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Spec mapping (AQA 7037): The Non-Examined Assessment is the third assessment component — the Independent Investigation, worth 60 marks (20% of the A-Level). It is the most AO3-heavy part of the qualification (skills lead, supported by AO2 application and AO1 knowledge — roughly AO3 30% / AO2 40% / AO1 30% of the NEA marks). It assesses your ability to run the full geographical enquiry process — purpose → methods → data → presentation → analysis → conclusion → evaluation — on a question you define yourself, rooted in the 7037 specification content.
The NEA is the one component where you control the pace, can redraft, and can bank marks before you ever sit a paper. This lesson is a complete, practical guide to every stage: choosing a question, the enquiry process, primary and secondary data, sampling, risk assessment, presentation and statistics, the mark bands, and the pitfalls that quietly cost the most.
The NEA must be an independent investigation into a geographical question or issue. AQA requires that it:
The investigation must connect to the 7037 specification — your question should relate to one or more course topics so that you can apply real geographical theory.
Key Point: "Independent" is the load-bearing word. Your teacher may advise on methodology and give feedback on one draft, but the question, fieldwork, analysis and conclusions must be your own. Moderators check authenticity; work that looks heavily steered or ghost-written is flagged. Keep a fieldwork log and dated photos of yourself collecting data — they double as evidence of authenticity and as appendix material.
Every strong NEA follows the geographical enquiry route. Think of it as a chain in which each stage is justified by the one before it: your methods must serve your question, your analysis must interrogate your data, and your evaluation must reflect honestly on the methods.
graph TD
A["1. Purpose: question + theory + location"] --> B["2. Methods: primary + secondary + sampling + risk"]
B --> C["3. Data collection: fieldwork"]
C --> D["4. Presentation: range of techniques"]
D --> E["5. Analysis: patterns, anomalies, statistics, theory"]
E --> F["6. Conclusion: answer the question, link to theory"]
F --> G["7. Evaluation: reliability, limitations, improvements"]
The mark scheme maps almost one-to-one onto this chain, so a report that visibly moves through these stages is already structured for the top bands.
The NEA is marked against four assessment criteria:
| Section | Marks | AO emphasis | What moderators reward |
|---|---|---|---|
| 1. Purpose of the investigation (introduction & planning) | 6 | AO3 | A clear geographical question; justification; explicit link to specification theory; a focused literature review; defined location and route to enquiry |
| 2. Methods of data collection | ~18 | AO3 | Appropriate primary methods; justified sampling; risk assessment; ethics; relevant secondary data; awareness of limitations |
| 3. Data presentation, analysis & interpretation | ~18 | AO3 + AO2 | A range of presentation techniques; correct statistics; interpretation tied to theory; identification of patterns, trends and anomalies |
| 4. Conclusions & evaluation | ~18 | AO1 + AO2 | Conclusion that answers the question and links to theory and wider context; critical evaluation of method, reliability and limitations; improvements and extensions |
| Total | 60 | — | — |
Each section is marked across three levels (the precise band totals are set in AQA's mark grid each series; treat the figures above as the standard weighting). The headline point is stable: Section 1 is worth only 6 marks, while collection, analysis and evaluation are each worth roughly three times that.
| Level | Character of the work |
|---|---|
| Top level | Thorough, detailed, well-justified, analytical and evaluative; strong, sustained links to theory; a genuine range of techniques; critical self-reflection |
| Middle level | Sound and generally appropriate; some justification and analysis but lacking depth; some links to theory; limited range of techniques |
| Lower level | Basic, poorly justified, descriptive rather than analytical; weak/absent theory links; very limited techniques |
Exam Tip: Many students lavish words on a beautiful introduction worth only 6 marks and then sprint through the 36 marks of analysis and evaluation. Allocate effort to where the marks live. A useful rule: the introduction should be the shortest substantial section in your report.
