i made a page that might help you visualize what’s going on in the UTFGrids.
page: https://jumear.github.io/stirfry/iNat_UTFGrid_data_interpreter
code: https://github.com/jumear/stirfry/blob/master/iNat_UTFGrid_data_interpreter.html
example usage: https://jumear.github.io/stirfry/iNat_UTFGrid_data_interpreter?z=0&x=0&y=0&geo=true&photos=true&geoprivacy=open&taxon_geoprivacy=open&quality_grade=research&spam=false (it may take a moment or two for the data to be retrieved.)
if i had made my own version of your page, i probably would have worked with standard places as the basis for place selection / guessing because they have the advantage of being able to capture obscured observations. however, the disadvantage is that standard places are not uniform in size, and they don’t cover the ocean very well. also, not all the boundaries in the system are properly defined, nor are they always undisputed.
if you’re trying to just select a particular point with observations in a somewhat randomized way, i think i would just use the &order_by=random parameter. this won’t give you a way to eliminate the bias towards places with a lot of observations, but it could give you a better way to work around the problems of repeated location selection and bias towards places where observers are awake and actively submitting, without doing too much extra work.
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the UTFGrid selection method would be a more complicated method – especially just to grasp, if you’ve never come across UTFGrids before – but it’s the only method that i can think of for selecting a point which provides a potential means to eliminate bias towards places with lots of observations. (you would weight your selection by observations per cell, but you would keep the basis for each cell between a high and low range that will allow you to exclude places with very few observations and also mitigate the bias for high-observation places.)