"coarse data" is a correct and usable phrase in written English.
You can use it when referring to data which contains information in a low-level of detail or information that has not yet been filtered or processed. For example, "The coarse data revealed several possible correlations."
The good news is that measurements can be made that will yield meaningful if coarse data about the security culture and how resistance to basic security controls is changing over time.
Warranty data contain useful information about product quality and reliability, but they are usually coarse data because they may be aggregated, delayed, censored, missing or vague.
What's more, de Montjoye showed that even "coarse" data provides "little anonymity". He lowered the "resolution" on his data by looking only at areas where purchases happened, not specific shops, and 15-day time frames in which they happened, not specific dates.
Choice of temporal precision is also influenced by the relative ease in obtaining coarse data, their compatibility with available energy price data, and avoidance of computational overheads when data sets expand.
We found that even when driven with relatively coarse data (10 m DEMs), V-BET produced a relatively accurate approximation of valley bottoms across the entire watersheds of these diverse physiographic regions.
Unsteady wind is used to exemplify this: turbulent wind speeds on a (rotating) wind turbine blade are synthesized from a coarse data grid via the introduced method of velocity increments.
As a result of the fine-coarse data reporting, it is possible to provide power consumption statistics to the consumers, which might help to reduce the overall consumption by changing customer behavior, as pointed out in several works [1 5].
We used this resolution to match our coarsest data, which was the PM2.5 data.
This is problematic since the tracking includes reading the location with at least city level accuracy every few hours, and even coarse location data can be used to create unique travel fingerprints, which can then be linked with other data sources for re-identification.
To classify coarse satellite data the traditional supervised and unsupervised techniques are not very useful because of mixed pixels in the classified data and poor accuracy.
However, because of Landsat's lower observation frequency, phenological methods developed with coarse resolution data are not directly transferable.
