Definitive glossary for understanding position accuracy, precision, and related geospatial surveying concepts, including standards, measurement, and reporting.
Position accuracy and precision underpin all reliable surveying, mapping, and geospatial data management. This comprehensive glossary provides deep technical definitions, explanations, and practical context for the core concepts, standards, and procedures that govern accuracy in modern surveying. Each entry answers not only “what” a term means, but also “how” it is measured, applied, and regulated, drawing on global standards including ICAO, FGDC, NGS, and authoritative surveying manuals.
Definition and Principles:
Accuracy in surveying is the degree of conformity between a measured or calculated position and its true or accepted reference value, usually defined by a geodetic datum such as WGS84 or NAD83. It quantifies how close reported coordinates are to their physical position on earth and is strictly defined in standards like ISO 19157 and ICAO Annex 15. For example, ICAO specifies positional accuracy as the closeness of agreement between measured and true values, with required levels (e.g., 1 m or 5 m) for aeronautical features.
Application:
Mandatory for legal boundary surveys, engineering design, cadastral mapping, aviation navigation (e.g., RNAV, RNP), and any spatial dataset requiring reliable georeferencing.
Expression:
Reported as a linear distance (e.g., 2 cm, 1 foot) or as the radius of a confidence circle (e.g., “95% of positions are within 0.5 m of the true location”). Often expressed with a statistical basis, such as the 95% confidence interval or RMSE (Root Mean Square Error).
Example:
A new geodetic control point determined by survey is found by a higher-order check to be within 1.5 cm of its true position; its positional accuracy is 1.5 cm. In aviation, ICAO requires airport reference points to have horizontal position accuracy within 1 meter at a 95% confidence level.
Standards:
ICAO Annex 15, ICAO ADQ Manual, and FGDC NSSDA provide definitions and test/reporting protocols for accuracy.
Definition and Principles:
Precision refers to the repeatability, consistency, or refinement of measurement. It is the degree to which repeated measurements under identical conditions yield similar results, regardless of how close they are to the true value. Quantified by statistical dispersion (standard deviation or variance).
Application:
Essential in survey operations for evaluating measurement reliability, instrument calibration, control network establishment, and least squares adjustments.
Expression:
Expressed as standard deviation (σ), variance (σ²), or measurement spread (e.g., ±0.003 m). In GNSS, often the standard deviation of position solutions over time.
Example:
A baseline measured five times yields values tightly clustered (e.g., standard deviation = 0.001 m), indicating high precision, regardless of proximity to the actual baseline length.
Standards:
Distinguished from accuracy in ICAO Annex 15, ISO 19157, and all major metrology references.
Definition and Principles:
The degree to which coordinates of a point in a spatial dataset match its actual ground location, referenced to a geodetic datum. A subset of spatial data quality, and critical for mapping, navigation, and engineering.
Application:
Vital for all spatial datasets: mapping, cadastral surveys, infrastructure planning, and aviation (e.g., runway thresholds, navigation aids, obstacles).
Measurement/Expression:
Assessed by comparing dataset coordinates to a higher-accuracy “ground truth.” Expressed as RMSE, standard deviation, or a 95% confidence circle/ellipse. Example: “1.5 meters at 95% confidence” means 95% of features fall within 1.5 m of their true location.
Example:
A GIS layer of hydrants claims 1.5 m horizontal positional accuracy at 95% confidence; 95% of hydrants checked fall within 1.5 m of surveyed positions.
Standards:
ICAO ADQ and FGDC NSSDA specify positional accuracy reporting requirements and test methods.
Definition and Principles:
Relative accuracy measures the correctness of spatial relationships between points within the same dataset, regardless of their absolute georeferencing. Focuses on internal consistency.
Application:
Critical in construction layout, project surveys, and mapping where the correct inter-feature relationship is more important than global position. Ensures correct distances/angles within a dataset even if globally offset.
Measurement/Expression:
Assessed by comparing measured distances/angles between points in the dataset against ground measurements. Expressed as maximum deviation (e.g., all parcel corners within 1 cm of each other) or as a statistical metric.
Example:
In a new subdivision, relative accuracy of 0.008 meters means all lot corners are within 8 mm of each other, even if the block is shifted 10 cm from the geodetic reference.
Standards:
ICAO Annex 15, FGDC, and ISO 19157 require separate documentation of relative accuracy, especially for internal network adjustment.
Definition and Principles:
Network accuracy is the uncertainty in a control point’s position relative to the geodetic datum. Measures how well a point is tied to the national or global reference framework.
Application:
Essential for geodetic control networks, GNSS base stations, and surveys requiring integration into the national/international coordinate system.
Measurement/Expression:
Determined by propagating uncertainties through a network adjustment (often least squares). Reported at a confidence level (typically 95%) as a linear value (e.g., 0.01 m at 95% confidence).
Example:
A first-order geodetic control point’s published network accuracy is 0.005 m at 95% confidence, indicating the true position is within 5 mm of reported coordinates, 95% of the time.
Standards:
NGS defines network accuracy in NSRS; ICAO mandates network accuracy for aeronautical features.
Definition and Principles:
Local accuracy quantifies the uncertainty in the position of a control point relative to other directly connected points in the same network/project. Measures internal geometric fidelity.
Application:
Vital for construction layout, engineering projects, and surveys requiring precise internal geometry (e.g., building corners, road alignments).
Measurement/Expression:
Analyzed via residuals/discrepancies within the project network after adjustment. Reported as maximum deviation or standard deviation at a 95% confidence level (e.g., 0.005 m at 95%).
