It was widely reported a few years ago that the tech was pretty useless for triangulation amongst a bunch of other worrying behavior by the company and police themselves: https://www.techdirt.com/2021/08/26/chicago-pd-oversight-say...
If you built towers for microphone above the cityscape you could get an accurate triangulation, but obviously nobody's doing that.
A sensor 4 blocks away will often record a much louder sound than one half a block away, because of buildings that are variously absorbing or reflecting sound.
That makes triangulation utterly useless.
You can localize to maybe a 20 square block area, but that's not a whole lot for cops to go on.
Mind if I ask how a sound recorded by two different sensors can be identified as the same original sound if the loudness is different? Unless the frequency of loud sounds is less than the time taken by the sounds to reach the sensors, wouldn’t it be hard to pick up two sounds spread apart by say a second but know that they are the same sound?
For example, if I’m on the phone with a friend during a thunderstorm and we are on opposite sides of the city, will we hear and be able to identify the same thunderclap?
Does sound have usable distinguishing features other than just loudness?
Than raw, peak amplitude? yes.
Frequency and amplitude variation over time will be roughly consistent in a linear system (i.e. the sound will get louder and quieter over time). If you have a stable, well-qualified listening system (such as, for example, a set of fixed recording locations in an environment that is acoustically static), you can build a map of how sound sources map to each recorder, and use principles of directed arrays to get very accurate location projections.
To use your example, yes it would be difficult for you and a single friend to confirm that you heard the same sound, but you could come up with a probability that if you heard a sound with the same characteristics with a certain time difference and loudness difference, it is likely to have been from a certain location. If, however, you have 4 friends, it gets much less likely that all 4 heard a different sounds calibrated exactly to seem like they came from a specific source when the data is combined.
Back in the shotspotter example, your microphones are in fixed positions, so you can figure out in advance what the echoes are going to be for any given originating sound, and do a reasonable job of triangulation with 3 sensors. You also have some other givens, eg it's reasonable to assume that the location of any actual gunshot is 1-2 meters above ground level; this means you'll be slightly wrong about rooftop snipers/shootouts but those are vanishingly rare compared to street shootings. I don't think anyone claims the product to be reliable for detecting shootings in building interiors, where you'd be competing with televisions and other false positives.
As I mentioned earlier, a product like this can get away with a relatively low success rate, as long as it's slightly better than the information that comes in from 911 calls and the rate of false positives if either tolerably low or negligently unaccounted for.
> If this result is attributable in part to missing or nonmatched records of investigatory stops that did take place as a direct consequence of a ShotSpotter alert, CPD’s record-keeping practices are obstructing a meaningful analysis of the effectiveness of the technology.
Should have been the end of the article right there, but techdirt decided to write one anyway.
And false positives really aren't that big a deal anyway. You get false police reports, at least with the sensors you can recalibrate and adjust.
If only we took companies out to defend their product at face value we could waste even more time and resources!
So much so that it's "obstructing a meaningful analysis of the effectiveness of the technology". Their words, not mine.
> Everywhere it's installed officers find usefulness in triangulating where shots are popped off.
At best, cops are going on 'feels' here because, presumably, the data aren't sufficient to provide a solid basis for analysis. Or the police are withholding data that proves the system works well, which prima facie seems unlikely.
I don't know what the real numbers are, but from that article it seems noone does...
A proper reporting of numbers would be nice.
I would rather the cameras go to the gunshot noises than stay bolted to a pole outside my house, which is where they are now.
Also, a faster response could result in a better rate of finding crime. "89% of ShotSpotter’s reports led police to find no gun-related crime" does not equal "89% of reports weren't gunshots". It could be that police show up too late.
In a sort of magical way where there is a drone that can easily be dispatched from within it's range that has the intelligence to not only navigate to the scene through a complex city environment but also find and gather evidence. I bet any current drone deployment is likely to be slower than dispatching people patrolling and there will be far fewer of them than regular cops. It's basically fantasy when the Shot Spotter stuff doesn't even seem to work.
Drone navigation has been progressing quite a lot over the past decade, and law enforcement in some places is already using them in an automated way, as the article mentions. Also, the suggestion above was forward looking, it wasn't suggesting that the NYPD should glue these systems together today.
It doesn't tell a gunshot from a dumpster lid slamming shut.
It does produce almost entirely false positives.
It doesn't identify the location of a gunshot to any useful level of precision.
I suppose if you want to start a police state, and make every fourth resident a cop, you might be able to mitigate the 90% false-positive rate, and the horrible location-identification rate, but at that point, do you even need shot-spotter?
Right now, police have data about where loud sounds generally come from, and video from fixed poles where crime happened in previous years. Both of these aren't great data points on their own.
The intersection of that data: [Video around locations where loud noises just happened] is probably much better than disjointed data points of [video of where crime happened in previous years] and [general locations of where loud sounds are happening].