Fireworks: Fun & Dangerous

Judy Chang, Gary Burnett, Andrew Mikofalvy

We chose to use the National Electronic Injury Surveillance System (NEISS) as our dataset, accumulating the injury reports from 2009 to 2014.  The data logs all injuries related to consumer products reported by a probability sample of hospitals across the country. We filtered the dataset to only look at injuries caused by fireworks. We want to tell this story because we want to raise awareness about the dangers of using fireworks. Our audience is consumers who may purchase fireworks to celebrate holidays, such as July 4th.

We only looked at fireworks-related injuries, and we counted the number of records by the body part injured via Tableau. Our goal was to see which parts of the body are most commonly injured by fireworks, and we found:

Total
Hand 329
Eyeball 260
Finger 201
Face 177
Foot 62
Trunk, upper 61
Leg, upper 49
Ear 49
Leg, lower 45
Arm, lower 35
Trunk, lower 29
>50% body 29
Ankle 27
Head 27
Neck 27
Mouth 16
Wrist 15
Knee 15
Arm, upper 13
Shoulder 9
Pubic region 9
Toe 9
Elbow 8
Not recorded 3
Internal 1
25-50% of body 1

The most common injuries are in the face, fingers, eyeballs, and hands. We wanted to demonstrate the gravity of these injuries by highlighting these body parts on the human body. We noticed there are roughly 4 clusters for the number of injuries: 0-10, 10-30, 30-100, and more than 100. Our data sculpture is hence a mannequin, where we painted each body part with the shade of red that corresponds to the number of injuries. We used yellow strings on the mannequin to demonstrate the boundaries of the body parts recorded in the dataset.

We also detached the hands to further illustrate the by-far most injured part of the body. The hands of the mannequin are also holding a firework, to show the audience the “source” of the red paint, and a stop sign, that warns the audience that fireworks cause at least 1500 injuries every year and to use caution when they use fireworks.

IMG_4365

Our dataset is only a subset of all fireworks related injuries; however, the number of injuries by body part is representative of the fireworks related injuries nationwide.

Activity Log: Feb 8, 2016

I chose yesterday, Monday February 8th, for the log because it’s my day with the most activities over the weekend. In chronological order,

  • Worked out
    • Number of steps, distance, pace – iPhone
    • List of exercises – my own log
  • Organized Gmail and Calendar
    • When I’m available this coming Sat/Sun – answered a WhenIsGood
    • My weekly schedule – added time commitments on Google Calendar
  • Youtube and Netflix surfing
    • What I watched – Youtube/Netflix uses my history to make recommendations
  • Lyft’ed to the Hawthorne
    • Where I was and where I’m going
  • Snapchatted a bunch
    • How many pictures I took and who I’ve sent them to
  • Bought some drinks
    • What,when, and how much I bought
  • Uber’ed back to Apartment
    • Where I was and where I’m going
  • Venmo’d friend for Uber
    • Who and how much I charged
  • Uber’ed to House of Blues
    • Where I was and where I’m going
  • House of Blues Staff scanned my concert ticket
    • That I attended the concert and when I entered the venue
  • Bought some more drinks 
    • What, when, and how much I bought
  • Snapchatted even more
    • How many pictures I took and who I’ve sent them to
  • Uber’ed back to Apartment
    • Where I was and where I’m going
  • Venmo’d friend for concert and Uber
    • Who and how much I charged
  • Called parents on Skype
    • Who, how long, and when I made the call
  • Throughout the day: Facebook surfing
    • Who/when/where/what of posts I liked, videos I watched, posts I’ve seen, and comments I’ve made
  • Throughout the day: Texting
    • Who/when/what I texted
  • Throughout the day: Listening to Spotify
    • Who/when/what music I like, dislike, follow, and etc.
  • Throughout the day: iPhone Walker
    • Where/how much/when/how fast I walked from place to place

2015 Year in Music

At the beginning of each year, Spotify refreshes its “Year in Music,” where it ranks the top artists and albums of the year and also publishes interesting statistics, such as how many tracks and how much time we’ve listened to music this year. The content can be the sum of all Spotify users, but can also be customized specifically to the user him/herself. Below shows an example screenshot, where Spotify totals that the world listened to 21 million different artists in 2015.

changycj_yearinmusic

Spotify 2015 Year in Music

On the website, the user can scroll through multiple panels, each of a different statistic. As the user browses through the website, different songs will play depending on the context. For example, on the Top Tracks panel, the song “Lean On” by Major Lazer plays, as it is the top track in 2015, according to Spotify.

Spotify publishes this content for its users to summarize global trends and also expose music habits and preferences the user him/herself may not know about. Overall, I think the statistics are very interesting, as they are very relatable, but I thought Spotify could have made it even better by having better visualization of these data. For example, a line chart could be drawn for the number of tracks we’ve listened to over years, instead of simply a line of text saying that we’ve listened to “167,493 more than last year.” Similarly, pie charts could be constructed comparing the top genres/artists across seasons, instead of separate panels with one-liners, “We loved Ellie Goulding, Wiz Khalifa, and Major Lazer this Spring” or “We finished the year strong with a lot of Justin Bieber, The Weeknd, and Drake.”

With these visual changes, I think Spotify can make its Year In Music incredibly relatable and interesting for its users, as they could explore their own taste but also compare it to the rest of the world’s.