The FBI has finally released crime statistics for 2023!
Let's have a short thread.
First thing up is recent violent crime trends:
Now let's focus in on homicides.
The homicide statistics split by race show the same distribution they have for years.
As with every crime, it's still men doing the killing, but it's also largely men doing the dying.
What about Hispanics? Their data is still a mess, but here it is if you're interested.
The age-crime curve last year looked pretty typical. How about this year?
Same as always. Victims and offenders still have highly similar, relatively young ages.
Everything else, from locations to motives to weapons is pretty similar to previous years. What's different is that the OP might show incorrect numbers.
For the past two years, the FBI has silently updated their numbers after about two weeks.
You can use the web archive to see that the data from the OP is the data shown at release last year, and the data from 2023 is the 2022 data with the FBI's suggested reductions (i.e., -11.6% homicides, -2.8% aggravated assaults, -0.3% robberies, etc.).
But you can see on their site now that they've adjusted the numbers up, so the reduction they suggested has brought us down to a figure that's less impressive than my chart shows. The difference isn't huge so I showed the OP without updating to their new data.
For reference, 2022 as reported then had a homicide rate of 6.3/100k, and they silently updated that to 7.48/100k. The 2023 data they provided today actually has a murder rate of 6.61/100k, higher than last year's initially-reported number, but lower than the updated number. To make matters worse, if you use their Expanded Homicides Report, you get a rate of 5.94 for 2022 and 5.24 for 2023.
Methodology matters and we get to see inconsistency in this year's data, not even data that's been updated or anything. It's a mess, so take everything with a grain of salt and, in the interest of caution, only interpret trends. Trends are mostly common between all data sources even if the absolute magnitudes are off, constantly updated, etc.
The idea is to put large, powerful animals like bulls or lions in the ring with several dogs, and the winner lives.
The sport has existed for thousands of years. One of our first records is of Indians showing it to Alexander the Great.
The first record in England comes from 1610 and features King James I requesting the Master of the Beargarden—a bear training facility—to provide him with three dogs to fight a lion.
Two of the dogs died and the last escaped because the lion did not wish to fight and retreated.
For one, there's no supportive pattern of sanctions. For two, you can develop in near-autarky, and before post-WW2, that was comparatively what the most developed countries were dealing with.
I'm not talking fatalities, but bites, because bites are still a bad outcome and any dog who bites should be put down.
If we take the annual risk a dog bites its owner, scale it for pit bulls and Golden Retrievers, and extrapolate 30 years...
How do you calculate this?
Simple.
First, we need estimates of the portion of the U.S. population bitten by dogs per year. Next, to adjust that, we need the portion of those bites that are to owners. So, for overall dogs, we get about 1.5% and roughly ~25% of that.
Then, to obtain lifetime risk figures, we need to pick a length for a 'lifetime'. I picked thirty years because that's what I picked. Sue me. It's about three dog lifetimes.
P(>=1 bite) = 1-(1-p)^t
It's pure probability math. To rescale for the breed, we need estimates of the relative risk of different dog being the perpetrators of bites. We'll use the NYC DOHMH's 2015-22 figures to get the risk for a Golden Retriever (breed = "Retriever" in the dataset) relative to all other dogs, and Lee et al. 2021's figures to get the risk for a pit bull. The results don't change much just using the NYC figures, they just became significantly higher risk for the pit bulls.
To rescale 'p' for b reed, it's just p_{breed} = p_{baseline} \times RR_{breed}.
Then you plug it back into the probability of a bite within thirty years. If you think, say, pit bulls are undercounted for the denominator for their RR, OK! Then let's take that to the limit and say that every 'Black' neighborhood in New York has one, halve the risk noticed for them, and bam, you still get 1-in-5 to 1-in-2.5 owners getting bit in the time they own pit bulls (30 years).
And mind you, bites are not nips. As Ira Glass had to be informed when he was talking about his notorious pit bull, it did not just "nip" two children, it drew blood, and that makes it a bite.
Final method note: the lower-bound for Golden Retriever risk was calculated out as 0.00131%, but that rounded down to 0. Over a typical pet dog lifespan of 10-13 years, an individual Golden Retriever will almost-certainly not bite its owner even once, whereas a given pit that lives 11.5 years will have an 18-33% chance of biting, and if we use the DOHMH RRs, it's much higher. If we use the DOHMH RR and double their population, that still holds.
The very high risk of a bite associated with a pit bull is highly robust and defies the notion that '99.XXXX% won't ever hurt anyone.' The idea that almost no pit bulls are bad is based on total fatality risk and it is a farcical argument on par with claiming that Great White Sharks shouldn't be avoided because they kill so few people.
Frankly, if we throw in non-owner risk, the typical pit bull *will* hurt some human or some animal over a typical pet dog's lifespan. And because pit bulls live a little bit shorter, you can adjust that down, but the result will still directionally hold because they are just that god-awful of a breed.
Final note:
Any dog that attacks a human or another dog that wasn't actively attacking them first should be put down. That is a big part of why this matters. These attacks indicate that the dogs in question must die.