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 played World of Warcraft

This was a person. She ate the food at convenience stores.

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

This was a person. He chewed gum constantly.

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

This was a person. He did not sleep with a pillow.

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

This was a person. She occasionally shop lifted.

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

This was a person. She was the richest of her friends. She wanted to learn magic.

This was a person. He chewed nicotine gum.

This was a person. She taught guitar.

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

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

This was a person. He was allergic to peanuts. He loved wine.

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

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

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

This was a person. She chewed gum constantly.

This was a person. He only ever had sex with one person.

This was a person. He looked like a cartoon.

This was a person. She has a suit at the cleaners. She helped people.

This was a person. He sang. He was a good dancer.

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

This was a person. She ate a lot of fish. She could play trumpet.

This was a person. She was good to people. She wore a lot of black clothing.

This was a person. He excelled at woodworking. He helped the widow next door.

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

This was a person. She had crazy hair colors.

This was a person. She was a drunk.

This was a person. He chewed nicotine gum.

This was a person. She helped people.

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

This was a person. She was an awful driver. She loved to fish.

This was a person. He had extensions.

This was a person. He had VD.

This was a person. She never raised hervoice.

This was a person. He has a suit at the cleaners. He loved Elvis.

This was a person. She could fall asleep anywhere. She threw a lot of parties.

This was a person. He was cockeyed.

This was a person. She made pie.

This was a person. He scalped tickets

This was a person. He could play banjo.

This was a person. She had a gun.

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

This was a person. She led the choir. She smoked pot at music festivals.

This was a person. He was a Mac evangelist.

This was a person. He could always get tickets.

This was a person. He ate the food at convenience stores.

This was a person. He was a tailor.

This was a person. He was afraid of flying. He was a plumber.

This was a person. She was lactose intolerant.

This was a person. She was homophobic.

This was a person. He couldn't grow a mustache. He was a cat person.

This was a person. He has a suit at the cleaners.

This was a person. She was polite to salesmen. She always had a clean car.

This was a person. She didn't have a family. She called in sick to work 9 times last month and was written up at work.

This was a person. She made pie.

This was a person. She played chess.

This was a person. He led the choir.

This was a person. He had tropical fish.

This was a person. He took a lot of photos. He was lactose intolerant.

This was a person. She had crazy hair colors.

This was a person. She was a dog person.

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

This was a person. He had crazy hair colors.

This was a person. He was a tailor.

This was a person. He had a hangover yesterday.

This was a person. He had extensions. He plays fantasy football.

This was a person. He survived cancer.

This was a person. She played soccer every weekend. She knew a lot of cops.

This was a person. He constantly wore sunglasses.

This was a person. She was a wedding photographer.

This was a person. He wore Spandex.

This was a person. She was a scout leader. She played chess.

This was a person. He was a street vendor.

This was a person. He could play harmonica.

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

This was a person. She had crazy hair colors.

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

This was a person. He loved wine.

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

This was a person. She wanted to learn magic. She loved chocolate.

This was a person. She was a Mac evangelist.

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

This was a person. He was a virgin. He was a DJ.

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

This was a person. She wrote for the newspaper.

This was a person. He volunteered at a middle school, because he had a crush on one of the other tutors. He was a historian.

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

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

This was a person. She loved to fish. She constantly wore sunglasses.

This was a person. She was a crossword whiz.

This was a person. She has four overdue books.

This was a person. She did not sleep with a pillow.

This was a person. He was afraid of heights.

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

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

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

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

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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