GELE common mistakes
GELE · Understand the traps and how to avoid them.
Computing a grid (state plane / UTM) answer by applying only the map projection scale factor to ground distances, leaving out the elevation (ellipsoid) factor - e.g. reporting a boundary line as 1000.000 m grid when the ground distance at 1200 m elevation needs a combined factor of about 0.99981.
Candidates train almost exclusively on flat-earth problems where ground distance equals grid distance, and the elevation factor is invisible on a map, so the reduction step is silently dropped under time pressure.
What to do instead
Before any coordinate computation, write the full reduction chain on the answer sheet: ground → ellipsoid (EF = R/(R+h)) → grid (SF from the projection), CF = EF × SF. Apply CF once to linear quantities and CF² to areas, and sanity-check that the combined correction is a few tens of ppm, never a few percent.
Swapping orthometric heights for ellipsoidal heights (or vice versa) when a problem gives a GPS-derived height and a geoid undulation, and adding or subtracting N with the wrong sign.
Both are written as 'h' or 'H' on the candidate's scratch work, and the relation h = H + N is easy to invert because the numeric difference is large enough to look plausible either way.
What to do instead
Adopt and write down one convention (h = ellipsoidal/GPS height, H = orthometric/leveled height, h = H + N) and label every height in the problem statement with its type before computing. Cross-check the magnitude of the shift against the local geoid range, and never combine heights from different vertical datums without an explicit transformation.
Treating an area computed from record coordinates as the legally controlling quantity and 'correcting' record bearings or distances to force the coordinates to close and to match the stated area.
Coordinate geometry is the easiest thing to compute in an exam setting, so candidates invert the surveyor's hierarchy of evidence and let the arithmetic outrank the monuments and the written record.
What to do instead
Apply the hierarchy explicitly in every cadastral problem - natural monuments, artificial monuments, then courses (bearings/distances), then area - and state it in the solution. Where area and courses conflict, hold the courses and treat the area statement as 'more or less' unless the problem says otherwise.
Converting older units with rounded factors, or mixing 66-foot Gunter's chains with 100-foot engineer's chains, when computing lengths and areas from a historical deed (e.g. converting links with 0.66 ft to two decimals and then squaring).
Unit conversion is treated as a trivial preliminary step rather than part of the measurement problem, and both chain types are simply called 'chains' in old descriptions.
What to do instead
Write the unit identity with full precision before touching the calculator (1 link = 0.66 ft exactly, 1 chain = 66 ft = 100 links, 1 acre = 43,560 ft² = 10 chain²), carry full precision to the final rounding, and convert areas using squared linear factors. Check that the answer's order of magnitude is physically sensible for the parcel described.
Propagating uncertainty by adding standard deviations arithmetically (σ_total = σ1 + σ2 + …) instead of in quadrature, and ignoring correlation when the same control station or baseline is reused in several derived quantities.
Summing is the intuitive 'worst case' impulse, and covariance terms are invisible in a simple error-budget list even though they dominate when observations come from one shared reference.
What to do instead
Build the problem as a matrix statement and propagate properly: Σyy = A Σxx Aᵀ. When the same observation feeds multiple outputs, keep the off-diagonal covariance terms; only drop them if the problem states the observations are statistically independent, and say so explicitly.
Deleting an observation because its residual looks large, without a statistical test, and then quoting the adjusted results from the original (pre-deletion) adjustment instead of re-running it.
Residuals are visually salient and 'obviously wrong' outliers feel like blunders, while the correct workflow - test, reject, readjust, re-test - costs another full computation.
What to do instead
Screen with a standardized residual (or tau / data-snooping test) against the a posteriori reference variance, remove only on test failure, then re-adjust from scratch and re-examine the remaining residuals and the redundancy number. Report results from the final pass only.
Treating a scale bar or stated map scale as a statement of positional accuracy - e.g. concluding that a 1:24,000 map is 'accurate to 2 m' or measuring a parcel on the map and reporting the scaled value as ground truth.
Scale, resolution and accuracy are all expressed as ratios or small numbers, and map-reading exercises encourage candidates to trust a ruler over metadata.
What to do instead
Separate the concepts on paper: scale converts map distance to ground distance (and scale² converts area); accuracy comes from the map's stated horizontal accuracy standard (e.g. NMAS/ASPRS class) or the source data, not from the scale number. Apply scale² when converting measured areas, and attach the stated accuracy as an uncertainty rather than a correction.
Entering degrees-minutes-seconds angles into the calculator as decimal degrees (45°30' typed as 45.30) and then summing or reducing angles, producing answers that are off by tens of minutes but still look reasonable.
The DMS notation on the exam paper matches the printed calculator display closely enough that the conversion step feels unnecessary, and the sexagesimal base-60 error hides in the third or fourth digit of the final bearing.
What to do instead
Convert every angle to decimal degrees at the start, compute, and convert back at the end; then run a structural check - a closed traverse's interior angles must sum to (n−2)·180°, azimuths must stay in [0°, 360°) after each addition, and back-bearings must differ by exactly 180°.
Reading an instrument specification such as ±(2 mm + 2 ppm) as a flat ±2 mm, or quoting an angular accuracy (e.g. ±5") as if it were a linear error, when computing the expected precision of a measured line.
Accuracy specifications are terse and the additive constant is the only number that looks like a real uncertainty; exam problems reward whichever value produces a tidy answer.
What to do instead
Expand the specification over the actual distance: σ_D = a + b·D (with D in the same units as the ppm term, i.e. mm + ppm × km), and keep angular and linear contributions in separate budgets, combining them in quadrature. State the distance at which you evaluated the specification so the answer is verifiable.
Computing photographic scale as f/H when the terrain being imaged sits well above datum, and confusing the pixel-derived ground sample distance with the image scale or with the film/pixel size.
The f/H form is the one memorized from the flat-terrain derivation, and GSD is usually presented as a single number that hides the flying height above ground versus above datum distinction.
What to do instead
Use S = f/(H − h) with H and h in the same datum, and derive GSD = (pixel size) × (H − h)/f directly from that same expression so the two answers are consistent. Check the result against the flying height above ground, not above sea level, before finalizing.