Fire data isn’t ugly Presenting fire data effectively series Episode: Table it

July 2015

A makeover of fire department data to transform it from unclear and underperforming to powerfully informative.

Traffic accident

Ok, so it’s technically not fire data here but I came across this report and I am a crazy person who redoes charts in my spare time couldn’t pass up the opportunity to cover tables. Tables aren’t technically a chart, I know, but they are a form of data visualization. And I love data viz in all its forms!

Data visualization is the presentation of data in a pictorial or graphical format, produced in an effort to help people understand the data by placing it in a visual context. It’s been around for at least several millennia (and possibly a million years older than that) so it’s not some newfangled idea. It just has newer forms.

Exhibit: Fatal Table Traffic accidents with cell phones

Year Total Fatal Injury Deaths Injured2008 392 4 140 4 1932009 415 3 143 3 1952010 519 6 173 6 2602011 480 3 171 3 2282012 523 6 183 6 2622013 471 6 148 7 209Total 2,800 28 958 29 1,347

Accidents People

Year Total Fatal Injury Deaths Injured2008 392 4 140 4 1932009 415 3 143 3 1952010 519 6 173 6 2602011 480 3 171 3 2282012 523 6 183 6 2622013 471 6 148 7 209Total 2,800 28 958 29 1,347

Accidents People

First, I get that the data here is about traffic accidents where a driver was using a cell phone, but I think we all know what I’m going to do with that clipart. (Delete it.)

If I had a picture of a wreck caused by a cell phone I would definitely use it here.

Spare tables Tables aren’t the most glamorous

Tables aren’t the most glamorous data viz but when you need to convey a lot of information in many different categories, tables do the job. They can do the job without all the colors, though.

Year Total Fatal Injury Deaths Injured2008 392 4 140 4 1932009 415 3 143 3 1952010 519 6 173 6 2602011 480 3 171 3 2282012 523 6 183 6 2622013 471 6 148 7 209Total 2,800 28 958 29 1,347

Accidents People

We don’t need the bold gridlines separating different categories.

We also don’t need bolding on the headers. Bolding doesn’t print very well and can strain eyes with letters bleeding together.

Year Total Fatal Injury Deaths Injured2008 392 4 140 4 1932009 415 3 143 3 1952010 519 6 173 6 2602011 480 3 171 3 2282012 523 6 183 6 2622013 471 6 148 7 209Total 2,800 28 958 29 1,347

Accidents People

If we were working with 20 years, I’d probably put a (really) light grey shading between rows to help readers. We can get away without it here because our table is pretty short.

You can choose to shade anything, but eyes are sensitive to shading and don’t need a huge contrast for this.

Year Total Fatal Injury Deaths Injured2008 392 4 140 4 1932009 415 3 143 3 1952010 519 6 173 6 2602011 480 3 171 3 2282012 523 6 183 6 2622013 471 6 148 7 209Total 2,800 28 958 29 1,347

Accidents People

It took me a bit to realize why they were separating accidents from people. More people can die or be injured than there are accidents. The same is true for fire casualties where one fire may injure multiple people.

Instead of separating that out, I changed the column titles.

Year Accidents Fatal Accidents Injury Accidents Deaths Injuries2008 392 4 140 4 1932009 415 3 143 3 1952010 519 6 173 6 2602011 480 3 171 3 2282012 523 6 183 6 2622013 471 6 148 7 209Total 2,800 28 958 29 1,347

We’re sensitive to proximity. If you have similar data like fatalities and fatal accidents, put them together. This helps reinforce the idea of multiple fatalities as well.

Year Accidents Fatal Accidents Deaths Injury Accidents Injuries2008 392 4 4 140 1932009 415 3 3 143 1952010 519 6 6 173 2602011 480 3 3 171 2282012 523 6 6 183 2622013 471 6 7 148 209Total 2,800 28 29 958 1,347

I moved Accidents to the end so now the totals are in ascending order. (And if we’re being honest, fatalities are the main interest for most readers.)

I’ve also removed the header “Year” because it’s obvious, and right aligned the titles to match each column (barely noticeable here).

Fatal Accidents Deaths Injury Accidents Injuries Accidents2008 4 4 140 193 3922009 3 3 143 195 4152010 6 6 173 260 5192011 3 3 171 228 4802012 6 6 183 262 5232013 6 7 148 209 471Total 28 29 958 1,347 2,800

More subtle changes: I’ve added some white space between the column titles and after 2013 to separate totals. I did this by simply adjusting the row height.

I’ve also shifted the columns, trying to find that sweet spot of “close” but not “crowding.”

Fatal Accidents Deaths Injury Accidents Injuries Accidents

2008 4 4 140 193 3922009 3 3 143 195 4152010 6 6 173 260 5192011 3 3 171 228 4802012 6 6 183 262 5232013 6 7 148 209 471

Total 28 29 958 1,347 2,800

We’re fine here but if you want to emphasize something, you can using a bit of color. I picked 2013 because that was the year this report was featuring.

Fatal Accidents Deaths Injury Accidents Injuries Accidents

2008 4 4 140 193 3922009 3 3 143 195 4152010 6 6 173 260 5192011 3 3 171 228 4802012 6 6 183 262 5232013 6 7 148 209 471

Total 28 29 958 1,347 2,800

Remove to Improve Tables aren’t charts but the same principles apply

Before

After

Year Total Fatal Injury Deaths Injured2008 392 4 140 4 1932009 415 3 143 3 1952010 519 6 173 6 2602011 480 3 171 3 2282012 523 6 183 6 2622013 471 6 148 7 209Total 2,800 28 958 29 1,347

Accidents People

Fatal Accidents Deaths Injury Accidents Injuries Accidents

2008 4 4 140 193 3922009 3 3 143 195 4152010 6 6 173 260 5192011 3 3 171 228 4802012 6 6 183 262 5232013 6 7 148 209 471

Total 28 29 958 1,347 2,800

Clean is stylish Keep your tables just as clean as your Charts. Your printer ink bill will thank you, too.

Hello! I’m Sara Wood and I love converting fire service members into

NFIRS operatives. I’m the State NFIRS program manager for Kansas and

enjoy providing classes to help bring fire departments into the era of data

driven decisions. If you need help creating a presentation or analyzing

your data, I’d love to hear from you!