IB Geography HL cheat sheet

Population structure: dependency ratio = (population aged 0–14 + population aged 65+) ÷ population aged 15–64 × 100; a rising ratio signals growing dependent load on the working-age cohort.

Demographic Transition Model stages: high fluctuating (stage 1), early expanding (2), late expanding (3), low fluctuating (4), and declining (5); natural increase = CBR − CDR per 1,000.

Doubling time (years) ≈ 70 ÷ annual growth rate (%); a 2% rate doubles population in about 35 years.

Population momentum means a youthful age structure keeps births high for decades even after fertility falls to replacement (about 2.1 children per woman).

Pro-natalist policies raise births (paid parental leave, childcare subsidies); anti-natalist policies lower them (China's one-child policy 1979–2015, later two- and three-child relaxations).

Climate controls: latitude drives insolation and temperature; altitude lowers temperature at roughly 6.5 °C per 1,000 m; continentality widens annual range; ocean currents warm or cool coasts (Gulf Stream vs. Humboldt).

Enhanced greenhouse effect: human emissions of CO2, CH4, and N2O trap outgoing longwave radiation; CO2 has risen from about 280 ppm pre-industrial to over 420 ppm.

Resource classification: renewable vs. non-renewable; stock vs. flow; and the ecological footprint (global hectares per capita) measures demand on biocapacity.

Drainage basin stores and flows: precipitation input; interception, infiltration, percolation, groundwater flow, surface runoff, and evapotranspiration outputs; hydrograph lag time shortens with urbanisation and compaction.

Storm hydrograph reading: rising limb, peak discharge, lag time, falling limb; steep impermeable rock, deforestation, and paved surfaces raise peak and shorten lag.

Water scarcity: physical scarcity (demand exceeds supply) vs. economic scarcity (lack of infrastructure/finance); about 4 billion people face severe water scarcity at least one month per year.

Oceans: thermohaline circulation is driven by temperature and salinity differences; El Niño involves weakened/reversed trade winds and warm water pooling in the eastern Pacific, shifting rainfall.

Ocean acidification: CO2 dissolves to form carbonic acid, lowering pH (about 0.1 unit since pre-industrial); harms calcifying organisms such as corals and pteropods.

Urban models: Burgess concentric zone, Hoyt sector, Harris–Ullman multiple nuclei; land value peaks in the CBD and declines outward, producing bid-rent gradients.

Urbanisation measures: urban growth rate, percentage urban, and megacity definition (over 10 million inhabitants); counter-urbanisation moves people from cities to rural areas.

Food security depends on availability, access, utilisation, and stability; the Green Revolution raised yields via high-yield varieties, irrigation, and fertiliser, with uneven social and environmental outcomes.

Globalisation indicators: trade-to-GDP ratio, FDI flows, remittances, and time–space convergence via transport and digital connectivity.

Core–periphery model: core regions concentrate capital and high-value activity; periphery supplies labour and raw materials; semi-periphery occupies an intermediate position.

KOF Globalisation Index measures economic, social, and political globalisation on a 0–100 scale; higher scores indicate deeper integration.

Transnational corporations (TNCs) shape global production networks; outsourcing and offshoring shift labour-intensive stages to lower-cost locations.

Global shift of manufacturing to East and South Asia since the 1970s; deindustrialisation in older industrial regions produces structural unemployment.

International migration: push–pull factors, remittance flows, brain drain from periphery, and diaspora networks; refugees are protected under the 1951 Convention.

Global risks: financial contagion, pandemics, climate change, and supply-chain disruption; global governance involves UN, WTO, IMF, and NGOs with uneven authority.

Glocalisation: TNCs adapt products and marketing to local cultures (e.g., fast-food menu variation) while retaining global brand identity.

Map scales: representative fraction (1:50,000), linear, and statement scales; large scale shows small area in more detail.

Contour interpretation: closely spaced contours mean steep slopes; V-shapes pointing uphill mark valleys; hachures mark depressions.

Sampling: random (unbiased), systematic (regular interval, e.g., every 10 m), and stratified (proportional to subgroups); state sample size and justify choice.

Central tendency and spread: mean, median, mode; range, interquartile range, and standard deviation; use IQR to describe dispersion robustly.

Graph selection: bar charts for discrete categories, histograms for continuous data, scatter graphs for correlation, and dispersion diagrams for spread.

Spearman's rank correlation coefficient: rs = 1 − (6Σd²) ÷ (n(n² − 1)); values near +1 or −1 indicate strong positive or negative correlation.

Chi-squared test compares observed and expected frequencies: χ² = Σ((O − E)² ÷ E); degrees of freedom = (rows − 1)(columns − 1).

Fieldwork design: hypothesis, sampling strategy, data collection method, risk assessment, and evaluation of reliability, accuracy, and bias; the IA is a written report of about 2,500 words.

Distinguish correlation from causation; a statistical association does not establish a causal mechanism without a plausible process.

Evaluate data sources: reliability (consistency), validity (measures the intended variable), accuracy (closeness to true value), and bias (systematic distortion).

Identify scale: local, national, regional, and global processes interact; a phenomenon may appear differently at different scales.

Recognise model limitations: the DTM assumes uniform societal progression and ignores migration and policy shocks.

Use the concept of feedback: positive feedback amplifies change (permafrost thaw releasing methane), negative feedback dampens it (increased cloud albedo).

