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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  volunteered at a middle school, because  he  had a crush on one of the other tutors. He  was a tailor. This was a person. She ate a lot of chinese food. This was a person. He could fall asleep anywhere. This was a person. She chewed nicotine gum. This was a person. She was polite to telemarketers. She never finished the book club book. This was a person. She knew a lot of cops. This was a person. He had a lot of shoes. This was a person. She  was a mason. She has a suit at the cleaners. This was a person. He  dropped his work laptop last week, destroyed it and couldn't sleep for days. This was a person. She did not own a fridge. She was left-handed. This was a person. He was the unofficial relationship guru among   friends. This was a person. He was the unofficial relationship guru among   friends. He has four overdue books.  This was a person. He wore earphones all the time. This was a person. She  could always get tickets. She knew a lot of cops. This was a person. She was gluten intolerant. This was a person. He  finished reading all of the works of Homer. This was a person. He always had gum. This was a person. He loved chocolate. This was a person. He  was a teacher. He  scalped tickets This was a person. She could fix anything. She was a dog person. This was a person. He threw a lot of parties. This was a person. She was a trivia master. This was a person. He was a wedding photographer. He was gluten intolerant. This was a person. He was 4 days sober. This was a person. He always brought lunch. This was a person. He wore bow ties. This was a person. He had a gun. This was a person. He had tropical fish. He loved oreos. This was a person. He has two living parents. This was a person. She ate a lot of fiber. This was a person. She had an unfortunate nickname. This was a person. He threw a lot of parties. This was a person. She was rough and tumble. This was a person. She  finished reading all of the works of Homer. This was a person. He had an awesome home theatre. This was a person. He made pie. This was a person. She did not sleep with a pillow. She was a wedding photographer. This was a person. She was the richest of  her friends. This was a person. She  volunteered at a middle school, because  she  had a crush on one of the other tutors. She  was playing nine games of Words with Friends on  her  phone. This was a person. He always brought reusable bags into the store. This was a person. He loved to fish. This was a person. He was working  his  way through all of the shows on Netflix. This was a person. She can't watch the end of T2 without crying This was a person. She has four overdue books.  This was a person. She ate the food at convenience stores. This was a person. She got thrown off of a golf course. This was a person. He overtipped. This was a person. She was afraid of the water. This was a person. She led the choir. This was a person. She had a bunch of cameras. This was a person. She ate a lot of kale. She was polite to salesmen. This was a person. He  scalped tickets This was a person. He was sporty. This was a person. He threw a lot of parties. This was a person. He  was a historian. This was a person. She did not sleep with a pillow. This was a person. She picked up litter.  This was a person. She slept around. This was a person. He  was learning to use CAD software. He just had  his car stereo repaired. This was a person. He was the unofficial relationship guru among   friends. This was a person. She had a green thumb. This was a person. He  had a hangover yesterday. This was a person. She loved Elvis. This was a person. She had crazy hair colors. This was a person. She had a sexy phone voice. This was a person. She made pie. This was a person. He could play banjo. He did not own a fridge. This was a person. She was rough and tumble. She had just paid a crapload of money to get    teeth fixed. This was a person. He had a bunch of tattoos. This was a person. He  had a personal injury lawsuit working its way through the courts. This was a person. She loved James Bond movies. This was a person. She was working  her  way through all of the shows on Netflix. She was afraid of the water. This was a person. She  played soccer every weekend. This was a person. He couldn't grow a mustache. This was a person. He had tropical fish. This was a person. He played chess. He has a music afficianado. This was a person. He smoked pot at music festivals. This was a person. He loved wine. This was a person. He  only ever had sex with one person. He  did open mike. This was a person. He spent a lot of time with old people. This was a person. He never finished the book club book. This was a person. He had bonsai trees. He always had a clean car. This was a person. He had a sexy phone voice. This was a person. He made scrapbooks. He occasionally shop lifted. This was a person. He knew how to get free satelittle television. This was a person. He loved wine. He was very careful about conserving gasoline. This was a person. She picked up litter.  This was a person. She  had a personal injury lawsuit working its way through the courts. This was a person. He plays fantasy football. This was a person. He could play banjo. He had long fingernails. This was a person. She never finished the book club book. She sang at parties. This was a person. He occasionally shop lifted. He never tried gin. This was a person. He  played soccer every weekend. He was kind of addicted to ebay. This was a person. He never raised  hisvoice. He wanted to learn magic. This was a person. She was left-handed. This was a person. She wore Spandex. This was a person. He  played soccer every weekend. This was a person. He knew a lot of cops. This was a person. He always had gum. This was a person. He spent a lot of time with old people.

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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