Your title must be a question — an AQA requirement, not a stylistic preference. A question gives focus, makes the investigation testable, and hands you a clear thing to answer in your conclusion.
A strong NEA title is (1) a question, not a topic; (2) focused enough to investigate thoroughly within ~4,000 words; (3) testable with primary data you can realistically collect; (4) rooted in geographical theory; (5) feasible to complete safely and ethically in the time available; and (6) analytical rather than descriptive.
| Quality | Title | Why |
|---|---|---|
| Good | "To what extent does environmental quality in residential areas of [Town X] vary with distance from the CBD?" | Focused, testable, linked to Burgess/Hoyt, measurable primary data (EQS), clear spatial frame |
| Good | "How does sediment size and roundness change along [Beach Y] from north to south, and does longshore drift explain the pattern?" | Specific location, testable hypothesis, coastal-process theory, measurable data (callipers, Power's roundness) |
| Good | "Is there a statistically significant relationship between deprivation and flood risk in [City Z]?" | Synoptic (physical + human), testable with statistics, uses IMD + flood maps |
| Bad | "Climate change" | Not a question; far too broad; no focus |
| Bad | "A study of rivers" | Not a question; no measurable focus |
| Bad | "Is global warming causing more hurricanes?" | Not investigable with student primary fieldwork; wrong scale |
| Bad | "Why is London a world city?" | Too broad; primary data near-impossible; no clear method |
| Specification topic | Possible NEA question |
|---|---|
| 3.1.1 Water and Carbon Cycles | "How does land use affect infiltration rates in [Catchment X]?" |
| 3.1.2 Coastal Systems | "To what extent does longshore drift control beach morphology at [Location Y]?" |
| 3.1.5 Hazards | "How does flood-risk perception differ between floodplain and non-floodplain residents in [Town Z]?" |
| 3.2.2 Changing Places | "How and why do perceptions of place identity differ between long-term residents and recent migrants in [Area W]?" |
| 3.2.3 Urban Environments | "Does the Burgess model accurately predict land-use distribution in [City V]?" |
Exam Tip: The strongest titles are often synoptic, fusing physical and human geography — how flood risk affects house prices, or how coastal erosion reshapes community identity. Synopticity is explicitly valued and gives you richer theory to evaluate against.
Primary data is data you collect through fieldwork. AQA requires it — a desk study on secondary sources alone does not meet the specification.
| Method | What it measures | Example application |
|---|---|---|
| Questionnaires | Perceptions, opinions, behaviours | Hazard-risk perception, place identity, shopping habits |
| Environmental Quality Survey (EQS) | Built/natural environment via a scoring rubric | Urban quality transects, comparing residential areas |
| Field measurements | Velocity, depth, width, gradient, sediment size | River channel surveys, beach profiles |
| Land-use mapping | Distribution of land uses | Testing urban models; tracking change |
| Pedestrian/traffic counts | Volume and patterns of movement | Sphere of influence; retail hierarchy |
| Photographic/field-sketch surveys | Visual evidence of features, land use, condition | Supporting qualitative analysis; documenting change |
| Soil/vegetation sampling | Depth, pH, organic content; species diversity | Succession and ecology studies |
| Weather measurements | Temperature, wind, humidity, precipitation | Urban heat island; microclimate |
Your sampling choice must be justified, not merely named — this is a frequent Level-2 ceiling.
| Strategy | How it works | Best when | Limitation |
|---|---|---|---|
| Random | Points chosen via random numbers | Area is fairly uniform; bias must be minimised | Can cluster by chance; may miss key features |
| Systematic | Regular intervals (e.g. every 50 m) | Even coverage of a transect/area is wanted | May lock onto a repeating pattern |
| Stratified | Area split into sub-groups; sample each | Distinct zones exist (e.g. land-use types) | Needs prior knowledge; may miss transitions |
| Pragmatic/opportunistic | Where safe access allows | Access is restricted (private land, safety) | Most bias-prone; must be acknowledged |
Exam Tip: Level 3 explains why the strategy fits the question and how it might bias results. Write "I used stratified sampling because the town has three clearly distinct housing zones, ensuring each was represented; however, this risked under-sampling the transition zones between them" — not just "I used stratified sampling."
