The Banana massacre Tragedy Casualties Visualization Tool - Cockeyed.com

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Disaster Casualties Visualization Tool: The Banana massacre: 1928

100 Dead Men and Women

The Banana massacre was a massacre of striking workers for the United Fruit Company on December 6, 1928 in the town of Ciénaga, Colombia. The number of dead has not been accurately noted.

This was a person. He abused cars.

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

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

This was a person. He loved nachos.

This was a person. He was polite to telemarketers.

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

This was a person. She taught guitar. She was a glassblower.

This was a person. He led the choir.

This was a person. He was lactose intolerant.

This was a person. She was playing nine games of Words with Friends on her phone.

This was a person. He loved pot.

This was a person. She made scrapbooks.

This was a person. He got a terrible haircut a few days ago and shaved his head to escape from it.

This was a person. She had VD.

This was a person. She was a historian. She played soccer every weekend.

This was a person. She never raised hervoice.

This was a person. She could play cricket.

This was a person. He was the richest of his friends.

This was a person. She scalped tickets She wore earphones all the time.

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

This was a person. He was a Mac evangelist. He took a lot of photos.

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

This was a person. He had a hangover yesterday.

This was a person. She always made the mashed potatoes at family get-togethers. She was awaiting judgement for a DUI last month.

This was a person. He was a skilled woodcarver. He wrote for the newspaper.

This was a person. She always had gum. She was cockeyed.

This was a person. He had a bunch of cameras. He could play harmonica.

This was a person. She was a cat person.

This was a person. He taught guitar.

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

This was a person. She chewed gum constantly.

This was a person. He picked up litter.

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

This was a person. She collected figurines.

This was a person. He occasionally shop lifted.

This was a person. She did not sleep with a pillow. She had bonsai trees.

This was a person. He always brought lunch.

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

This was a person. He chewed nicotine gum.

This was a person. He was a pharmacist.

This was a person. He finished reading all of the works of Homer.

This was a person. He was the richest of his friends. He loved oreos.

This was a person. She chewed gum constantly. She had long fingernails.

This was a person. He had VD.

This was a person. He led the choir. He had just paid a crapload of money to get teeth fixed.

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

This was a person. He loved to fish.

This was a person. He was always in a hurry.

This was a person. He was a trivia master. He took careful notes after every meal.

This was a person. She tutored kids.

This was a person. She loved to make out.

This was a person. He was polite to salesmen.

This was a person. She always made the mashed potatoes at family get-togethers.

This was a person. She collected figurines.

This was a person. He was a skilled woodcarver. He was a glassblower.

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

This was a person. She led the choir.

This was a person. He was polite to telemarketers.

This was a person. She had a green thumb. She played World of Warcraft

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

This was a person. He had an unfortunate nickname. He was a cheapskate.

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

This was a person. She had a lot of Beatles records. She had tropical fish.

This was a person. He collected figurines.

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

This was a person. He excelled at woodworking. He liked to argue.

This was a person. He occasionally shop lifted.

This was a person. She was sleeping with her ex.

This was a person. She did open mike.

This was a person. He loved to fish. He ate a lot of fish.

This was a person. She was a hoarder.

This was a person. She was the unofficial relationship guru among friends.

This was a person. She ate a lot of fish.

This was a person. She has four overdue books. She loved MASH.

This was a person. He always wore a hat.

This was a person. She was a drunk.

This was a person. She was a tailor.

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

This was a person. She volunteered at a middle school, because she had a crush on one of the other tutors.

This was a person. She took a lot of photos.

This was a person. She ate a lot of hot dogs. She was very good at cleaning.

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

This was a person. She always brought lunch.

This was a person. She could fix anything.

This was a person. He was a plumber.

This was a person. She always had gum.

This was a person. She was a poet.

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

This was a person. She was a pharmacist. She scalped tickets

This was a person. She was a glassblower.

This was a person. He was a DJ.

This was a person. He loved cigarettes.

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

This was a person. He chewed nicotine gum.

This was a person. She had a personal injury lawsuit working its way through the courts.

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

This was a person. She was polite to salesmen. She wore Spandex.

This was a person. She never finished the book club book.

This was a person. He always wore a hat.

This was a person. She was a skilled woodcarver. She kept to herself mostly

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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