Why is "Justifying a method with 'because it is easy/quick' instead of why it suits the hypothesis" a common mistake in Human Geography Research and Fieldwork?

Why it happens: Students remember that a method was convenient rather than why it produces the right, representative data for the enquiry. How to avoid it: Justify every method against the **hypothesis and data needed**: 'systematic sampling every 200 m gave even coverage of the transect so the change in EQI with distance from the CBD was fairly represented'. Convenience alone is a Band 1 answer. Source: Edexcel IAL Geography Unit 2 examiner reports

Why is "Describing what you did when the question asks you to evaluate or assess" a common mistake in Human Geography Research and Fieldwork?

Why it happens: Recounting the method feels safe, so students narrate the fieldwork instead of judging its reliability and validity. How to avoid it: For 'evaluate/assess', **weigh strengths against weaknesses** using the words **reliability** and **validity**, give **specific limitations** (EQI subjectivity, sample size, snapshot) and reach a **judgement** with targeted improvements. Source: Edexcel IAL Geography Unit 2 examiner reports

Why is "Listing vague limitations like 'human error' or 'the weather' without saying how they affected the data" a common mistake in Human Geography Research and Fieldwork?

Why it happens: Students know limitations are expected but reach for generic phrases rather than method-specific problems. How to avoid it: Name a **specific limitation and its effect**: 'scoring the EQI alone (one observer) reduced validity because the scores reflected one person's opinion of what counts as littered'. Then give a **targeted improvement**. Source: Edexcel IAL Geography Unit 2 examiner reports

Why is "Treating EQI, bipolar or Likert scores as fully objective facts" a common mistake in Human Geography Research and Fieldwork?

Why it happens: Because the scores are numbers, students assume they are as objective as a measured length or count. How to avoid it: State that EQI/bipolar and Likert scores are **based on personal judgement or perception**, so they are **partly subjective**; reduce this with **defined scoring sheets, independent scoring and averaging**, and **triangulate** with objective data (counts, census, IMD) before drawing firm conclusions. Source: Edexcel IAL Geography Unit 2 examiner reports

Edexcel International A Levels Geography (XGE01-YGE01)

Human Geography Research and Fieldwork

Q: Why is "Giving invented or generic fieldwork with no real place, figures or detail" a common mistake in Human Geography Research and Fieldwork?

Why it happens: Students who did not engage with their own investigation fall back on textbook-style, place-less descriptions. How to avoid it: Use **your real investigation specifics** in Section B — the actual city/transect, number of sites, interval, indicators and results — and **apply methods to the specific resource** in Section C. Specific detail (who/where/how many) is what reaches Band 3. Source: Edexcel IAL Geography Unit 2 examiner reports

Q: Why is "Writing a conclusion that does not link back to the original hypothesis or enquiry question" a common mistake in Human Geography Research and Fieldwork?

Why it happens: Students treat the conclusion as a general summary of the trip rather than a direct answer to what they set out to test. How to avoid it: Always **return to the hypothesis**: state whether the data supported or rejected it, quote the key result (e.g. the chi-squared / rs value and significance), and answer the enquiry question explicitly. Source: Edexcel IAL Geography Unit 2 examiner reports

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Detailed Study Notes

Detailed notes on Urban Problems, Planning and Regeneration for Edexcel International A Levels Geography, covering key concepts, explanations, examples, and exam-focused revision points.

Human Geography Research and Fieldwork (Urban) — Pearson Edexcel IAL Geography (XGE01/YGE01) Study Notes (Unit 2)

How to plan, carry out and write up an urban fieldwork investigation for Unit 2 (WGE02: 'Urban Problems, Planning and Regeneration'): the six-step enquiry process; forming an enquiry question and hypothesis and choosing systematic, random or stratified sampling; collecting primary data (Environmental Quality Index and bipolar surveys, pedestrian and traffic counts, Likert-scale questionnaires, land-use mapping) and secondary data (census, the Index of Multiple Deprivation, house prices, regeneration reports, historical maps); presenting data (choropleth maps, located bars, scatter graphs); analysing it with chi-squared or Spearman's rank; and evaluating reliability and validity before concluding against the hypothesis — ready for Section B (your OWN investigation) and Section C (an UNFAMILIAR urban location).

At a glance

Fieldwork is a 6-step enquiry: question/hypothesis → plan + risk assessment → collect data → present → analyse (incl. statistics) → conclude + evaluate.
A hypothesis is a precise, testable statement (e.g. 'environmental quality improves with distance from the CBD') that data can support or reject; pair it with a null hypothesis.
Sampling can be systematic (every nth site — even coverage), random (random numbers — no bias, can cluster) or stratified (proportional to sub-areas/groups).
Urban primary data: Environmental Quality Index / bipolar surveys, pedestrian and traffic counts, Likert-scale questionnaires, land-use mapping and transects.
Urban secondary data: census, the Index of Multiple Deprivation (IMD), house-price data, council regeneration reports and historical maps.
Presentation: area data → choropleth map; sequential/located data → located bars; relationship between two variables → scatter graph.
Statistics: chi-squared tests a difference from expected frequencies; Spearman's rank tests correlation — always compare to a critical value at 95% and interpret geographically.
Evaluation weighs reliability (would repeats agree?) and validity (does the EQI really measure quality of life?); conclusions must link back to the hypothesis.

What you’ll learn

Mapped to the Pearson Edexcel International A Levels XGE01-YGE01 syllabus (2016-onwards).

WGE02 — Understand the enquiry process for an urban fieldwork investigation: forming an enquiry question and hypothesis (with a null hypothesis), planning methodology and a risk assessment, and choosing an appropriate sampling strategy.
WGE02 — Collect and use urban primary data (Environmental Quality Index / bipolar surveys, pedestrian and traffic counts, Likert-scale questionnaires, land-use mapping) and secondary data (census, IMD, house prices, regeneration reports, historical maps).
WGE02 — Select and justify appropriate data-presentation techniques (choropleth maps, located bars, line and scatter graphs, radar diagrams) that match the data type.
WGE02 — Analyse fieldwork data, including averages, anomalies and inferential statistics (chi-squared and Spearman's rank), comparing results with a critical value at the 95% significance level.
WGE02 — Evaluate the reliability and validity of methods and reach a conclusion that links directly back to the hypothesis, for both Section B (own investigation) and Section C (unfamiliar context).

The urban fieldwork enquiry process (6 steps)

Every investigation follows the same route: question, plan, collect, present, analyse, conclude and evaluate.

