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Disaster Casualties Visualization Tool: The Boyd Massacre: 1809

68 Dead Men and Women

The Boyd Massacre Maori residents of northern New Zealand killed and ate between 66 and 70 people as revenge for the whipping of a young Maori chief in 1809.

This was a person. He picked up litter.  He loved MASH. This was a person. He ate a lot of raisins. He loved cigarettes. This was a person. He was afraid of heights. This was a person. He  only ever had sex with one person. This was a person. She  was a teacher. This was a person. She  went to church. This was a person. She just had the bandages removed from her plastic surgery. This was a person. She loved pot. This was a person. He was a good dancer. He overtipped. This was a person. She threw a lot of parties. She was a wedding photographer. This was a person. He always wore a hat. This was a person. She always had gum. This was a person. He had a lot of music on vinyl. He was a glassblower. This was a person. She played piano. She was a skilled woodcarver. This was a person. He had a green thumb. This was a person. She had a bunch of cameras. This was a person. She was a skilled woodcarver. This was a person. He was cockeyed. He once robbed a bank. This was a person. He made scrapbooks. He was balding. This was a person. He tutored kids. He loved fine dining. This was a person. She  was a translator. This was a person. He survived cancer. This was a person. She never finished the book club book. This was a person. She  was sort of selfish in bed. This was a person. He  was awaiting judgement for a DUI last month. He  did open mike. This was a person. He was the richest of  his friends. This was a person. She  kept buying vegetables, even though they always just went bad in  her fridge. She  was a translator. This was a person. She lived on breakfast bars. This was a person. He wore bow ties. This was a person. She was cockeyed. She was an expert barbecuer. This was a person. She  was a DJ. This was a person. He was a cheapskate. This was a person. He  was afraid of flying. This was a person. She had a gun. She was divorced. This was a person. She has four overdue books.  This was a person. He was polite to telemarketers. He always brought reusable bags into the store. This was a person. She  dropped her work laptop last week, destroyed it and couldn't sleep for days. This was a person. He wore earphones all the time. This was a person. He was always in a hurry. He was a Mac evangelist. This was a person. She was a cat person. This was a person. He was good at job interviews. This was a person. She ate a lot of raisins. She slept around. This was a person. He loved cigarettes. This was a person. She was left-handed. This was a person. He never raised  hisvoice. This was a person. He helped people. This was a person. He never finished the book club book. He was cockeyed. This was a person. She  had a hangover yesterday. This was a person. She looked like a cartoon. This was a person. He had a lot of Beatles records. He was divorced. This was a person. She was polite to salesmen. She sang. This was a person. She tutored kids. This was a person. He constantly wore sunglasses. This was a person. He  was a drunk. This was a person. He  called in sick to work 9 times last month and was written up at work. He  kept buying vegetables, even though they always just went bad in  his fridge. This was a person. He wanted to learn magic. This was a person. She once robbed a bank. This was a person. He tutored kids. This was a person. He kept to himself mostly This was a person. She had a lot of music on vinyl. This was a person. He didn't get along with animals. He plays fantasy football. This was a person. She chewed gum constantly. She ate a lot of kale. This was a person. He was working  his  way through all of the shows on Netflix. This was a person. He had a podcast. He had bonsai trees. This was a person. He was a cheapskate. This was a person. She  was a DJ. This was a person. She wore earphones all the time. This was a person. She  was kind of an awesome lover.

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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