maps

What You Need to Know

AP Human Geography loves maps because maps are arguments: they show patterns, make comparisons, and (sometimes) mislead. On the exam, you need to (1) identify common map types, (2) choose the right map for a dataset, and (3) critique what a map hides or distorts.

Core idea (high-yield)

• Reference maps answer “Where is it?” (location, boundaries, physical features).

• Thematic maps answer “What’s the pattern of a variable?” (population density, language, migration, climate, etc.).

• Every map involves generalization (simplifying reality) and often distortion (especially with projections and classification).

Warning: The most common APHG trap is using the right-looking map type with the wrong kind of data (especially choropleths with raw counts).

Must-know families of maps

• Reference: political, physical, topographic

• Thematic: choropleth, proportional/graduated symbol, dot distribution (dot density), isoline/contour, flow-line, cartogram, qualitative thematic

• Geospatial tools (often tested alongside map types): GIS, GPS, remote sensing

• Projection types and distortion: Mercator, Peters, Robinson, azimuthal; distortion of shape, area, distance, direction

One essential “formula” concept: scale

You may need to interpret/compare map scales.

• Representative fraction (RF):
  $RF = \frac{\text{map distance}}{\text{ground distance}}$

• If a map has RF $1:n$, then:
  $\text{ground distance} = \text{map distance} \times n$

Step-by-Step Breakdown

Use this quick process when you’re given a map (or asked to pick the best map type).

A. How to identify a map type fast

1. Check what is being symbolized

   • Areas (shaded regions) → likely choropleth (or qualitative thematic)

   • Points/dots → dot distribution

   • Circles/squares sized differently → proportional/graduated symbol

   • Lines with arrows/thickness → flow-line map

   • Lines connecting equal values (like contour lines) → isoline

   • Areas resized/warped → cartogram

2. Ask: is the variable discrete or continuous?

   • Continuous surface (temperature, elevation, pressure) → isoline

   • Counts/attributes by region → choropleth or symbol maps

3. Check the legend for clues

   • Value ranges (class breaks) + shaded polygons → choropleth

   • “1 dot = X people” → dot distribution

   • Arrow widths correspond to amount moved → flow

B. How to choose the right thematic map for data (exam-style)

1. Decide what you want to communicate

   • Rates/percentages/densities across regions → choropleth

   • Totals at places/regions (GDP, total population, votes) → proportional symbol

   • Precise spatial distribution within regions (clusters) → dot distribution

   • Movement (migration, trade, commuting) → flow-line

   • Continuous change (temp, elevation) → isoline

   • Show how variable “reweights” the world (population, CO₂) → cartogram

2. Check if normalization is needed

   • Choropleths almost always need normalized data (per capita, per area, percent).

3. Assess audience and comparability

   • If readers must compare exact totals quickly → proportional symbols often beat choropleths.

   • If you need to highlight “core vs periphery” patterns → choropleth or cartogram.

Mini worked example (map choice)

• Dataset: “Total number of migrants leaving each country last year”

  • Best: proportional symbol (totals by unit) or flow-line if destinations are included.

• Dataset: “Percent foreign-born by state”

  • Best: choropleth (rate by area).

Key Formulas, Rules & Facts

A. Reference map types (know what each is for)

B. Thematic map types (the big ones)

C. Choropleth classification + color (frequent FRQ analysis points)

• Class breaks matter (the same data can “look different” depending on the scheme):

  • Equal interval: same numeric range per class

  • Quantile: same number of observations per class

  • Natural breaks (Jenks): classes follow data clusters

  • Standard deviation: highlights deviation from mean

• Color scheme

  • Sequential (light → dark): low → high of a single variable

  • Diverging (two colors around a midpoint): above/below average

  • Qualitative (distinct hues): categories, not magnitude

D. Projection types + distortion (must recognize the logic)

All flat maps distort at least one of: shape, area, distance, direction.

E. Scale (high-yield rules)

Examples & Applications

Example 1: Choropleth vs proportional symbol (classic AP trap)

• Prompt: “Map the number of COVID cases by county.”