Example:
A construction project’s local network has a maximum relative error between points of 5 mm, ensuring highly consistent placement of all features.
Standards:
FGDC and NGS require separate reporting of local accuracy; referenced in ICAO for aerodrome mapping.
Definition and Principles:
Vertical accuracy is the closeness of a measured or estimated elevation (z-coordinate) to the true elevation, referenced to a vertical datum (e.g., NAVD88, WGS84 ellipsoid).
Application:
Critical for topographic mapping, floodplain management, engineering, DEMs, and aviation (e.g., runway elevations, obstacle clearance).
Measurement/Expression:
Compared to independently surveyed ground truth points; reported as RMSE or at a 95% confidence level (e.g., 0.25 m at 95%).
Example:
A DEM advertises 0.15 m vertical accuracy at 95% confidence, meaning 95% of points are within 15 cm of actual ground elevation.
Standards:
ICAO Annex 15, FGDC, and ASPRS provide vertical accuracy requirements and test/reporting protocols.
Definition and Principles:
Significant figures are digits in a number that convey meaningful measurement information and indicate the certainty of reported values, reflecting instrument/method limits.
Application:
Applied in measurement reporting, calculations, and data entry to prevent overstating precision. Critical in communicating survey results and integrating data.
Usage/Expression:
Number of significant figures determined by the smallest reliable increment. E.g., if a GNSS receiver’s accuracy is 1 cm, report 123.46 m, not 123.4567 m.
Example:
If instrument accuracy is 1 cm, distance is reported as 123.46 m, not more, reflecting the true precision.
Standards:
ISO 80000, national surveying standards, and metrology guidelines.
Blunders (Gross Errors):
Human mistakes (e.g., misreading instruments, data entry errors). Not statistical; must be detected and eliminated, as they bias results.
Systematic Errors:
Consistent, repeatable errors from equipment/calibration, environment, or faulty procedures. Can often be corrected (e.g., by instrument calibration or applying correction factors).
Random Errors:
Unpredictable, inherent fluctuations (e.g., instrument noise, atmospheric effects). Treated statistically, minimized via redundancy and adjustment.
Error Treatment:
Check for blunders; investigate/correct systematic errors; minimize random errors statistically.
Standards:
ICAO, FGDC, and major surveying standards require error analysis, documentation, and correction/elimination protocols.
Definition and Principles:
A quality metric in traverse and level loop surveys, describing the proportional relationship between the total traverse length and misclosure (total error returning to the starting point).
Application:
Used in classic traverses and level loops as a primary check on survey internal consistency; minimum ratios required for survey acceptance.
Calculation/Expression:
Closure ratio = Total length / Linear misclosure.
E.g., 10,000 m traverse with 0.5 m misclosure = 1:20,000.
| Total Traverse Length | Misclosure | Closure Ratio |
|---|---|---|
| 10,000 m | 0.5 m | 1:20,000 |
Acceptance Criteria:
Survey standards specify minimum ratios (e.g., 1:10,000 for cadastral, 1:20,000 for high-order geodetic).
Definition and Principles:
A statistical measure of average error magnitude between measured/predicted values and the true values. Summarizes average distance between surveyed points and their true positions.
Calculation:
For horizontal position:
[
RMSE = \sqrt{\frac{\sum_{i=1}^{n} \left[(x_i - x_{i,ref})^2 + (y_i - y_{i,ref})^2\right]}{n}}
]
Application:
Standard for accuracy assessment in spatial data quality (FGDC NSSDA, ASPRS, ICAO ADQ).
Reporting:
RMSE is multiplied by a confidence factor for desired confidence level (e.g., RMSE × 1.7308 for 95% in NSSDA).
Example:
If RMSE = 0.30 m, 95% confidence accuracy = 0.30 × 1.7308 = 0.52 m.
Definition and Principles:
A confidence level quantifies the probability that positional error does not exceed a value. Confidence circle (2D) or ellipse (anisotropic error) is the area within which the true position is expected to lie with a given probability (e.g., 95%).
Application:
Used in all positional accuracy reporting for geodetic control, navigation, and mapping. ICAO and FGDC require 95% confidence level reporting.
Calculation/Expression:
Radius of confidence circle = RMSE × confidence factor (e.g., 1.7308 for 95%). For anisotropic errors, ellipse axes reflect standard deviations in each direction.
Example:
A point with 0.2 m RMSE has a 95% confidence circle radius of 0.346 m, i.e., 95% probability that the true location is within 0.346 m.
Standards:
ICAO ADQ Manual, FGDC NSSDA, and ISO standards require confidence level reporting for positional data.
For a deeper dive into any term or to request expert consultation on survey data accuracy, please contact us or schedule a demo .
Accuracy refers to how close a measured position is to the true or accepted value, while precision measures how repeatable or consistent multiple measurements are, regardless of their closeness to the true value.
Position accuracy is typically measured by comparing surveyed coordinates to a ground-truth reference and reported using statistical metrics such as RMSE or as a confidence interval (e.g., 95% confidence level).
Network accuracy reflects how well a point is tied to a geodetic datum, while local accuracy measures internal consistency within a project or network. Reporting both ensures clarity in both absolute and relative positioning.
RMSE (Root Mean Square Error) quantifies the average positional error between surveyed points and their true positions. It's widely used for expressing spatial data accuracy and is often adjusted for a desired confidence level.
Significant figures ensure that reported measurements reflect the true precision and reliability of the data, preventing overstating the certainty of survey results.
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