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. She was awaiting judgement for a DUI last month. She was kind of an awesome lover.

This was a person. She was afraid of flying.

This was a person. He was living with his sister.

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

This was a person. She abused cars.

This was a person. She had an unfortunate nickname.

This was a person. She was a street vendor.

This was a person. She was thrifty.

This was a person. She got thrown off of a golf course. She knew how to get free satelittle television.

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

This was a person. He was cockeyed.

This was a person. He loved to fish.

This was a person. She was divorced.

This was a person. He could play banjo. He was a hoarder.

This was a person. She talked during movies.

This was a person. He was rough and tumble.

This was a person. He looked like a cartoon. He sang at parties.

This was a person. He loved wine.

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

This was a person. She was afraid of flying.

This was a person. She played piano.

This was a person. He taught guitar.

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

This was a person. He loved to fish.

This was a person. She never carried cash.

This was a person. She was a trivia master.

This was a person. He loved Elvis. He was divorced.

This was a person. He was polite to telemarketers.

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. He wrote for the newspaper.

This was a person. She was an expert barbecuer. She had a sailboat as a teen.

This was a person. He looked like a cartoon. He has four overdue books.

This was a person. He loved James Bond movies. He had an awesome home theatre.

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

This was a person. She sang at parties.

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

This was a person. He never drank coffee.

This was a person. He talked during movies. He never raised hisvoice.

This was a person. She snored.

This was a person. He never raised hisvoice. He had bonsai trees.

This was a person. He always had gum.

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

This was a person. She could fix anything.

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

This was a person. She was a DJ.

This was a person. He could fix anything.

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

This was a person. He watched a lot of sports on TV, but never played them himself. He was a cat person.

This was a person. She looked like a cartoon. She was left-handed.

This was a person. She went to the horseraces. She chewed nicotine gum.

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

This was a person. She had a twin. She was learning to speak French.

This was a person. He was a translator. He was an awful driver.

This was a person. She always had a clean car. She looked like a cartoon.

This was a person. She wore a lot of jewelry. She was a cat person.

This was a person. She sang at parties.

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

This was a person. He constantly wore sunglasses.

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

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

This was a person. She knew a lot of cops.

This was a person. He loved wine.

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

This was a person. He had a gun. He always brought lunch.

This was a person. She was a tailor.

This was a person. She was a translator.

This was a person. He had an unfortunate nickname.

This was a person. He was allergic to peanuts. He could play cricket.

This was a person. He lived on breakfast bars.

This was a person. She had a twin. She had been having trouble tying her shoes because she pulled a muscle in her abs.

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

This was a person. She played World of Warcraft

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

This was a person. He was a plumber. He had a bunch of tattoos.

This was a person. He made pie. He was a glassblower.

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

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

This was a person. He was an awful driver.

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

This was a person. She loved pot.

This was a person. She sang at parties.

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

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

This was a person. He occasionally shop lifted.

This was a person. She didn't have a family.

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

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

This was a person. He was an expert barbecuer.

This was a person. She was a scout leader. She abused cars.

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

This was a person. He was allergic to peanuts. He was polite to salesmen.

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

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

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

This was a person. She was a virgin.

This was a person. She had a hangover yesterday.

This was a person. He had long fingernails.

This was a person. He was afraid of heights.

This was a person. He helped people.

This was a person. She could fall asleep anywhere.

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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