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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 wore Spandex. He sang at parties. This was a person. He took a lot of photos. This was a person. She spends a lot of time watching tennis. This was a person. She threw a lot of parties. This was a person. She ate a lot of chinese food. She was very careful about conserving gasoline. This was a person. He went to the horseraces. He had just paid a crapload of money to get    teeth fixed. This was a person. She  could always get tickets. This was a person. He had a bunch of tattoos. This was a person. He ate a lot of chinese food. This was a person. He was a crossword whiz. This was a person. He was polite to salesmen. This was a person. He was left-handed. This was a person. He didn't get along with animals. This was a person. He was afraid of the water. This was a person. He was a secret Buddahist. This was a person. He was 4 days sober. He  had been having trouble tying  his shoes because  he  pulled a muscle in his abs. This was a person. He smoked pot at music festivals. 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 loved cigarettes. He played a lot of social card games.  This was a person. She was a secret Buddahist. This was a person. He had an awesome home theatre. This was a person. He watched a lot of sports on TV, but never played them  himself. He constantly wore sunglasses. This was a person. She picked up litter.  She made scrapbooks. This was a person. He grew up on a farm.  This was a person. She had a lot of Beatles records. This was a person. He was thrifty. This was a person. He had an awesome home theatre. He took a lot of photos. This was a person. She  dropped her work laptop last week, destroyed it and couldn't sleep for days. This was a person. She was afraid of the water. This was a person. She  was a DJ. This was a person. He always had a clean car. This was a person. She ate a lot of raisins. This was a person. He ate a lot of fish. This was a person. He ate a lot of chinese food. This was a person. He ate a lot of fish. This was a person. He ate the food at convenience stores. This was a person. She was an expert barbecuer. She fixed bikes. This was a person. She has a suit at the cleaners. She could fix anything. This was a person. She was always in a hurry. She loved Elvis. This was a person. She played World of Warcraft This was a person. He always wore a hat. This was a person. He had an unfortunate nickname. He was cockeyed. This was a person. She ate a lot of hot dogs. This was a person. He wore Spandex. He wore a lot of hats.  This was a person. He played piano. He never ate breakfast. This was a person. He was rebuilding an engine. This was a person. He  was training to run a marathon. He  was a translator. This was a person. She knew a lot of cops. She was the richest of  her friends. This was a person. He was rough and tumble. This was a person. He was a cat person. This was a person. He  called in sick to work 9 times last month and was written up at work. This was a person. He wore a lot of jewelry. This was a person. She was always in a hurry. This was a person. She could fix anything. This was a person. She ate a lot of kale. This was a person. She had extensions. This was a person. He didn't get along with animals. This was a person. She chewed gum constantly. This was a person. She watched a lot of sports on TV, but never played them  herself. This was a person. He  always bought flowers for  his  desk. This was a person. She was an awful driver. This was a person. He had an awesome home theatre. This was a person. He taught guitar. This was a person. He always had gum. This was a person. He  was learning to speak French. This was a person. She had bonsai trees. She could play cricket. This was a person. He ate a lot of fish. 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 was sporty. This was a person. He just had the bandages removed from  plastic surgery. This was a person. She did not own a fridge. She was a cheapskate. This was a person. He watched a lot of sports on TV, but never played them  himself. This was a person. She  was a historian. This was a person. She  collected figurines. This was a person. She has a suit at the cleaners. This was a person. She  was sort of selfish in bed. She  was a teacher. This was a person. She tutored kids. This was a person. She could juggle. She loved pot. This was a person. She played a lot of social card games.  This was a person. She  was playing nine games of Words with Friends on  her  phone. She was a crossword whiz. This was a person. She prefered bread without crusts. This was a person. He ate a lot of chinese food. This was a person. She always had a clean car. This was a person. She played chess. This was a person. He was allergic to peanuts. He loved cigarettes. This was a person. She was a glassblower. This was a person. She  was a historian. This was a person. He was a crossword whiz. This was a person. He could play cricket. He never finished the book club book. This was a person. He  was a pharmacist. This was a person. She ate a lot of hot dogs. She was very good at cleaning. This was a person. She was allergic to peanuts. This was a person. She  was afraid of flying. She  could always get tickets. This was a person. He was a trivia master. This was a person. She smoked pot at music festivals. This was a person. He spent a lot of time with old people. He couldn't stand having the air-conditioner blow on   himself . This was a person. He was a cat person. This was a person. He excelled at woodworking. This was a person. She wore a lot of jewelry. She played piano. This was a person. She was living with her sister. She  was learning to use CAD software.

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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