Unit 2 fieldwork is assessed as an enquiry process, not just a set of methods. Examiners want you to show you understand why each stage is done and how they link together. The same six steps apply to an urban 'Urban Problems, Planning and Regeneration' investigation.

Form an enquiry question and hypothesis. Turn a broad aim ('study regeneration') into a precise enquiry question ('Has regeneration improved quality of life in area X?') and a testable hypothesis ('Environmental quality is higher in the regenerated area'), with a null hypothesis for the statistics.
Plan the methodology and a risk assessment. Decide what data, where, how much and which sampling strategy; assess urban hazards (traffic, personal safety) and controls.
Collect primary and secondary data. Gather primary data in the field (EQI, counts, questionnaires) and secondary data (census, IMD, house prices, historical maps).
Present the data. Choose techniques that match the data type (choropleth, located bars, scatter graphs).
Analyse and interpret (including statistics). Use averages, spot anomalies, and run a chi-squared or Spearman's rank test compared with a critical value.
Conclude and evaluate. Answer the hypothesis directly, then judge reliability and validity and suggest improvements.

This cycle is exactly what Section B (your own investigation) and Section C (an unfamiliar location) test — you either recall your own enquiry or apply the process to a new place.

The fieldwork enquiry cycle

1. Question + hypothesis + null hypothesis 2. Plan method + risk assessment sampling strategy 3. Collect data primary + secondary 4. Present data choropleth, bars, scatter graphs 5. Analyse averages, anomalies, chi-squared / Spearman 6. Conclude + evaluate reliability + validity refine the enquiry and repeat if needed

Section B recalls YOUR cycle; Section C applies it to an unfamiliar location.

Fieldwork is examined as a linked enquiry process. Knowing the six steps lets you structure both an own-investigation answer and an unfamiliar-context answer.

Six steps: question/hypothesis → plan + risk assessment → collect → present → analyse → conclude + evaluate.
Start every enquiry with a testable hypothesis and a null hypothesis.
Section B recalls your own investigation; Section C applies the same process to a new place.
Examiners reward understanding WHY each stage is done, not just naming methods.

See the full worked example for human geography research and fieldwork →

Forming a hypothesis and choosing a sampling strategy

A precise, directional hypothesis plus the right sampling strategy make results representative and testable.

Forming a hypothesis. A good hypothesis is precise, directional and measurable. For an urban transect from the CBD outwards, examples include:

'Environmental quality improves with distance from the CBD.'
'Quality of life declines towards the inner city.'
'Regeneration has improved environmental quality in area X compared with a neighbouring un-regenerated area.'

Each names a variable (environmental quality, quality of life), a direction of change, and a place, and each comes with a null hypothesis ('there is no difference/relationship') needed for the statistics.

Choosing a sampling strategy. You cannot survey every street, so you sample — and the method must fit the hypothesis:

Strategy	How	Best for
Systematic	Every nth site/person (e.g. every 200 m; every 5th person)	Even coverage along a transect — change with distance from the CBD
Random	Chosen using random numbers	Removing observer bias; but points can cluster and leave gaps
Stratified	Split into sub-areas/groups and sample each in proportion	Fair comparison of sub-areas (CBD, inner city, suburb) or groups (age bands)

For a CBD-to-suburb transect, systematic sampling (e.g. every 200 m, 11 sites) gives the even coverage a distance hypothesis needs and a clean variable for a statistical test. For comparing a regenerated vs un-regenerated area, stratified sampling of equal numbers in each area is fairer.

Systematic sampling along a transect (every 200 m)

CBD dense, high-rise SUBURBS low-density, green

site 1 0 m site 6 1000 m site 11 2000 m

Fixed 200 m interval → even coverage → a clean distance variable for a stats test

Systematic sampling at a fixed interval gives even coverage of the whole transect, so a change-with-distance hypothesis is fairly tested — random points could cluster and leave gaps.

Hypothesis = precise + directional + measurable, with a null hypothesis for stats.
Systematic sampling (every nth) suits change-with-distance transects.
Random sampling removes bias but can cluster; stratified samples sub-areas/groups in proportion.
Match the strategy to the hypothesis, not to convenience.

See the full worked example for human geography research and fieldwork →

Collecting primary and secondary data in the city

Field techniques (EQI, counts, questionnaires) gather primary data; census, IMD and maps supply secondary data.

A strong urban investigation combines primary data (collected first-hand) with secondary data (already published), so results can be compared and put in context.

Primary data techniques (urban):

Environmental Quality Index (EQI) / bipolar surveys — score indicators (litter, greenery, building quality, noise, graffiti) on a scale (e.g. −2 to +2) and total them for each site.
Pedestrian counts and traffic counts — count people/vehicles passing a fixed point for a set time (e.g. 10 minutes) at several sites and times.
Questionnaires — structured, with a Likert scale (strongly agree … strongly disagree, scored 5–1) to quantify quality-of-life perceptions.
Land-use mapping / transects — record the use of each building/plot along a route; housing-quality surveys; photographs and field sketches.

Secondary data (urban):

Census data — population, age, ethnicity, housing tenure, car ownership.
Index of Multiple Deprivation (IMD) — a combined deprivation score/rank for each small area.
House-price data and local authority regeneration reports.
Historical maps and aerial photographs — to show land-use change over time.

The best answers name the tool AND the indicator/variable it measures, and use both data types together (e.g. EQI scores checked against IMD and house prices).

Environmental Quality Index (EQI) scoring sheet

negative (−2) positive (+2)

−2 −1 0 +1 +2

Litter score −1

Greenery / open space score 0

Building quality score +1

Noise / traffic score −2

Graffiti / vandalism score −1

TOTAL EQI = −3 Add every indicator → one value per site

An EQI turns environmental quality into a comparable number by scoring fixed indicators on a bipolar scale and totalling them — but the scores are opinion-based, so define each score and have two people score independently.

Primary (urban): EQI/bipolar survey, pedestrian and traffic counts, Likert questionnaires, land-use mapping.
Secondary (urban): census, Index of Multiple Deprivation, house prices, regeneration reports, historical maps.
Name the tool AND the variable it measures; use primary and secondary data together.
Standardise counts (same time/length) and define EQI scores to keep data comparable.

See the full worked example for human geography research and fieldwork →

Presenting urban data: matching technique to data type

Area data suits a choropleth; sequential data suits located bars; relationships suit a scatter graph.

