Hazardous Enquiry Skills

A geographical enquiry is an investigation into a geographical question or hypothesis using primary or secondary data, following a structured process [1 mark]. It includes: forming a question, designing a method, collecting and presenting data, analysing patterns, drawing a conclusion, and evaluating the process [1 mark].

1. Question / hypothesis — what are you investigating? [1 mark]
2. Method / data collection — what data, from what source? [1 mark]
3. Data presentation — maps, graphs, tables [1 mark].
4. Analysis — patterns, correlations, comparisons [1 mark].
5. Conclusion — does the data support the hypothesis? [1 mark]
6. Evaluation — limitations + improvements [1 mark].

Enquiry question: 'Are vulnerability factors more important than earthquake magnitude in determining death tolls?' [1 mark]

Hypothesis: 'Earthquakes in countries with low vulnerability (high HDI, enforced building codes) cause fewer deaths than earthquakes of similar magnitude in countries with high vulnerability (low HDI, weak codes).' [1 mark]

Why it is suitable.

It is investigable using secondary data — USGS provides earthquake magnitudes + locations; UN OCHA provides death tolls; UN HDI provides vulnerability data. No need for unsafe primary fieldwork [1 mark].

It addresses spec 3.2b directly + allows clear comparison across countries; it can be tested with statistics (e.g. Spearman's rank correlation) and visualised with maps and scatter graphs. A specific, testable question scores higher than a vague one [1 mark].

Sample selection. Choose 10–15 earthquakes of magnitude 6.5+ since 2000, spread across developed countries (Japan, USA, NZ, Italy) and developing/emerging countries (Haiti, Türkiye, Iran, Nepal, Afghanistan). A spread of 10–15 events gives enough data points for statistical analysis while remaining manageable [1 mark].

Data sources. Use the USGS Earthquake Hazards Program for magnitude and location data; UN OCHA + Reliefweb for death tolls and cost data; UN Human Development Report for HDI scores; World Bank for GDP per capita and population density [1 mark].

Tabulation. Create a structured table with columns: country, year, magnitude, deaths, GDP per capita, HDI, building code year, deaths per million people. Calculate deaths per unit magnitude to allow fairer comparison [1 mark].

Reliability checks. Cross-reference death tolls across sources (UN OCHA, government, Reliefweb, USGS) — note that developing-country counts are often estimates with significant uncertainty (Haiti 2010 estimates range from 100k to 316k). Add uncertainty bars where appropriate [1 mark].

A clear method ensures the data is reliable, comparable, and traceable — essential for the analysis and evaluation stages.

An effective hazard enquiry uses MULTIPLE presentation techniques — each chosen for the pattern it best reveals.

Choropleth map of deaths or HDI. A world choropleth map shaded by total earthquake deaths per country (2000–present) reveals SPATIAL CLUSTERS — high deaths in the Himalayan belt, Anatolian fault region, Indonesian archipelago, Andes. Sequential colours (light yellow → dark red) avoid the rainbow trap; 4–6 intervals justified by natural breaks. Add KEY, TITLE, SCALE, NORTH ARROW, SOURCE [2 marks].

Scatter graph of magnitude vs deaths. This reveals RELATIONSHIPS. Plot magnitude (X-axis, linear) against deaths (Y-axis, logarithmic — because deaths vary from ~10 to 300,000). Colour each point by development status (blue = developed, red = developing). The expected pattern: developing countries cluster ABOVE the trend line at high deaths; developed countries below it. This visually proves the vulnerability hypothesis [2 marks].

Multiple bar chart for paired comparisons. Bar charts side-by-side compare individual quakes — e.g. Japan M9.1/16k vs Haiti M7.0/230k. Visual contrast is striking. Useful for stand-out cases that scatter plots may compress [1 mark].

Summary table. Lists all data so readers can verify + recalculate. Should include uncertainty bars for developing-country death tolls (e.g. Haiti 2010: 100k–316k estimates). Tables underpin every other presentation [1 mark].

Together, these four techniques reveal SPATIAL patterns (choropleth), RELATIONSHIPS (scatter), CONTRASTS (bar chart), and underlying DATA (table) — a complete analytical picture.

Secondary-data hazard enquiries are powerful but face several limitations that the evaluation stage must address.

Sample limitations. A study of 10–15 earthquakes is a small sample; statistical patterns may not generalise. Larger samples (50+ events spanning longer periods) would strengthen conclusions. Different time periods (e.g. pre-1990 vs post-1990) might be analysed to capture changes in code enforcement over time [1 mark].

Death-count uncertainty. Death tolls for developing countries are often estimates with significant uncertainty — Haiti 2010 estimates range from 100,000 to 316,000. Some governments under-report (political reasons); others over-report (to attract aid). Cross-referencing multiple sources (UN OCHA, ReliefWeb, USGS, national governments) reduces but cannot eliminate this [1 mark].

Composite indicators mask specifics. HDI is a composite of life expectancy + education + income — it does not directly measure BUILDING CODE ENFORCEMENT or GOVERNANCE quality. A country with high HDI but weak enforcement (e.g. Türkiye 2023) can still suffer catastrophic losses. Future studies should integrate specific governance metrics (e.g. Transparency International Corruption Index) [1 mark].

Correlation vs causation. A strong correlation between HDI and deaths does not PROVE causation — confounding variables (climate, geology, population density) may also matter. The role of CODE ENFORCEMENT specifically can only be tested with detailed case-by-case data [1 mark].

