An ArcGIS user performs a spatial adjustment on a dataset. What factor can indicate if the results are acceptable?

The root mean square (RMS) error is a critical factor in assessing the accuracy of the results from a spatial adjustment. It provides a quantitative measure of the discrepancies between the original and adjusted datasets. A lower RMS error indicates that the points have been positioned more accurately, meaning that the transformation has improved the spatial alignment of the dataset effectively.

While visual inspections can provide a general sense of whether the adjustment looks reasonable, they are subjective and can be misleading. The output coordinate system, while important for ensuring that data aligns correctly for further analysis, does not directly reflect the quality of the adjustment. Similarly, the number of links used in the transformation is significant, but an adequate number of links does not guarantee a precise adjustment; it is still vital to evaluate the RMS error to confirm that the adjustment meets the required level of accuracy.

Thus, RMS error serves as a definitive metric to evaluate the success of spatial adjustments in terms of precision and accuracy.