30 Required Models & Theories for AP Human Geography

What You Need to Know

You’re expected to recognize, define, sketch (when relevant), and apply the most-used AP Human Geography models and theories—then evaluate limits/assumptions. Most exam questions aren’t “recite the model”; they’re “use the model to explain a real place” (and note why it doesn’t perfectly fit).

FRQ super-rule: Always do (1) define, (2) apply to the prompt’s place, (3) add a limitation/exception.

The “30” list (high-frequency models/theories)

Use this as your master checklist.

Step-by-Step Breakdown

Use this workflow for any model/theory question (MCQ or FRQ).

1. Identify the unit/theme

   • Urban land use? (Burgess/Hoyt/Multiple Nuclei/Bid-rent)
   • Population? (DTM/ETM/pyramids)
   • Migration? (Push–pull/Ravenstein/Zelinsky/Gravity)
   • Development/industry/ag? (Rostow/Wallerstein/Weber/von Thünen/Clark–Fisher)

2. Name + define the model in 1 sentence

   • Example: “The DTM explains how birth/death rates shift as a country industrializes, changing natural increase.”

3. Apply to the specific place in the prompt (2 concrete details)

   • Use real indicators: TFR, CBR/CDR, aging, informal settlements, corridors, ports, coal/iron, labor cost, etc.

4. Add one limitation/exception (this is where points hide)

   • Models often assume: isotropic plain, closed economy, stable politics, universal path, no colonial legacy, etc.

Mini worked examples (how to “apply + limit” fast)

1. von Thünen

   • Apply: “A city market encourages dairy/market gardening close in because they’re perishable and costly to ship.”
   • Limit: “Refrigeration, highways, and global trade break the neat ring pattern.”

2. DTM

   • Apply: “Country X has a high CBR and rapidly falling CDR due to sanitation → Stage 2 rapid growth.”
   • Limit: “War, HIV/AIDS, or state policies can interrupt the expected pathway.”

3. Central Place Theory

   • Apply: “Higher-order services (specialty hospital) cluster in a regional city because they need a larger threshold population and people will travel farther (greater range).”
   • Limit: “Physical barriers + online services + uneven wealth distort hexagonal spacing.”

Key Formulas, Rules & Facts

Core quantitative tools (you can actually compute)

Urban models: must-know labels

• Burgess (Concentric Zones) (inside → out): CBD → Zone of Transition → Working-Class → Middle-Class → Commuter/Suburbs.
• Hoyt (Sectors): High-rent and industry form wedges along rail/roads/water.
• Multiple Nuclei: Separate nodes (CBD, manufacturing, university, airport) shape land use.
• Latin American (Griffin–Ford): CBD + commercial spine to elite sector, with zone of maturity and peripheral squatter settlements; in situ accretion in between.
• Southeast Asian (McGee): Colonial CBD + port/old city, with commercial spine and mixed land uses; strong ethnic quarters.
• Bid-rent: Different land users “bid” for access; retail usually bids highest near the center.

Population models: high-yield stage clues

• DTM Stage 1: High CBR, high CDR (little growth).

• Stage 2: CDR drops fast (sanitation/medicine) → population boom.

• Stage 3: CBR drops (urbanization, contraception, women’s education).

• Stage 4: Low CBR/CDR (stable/slow growth).

• Stage 5 (often taught): CBR below CDR (natural decrease), aging.

• ETM (classic framing):

  • Stage 1: Pestilence/famine
  • Stage 2: Receding pandemics
  • Stage 3: Degenerative diseases
  • Stage 4/5 (often added): Delayed degenerative + re/emerging infections

Development/industry/agriculture: what each assumes

• Rostow: Development is linear; growth comes from investment/industrialization.
• Wallerstein: Development is relational (core–periphery); inequality is structural.
• Weber: Firms choose sites to minimize transport + labor (agglomeration can pull firms together).
• von Thünen assumes: One market city, isotropic plain, equal transport in all directions, farmers maximize profit.
• Clark–Fisher: As development rises, employment shifts primary → secondary → tertiary (and beyond).

