Sharpeville Massacre Tragedy Casualties Visualization Tool - Cockeyed.com

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Disaster Casualties Visualization Tool: Sharpeville Massacre: 1960

69 Dead Men and Women

The Sharpeville massacre at the police station in the South African township of Sharpeville. After a day of demonstrations the South African police opened fire on the crowd of black protesters killing 69 people.

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

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 played piano.

This was a person. He had a podcast.

This was a person. He smoked pot at music festivals. He couldn't swim.

This was a person. She wore a lot of jewelry.

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

This was a person. She loved wine.

This was a person. He spent a lot of time with old people.

This was a person. He wore a lot of hats. He picked up litter.

This was a person. She loved cigarettes.

This was a person. She wore a lot of hats.

This was a person. She was left-handed.

This was a person. He was good at job interviews. He ate a lot of chinese food.

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

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

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

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

This was a person. He was a translator. He scalped tickets

This was a person. She never carried cash.

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

This was a person. She played piano.

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

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

This was a person. He could fall asleep anywhere.

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

This was a person. She helped strangers.

This was a person. She wore bow ties.

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

This was a person. She went to the horseraces.

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

This was a person. She went to church.

This was a person. He made scrapbooks.

This was a person. She was a crossword whiz.

This was a person. She could play banjo.

This was a person. She was a poet.

This was a person. He loved oreos. He snored.

This was a person. She was rough and tumble.

This was a person. He could play harmonica. He was polite to salesmen.

This was a person. She was a wedding photographer.

This was a person. He did not sleep with a pillow. He was homophobic.

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

This was a person. She always had gum.

This was a person. He always wore a hat.

This was a person. He was a mason.

This was a person. She did not sleep with a pillow. She was the richest of her friends.

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

This was a person. He picked up litter. He helped strangers.

This was a person. She was a good dancer.

This was a person. She loved James Bond movies.

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

This was a person. She was a mason.

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

This was a person. He lived on breakfast bars.

This was a person. She was learning to speak French.

This was a person. She scalped tickets

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

This was a person. She had tropical fish. She could play banjo.

This was a person. He was the unofficial relationship guru among friends. He was a cheapskate.

This was a person. He had a bunch of tattoos. He could fall asleep anywhere.

This was a person. She was a mason.

This was a person. He has two living parents.

This was a person. She wore a lot of hats. She smoked pot at music festivals.

This was a person. She was sort of selfish in bed.

This was a person. He wore a lot of hats.

This was a person. She was a pharmacist.

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

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

This was a person. He chewed nicotine gum. He couldn't swim.

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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The Weight of Clothing

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