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 has a suit at the cleaners.

This was a person. She had crazy hair colors.

This was a person. She was a wedding photographer.

This was a person. He had bonsai trees.

This was a person. She played a lot of social card games. She has four overdue books.

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

This was a person. He could play banjo.

This was a person. She spends a lot of time watching tennis. She smoked pot at music festivals.

This was a person. He had a hangover yesterday.

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

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

This was a person. She was a hoarder.

This was a person. She went to the horseraces.

This was a person. He was an awful driver.

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

This was a person. He had bonsai trees.

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

This was a person. She was sporty.

This was a person. She wrote for the newspaper.

This was a person. He wanted to learn magic. He ate a lot of hot dogs.

This was a person. She never drank coffee.

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

This was a person. He sang at parties.

This was a person. He loved to make out.

This was a person. He loved cigarettes. He was sporty.

This was a person. He was a Mac evangelist.

This was a person. He wore bow ties.

This was a person. He was very good at cleaning. He played a lot of social card games.

This was a person. She slept around.

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

This was a person. He has four overdue books.

This was a person. He was sleeping with his ex. He loved to make out.

This was a person. She was the richest of her friends.

This was a person. He loved oreos.

This was a person. She was a dog person.

This was a person. He was afraid of heights.

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

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

This was a person. She was working her way through all of the shows on Netflix. She just had her car stereo repaired.

This was a person. She had just paid a crapload of money to get teeth fixed.

This was a person. He was a street vendor.

This was a person. He was a hoarder.

This was a person. He was a crossword whiz.

This was a person. He played soccer every weekend. He was afraid of heights.

This was a person. He wrote for the newspaper.

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

This was a person. She was a street vendor.

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

This was a person. She had a green thumb.

This was a person. He had a gun.

This was a person. He could play harmonica.

This was a person. He had a personal injury lawsuit working its way through the courts. He was an awful driver.

This was a person. She kept to herself mostly

This was a person. She loved fine dining.

This was a person. She had a gun. She threw a lot of parties.

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

This was a person. He taught guitar.

This was a person. She had a green thumb.

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

This was a person. He never carried cash.

This was a person. He could play cricket.

This was a person. He was a tailor.

This was a person. He loved James Bond movies.

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

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

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

This was a person. He wore Spandex.

This was a person. She had a twin.

This was a person. She was rebuilding an engine. She had an awesome home theatre.

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

This was a person. She loved chocolate.

This was a person. She had braces on her teeth. She watched a lot of sports on TV, but never played them herself.

This was a person. He was a poet.

This was a person. She had tropical fish.

This was a person. She went to church.

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

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

This was a person. She was rebuilding an engine.

This was a person. He was a pharmacist.

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

This was a person. She always bought flowers for her desk.

This was a person. He played piano.

This was a person. He had an unfortunate nickname.

This was a person. She was a DJ.

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

This was a person. He sang.

This was a person. She was a mason.

This was a person. She was the richest of her friends.

This was a person. She did open mike. She was kind of addicted to ebay.

This was a person. He picked up litter. He was balding.

This was a person. He overtipped.

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

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

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

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

This was a person. He made scrapbooks.

This was a person. She had an awesome home theatre. She lived on breakfast bars.

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

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

This was a person. She wrote for the newspaper.

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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