What describes how closely the non-spatial features of a dataset match their real-world locations?

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The correct choice is spatial accuracy. This term refers to how closely the locations represented in a dataset correspond to their actual locations in the real world. Spatial accuracy is crucial in GIS because it affects the reliability of the analysis and the decisions made based on the data. For instance, if the spatial accuracy is poor, the features might be plotted at incorrect locations, leading to misunderstandings or errors in spatial analysis.

Attribute accuracy, on the other hand, pertains to the correctness of the data attributes associated with geographic features — such as identifying a building's purpose or population numbers. Data integrity encompasses the overall reliability and accuracy of the data as a whole, including both spatial and attribute aspects. Geographic precision typically relates to the level of detail in the dataset but does not explicitly measure the correlation between spatial features and their physical locations in the environment. Thus, understanding spatial accuracy is essential for effective GIS applications, ensuring that the representations within the system accurately reflect reality.