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 a glassblower.

This was a person. She led the choir.

This was a person. She was lactose intolerant.

This was a person. He always brought lunch.

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

This was a person. She could fall asleep anywhere. She was the richest of her friends.

This was a person. He loved Elvis.

This was a person. She wanted to learn magic.

This was a person. He was lactose intolerant.

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

This was a person. She could always get tickets.

This was a person. He loved pot.

This was a person. He had a twin.

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

This was a person. He was afraid of flying.

This was a person. She didn't get along with animals.

This was a person. She always brought lunch.

This was a person. She was rough and tumble.

This was a person. She looked like a cartoon.

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

This was a person. She was a trivia master.

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

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

This was a person. He talked during movies.

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

This was a person. He ate a lot of chinese food. He did not own a fridge.

This was a person. He went to church.

This was a person. She loved oreos.

This was a person. She loved oreos. She was gluten intolerant.

This was a person. She did open mike. She talked during movies.

This was a person. She was a trivia master.

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

This was a person. He went to church. He never carried cash.

This was a person. He went to church. He was rebuilding an engine.

This was a person. She was a cheapskate.

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

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

This was a person. He was polite to telemarketers.

This was a person. She loved oreos.

This was a person. She had a twin.

This was a person. He loved chocolate.

This was a person. She was a DJ.

This was a person. He was divorced. He could juggle.

This was a person. She wore Spandex.

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 a tailor.

This was a person. He was left-handed.

This was a person. She went to the horseraces.

This was a person. He prefered bread without crusts.

This was a person. She was rebuilding an engine.

This was a person. He was gluten intolerant.

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

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

This was a person. She had a podcast.

This was a person. She was a mason. She had a bunch of tattoos.

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

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

This was a person. He never drank coffee.

This was a person. She was lactose intolerant. She loved wine.

This was a person. He got thrown off of a golf course. He loved MASH.

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

This was a person. He was a vegetarian. He was a glassblower.

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

This was a person. She helped strangers.

This was a person. She was the richest of her friends. She had a bunch of cameras.

This was a person. He was a hoarder. He was cockeyed.

This was a person. He scalped tickets

This was a person. He was a translator.

This was a person. He can't watch the end of T2 without crying He watched a lot of sports on TV, but never played them himself.

This was a person. He has four overdue books.

This was a person. He sang.

This was a person. She was a plumber.

This was a person. She could play harmonica.

This was a person. She had a sexy phone voice.

This was a person. He threw a lot of parties.

This was a person. She had a bunch of tattoos. She talked during movies.

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

This was a person. He had a gun.

This was a person. She was rebuilding an engine.

This was a person. She was a teacher.

This was a person. He ate a lot of kale. He was polite to salesmen.

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

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

This was a person. He was a secret Buddahist.

This was a person. He loved to make out.

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

This was a person. She could fix anything. She was a cat person.

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

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

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

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

This was a person. She never ate breakfast.

This was a person. He brought great food to social gatherings.

This was a person. She was afraid of flying. She was a mason.

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

This was a person. She has two living parents.

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

This was a person. She was afraid of flying.

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

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

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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