Presentation marks are won by choosing the technique that matches the data type — not by making everything a bar chart.

Choropleth map — for area data covering a whole map, such as IMD deprivation or census values by ward. Shade each area by class (light = low, dark = high) to reveal the spatial pattern (e.g. deprived inner core fading to wealthier suburbs). Use sensible class boundaries and a clear key.
Located bar charts / proportional symbols — for values at points, such as EQI totals or pedestrian counts along a transect, plotted on a base map so the pattern reads spatially.
Line graph — for continuous change, e.g. EQI or pedestrian count against distance from the CBD.
Scatter graph — for the relationship between two variables (e.g. EQI vs pedestrian count, or house price vs distance), with a best-fit line; this sets up a Spearman's rank test.
Radar / rose diagrams — to display the profile of EQI indicators at a site.

The golden rule: sequential/located → bars or line; area → choropleth; relationship → scatter. Poor presentation (a badly classed choropleth, or a graph type that hides the pattern) weakens the analysis that follows.

Choropleth map: deprivation (IMD) across a city

inner city (most deprived) suburb ward

Key: IMD least deprived low high most deprived

Darker = more deprived. The spatial pattern (deprived core → wealthier edge) is read at a glance. Choropleths suit AREA data; use sensible class boundaries and a clear key.

A choropleth is ideal for area data such as deprivation, showing the spatial pattern instantly — but it implies each ward is uniform, and the pattern depends on the class boundaries chosen.

Area data (IMD, census by ward) → choropleth map with a clear key.
Located values (EQI, counts) → located bars / proportional symbols on a base map.
Relationship between two variables → scatter graph with best-fit line (sets up Spearman's rank).
Match the technique to the data type; class choropleths sensibly.

See the full worked example for human geography research and fieldwork →

Analysis and statistics: chi-squared and Spearman's rank

Use averages and anomalies, then a chi-squared or Spearman's rank test compared with a 95% critical value.

Analysis moves beyond describing the data to testing it. Start with averages (mean/median) and by identifying anomalies (points off the trend — check for error, then explain rather than delete). Then use an inferential statistic.

Chi-squared ( $\chi^{2}$ ) tests whether observed frequencies differ significantly from expected frequencies (a test of difference, not correlation):

$\chi^{2} = \sum \frac{(O - E)^{2}}{E}$

Worked example — pedestrian counts of 90, 70, 50, 30 at four sites (total 240) against an even expected value of $240 \div 4 = 60$ :

$(90-60)^{2}/60 = 15$ ; $(70-60)^{2}/60 = 1.67$ ; $(50-60)^{2}/60 = 1.67$ ; $(30-60)^{2}/60 = 15$ .
$\chi^{2} = 15 + 1.67 + 1.67 + 15 = 33.34$ .
Degrees of freedom $= 4 - 1 = 3$ ; the 95% critical value is 7.82.
$33.34 > 7.82$ , so reject the null hypothesis: footfall is genuinely uneven, concentrating in the CBD.

Spearman's rank ( $r_s$ ) tests the strength and direction of a correlation between two ranked variables, from −1 to +1:

$r_s = 1 - \frac{6 \sum d^{2}}{n(n^{2}-1)}$

For example, $r_s = +0.81$ for EQI against distance from the CBD is a strong positive correlation; if it exceeds the 95% critical value for that $n$ , reject the null hypothesis. Always compare with the critical value and state the geographical meaning — a number alone is incomplete, and correlation is not proof of cause.

Analysing fieldwork data with statistics

Spearman's rank (correlation) EQI score distance from CBD → rs ≈ +0.81 (strong positive) exceeds 95% critical value → reject null

Chi-squared (difference) CBD inner suburb outer 90 70 50 30 expected 60 chi-squared = 33.34 (df = 3) 33.34 > 7.82 critical value → reject null

Spearman's rank tests a correlation between two variables (left); chi-squared tests whether observed frequencies differ from expected (right). Both are only finished when compared with the 95% critical value and interpreted geographically.

Start with averages and anomalies (check for error, then explain — do not delete).
Chi-squared = sum of (O − E)²/E tests a difference from expected frequencies; df = categories − 1.
Spearman's rank tests correlation between two variables (−1 to +1).
Always compare the result with the 95% critical value and state the geographical meaning.

See the full worked example for human geography research and fieldwork →

Evaluation and conclusion: reliability, validity and the hypothesis

Judge reliability and validity, give specific limitations and improvements, and answer the hypothesis directly.

The final steps separate the top answers from the rest. Evaluation and conclusion are where AO3 marks are concentrated.

Evaluate reliability and validity — and keep them distinct:

Reliability — would the method give the same result if repeated? Raised by larger samples, repeated measurements, independent scoring and a standard procedure. (A single EQI score at each site = low reliability.)
Validity — does the data actually measure what the hypothesis needs? Weakened by subjectivity (the EQI is opinion-based), bias (asking only one group in a questionnaire), snapshot conditions (one afternoon, one weather), or measuring the wrong variable.

Give specific limitations with their effect ('scoring the EQI alone reduced validity because the scores reflected one person's opinion') and targeted improvements ('two scorers averaging; repeat on several days; triangulate with IMD data').

Conclude by answering the hypothesis. State whether the data supported or rejected it, quote the key result (the chi-squared or $r_s$ value and its significance), and answer the enquiry question directly. A conclusion that does not link back to the hypothesis loses marks.

Reliability vs validity → conclusion

RELIABILITY consistent if repeated tight group = consistent, but not on target ↑ bigger samples, repeats, standard method

VALIDITY measures the right thing on the bullseye = measures what the hypothesis needs ↓ subjectivity, bias, snapshot weaken it

CONCLUSION: answer the hypothesis quote the key statistic + significance; answer the enquiry question directly

Reliability is consistency (a tight group); validity is measuring the right thing (on the bullseye). Evaluate both, then write a conclusion that answers the hypothesis and quotes your key result.

Reliability = same result if repeated; validity = measures what the hypothesis needs.
Give specific limitations with their effect, then targeted improvements.
Conclusion must answer the hypothesis and quote the key statistic and its significance.
Do not narrate the method when asked to evaluate — weigh strengths against weaknesses and judge.

