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 has a suit at the cleaners. He did not sleep with a pillow.

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

This was a person. She could play cricket.

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

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

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

This was a person. She was a translator.

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

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

This was a person. He had a bunch of cameras. He ate a lot of chinese food.

This was a person. He was homophobic.

This was a person. She played World of Warcraft

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

This was a person. She made her own beer and was kind of stingy with it.

This was a person. She wore Spandex.

This was a person. He survived cancer.

This was a person. He has a suit at the cleaners. He couldn't grow a mustache.

This was a person. He was good at job interviews.

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

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

This was a person. He could play banjo.

This was a person. He was a hoarder.

This was a person. She had a hangover yesterday.

This was a person. She was an expert barbecuer.

This was a person. He knew how to get free satelittle television. He loved museums.

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

This was a person. He was very good at cleaning.

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

This was a person. She occasionally shop lifted.

This was a person. She was working her way through all of the shows on Netflix.

This was a person. She was a vegetarian.

This was a person. She always had gum.

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

This was a person. He was a Mac evangelist.

This was a person. She ate a lot of kale. She always brought reusable bags into the store.

This was a person. He had a green thumb. He had a lot of shoes.

This was a person. She was awaiting judgement for a DUI last month.

This was a person. He was living with his sister. He loved to make out.

This was a person. He was polite to telemarketers.

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

This was a person. She was very good at cleaning.

This was a person. She survived cancer.

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

This was a person. She called in sick to work 9 times last month and was written up at work. She was a drunk.

This was a person. She couldn't grow a mustache.

This was a person. He played soccer every weekend.

This was a person. He had a personal injury lawsuit working its way through the courts. He was afraid of heights.

This was a person. She took careful notes after every meal.

This was a person. He scalped tickets

This was a person. She was a translator. She was kind of addicted to ebay.

This was a person. He got thrown off of a golf course. He played guitar on the streets for money.

This was a person. He was cockeyed.

This was a person. He was kind of addicted to ebay. He was a secret Buddahist.

This was a person. She made pie.

This was a person. She never drank coffee. She ate a lot of chinese food.

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

This was a person. She made pie. She wanted to learn magic.

This was a person. He once robbed a bank.

This was a person. She survived cancer.

This was a person. She played World of Warcraft She once robbed a bank.

This was a person. She just had the bandages removed from plastic surgery. She chewed nicotine gum.

This was a person. She could play harmonica.

This was a person. She knew how to get free satelittle television. She was balding.

This was a person. He constantly wore sunglasses.

This was a person. He had a twin. He was a historian.

This was a person. He was a skilled woodcarver.

This was a person. She just had her car stereo repaired.

This was a person. He was afraid of the water.

This was a person. She looked like a cartoon. She wore a lot of hats.

This was a person. She had a lot of music on vinyl.

This was a person. She wore bow ties. She was an expert barbecuer.

This was a person. He was very careful about conserving gasoline.

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

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

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

This was a person. He was good to people. He had a tiny wine collection.

This was a person. She did not own a fridge.

This was a person. He had just paid a crapload of money to get teeth fixed. He never answered the phone.

This was a person. She has a suit at the cleaners. She talked during movies.

This was a person. She loved Elvis.

This was a person. She never drank coffee.

This was a person. She dropped her work laptop last week, destroyed it and couldn't sleep for days. She could always get tickets.

This was a person. She kept to herself mostly

This was a person. She was left-handed. She got thrown off of a golf course.

This was a person. He loved to make out. He was a mason.

This was a person. He had an unfortunate nickname.

This was a person. He loved pot.

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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