• Better choice: proportional symbol (totals), or choropleth only if you map rate (cases per population).

• Key insight: choropleths can imply big counties are “worse” just because they’re large on the map.

Example 2: Dot density for internal clustering

• Prompt: “Show where people live within a state, not just state averages.”

• Use: dot distribution (e.g., 1 dot represents a fixed number of people).

• Key insight: reveals urban clusters and empty rural areas that choropleths can hide.

Example 3: Isoline for continuous phenomena

• Prompt: “Map precipitation across a region.”

• Use: isoline (isohyets = equal rainfall).

• Key insight: precipitation doesn’t change at political borders, so isolines match reality better.

Example 4: Flow-line for migration/trade

• Prompt: “Show major migration streams from Mexico to the U.S. and their volumes.”

• Use: flow-line.

• Key insight: thickness conveys magnitude; arrows convey direction; can illustrate channels/corridors.

Common Mistakes & Traps

1. Bold mistake: Using raw totals on a choropleth

   • What goes wrong: you shade areas by total population/GDP/cases.

   • Why it’s wrong: choropleths imply the value is spread evenly across the area and exaggerate large regions.

   • Fix: use rates (per capita/per area) or switch to proportional symbols.

2. Bold mistake: Confusing large-scale vs small-scale maps

   • What goes wrong: you say “large-scale = large area.”

   • Why it’s wrong: large-scale means more detail, smaller area.

   • Fix: remember: “large scale, large detail.”

3. Bold mistake: Treating categories like quantities

   • What goes wrong: you interpret a qualitative thematic map (religion, language) as if darker = “more.”

   • Why it’s wrong: categories are different kinds, not ordered amounts.

   • Fix: check legend: if it lists names not ranges, it’s categorical.

4. Bold mistake: Ignoring classification (breaks) on choropleths

   • What goes wrong: you compare two maps with different class breaks as if they’re equivalent.

   • Why it’s wrong: breaks control what looks “high” or “low.”

   • Fix: always read the ranges and number of classes.

5. Bold mistake: Overreading dot density precision

   • What goes wrong: you assume each dot is an exact location.

   • Why it’s wrong: dots are often randomly placed within a unit or constrained by a mask; they show pattern, not addresses.

   • Fix: interpret at the pattern level (clusters, corridors), not point-by-point.

6. Bold mistake: Forgetting projection distortion

   • What goes wrong: you interpret Greenland/Africa sizes literally on Mercator.

   • Why it’s wrong: projections distort area/shape/distance/direction.

   • Fix: name the likely distortion: Mercator = area distortion near poles.

7. Bold mistake: Missing what a cartogram is “arguing”

   • What goes wrong: you dismiss it because it “looks wrong.”

   • Why it’s wrong: the distortion is the point—size reflects the mapped variable.

   • Fix: state what size represents (population, emissions, GDP) and note the tradeoff (less geographic accuracy).

8. Bold mistake: Not matching continuous vs discrete data

   • What goes wrong: you map temperature by county choropleth and imply sharp border changes.

   • Why it’s wrong: temperature is continuous.

   • Fix: use isoline (or raster/remote sensing) for continuous surfaces.

Memory Aids & Quick Tricks

Quick Review Checklist

• You can distinguish reference vs thematic maps and explain why each is used.

• You can identify at a glance: choropleth, dot density, proportional symbol, isoline, flow-line, cartogram.

• You know: choropleths need normalized data (rates/percents/densities).

• You can explain how classification (breaks) and color schemes change interpretation.

• You can compare large-scale (detailed, small area) vs small-scale (less detailed, large area).

• You can name the four key projection distortions: shape, area, distance, direction.

• You can describe what Mercator distorts (area near poles) and why Peters exists (equal area).

• You can critique a map for what it hides: aggregation, generalization, dot placement, symbol overlap, projection bias.

You’re aiming to do two things on test day: name the map type and say why it’s the best (or misleading) for that data—that’s where the points are.