when it comes to making data useful, i think there are bigger things to worry about than whether or not observations are recorded with positional accuracy values. i would rather have good coordinates without associated positional accuracy than bad coordinates with positional accuracy. and no one should ever just assume that coordinates or positional accuracy values recorded for any given observation in iNaturalist are necessarily reliable.
i’ve given this example in other threads before, but i’ll provide a better visualization here than i have before (below). at a park in my area, there have been now 9 observations of a specific patch of Heteropterys glabra plants. it’s grown a little over time, but the entire patch probably is not bigger than 5m in diameter. here are the observations in iNaturalist: https://www.inaturalist.org/observations?place_id=65598&taxon_id=542500, and here is a visualization of those observations with their accuracy circles, based on the data recorded in the system (only 6 of 9 of the observations had positional accuracy recorded):
if the locations of the observations were reliable, then all of the circles would overlap, but that’s not the case. one of the non-overlapping circles has a relatively small positional accuracy value recorded – just 15m – but if you compare it with the other observations, it looks like the true positional accuracy value should be more like 30m. and more to the point of this particular thread, if you look at the 3 observations without positional accuracy recorded (no circles), they look like they more reliably represent the true location of the patch of plants than some of the observations with positional accuracy recorded.
so, yes, if you can record good values for coordinates and positional accuracy, then please do. and i’m not against educating certain people about what positional accuracy is and how to record it properly. but i don’t think everyone needs to be burdened with the technical intricacies of all of this. leave it to the scientists and analysts to correct for errors in the data they want to analyze, and leave it to the engineers to figure out new technical approaches to capture better data.