Exposure weighting absent. Deaths reflect both vulnerability AND exposure (population at risk). Two M7.0 quakes — one in rural Iran (low exposure), one in central Port-au-Prince (huge exposure) — produce very different deaths regardless of vulnerability. Better enquiries weight deaths by population density at the epicentre [1 mark].

Suggested improvements. (a) larger sample, (b) building code + enforcement data, (c) exposure weighting, (d) multi-hazard comparison (cyclones + floods + earthquakes) to test if vulnerability generalises, (e) longitudinal study tracking changes over decades [1 mark].

A reflective evaluation strengthens the enquiry by recognising both what was achieved and what remains uncertain.

1. Question + hypothesis. Question: 'Does building code enforcement reduce earthquake death tolls?' Hypothesis: 'Countries with strict and enforced building codes (Japan, Chile, USA) suffer fewer deaths per unit magnitude than countries with weak or unenforced codes (Haiti, Türkiye 2023, Pakistan).' Addresses spec 3.2b + 3.3a [1 mark].

2. Method. Sample 15 earthquakes M6.5+ since 2000 across enforced-code countries (Japan, Chile, USA, NZ, Italy), partial-enforcement countries (Türkiye, Iran, Mexico), and weak-enforcement countries (Haiti, Pakistan, Nepal). Sources: USGS (magnitude, location), UN OCHA (deaths, cost), national reports on building code dates + enforcement (Turkish Earthquake Engineering Society; Japan Building Standard Law; California Office of Emergency Services). Cross-reference death counts across UN, government + Reliefweb [1 mark].

3. Data presentation. (a) Choropleth map of total earthquake deaths 2000+; (b) scatter graph of magnitude vs deaths, colour-coded by enforcement strength; (c) bar chart pairs (Japan Tōhoku M9.1/16k vs Haiti 2010 M7.0/230k; Chile Maule M8.8/525 vs Türkiye 2023 M7.8/60k); (d) summary table with uncertainty bars [1 mark].

4. Analysis. Calculate mean deaths per earthquake by enforcement category: enforced ~10k, partial ~30k, weak ~150k (rough). Plot HDI/enforcement score vs deaths — strong negative correlation. Spearman's rank correlation likely > 0.7 (significant). Outliers: Christchurch 2011 (M6.3/185 — high for enforced country) due to liquefaction; spec mentions this matters [1 mark].

5. Conclusion. Data strongly supports the hypothesis: enforced building codes reduce earthquake deaths by 1-2 orders of magnitude for similar-magnitude quakes (Japan M9.1/16k vs Haiti M7.0/230k = ~14× fewer deaths for ~700× more energy). Chile (M8.8/525) shows even emerging countries can dramatically reduce deaths with enforcement. Türkiye 2023 (~60k+) shows code reform without enforcement saves few lives [2 marks].

6. Evaluation. Limitations: small sample (15 events); death-count uncertainty; enforcement quality hard to quantify; confounding variables (population density, geology). Improvements: larger sample (50+); detailed enforcement scoring (Transparency Index proxy); weight by exposure; longitudinal study of countries over multiple events. Despite limitations, the consistency + magnitude of the pattern strongly supports the conclusion: ENFORCEMENT is the single most important determinant of earthquake death tolls [2 marks].

This judgement requires distinguishing what 'validity' means and recognising the unique constraints of hazard research.

Why secondary-data enquiries are sometimes seen as 'less valid'.

(1) Lack of primary measurement. Students do not collect data themselves — so cannot verify accuracy, equipment calibration, or measurement technique. Traditional river/coast fieldwork involves direct ranging-pole transects, sediment analysis, etc. — students see + own the data [2 marks].

(2) Distance from phenomenon. A choropleth of earthquake deaths is abstract — students never feel ground shaking, hear an EEWS alert, or witness damage. Fieldwork on coasts/rivers engages the senses + builds intuition [1 mark].

(3) Data quality variable. Secondary data is only as good as its sources. Developing-country death counts vary widely (Haiti 2010: 100k–316k estimates); national governments may under- or over-report; some sources biased [1 mark].

Why secondary-data enquiries are equally or MORE valid for hazards.

(4) Safety + ethics. Active hazards kill — students cannot safely study active fault zones, volcanoes, or post-disaster zones. Secondary data is the ONLY feasible approach. A 'less valid' enquiry that students could not safely do at all is the wrong comparison [2 marks].

(5) Scale + statistical power. Secondary data allows global comparison (15+ countries, 50+ events) — far more powerful than the small geographic scope of any primary study. Statistical analysis (correlations, regressions) becomes meaningful at global scale [2 marks].

(6) Professional standard. Real hazard scientists use overwhelmingly secondary + remote data — USGS catalogues, satellite InSAR, GIS overlays. Students working with these sources are practising AUTHENTIC contemporary methods, not amateur-level primary measurement [1 mark].

(7) Multiple-source triangulation. Cross-referencing UN OCHA + USGS + government data + media reports forces students to assess reliability + uncertainty — a critical skill that primary fieldwork (single source = your own) often lacks [1 mark].

Conclusion. The premise is misleading. For hazards, secondary-data enquiries are not 'less valid' — they are the appropriate + only safe method, and they teach skills (source evaluation, GIS, statistical analysis) that match how professional hazard scientists actually work. Primary fieldwork suits rivers/coasts/farms; secondary-data enquiry suits hazards. Both are valid when matched to the phenomenon being studied. The strongest geography education combines both approaches across the syllabus.