AQA expects a documented risk assessment and ethical awareness — and these earn marks in Section 2.
| Hazard | Risk | Control measure |
|---|---|---|
| Working near a river/coast | Slips, deep/fast water, being cut off by tide | Check tide tables; stay back from edges; never wade alone; fieldwork in pairs |
| Roadside surveys | Traffic | High-vis clothing; survey from the pavement; avoid peak traffic |
| Lone working | Injury/incident with no help | Carry a charged phone; share your route and return time; work in daylight |
| Questionnaires with the public | Personal safety; informed consent | Survey in public, populated places; never record names of minors; explain purpose and right to decline |
| Weather | Exposure, heatstroke | Check forecast; appropriate clothing; reschedule if unsafe |
Ethics: gain informed consent for questionnaires, keep responses anonymous, avoid pressuring participants, respect private land and protected sites, and never put yourself or others at risk for data.
Good data starts with a disciplined collection day. Pilot your method first; standardise your procedure so every site is measured identically (same EQS rubric, same instrument, same recorder where possible) to reduce observer bias; record the context (date, time, weather) because you will need it in your evaluation; and photograph each site for your appendix and as authenticity evidence. Capture data straight onto a pre-printed recording sheet rather than loose paper — a structured sheet prevents the missing values that wreck statistical tests later.
| Before the day | On the day | After the day |
|---|---|---|
| Pilot the method; finalise recording sheet; check kit and tide/weather | Standardise procedure across sites; log time/weather; photograph sites | Digitise data immediately; flag anomalies; back up |
Exam Tip: The notes you make during fieldwork about what went wrong — a broken tape, an uncooperative respondent, a sudden downpour — are pure gold for the evaluation section. Keep a running "limitations log" in your field notebook; trying to reconstruct these honestly three months later is far weaker than recording them live.
Secondary data — collected by others — provides context, comparison and a backdrop for your primary findings.
| Source | What it provides | Where to find it |
|---|---|---|
| Census data | Demographics, housing, employment | ONS / nomisweb |
| Index of Multiple Deprivation | Deprivation by LSOA across seven domains | MHCLG / local authority |
| GIS / flood data | Flood maps, land use, transport networks | Digimap, Environment Agency, Google Earth |
| Satellite imagery | Land cover, urban extent, change over time | Google Earth, Sentinel Hub, NASA Earthdata |
| OS / historical maps | Topography, infrastructure, change | Digimap; National Library of Scotland |
| Published statistics | Climate, economic, environmental data | Met Office, World Bank, DEFRA |
| Academic literature | Theory, models, prior findings | Google Scholar, JSTOR |
Exam Tip: The best NEAs integrate secondary data into the analysis rather than parking it in the introduction — e.g. overlaying your EQS scores on an IMD choropleth to test whether environmental quality tracks deprivation, or comparing your beach profiles with Environment Agency survey data from earlier years.
AQA rewards a genuine range of presentation techniques; relying on a couple of bar charts caps you. Match the technique to the data: bar charts for discrete categories, histograms for frequency distributions, line graphs for change over time/transect, scatter graphs for relationships, box-and-whisker for spread and outliers, proportional symbols / choropleth / isoline / desire-line maps for spatial data, and cross-sections for landscape variation.
Spearman's rank correlation coefficient (rₛ) — tests the strength and direction of a relationship between two ranked variables; output between −1 and +1; compare the calculated value with the critical value at the 0.05 level for your sample size. Example: does distance from the CBD correlate with EQS? It is computed as:
rs=1−n(n2−1)6∑d2
where d is the difference between the two ranks for each pair and n is the number of pairs.
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