Examples & Applications

1) Gravity Model (migration/shopping flows)

• Setup: City A P_1 = 2,000,000, City B P_2 = 500,000, distance d = 100.
• Insight: I = \frac{2,000,000 \times 500,000}{100^2} is much larger than if distance doubles (because distance is squared), so nearby big cities dominate interaction.
• Exam twist: If two options have similar distance, population sizes decide; if populations similar, distance decides.

2) Rank-size vs primate (urban system diagnosis)

• Setup: Largest city is 12 million; second is 3 million.
• Insight: Since 12 \text{ million} > 2 \times 3 \text{ million}, it fits primate dominance.
• Application: Explain with colonial primacy, centralized investment, or political centralization.

3) Weber (industrial location)

• Setup: A steel mill needs bulky inputs (iron ore + coal) and sells to a major metro market.
• Insight: If inputs lose weight during processing, the plant tends to locate near the inputs (to reduce transport of bulky raw materials). If market access dominates, it shifts toward the market.
• Limitation: Modern firms may prioritize skilled labor, just-in-time logistics, or tax incentives over classic least-cost.

4) Latin American city model (inequality pattern)

• Setup: City shows elite high-rise development along one corridor from CBD; large informal settlements on periphery.
• Insight: That corridor is the spine/elite sector; peripheral informal housing matches periferico.
• Limitation: Rapid globalization can produce edge cities and polycentric patterns not captured by the model.

Common Mistakes & Traps

1. DTM stage mix-ups: You call a country “Stage 3” just because it’s growing.

   • Why wrong: Stage 3 is defined by falling CBR, not just growth.
   • Fix: Look for why CBR is dropping (contraception, women’s education, urbanization).

2. Treating models as universal laws: You force-fit Burgess or von Thünen to every city/region.

   • Why wrong: Many models assume flat land, single center, no planning, older industrial context.
   • Fix: Always add a limitation (topography, zoning, highways, globalization, history).

3. Confusing rank-size with primate:

   • Why wrong: Rank-size is a distribution pattern; primate is one-city dominance.
   • Fix: Rank-size uses P_n = \frac{P_1}{n}; primate checks if the largest is disproportionately big (often >2\times #2).

4. Diffusion type confusion (contagious vs hierarchical):

   • Why wrong: You describe “spread through social media influencers” as contagious.
   • Fix: Influencer/elite-to-others = hierarchical; neighborhood-to-neighborhood = contagious.

5. Misreading population pyramids: You see a wide base and say “aging population.”

   • Why wrong: Wide base = high youth dependency and likely high CBR.
   • Fix: Aging shows as a wide/top-heavy upper structure and higher old-age dependency.

6. Overstating Heartland/Rimland today:

   • Why wrong: Modern power includes air, cyber, trade networks, nukes.
   • Fix: Use them as historical/geostrategic lenses, then critique modern limits.

7. Weber without agglomeration: You only mention transport costs.

   • Why wrong: Clustering benefits (shared labor pool, suppliers) can outweigh transport.
   • Fix: Add “agglomeration economies can pull firms into clusters even if costs rise.”

Memory Aids & Quick Tricks

Quick Review Checklist

• You can name + define each of the 30 models/theories in one sentence.
• You can sketch/label: Burgess, Hoyt, Multiple Nuclei, Latin American, Southeast Asian, CPT hexagons.
• You know the 3 key computations: Gravity I = \frac{P_1P_2}{d^2}, **Rank-size** P_n = \frac{P_1}{n}, **Dependency ratio** \frac{(0\text{–}14)+(65+)}{15\text{–}64}\times 100.
• You can match DTM/ETM stages to real indicators (CBR/CDR, aging, disease types).
• You can apply Weber (inputs/market/labor/agglomeration) and von Thünen (perishability/transport).
• You can contrast Rostow (linear) vs Wallerstein (structural core–periphery).
• You always add one limitation/assumption when applying a model.

You’ve got this—focus on application + limitations, and these models will start feeling like free points.