See the full worked example for human geography research and fieldwork →

Quick recap

Fieldwork is a six-step enquiry: question/hypothesis → plan + risk assessment → collect → present → analyse → conclude + evaluate.
A hypothesis is precise, directional and measurable, with a null hypothesis; sampling (systematic/random/stratified) must fit the hypothesis.
Urban primary data: EQI/bipolar surveys, pedestrian and traffic counts, Likert questionnaires, land-use mapping; secondary: census, IMD, house prices, regeneration reports, historical maps.
Present by data type: area → choropleth; located → bars; relationship → scatter graph.
Chi-squared tests a difference from expected frequencies; Spearman's rank tests a correlation — both compared with the 95% critical value.
Evaluate reliability (consistency) and validity (measuring the right thing) with specific limitations and improvements.
The conclusion must answer the hypothesis and quote the key statistic and its significance.

Memorise this

Verbatim phrases and definitions Edexcel mark schemes credit.

Enquiry: question/hypothesis → plan + risk assessment → collect → present → analyse → conclude + evaluate.
Hypothesis = precise + directional + measurable, with a null hypothesis.
Systematic = even coverage (transect); random = no bias but clusters; stratified = sub-areas in proportion.
Urban primary: EQI, pedestrian/traffic counts, Likert questionnaires, land-use mapping.
Urban secondary: census, Index of Multiple Deprivation (IMD), house prices, regeneration reports, historical maps.
Area data → choropleth; located data → bars; relationship → scatter graph.
Chi-squared = Σ(O−E)²/E (difference); Spearman's rank (correlation) — compare to the 95% critical value.
Reliability = consistency; validity = measuring the right thing; conclusion links back to the hypothesis.

How it’s examined

Urban fieldwork is examined in Unit 2 (WGE02: Geographical Investigations) through Section B and Section C. Section B tests your OWN investigation ('Urban Problems, Planning and Regeneration'): expect questions (2-8 marks) asking you to state your hypothesis and sampling strategy, justify a method or a presentation technique, or assess how confident you can be in your conclusion — answered with your REAL specifics (the actual city/transect, number of sites, interval, indicators, results). Section C gives an UNFAMILIAR urban location in the resource and asks how a geographer could investigate it (e.g. 'suggest how to investigate whether regeneration has improved quality of life at this location') — you must apply the enquiry process to that specific place, not list generic methods. Higher-tariff questions (6-8 marks) are levels-based AO3 (Band 1-3): the discriminators are SPECIFICITY (who/where/how many/why chosen/what limitation), correct use of 'reliability' and 'validity', showing statistical working (chi-squared/Spearman's rank compared with a critical value), and a conclusion that links back to the hypothesis. There are no 15-20 mark essays in this fieldwork/skills content; the maximum tariff is around 8 marks.

Sources: Pearson Edexcel International Advanced Level Geography (YGE01/XGE01) specification (Issue 3, 2016 onwards); Edexcel IAL Geography Unit 2 examiner reports; Edexcel IAL Geography Unit 2 (WGE02) sample assessment materials and mark schemes. Last reviewed 2026-07-13.

Take this whole topic with you

Step-by-step worked examples — Human Geography Research and Fieldwork

These eight walkthroughs take you through the urban fieldwork enquiry process step by step — forming a hypothesis, choosing a sampling strategy, designing an Environmental Quality Index and a Likert-scale questionnaire, running a chi-squared calculation on collected data, reading an urban dataset and evaluating a method — and show exactly how to structure answers to Unit 2 Section B (your own investigation) and Section C (an unfamiliar urban context) fieldwork questions.

Question type:

