Which term would NOT typically be associated with uncertainties in geospatial data?

Model accuracy is primarily concerned with the precision and reliability of a model's predictions based on the data and algorithms used. It reflects how well a model represents the real-world conditions or processes it is meant to simulate. While model accuracy can be influenced by uncertainties in the data input, the term itself does not directly represent uncertainties associated with the data itself.

In contrast, the other terms are directly related to the uncertainties in geospatial data. Measurement uncertainty refers to the doubt that exists about the result of a measurement, which is integral to assessing data quality. Contextual uncertainty deals with how the context in which data is collected or analyzed can lead to different interpretations, thus affecting the reliability of findings. Data integrity encompasses the accuracy and consistency of data, where any issues can also introduce uncertainty in the overall dataset.

Understanding the differences between these concepts helps to clarify why model accuracy, while important in evaluating the effectiveness of analyses, is not typically used to describe uncertainties inherent to geospatial data itself.