Which factor is important for determining the acceptability of spatial adjustment results?

RMS error, or Root Mean Square error, is a crucial metric in evaluating the quality of spatial adjustments. It quantifies the difference between the observed locations of points in the original dataset and their corresponding locations in the adjusted dataset. A lower RMS error indicates a better fit of the adjusted data to known control points or reference features, signifying more accurate results. By assessing RMS error, users can determine if the spatial adjustments have met the desired accuracy standards necessary for their specific applications.

Visual inspection of adjusted data, while valuable, relies on subjective judgment and can overlook numerical discrepancies. The output coordinate system is important for ensuring compatibility with other datasets, but it does not inherently reflect the quality of the spatial adjustment. Lastly, the number of links used in the transformation can provide insight into the potential accuracy but does not directly measure the quality of the adjustment itself; more links do not always equate to a more accurate result without considering how well they fit. Thus, RMS error emerges as the most definitive factor in evaluating the acceptability of spatial adjustment results.