1Turn an aim into a testable urban hypothesis (3 marks)
Getting startedIdentify & classify• hypothesis, enquiry question, environmental quality, Unit 2
Question
A student wants to investigate how environmental quality changes across a city from the inner city towards the suburbs. Write a suitable hypothesis and explain why it is testable. (3 marks)
Step-by-step solution
1. Step 1
  Start from the enquiry question: 'How does environmental quality change with distance from the CBD/inner city?' A hypothesis is a precise, testable statement you can support or reject with data.
2. Step 2
  Write a directional hypothesis with a measurable variable: 'Environmental quality improves with distance from the CBD.' It names the variable (environmental quality), the direction of change (improves) and where it is measured (along a transect out of the CBD).
3. Step 3
  Explain why it is testable: environmental quality can be scored objectively using an Environmental Quality Index (EQI) at fixed points, and the pattern can be tested statistically (e.g. a chi-squared or Spearman's rank test) against distance — so the data can either support or reject it. Always pair it with a null hypothesis ('there is no relationship') for the statistics.
Answer
Hypothesis: 'Environmental quality improves with distance from the CBD.' It is testable because environmental quality is a measurable variable (an EQI total score) that can be sampled at fixed points along a transect and correlated with distance to support or reject the statement; the null hypothesis is that there is no relationship.
Examiner tip
3 marks (AO3). (1) states a precise, directional hypothesis with a measurable variable; (2) links it to the enquiry question; (3) explains testability — objective measurement + can be supported/rejected (bonus for the null hypothesis). A vague aim ('to look at the city') scores nothing; examiners want a variable and a direction.
2Choose and justify a sampling strategy (4 marks)
Getting startedIdentify & classify• sampling, systematic, stratified, transect, Unit 2
Question
A student is testing how environmental quality changes along a 2 km transect running from the CBD to the suburbs. Which sampling strategy should they use, and why? (4 marks)
Step-by-step solution
1. Step 1
  Identify the three options. Random — sample points chosen using random numbers (removes bias but can cluster). Systematic — sample at a fixed interval (e.g. every 200 m). Stratified — split the route into sub-areas (e.g. CBD, inner city, suburb) and sample each in proportion.
2. Step 2
  Match strategy to the hypothesis. Because we are testing change with distance along a line, we need even coverage of the whole 2 km — so systematic sampling every 200 m (11 sample sites) is best.
3. Step 3
  Justify the choice: systematic sampling gives an evenly spread, representative transect, is quick and repeatable, and produces a clean distance variable for a Spearman's rank test. Random sampling might leave large gaps or clusters along the line.
4. Step 4
  Add rigour: at each site score the same EQI sheet and, for reliability, have two people score independently and take the mean, so one person's opinion does not distort the site value. Sampling also reduces the impossible workload of surveying every street on a 2 km route in the time available.
Answer
Use systematic sampling — score the EQI every 200 m along the 2 km transect (11 sites), with two people scoring independently and taking the mean at each site. It gives even, representative coverage of the whole transect, is quick and repeatable, and produces a clean distance variable for a Spearman's rank test, whereas random points could cluster and leave gaps.
Examiner tip
4 marks (AO3). (1) names the strategy; (2) links it to the aim (change with distance = even coverage); (3) justifies vs the alternatives; (4) adds a rigour detail (interval/number of sites/independent scoring). 'Systematic because it is easy' with no justification caps low — examiners reward the reason the method fits THIS hypothesis.
3Design an Environmental Quality Index survey (4 marks)
Getting startedWord problem• EQI, bipolar survey, environmental quality, Unit 2
Question
Describe how you would carry out an Environmental Quality Index (EQI) survey at each site along your urban transect. (4 marks)
Step-by-step solution
1. Step 1
  Define the tool: an EQI (bipolar) survey scores chosen indicators on a scale (e.g. −2 to +2), with a positive and a negative statement at each end (e.g. 'litter-free +2 … heavily littered −2').
2. Step 2
  Choose relevant urban indicators: litter/cleanliness, greenery/open space, building/housing quality, noise and traffic, graffiti/vandalism, and pedestrian safety. Use the same indicators at every site so the comparison is fair.
3. Step 3
  At each site, score every indicator against the printed statements and total the scores to give one overall EQI value for that site, which you can map or graph against distance from the CBD.
4. Step 4
  Reduce subjectivity and improve reliability: use a clearly defined scoring sheet with descriptions for each score, have two people score independently and take the mean, survey at the same time of day, and photograph each site for a record.
Answer
At each site score a fixed set of urban indicators (litter, greenery, building quality, noise/traffic, graffiti, safety) on a bipolar scale from −2 to +2 using a defined scoring sheet, then total the scores to give one EQI value per site. Use the same indicators everywhere, have two people score independently and average, survey at the same time of day, and photograph each site to reduce subjectivity.
Examiner tip
4 marks (AO3). (1) EQI = bipolar scale with paired statements; (2) relevant urban indicators kept the same at each site; (3) total the scores to give a comparable value; (4) at least one way to reduce subjectivity/improve reliability. Recognising and controlling the subjectivity of the EQI is the discriminator here.
4Design a Likert-scale questionnaire (5 marks)
Building confidenceWord problem• Edexcel IAL Geography Unit 2 style• questionnaire, Likert scale, quality of life, Unit 2
Question
A student wants to compare residents' perceptions of quality of life in a regenerated area with a non-regenerated area. Explain how they could design a questionnaire using a Likert scale, and how they would reduce bias. (5 marks)
Step-by-step solution
1. Step 1
  Define the tool: a Likert scale asks respondents to rate their agreement with a statement on an ordered scale (e.g. strongly agree / agree / neutral / disagree / strongly disagree, scored 5 to 1), turning opinions into numerical data that can be compared.
2. Step 2
  Write clear, relevant statements on quality of life: 'I feel safe walking here at night', 'There are enough green spaces', 'I am satisfied with local shops and services', 'This area has improved in the last five years'. Keep them short, neutral and one idea each (avoid leading or double-barrelled questions).
3. Step 3
  Use a structured (closed) questionnaire so every respondent answers the same statements on the same scale, making the two areas directly comparable, and add a few factual questions (age band, how long resident) to interpret the results.
4. Step 4
  Sample fairly: ask a similar number of people in each area (e.g. 30 each), use systematic sampling (every 5th person) or stratified by age to avoid only asking one group, and survey at varied times/days so you do not only catch, say, retired people at midday.
5. Step 5
  Reduce bias: ask questions in the same neutral wording each time, record answers exactly, guarantee anonymity so people answer honestly, and acknowledge that questionnaires still capture perception, not fact. Totalling the Likert scores lets you compare the two areas quantitatively and links to the enquiry question about regeneration and quality of life.
Answer
Design a structured questionnaire of clear, single-idea statements about quality of life (safety, green space, services, recent improvement), each rated on a 5-point Likert scale (strongly agree to strongly disagree, scored 5–1). Ask a similar number of people (e.g. 30) in each area using systematic or stratified sampling at varied times, keep the wording neutral and anonymous, and total the scores to compare the regenerated and non-regenerated areas — while acknowledging it measures perception, not fact.
Examiner tip
5 marks (AO3). (1) Likert = ordered agreement scale that quantifies opinion; (2) relevant, well-written statements (single idea, not leading); (3) structured/closed for comparability; (4) fair sampling of both areas (equal numbers, varied times, avoid one group); (5) reduce bias + honest acknowledgement it captures perception. Recognising the difference between perception and fact is a Level-3 discriminator.
5Match urban data to the right presentation technique (5 marks)
Building confidenceGraph or diagram• Edexcel IAL Geography Unit 2 style• data presentation, choropleth, scatter graph, Unit 2
Question
For an urban investigation a student has collected: (a) EQI totals along a transect, (b) the Index of Multiple Deprivation (IMD) score for each ward across a city, and (c) pedestrian count and EQI score paired at each site. Suggest and justify a suitable presentation technique for each. (5 marks)
Step-by-step solution
1. Step 1
  (a) EQI totals along a transect — distance vs score is a sequence of located values along a line, so use located bars on a base map or a line/bar graph against distance from the CBD, which shows how quality changes outward.
2. Step 2
  (b) IMD score per ward across a city — this is area data covering a whole map, so use a choropleth map: shade each ward by its deprivation class (light = least deprived, dark = most deprived) to show the spatial pattern at a glance.
3. Step 3
  (c) Pedestrian count paired with EQI — you are testing the relationship between two variables, so use a scatter graph with a best-fit line; this sets up a Spearman's rank correlation test.
4. Step 4
  State the general rule: choose the technique that matches the data type — sequential/located data → located bars or line graph; area data → choropleth map; relationship between two variables → scatter graph. Avoid a single graph type for everything.
5. Step 5
  Add a discriminator: for a choropleth, use sensible class boundaries (e.g. quintiles of the IMD) and a clear key; a poorly classed choropleth can hide the real pattern, so presentation choices must set up honest analysis.
Answer
EQI along a transect → located bars on a base map or a graph against distance (sequential located values). IMD per ward → choropleth map, shading each ward by deprivation class (area data). Pedestrian count vs EQI → scatter graph with best-fit line (relationship between two variables, ready for a Spearman's rank test). Match the technique to the data type, and class the choropleth sensibly with a clear key.
