Timeline for How to create an error map to support an average kernel density map?
Current License: CC BY-SA 3.0
8 events
| when toggle format | what | by | license | comment | |
|---|---|---|---|---|---|
| Dec 13, 2023 at 22:26 | comment | added | whuber | @MannyG The former, especially when the sampling points are not sampled randomly or systematically (i.e, on a grid). | |
| Jun 11, 2020 at 15:27 | history | edited | CommunityBot |
Commonmark migration
|
|
| Jan 9, 2013 at 15:07 | vote | accept | Aaron♦ | ||
| Jan 9, 2013 at 1:23 | comment | added | MannyG | @whuber, could you elaborate on (a) in your first paragraph? For example, are you referring to errors in data collection techniques that may persist across each forest gap (and therefore systematically manifest themselves in each KDE raster), or errors tied to the implementation of the focal function? | |
| Jan 8, 2013 at 23:40 | comment | added | whuber | @jeffrey Thank you, that is a consideration, too. The CV is obtained by dividing the (local) standard deviation grid by the (local) mean grid. I didn't mention it, but for such multiplicative summaries, some care should be taken to mask out areas where the denominator (the mean or minimum, as the case may be) is close to zero: the results may be unreliable there and likely will reflect nothing other than numerical imprecision and tiny inaccuracies in approximating the kernels. | |
| Jan 8, 2013 at 22:24 | comment | added | Jeffrey Evans | I would imagine that the coefficient of variation would be useful in this context. | |
| Jan 8, 2013 at 21:58 | history | edited | whuber | CC BY-SA 3.0 |
Clarification, spelling; added a link.
|
| Jan 8, 2013 at 20:24 | history | answered | whuber | CC BY-SA 3.0 |