GTLE Geography common mistakes
GTLE Geography · Understand the traps and how to avoid them.
Answering map-reading items from the legend's category names alone, without checking the scale bar or the grid coordinates attached to the feature in the item text.
Candidates recognize a symbol type (e.g., a contour interval or a land-use shading) and jump to the label that matches their memory of similar maps, skipping the numeric scale and coordinate cues the item deliberately provides.
What to do instead
Before choosing, restate the item's scale and coordinate values in your own words and test each option against those numbers; discard any option that only fits the symbol's name but contradicts the stated scale or location.
Treating a physical-geography process question as a vocabulary recall task, picking the term that sounds most technical rather than the term that matches the described mechanism.
Process items often include plausible jargon (isostasy, orographic lift, base level) and candidates under time pressure select by familiarity instead of tracing cause and effect through the stated conditions.
What to do instead
Write a one-line causal chain (input, process, output) from the item stem before reading the options, then choose only the option whose mechanism completes that chain; if none does, re-read the stem for a limiting condition you missed.
Calculating time-zone or solar-time differences using the longitude difference directly in hours without converting degrees to hours at 15 degrees per hour, or applying the wrong sign for east versus west.
The degree-to-hour conversion is easy to skip when the item supplies a table of city longitudes, and sign errors creep in because candidates memorize 'east is later' without anchoring it to a worked example.
What to do instead
Always show the division by 15 and label the result as hours ahead or behind a reference meridian before touching the options; do one mental check against a known pair (e.g., a location 30 degrees east is two hours ahead).
Selecting a climate classification from the temperature and precipitation figures without first checking hemisphere and seasonality, so a Southern Hemisphere station is misclassified as Northern.
Candidates pattern-match the wettest and driest months to a biome name but forget that July-warm versus January-warm reverses the seasonal signature, and the options usually include the mirrored distractor.
What to do instead
Identify the warmest and coldest months first and state the hemisphere explicitly, then evaluate the precipitation distribution; only after both steps choose the climate type.
Confusing push and pull factors in migration items, labeling a destination's opportunity as a reason people leave rather than a reason they move toward it.
Everyday language blurs 'attracted by jobs' with 'driven out by lack of jobs', and items often describe the same economic condition from both sides in the options.
What to do instead
For each option, ask 'does this operate at the origin or the destination?' and mark it O or D before deciding; the factor type follows from the location, not from whether the effect is positive or negative.
Answering population-pyramid items by eyeballing the widest bar instead of reading the age cohort labels and dependency-ratio definition the item asks about.
The widest bar draws the eye, and candidates report the modal age group when the item actually asks for the youth dependency ratio or the working-age share, which requires summing several bars.
What to do instead
Underline the exact quantity requested, write the formula (e.g., under-15 plus over-64 divided by 15 to 64), and estimate each component from the labeled axis before comparing options.
Matching a country or region to an economic indicator by reputation ('known for oil') rather than by the sector shares or per-capita figures given in the item.
Prior knowledge about a place feels authoritative, so candidates override the supplied data, especially when the item uses a fictional or anonymized region profile.
What to do instead
Treat supplied data as the only evidence: rank the sectors or indicators given, then map the dominant one to the economic structure; use outside knowledge only to interpret a term, never to replace the figures.
Misreading choropleth or dot-density maps as showing absolute totals when they encode rates or densities, and then ranking regions by raw count.
Darker shading intuitively suggests 'more people' or 'more of the thing', and candidates forget that a rate map divides by area or population, so a small dark region may have a lower total than a large pale one.
What to do instead
Read the map title and legend units first and say aloud whether the variable is a count, a rate, or a density; if it is a rate, refuse to make total-count comparisons the map cannot support.
Skipping the final step of multi-part items, such as computing a gradient and then failing to convert units or round to the precision the options use.
Candidates finish the conceptual work and stop at the first plausible number, so a correct method yields a wrong answer because meters were left as centimeters or a rise-over-run result was not divided by the map scale.
What to do instead
Adopt a fixed closing checklist for quantitative items: units stated, scale applied, sign correct, rounding matched to options; run it on every calculation before marking the answer.
Leaving a flagged item blank when the platform scores only submitted responses, instead of eliminating options and committing to a best guess.
Candidates plan to return to hard items, run out of time, and assume an unanswered item is neutral, when in most delivery formats an unanswered item scores the same as a wrong one.
What to do instead
At the two-minute warning, force an answer on every blank by crossing off impossible options and choosing among the remainder; log the item as a guess so you can revisit only if time remains.