Examiner tip
5 marks (AO3). One mark for each correctly matched technique WITH a reason (sequential/area/relationship), plus the general rule and/or the choropleth-classing point. Naming a technique without saying why it suits the data type only part-scores; examiners want the data-type reasoning.
6Carry out a chi-squared test (8 marks)
Building confidenceMulti-step problem• Edexcel IAL Geography Unit 2 style• chi-squared, statistics, pedestrian count, Unit 2
Question
A student counts pedestrians for 10 minutes at four sites along a transect out of the CBD to test whether footfall is evenly distributed across the city. The observed counts are: CBD 90, inner city 70, suburb 50, outer edge 30 (total 240). Carry out a chi-squared test and interpret the result at the 95% significance level (critical value for 3 degrees of freedom is 7.82). (8 marks)
Step-by-step solution
1. Step 1
  State the hypotheses. Null (H0): pedestrian counts are evenly distributed between the four sites (no difference from expected). Alternative (H1): the counts are not evenly distributed (footfall concentrates in the CBD). Chi-squared ( $\chi^{2}$ ) tests whether observed frequencies differ significantly from expected frequencies.
  $\chi^{2} = \sum \frac{(O - E)^{2}}{E}$
2. Step 2
  Work out the expected value (E). If footfall were even, the total of 240 would be split equally between the 4 sites, so $E = 240 \div 4 = 60$ pedestrians at every site.
  $E = \frac{240}{4} = 60$
3. Step 3
  Find $(O - E)$ for each site: CBD $90-60 = +30$ ; inner city $70-60 = +10$ ; suburb $50-60 = -10$ ; outer edge $30-60 = -30$ .
4. Step 4
  Square each difference and divide by E: CBD $30^{2}/60 = 900/60 = 15$ ; inner city $10^{2}/60 = 100/60 = 1.67$ ; suburb $(-10)^{2}/60 = 100/60 = 1.67$ ; outer edge $(-30)^{2}/60 = 900/60 = 15$ .
5. Step 5
  Sum the values to get chi-squared: $\chi^{2} = 15 + 1.67 + 1.67 + 15 = 33.34$ .
  $\chi^{2} = 15 + 1.67 + 1.67 + 15 = 33.34$
6. Step 6
  Work out the degrees of freedom: $df = (\text{number of categories} - 1) = 4 - 1 = 3$ .
  $df = 4 - 1 = 3$
7. Step 7
  Compare with the critical value. At the 95% level (p = 0.05) for $df = 3$ the critical value is 7.82. Because $33.34 > 7.82$ , the result is significant, so we reject the null hypothesis.
8. Step 8
  Interpret geographically: the difference from an even distribution is very unlikely (less than 5% chance) to be due to chance, so footfall is genuinely uneven — it concentrates in the CBD and falls towards the edge of the city, as expected where retail, offices and public transport cluster in the centre. Remember $\chi^{2}$ tests a difference from expected, not a correlation.
Answer
With an expected value of 60 at each site, $\chi^{2} = 15 + 1.67 + 1.67 + 15 = 33.34$ . Degrees of freedom = 4 − 1 = 3, and the 95% critical value is 7.82. Because 33.34 exceeds 7.82, the result is significant, so the null hypothesis is rejected: pedestrian footfall is genuinely unevenly distributed, concentrating in the CBD and declining towards the city edge.
Examiner tip
8 marks (AO3, levels-based). Level 3 needs: null + alternative hypotheses stated; correct expected value (240 ÷ 4 = 60); correct (O−E)²/E for each site; correct $\chi^{2}$ = 33.34; correct df = 3; comparison with the critical value (7.82); a decision (reject the null / significant at 95%); and a geographical interpretation. Common errors: forgetting to work out the expected value, wrong df, or stopping at the number without comparing to the critical value. State that $\chi^{2}$ shows a difference from expected, not proof of cause.
7Interpret an urban EQI dataset and spot an anomaly (6 marks)
StretchGraph or diagram• Edexcel IAL Geography Unit 2 style• environmental quality, anomaly, interpretation, regeneration, Unit 2
Question
An EQI transect from the CBD to the suburbs shows scores generally rising outwards (from about +2 near the centre to +14 in the suburbs), but one inner-city site scores an unexpectedly high +12. Interpret the pattern and suggest how you would treat the unexpected high score. (6 marks)
Step-by-step solution
1. Step 1
  Describe the overall pattern: EQI scores rise with distance from the CBD, from low near the centre (traffic, litter, dense old housing, little green space) to high in the suburbs (more greenery, newer/better housing, less congestion). This supports the hypothesis that environmental quality improves outwards.
2. Step 2
  Explain the process link: the inner city often has older, higher-density housing, more traffic and industry legacy, lowering EQI, while suburbs have planned, lower-density estates with gardens and open space, raising EQI — a classic environmental-quality gradient.
3. Step 3
  Identify the anomaly: the inner-city site scoring +12 does not fit the trend. A likely cause is recent regeneration — a site cleared and rebuilt with new housing, landscaping and green space that has locally lifted environmental quality above its neighbours.
4. Step 4
  Decide how to treat the anomaly: first check it is not a scoring error (re-score the EQI sheet, compare the two scorers, check the photograph). If it is genuine, label and explain it rather than delete it — anomalies are real geography and examiners reward recognising them.
5. Step 5
  Consider the effect on analysis: the regenerated site may weaken a simple correlation between distance and EQI. You could report the correlation with and without the anomalous point and explain the difference, being transparent about the decision.
6. Step 6
  Conclude by linking to validity: the anomaly shows the EQI reflects real change on the ground (a regeneration scheme), and that a single survey is a snapshot — repeating on different days, or using secondary data (regeneration reports, historical maps), would confirm whether the high score is due to planned regeneration.
Answer
EQI scores rise with distance from the CBD, from low in the congested, high-density inner city to high in the greener, lower-density suburbs, supporting the hypothesis. The inner-city site scoring +12 is an anomaly, most likely a recently regenerated site with new housing and green space. Check it is not a scoring error, then label and explain it rather than deleting it, report the correlation with and without it, and confirm the cause using secondary data such as regeneration reports — remembering a single survey is only a snapshot.
Examiner tip
6 marks (AO3, levels-based). Level 3: describes the outward-rising pattern, links inner-city vs suburban environments to the scores, identifies the high inner-city site as an anomaly (regeneration), and handles it properly — check for error, keep and explain (don't delete), consider with/without in analysis, and reflect on validity/snapshot and secondary data. Deleting anomalies without justification is penalised; recognising real geography is rewarded.
8Evaluate reliability and validity of an urban method (8 marks)
StretchMulti-step problem• Edexcel IAL Geography Unit 2 style• evaluation, reliability, validity, EQI, Unit 2
Question
A student judged the environmental quality of each site by scoring one EQI sheet on their own, once, in the middle of a single afternoon. Evaluate the reliability and validity of this method and suggest improvements. (8 marks)
Step-by-step solution
1. Step 1
  Define the two ideas up front: reliability = would the method give the same result if repeated (consistency)? Validity = does it actually measure what the hypothesis needs (real environmental quality, not just one person's mood)?
2. Step 2
  Validity weakness — subjectivity. An EQI is a bipolar opinion score, so one person deciding whether litter is '−2' or '−1' is a subjective judgement that varies between people and even for the same person on different days. A single scorer → scores reflect one opinion, weakening validity.
3. Step 3
  Reliability weakness — no repeats. Scoring each site once means there is no check on consistency; if the survey were repeated the scores could easily differ, so reliability is low.
4. Step 4
  Other validity issues — snapshot conditions. Surveying only one afternoon means conditions on the day (a market just packed up leaving litter, roadworks, weather) may not represent the normal area, and time of day affects traffic and crowding — a snapshot problem.
5. Step 5
  Improvement 1 — reduce subjectivity. Have two or more people score each site independently and take the mean, and use a clearly defined scoring sheet with a description for every score so judgements are consistent.
6. Step 6
  Improvement 2 — improve reliability. Repeat the survey at each site (e.g. on more than one day and at different times) and take the mean, so a single unusual moment does not distort the result.
7. Step 7
  Improvement 3 — triangulate with objective data. Combine the EQI with more objective measures — pedestrian/traffic counts, and secondary data such as census or IMD scores and house prices — so conclusions rest on more than one, partly objective, variable.
8. Step 8
  Overall judgement: as carried out, the method has low reliability and questionable validity because of the single, subjective, snapshot score. The improvements — independent scoring and averaging, repeats on different days, and triangulation with objective/secondary data — would make the data far more trustworthy, so any conclusion about environmental quality improving outwards would be much better supported.
Answer
Validity is weakened because an EQI is a subjective opinion score judged by one person, and one afternoon's conditions are only a snapshot that may not represent the area. Reliability is low because each site was scored only once, so repeats could differ. Improve it by having two or more people score independently with a defined sheet and averaging, repeating on several days and times, and triangulating with objective measures (pedestrian/traffic counts) and secondary data (census, IMD, house prices). As it stands the data is not trustworthy; the improvements would make any conclusion far better supported.
Examiner tip
8 marks (AO3, levels-based). Level 3: defines reliability AND validity correctly, identifies specific weaknesses (subjectivity = validity; no repeats = reliability; snapshot/time of day = validity), and gives targeted improvements that clearly address each named weakness (independent scoring, repeats, triangulation), ending with an overall judgement. Generic 'we could use more data' without linking to reliability/validity, or listing improvements with no weakness identified, caps at Level 1-2.

