To Dancing Outlaw's point about testing and conclusions - if you follow this link you see (scroll down) a map of NYC. The default is the case positivity by area of the city. there's a button at the top where you can swap it to the daily test rate per 100,000 people. While the correlation isn't perfect you see that parts of Manhattan look like they are doing great on test positivity then when you swap it you see it's because they are testing like crazy there so naturally the same absolute # of cases looks like a much smaller positivity rate because you are putting the same # of cases in a larger pool of tests.
IOW - there's no standardization making comparisons even across neighborhoods quite noisy data.
For example: The Chelsea neighborhood in Manhattan had a case positivity rate of 2.89% which is well below the target threshold. They test 1.286% of the population. Compare that to a "hot zone" of Gravesend in Brooklyn the case positivity rate is 9.51% which is well above the target threshold. They only tested .507% of the population however. If they raised the testing percentage to that of Chelsea and those additional tested were negative then the case positivity rate drops to 3.75% which puts them well below the target threshold. Now it's not certain that all those additional tests would be negative but since testing tends to be driven by symptoms it's not unreasonable to expect a higher rate of testing would yield lower case positivity percentages.
Sample size is considered "adequate" but I see no indication it is considered scientific (eg. margin of error can be calculated and findings can be extrapolated)
So, the testing could be useful within a neighborhood assuming the same process and roughly same proportion of population captured (eg. to see trends in the neighborhood) but comparing one neighborhood to another when sample proportion is not controlled is a bit of a fools game.
COVID-19: Latest Data - NYC Health