statement defined and story outlined on paper, I had a rough idea of what visualizations I wanted to create to mold a better story. Here’s the order in which I created the visuals:

Bar chart : To set the stage for my problem statement, I wanted to show how many fatal attacks occurred per month between the years of 1900–2018. This is similar to Zachary’s bar chart, but I decided to take a more simplistic design approach for clarity.

: To set the stage for my problem statement, I wanted to show how many fatal attacks occurred per month between the years of 1900–2018. This is similar to Zachary’s bar chart, but I decided to take a more simplistic design approach for clarity. Map : To give a clear visual linking back to the problem statement and story. This is also similar to the map in Zachary’s story, but I simplified it to highlight the states with the most fatal bear attacks rather than highlighting parks specifically (wanted to leverage more of the raw data for location).

: To give a clear visual linking back to the problem statement and story. This is also similar to the map in Zachary’s story, but I simplified it to highlight the states with the most fatal bear attacks rather than highlighting parks specifically (wanted to leverage more of the raw data for location). Stacked bar chart : To give a visual comparison of the types of bears involved in the fatal attacks.

: To give a visual comparison of the types of bears involved in the fatal attacks. Tree map : To visually represent how many deaths per year between 1900–2018.

: To visually represent how many deaths per year between 1900–2018. Highlight table : To better show what months had the most grizzly bear attacks.

: To better show what months had the most grizzly bear attacks. Horizontal bar chart : To show the top 10 most likely to be attacked by a bear locations.

: To show the top 10 most likely to be attacked by a bear locations. Summary tiles: While I wasn’t entirely sure whether I would use these, I created a few to accompany my story.

6. Put together dashboard using column layout

Now that I had the individual sheets created, all that was left was how to put it all together in a dashboard. I started sketching some layouts on paper before dragging and dropping everything in a Tableau dashboard. My first layout was the typical four quadrant layout. This layout helped me think through my story, but I didn’t like the order in which it would display my visuals so I went back to sketching. I really liked the idea of the golden ratio layout so I started piecing my visuals into that layout, but I found the smaller “grids” harder to fill. I went back to sketching again and thought about my story as if it was a narrative. If I were to put this in a PowerPoint deck, how would I present the sequence of visuals? I decided to keep it simple and went with the three column layout.

Here’s what my quick sketches looked like…

My quadrant layout sketch

Sketch of golden ratio layout

Final sketch — 3 column layout

7. Final Product

The final dashboard I created is unique in that it can standalone to tell a story based on data. While Zachary’s story on Vox was very well-written with nice graphical visualizations, I found it hard to follow along with the alternating text and visuals. What I’ve done is distill the message in the text and include it as visualizations in a dashboard, utilizing the chart titles to summarize the story.

A screenshot of the final product can be found at the end of the article. Also providing the link to the Tableau Public page if you’re curious to interact with it.

8. A note about the data

As I went through this data set, I had very mixed feelings about whether I should use it or not because the data was almost sporadic. What I mean by that is the data was collected and organized by someone based on data written to a Wikipedia page. Based on the citations from the Wikipedia page, it looks like most of the data was collected from newspaper articles, publications, or other news outlets. Selection bias is at play here as we are only taking into account the fatal bear attacks based on published stories that someone decided to write about. A more accurate/reliable way to get a hold of fatal bear attacks may be from hospital records.

Another thing I noticed was there were descriptions of the fatal bear attacks. For those attacks involving two or more people, the descriptions were the same for all, which may or may not be accurate. If I were to include descriptions of attacks in my data story, I would have had to normalize the data somehow.

In conclusion, I believe the data collection process can be improved to lessen the limitations and biases in our processing. This would produce a more accurate data story to the fatality of bear attacks.