Model Answers — Human Geography Research and Fieldwork

These nine exemplar answers respond to Unit 2 Section B (own-investigation) and Section C (unfamiliar-context) URBAN fieldwork questions, showing the specificity — who, where, how many, why chosen, what limitation — that moves a levels-based AO3 answer from Band 1 to Band 3.

Question 1
2 marks
Q (2 marks). State what is meant by a hypothesis in a geographical investigation.
Model answer
A hypothesis is a precise, testable statement predicting a relationship or difference, which the collected data can then support or reject.

For example, 'environmental quality improves with distance from the CBD' names a measurable variable and a direction, so it can be tested with fieldwork evidence.
Why this scores
2 marks (AO3). (1) hypothesis = a testable/predictive statement; (2) it can be supported or rejected by data (or an example that names a measurable variable). 'A guess' or 'a question' is too weak — stress testable and measurable.
Question 2
3 marks
Q (3 marks). Distinguish between primary and secondary data, giving one urban example of each.
Model answer
Primary data is information the student collects first-hand in the field, such as scoring an Environmental Quality Index along a transect, or carrying out pedestrian and traffic counts at a set of sites.

Secondary data is information collected by someone else and already published, such as census data, the Index of Multiple Deprivation (IMD), house-price data, or council regeneration reports.

The key difference is the source: primary data is gathered by the investigator during the fieldwork, whereas secondary data already exists and is obtained from another source. Good urban investigations use both so field results can be compared and put in context.
Why this scores
3 marks (AO3). (1) primary = collected first-hand by the student, with an urban example; (2) secondary = already exists/collected by others, with an urban example; (3) the distinction is the SOURCE, and ideally that using both strengthens the study. A named urban example for each (EQI vs census/IMD) is what lifts it above a textbook definition.
Question 3
3 marks
Q (3 marks). For your own urban investigation, state the sampling strategy you used and give two reasons why it was appropriate.
Model answer
For my Urban Problems investigation I used systematic sampling, scoring an EQI and counting pedestrians at fixed 200 m intervals along a 2 km transect from the CBD to the suburbs, giving 11 sample sites.

It was appropriate because, firstly, my hypothesis tested change with distance from the CBD, so I needed even coverage of the whole transect — a fixed interval guarantees no large gaps. Secondly, it was quick and repeatable within the time available, and it produced an evenly spaced distance variable that I could use in a Spearman's rank test.
Why this scores
3 marks (AO3, Section B — own investigation). (1) names a specific strategy with a specific interval/number of sites; (2) reason 1 linked to the hypothesis (even coverage); (3) reason 2 (repeatable/quick/suits the statistics). Specificity — 'every 200 m, 11 sites' — is what marks out a genuine own-investigation answer from a generic one.
Question 4
Edexcel IAL Geography Unit 2 style4 marks
Q (4 marks). Justify why a choropleth map is a suitable way to present deprivation (IMD) data across a city, and explain one limitation of using it.
Model answer
A choropleth map is suitable because IMD data is area data — every ward across the whole city has a value — and a choropleth shades each ward by its deprivation class (light = least deprived, dark = most deprived). This makes the spatial pattern immediately visible, for example a core of high deprivation in the inner city fading outwards to the suburbs, which a table of numbers cannot show at a glance.

It also allows easy visual comparison between different parts of the city.