Apply multiple perspectives: economic, social, environmental, and political; weigh stakeholder interests and trade-offs in evaluation.

Avoid ecological fallacy: inferring individual behaviour from aggregate area data; also avoid the modifiable areal unit problem when changing boundaries.

Structure evaluation with evidence, counter-argument, and judgement; state the strength of conclusion and acknowledge uncertainty.

Hypothesis testing: state null and alternative hypotheses; choose a significance level (commonly 0.05); compare calculated statistic with critical value.

Interpret p-values: p < 0.05 rejects the null hypothesis at the 5% level; report the test statistic, degrees of freedom, and conclusion.

Data presentation critique: misleading axes, truncated scales, and area-proportional symbols can exaggerate differences.

Triangulation strengthens conclusions by combining multiple methods (e.g., questionnaire, transect, and secondary census data).

Qualitative data (interviews, photographs, field sketches) complements quantitative data; code responses to identify themes.

GIS layers (raster and vector) enable overlay analysis; buffering and spatial query support site-suitability decisions.

Ethical fieldwork: informed consent, anonymity, avoiding harm to participants, and minimising environmental impact.

Evaluate the IA: justify methods, discuss sampling limitations, and suggest realistic improvements rather than generic ones.

Concept mapping links processes across themes: population pressure → resource use → emissions → climate change → migration.

Systems thinking: identify inputs, outputs, stores, and flows; a change in one component propagates through the system.

Sustainability evaluation uses the three pillars (environmental, social, economic) and intergenerational equity.

Resilience and vulnerability: exposure, sensitivity, and adaptive capacity determine risk to hazards and climate change.

Compare case studies at contrasting levels of development to test generalisations; avoid overgeneralising from a single example.

Use the precautionary principle when evidence is uncertain but consequences are severe (e.g., ocean plastics, geoengineering).

Distinguish mitigation (reducing causes) from adaptation (reducing impacts); both are needed and have different cost and equity implications.

Synthesise a reasoned judgement: state criteria, weigh evidence, acknowledge limitations, and reach a defensible conclusion.

Command words: 'describe' states features; 'explain' gives reasons; 'evaluate' weighs strengths and weaknesses and reaches a judgement.

Use precise geographic terminology (e.g., 'infiltration', 'dependency ratio', 'core–periphery') rather than vague words like 'thing' or 'stuff'.

Support claims with specific data, place names, and dates; unsupported generalisations weaken answers.

Structure extended answers: introduction, developed points with evidence, counter-argument, and conclusion.

Reference secondary sources accurately and distinguish primary from secondary data in the IA.

Check units and scale when interpreting graphs and maps; misreading scale is a common error.

Communicate uncertainty: use phrases such as 'suggests', 'indicates', and 'is likely to' where evidence is incomplete.

Self-evaluate: identify one strength and one limitation of your own inquiry and propose a concrete improvement.

Calculate percentage change: ((new − old) ÷ old) × 100; distinguish absolute change from relative change.

Index numbers express change relative to a base year (base = 100); useful for comparing trends across different units.

Rates vs. totals: a rate (per 1,000 or per 100,000) allows comparison between populations of different sizes.

Choropleth maps show density or rate by area; they can mislead when areas vary greatly in size and population.

Population pyramids: wide base indicates high birth rates; narrowing top indicates lower life expectancy; bulges show migration or baby booms.

Scatter graphs reveal correlation direction and strength; outliers should be identified and explained, not ignored.

Moving averages smooth short-term fluctuations to reveal underlying trends in time-series data.

Precision and rounding: report to an appropriate number of significant figures and avoid spurious precision.

A strong judgement states a clear position, the criteria used, and the evidence supporting it.

Counter-arguments should be acknowledged and rebutted, not ignored; this demonstrates critical thinking.

Weigh short-term vs. long-term impacts (e.g., dam construction: immediate energy gains vs. long-term ecological costs).

Consider spatial scale of impact: local communities may bear costs while national or global actors gain benefits.

Use stakeholder analysis: identify who gains, who loses, and who has power in a decision.

Recognise value judgements: 'sustainable' and 'fair' are normative and require explicit criteria.

Evaluate the strength of evidence: sample size, representativeness, and consistency across sources.

Conclude with a qualified judgement rather than an absolute claim, reflecting the balance of evidence.

Formulate a focused research question that is answerable with available data and linked to a geographic concept.

Select a sampling frame and justify sample size; larger samples reduce sampling error but cost time.

Pilot studies test methods and refine questionnaires before full data collection.

Control variables where possible to isolate the effect under study (e.g., sample at same time of day).

Record data systematically with a clear coding scheme to enable later analysis.

Assess risk and obtain consent; fieldwork must comply with ethical and safety guidelines.

Plan analysis in advance: choose statistical tests and presentation methods suited to the data type.

Evaluate limitations honestly and suggest feasible improvements for future inquiry.

Integrate concepts from core, optional, and global themes to explain complex real-world issues.

Use case studies from contrasting places to illustrate processes and test explanations.

Distinguish description from analysis: analysis explains why patterns exist and what they imply.

Evaluate the role of different actors (states, TNCs, NGOs, individuals) in shaping outcomes.

Recognise that geographic knowledge is provisional and evolves with new data and methods.

Communicate findings clearly with appropriate maps, graphs, and text, citing sources.

Reflect on the inquiry process and identify transferable skills developed.

Reach a justified conclusion that answers the research question directly and acknowledges uncertainty.