7  Accuracy, Error, and Precision

So far, we have discussed many different data-related limitations to using geographic information for spatial analysis, such as resolution, abstraction (and other limits of the layer model), data bias, and the impacts of “cooked” data.

We also need to examine how the processes of measurement and representation introduce accuracy issues, errors, and varying levels of precision into our datasets.

7.1 Accuracy and Precision

Accuracy can be defined as the degree to which our data matches “true” values. Precision refers to the level of measurement and exactness of our data. A dataset can be:

  • Accurate but not precise- the measurements are close to the “true” value, but recorded at coarse increments

  • Precise but not accurate- the measurements are highly detailed but consistently off from the true value.

For example, let’s consider temperature data collected at the RDU ASOS station. In this dataset, accuracy would be defined by how close the value measured at the station is to the actual temperature outside. Precision would be, essentially, how many decimal places there are. For instance, is the station measuring to the degree, the half degree, the tenth of a degree?

7.2 Data Quality and Error

Accuracy and precision then inform data quality– which is the relative accuracy and precision of a particular dataset. Error refers to the combined effects of inaccuracies and imprecision

Error in a geographic dataset can take several different forms:

  • Positional error- inaccuracies or imprecision in location. Can be caused by:
    • Measurement limitations- GPS inaccuracy, sensor resolution limits, survey instrument errors
    • Map scale- smaller scales generalize more, reducing positional detail
    • Projection and transformation- coordinate system changed introduce displacement
    • Topological errors
  • Attribute error- inaccuracies or imprecision in the non-spatial data associated with features. Can be caused by:
    • Data entry or transcription mistakes
    • Outdated data- attributes no longer represent current conditions
    • Sensor/device errors- faulty readings from measurement device
    • Sensor/device limitations- precision or range of measurement is inherently restricted
  • Conceptual error- Mismatches between the data’s abstraction and the actual characteristics of what it represents. Can be caused by:
    • Inappropriate classification- categories too broad or narrow
    • Overgeneralization- simplifying complex features beyond what’s appropriate for analysis
    • Temporal mismatch- datasets from different dates are combined
    • Modeling assumptions

7.2.1 When do issues become errors:

Some problems are always errors– for example, a malfunctioning GPS unit or broken temperature sensor will always produce inaccurate data regardless of how the data is used.

Other potential issues only become errors in certain contexts. For instance:

  • Imprecise data is not inherently “wrong”, but if your analysis requires high precision, that imprecision becomes an error
  • Low resolution is not automatically an error, it just limits the detail of the data. However, if your research question demands final spatial detail, low resolution would be a source of error.

These are examples of context-dependent error, which are situations where the data’s characteristics (precision, resolution, scale) are mismatched with the requirements of the analysis and, therefore, become error.

7.2.2 Error vs. Uncertainty

Uncertainty describes the estimated amount of error that might be present in a dataset. It allows users to judge whether the data is suitable for a specific purpose. When potential error can be quantified, it can often be accounted for in analysis. The bigger problem arises when error cannot be measured (or when we don’t even know it exists), because it cannot be corrected or factored into decision-making.

Example: If a GPS is accurate within ±5 m, that’s uncertainty you can plan for. If the GPS occasionally drifts 30 m without warning, that’s unknown error.

7.3 Case Studies: Why Does Accuracy and Precision Matter

7.3.2 Landslide Risk

You are tasked with assessing landslide risk for a mountainous county: Your available data includes:

  • A digital elevation model
  • A soil type map
  • Rainfall records from a local station network

Consider the following questions:

  • Which parts of the dataset could lead to positional error, attribute error, or conceptual error?

  • Imagine a research question where the dataset limits here might not be a problem. What would that question look like?

  • Imagine a research question where the same dataset limits might cause a problem. What would that question look like?