The Happy Land Fire Tragedy Casualties Visualization Tool - Cockeyed.com

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Disaster Casualties Visualization Tool: The Happy Land Fire: 1990

87 Dead Men and Women

The Happy Land fire was an arson fire that killed 87 people trapped in an unlicensed social club, 1990.

This was a person. He just had the bandages removed from plastic surgery.

This was a person. He can't watch the end of T2 without crying

This was a person. He looked like a cartoon. He was rough and tumble.

This was a person. He was a vegetarian.

This was a person. He was a DJ.

This was a person. He ate a lot of raisins.

This was a person. She kept buying vegetables, even though they always just went bad in her fridge.

This was a person. She was working her way through all of the shows on Netflix. She took careful notes after every meal.

This was a person. She was a Mac evangelist.

This was a person. She spends a lot of time watching tennis.

This was a person. He prefered bread without crusts.

This was a person. He got thrown off of a golf course. He excelled at woodworking.

This was a person. She could play banjo. She looked like a cartoon.

This was a person. She always had a clean car. She never finished the book club book.

This was a person. She spent a lot of time with old people.

This was a person. He never finished the book club book. He ate the food at convenience stores.

This was a person. She ate a lot of hot dogs.

This was a person. He taught guitar.

This was a person. She had tropical fish.

This was a person. He looked like a cartoon.

This was a person. She was a wedding photographer.

This was a person. She had a bunch of cameras.

This was a person. He had a podcast.

This was a person. She was 4 days sober.

This was a person. She had braces on her teeth. She loved Elvis.

This was a person. She wanted to learn magic.

This was a person. He was polite to telemarketers.

This was a person. She could fall asleep anywhere.

This was a person. He had tropical fish.

This was a person. He was a cat person.

This was a person. He led the choir.

This was a person. He was working his way through all of the shows on Netflix. He never carried cash.

This was a person. She made pie. She snored.

This was a person. He was kind of an awesome lover.

This was a person. He was working his way through all of the shows on Netflix. He loved to fish.

This was a person. He prefered bread without crusts.

This was a person. She loved chocolate.

This was a person. She was lactose intolerant.

This was a person. He was a hoarder. He had a green thumb.

This was a person. She always brought reusable bags into the store.

This was a person. He was playing nine games of Words with Friends on his phone.

This was a person. He ate a lot of chinese food.

This was a person. He led the choir. He was obsessed with sitcoms.

This was a person. She grew up on a farm.

This was a person. She smoked pot at music festivals.

This was a person. She had bonsai trees.

This was a person. She always had gum. She once robbed a bank.

This was a person. He was a vegetarian.

This was a person. He slept around.

This was a person. He was a teacher. He just had his car stereo repaired.

This was a person. He dropped his work laptop last week, destroyed it and couldn't sleep for days.

This was a person. She was training to run a marathon.

This was a person. She never ate breakfast. She was good at job interviews.

This was a person. She was learning to use CAD software. She was a tailor.

This was a person. He had been having trouble tying his shoes because he pulled a muscle in his abs.

This was a person. He smoked pot at music festivals.

This was a person. He tutored kids. He was left-handed.

This was a person. He made scrapbooks.

This was a person. He ate a lot of raisins.

This was a person. He was sporty.

This was a person. She always had a clean car.

This was a person. He was a cheapskate.

This was a person. She had a lot of shoes.

This was a person. She got thrown off of a golf course. She was going blind.

This was a person. She was a DJ.

This was a person. He dropped his work laptop last week, destroyed it and couldn't sleep for days. He was a tailor.

This was a person. He was a hoarder.

This was a person. He was good to people.

This was a person. She was afraid of heights.

This was a person. She made pie.

This was a person. He wore a lot of hats.

This was a person. He could play cricket.

This was a person. She ate the food at convenience stores. She had just paid a crapload of money to get teeth fixed.

This was a person. She was polite to telemarketers.

This was a person. She snored.

This was a person. She kept to herself mostly

This was a person. He watched a lot of sports on TV, but never played them himself. He was the richest of his friends.

This was a person. He was sleeping with his ex.

This was a person. He played a lot of social card games.

This was a person. He had a lot of Beatles records.

This was a person. He loved to make out.

This was a person. She was a skilled woodcarver.

This was a person. She had extensions.

This was a person. He was a crossword whiz. He had a lot of music on vinyl.

This was a person. He got out of jail two years ago and never reunited with his family.

This was a person. She did open mike. She could fix anything.

This was a person. She was rebuilding an engine.

View a different tragedy with the Disaster Visualization Tool

Jonestown | The Titanic | Kent State Shootings | My Lai | Lockerbie bombing | Kent State Shootings | Tiananmen Square Massacre | The Banana massacre | The Boyd Massacre | Mountain Meadows massacre | Kfar Etzion Massacre | Sharpeville Massacre | The Miami Showband Massacre | The Novocherkassk massacre | Tokyo Subway Sarin attack | Sinking of the Titanic | The Happy Land Fire | Brooklyn Bridge stampede | 1990 Pilgrim Stampede | Luxor hot air balloon crash |

Mouse-over body descriptions are randomly generated, intended to inject humanity into the raw number of dead. It cannot accurately represent the actual people who died in the tragedy.

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