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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  was a DJ. He can't watch the end of T2 without crying This was a person. He had long fingernails. This was a person. She  was a poet. This was a person. She had crazy hair colors. This was a person. She was a secret Buddahist. This was a person. She was a skilled woodcarver. This was a person. She was an expert barbecuer. This was a person. She  was sort of selfish in bed. This was a person. She loved Elvis. This was a person. She  was kind of an awesome lover. She  was a pharmacist. This was a person. She took a lot of photos. This was a person. She was always in a hurry. She had a lot of Beatles records. This was a person. He had crazy hair colors. This was a person. She overtipped. This was a person. She excelled at woodworking. This was a person. He helped strangers. This was a person. He picked up litter.  This was a person. He was lactose intolerant. This was a person. He  had a hangover yesterday. This was a person. He taught guitar. This was a person. She helped people. This was a person. He was the richest of  his friends. This was a person. He was rebuilding an engine. He talked during movies. This was a person. He had extensions. He sang at parties. This was a person. He  was a DJ. He had a bunch of tattoos. This was a person. He loved chocolate. This was a person. He ate a lot of hot dogs. This was a person. She had a podcast. This was a person. He was very good at cleaning. He was left-handed. This was a person. She had a lot of shoes. This was a person. She had long fingernails. This was a person. She was left-handed. This was a person. She did not sleep with a pillow. This was a person. She constantly wore sunglasses. This was a person. She was working  her  way through all of the shows on Netflix. She knew a lot of cops. This was a person. He  dropped his work laptop last week, destroyed it and couldn't sleep for days. He  was a pharmacist. This was a person. He knew how to get free satelittle television. This was a person. He  scalped tickets This was a person. He was a wedding photographer. This was a person. He  was learning to speak French. He  was a tailor. This was a person. She  was afraid of flying. This was a person. He  was sleeping with  his  ex. This was a person. She never ate breakfast. This was a person. He played chess. This was a person. She was an expert barbecuer. This was a person. He was always in a hurry. This was a person. She ate the food at convenience stores. This was a person. She was polite to telemarketers. This was a person. She was thrifty. This was a person. She  only ever had sex with one person. This was a person. She  was a poet. This was a person. She wore bow ties. This was a person. She loved pot. This was a person. She was polite to salesmen. This was a person. She knew a lot of cops. This was a person. She wore Spandex. She played World of Warcraft This was a person. She knew a lot of cops. This was a person. He wore a lot of hats.  This was a person. He  was a mason. This was a person. He  was a street vendor. This was a person. She wrote for the newspaper. This was a person. He spends a lot of time watching tennis. He was obsessed with sitcoms. This was a person. He played World of Warcraft This was a person. She was thrifty. This was a person. He sang. This was a person. He had just paid a crapload of money to get    teeth fixed. This was a person. He had a green thumb. This was a person. He sang. This was a person. She  was kind of an awesome lover. This was a person. He once robbed a bank. He was really good at geography. This was a person. He had just paid a crapload of money to get    teeth fixed. He helped the widow next door. This was a person. He had braces on  his  teeth. This was a person. She  was sleeping with  her  ex. She  was learning to use CAD software. This was a person. She survived cancer. This was a person. He had a lot of music on vinyl. This was a person. She wore earphones all the time. This was a person. She had crazy hair colors. This was a person. He spends a lot of time watching tennis. This was a person. She loved nachos. This was a person. He  finished reading all of the works of Homer. This was a person. He snored. This was a person. She wore a lot of jewelry. This was a person. He has four overdue books.  He had lice. This was a person. She had extensions. This was a person. He did not sleep with a pillow. He chewed gum constantly. This was a person. She was a good dancer. This was a person. She loved Elvis. This was a person. He took a lot of photos. This was a person. She ate a lot of raisins. She loved nachos. This was a person. She loved chocolate. She tutored kids. This was a person. He had a bunch of tattoos. This was a person. He lived on breakfast bars. This was a person. She was good at job interviews. She had long fingernails. This was a person. She could play cricket. This was a person. She wanted to learn magic. This was a person. She sang. This was a person. He  had VD. This was a person. She was working  her  way through all of the shows on Netflix. This was a person. He plays fantasy football. This was a person. She  got a terrible haircut a few days ago and shaved  her  head to escape from it.

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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