One limitation is that a choropleth implies each ward is internally uniform, when in reality deprivation varies within a ward, so real pockets of wealth or poverty are hidden. The pattern also depends heavily on the class boundaries chosen — poorly chosen classes can exaggerate or hide the true pattern.
Why this scores
4 marks (AO3). (1) data is area data, so a choropleth fits; (2) it shows the spatial pattern/gradient; (3) a valid strength (comparison) or the shading detail; (4) a genuine limitation (implies uniform wards / class boundaries mislead). The command 'justify … and explain one limitation' needs BOTH sides — a strengths-only answer caps at half.
Question 5
Edexcel IAL Geography Unit 2 style5 marks
Q (5 marks). Explain how you planned for safety and reliability before carrying out fieldwork in an urban area. (5 marks)
Model answer
Before fieldwork I carried out a risk assessment identifying the main urban hazards and how to reduce each one.

Traffic on busy roads and junctions — I planned to count and survey from the pavement, use pedestrian crossings, and keep equipment out of the road.

Personal safety in unfamiliar or deprived areas — I worked in pairs or small groups, carried a charged phone, told a teacher my route, and avoided isolated areas, surveying in daylight only.

Crowds and antisocial behaviour — I stayed alert on crowded seafronts/high streets, kept valuables secure, and was polite and non-confrontational when asking questionnaire questions.

Weather / tiredness — I checked the forecast and took water and suitable clothing for a day walking a 2 km transect.

For reliability I planned to sample systematically at fixed 200 m intervals, score the EQI with two people independently and take the mean, and record data on a pre-printed recording sheet so the same procedure was followed at every site, making the survey consistent and repeatable.
Why this scores
5 marks (AO3). Credit specific urban hazards with a matched control measure (traffic + crossings; personal safety + pairs/daylight; crowds; weather), plus at least one reliability plan (systematic sampling / independent scoring / standard recording sheet). Generic 'be careful' scores nothing — examiners want urban-specific hazards (traffic, personal safety) and concrete precautions.
Question 6
Edexcel IAL Geography Unit 2 style5 marks
Q (5 marks). Explain how a geographer could measure pedestrian flows to compare how busy different parts of a city are, and identify one source of error in the method. (5 marks)
Model answer
Pedestrian flows can be measured with a pedestrian count. At each site the geographer counts the number of people passing a fixed point (e.g. a shopfront or corner) for a set time — for example 10 minutes — using a tally sheet or click counter, and records the count so sites can be compared.

To make the comparison fair and reliable, the count should be done for the same length of time at every site, at the same time of day and day of week, and ideally repeated and averaged, counting people moving in both directions.

One source of error is that pedestrian numbers vary strongly with time of day, day of the week and weather — a count taken at lunchtime on a market day is not comparable with a wet Monday morning — so a single count may not represent normal footfall, which is why standardising the time and repeating the count matters.
Why this scores
5 marks (AO3). (1) named method — count people passing a fixed point; (2) for a set time with a tally/counter; (3) standardise time/place so sites are comparable; (4) improve by repeating/averaging (or both directions); (5) a genuine, specific source of error (time of day/day/weather variation) not just 'human error'. Standardising the count time is the key methodological point.
Question 7
Edexcel IAL Geography Unit 2 style6 marks
Q (6 marks). For your own urban investigation, assess how confident you can be in the conclusion you reached. (6 marks)
Model answer
My investigation tested the hypothesis that environmental quality improves with distance from the CBD. My data supported it: a Spearman's rank value of +0.81 (significant at the 95% level, n = 11) showed a strong positive correlation between distance from the CBD and EQI score, so I concluded that environmental quality genuinely increases outwards, from the congested inner city to the greener suburbs.

I can be fairly confident in this conclusion for several reasons: I used systematic sampling for even coverage, scored the EQI with two people independently and averaged to reduce subjectivity, and my statistical test showed the pattern was unlikely to be due to chance.

However, my confidence is limited by real weaknesses. The EQI is partly subjective, the data was collected on a single afternoon (a snapshot that may not represent the area on other days), and the sample of 11 sites is quite small. There was also one anomaly — a regenerated inner-city site scored unexpectedly high, weakening the trend.

Overall, I am moderately confident: the significant correlation and careful sampling support the conclusion, but because of the snapshot, small sample and EQI subjectivity I would want to repeat the survey on several days, increase the sample size and add secondary data (census/IMD) before treating the conclusion as certain. Crucially, the conclusion links directly back to the original hypothesis, which is what makes it valid.
Why this scores
6 marks (AO3, levels-based, Section B). Level 3 (5-6): states the conclusion linked to the hypothesis, gives specific reasons for confidence (sampling, independent scoring, significant statistic with a real value), AND specific reasons against (snapshot, sample size, subjectivity, anomaly), reaching an overall judgement on confidence. Answers that only describe the conclusion, or say 'it could be better' without specifics, cap at Level 2. The link back to the hypothesis is essential.
Question 8
Edexcel IAL Geography Unit 2 style8 marks
Q (8 marks). A resource shows a map and photographs of an unfamiliar inner-city area where a large regeneration scheme has replaced old industrial land with new housing, shops and a park, next to older un-regenerated streets. Suggest how a geographer could investigate whether the regeneration has improved quality of life at this location. (8 marks)
Model answer
To investigate whether the regeneration has improved quality of life, a geographer should design a clear enquiry around a hypothesis such as: 'Quality of life and environmental quality are higher in the regenerated area than in the neighbouring un-regenerated streets.' The null hypothesis is that there is no difference.

Primary data collection. At several sites in each area (e.g. five in the regenerated zone and five in the un-regenerated streets, sampled systematically), the geographer could:

score an Environmental Quality Index (EQI) to compare litter, greenery, building quality and noise, with two people scoring independently;

carry out a Likert-scale questionnaire with residents on perceptions of safety, services and recent improvement, asking a similar number of people in each area;

do pedestrian counts and take photographs / field sketches of the built environment.

Secondary data. Compare census data, IMD deprivation scores and house-price data for the two areas, and use historical maps or aerial photographs and council regeneration reports to show change over time.

Presentation and analysis. Present EQI and questionnaire scores as located bars / a choropleth, compare the mean EQI and Likert totals of the two areas, and test whether any difference is statistically significant (e.g. a chi-squared test on responses, or Spearman's rank for EQI against distance from the scheme).

Reliability and validity. Sample equal numbers of sites in each area for a fair comparison, take repeat measurements and means, survey at the same time of day, and score the EQI in pairs to reduce subjectivity. Acknowledge limitations: a survey on one day is a snapshot, questionnaires capture perception not fact, and factors other than regeneration (e.g. who has moved in) affect quality of life, so conclusions should be cautious.

A conclusion would then state whether the evidence supports the hypothesis — most likely that the regenerated area shows higher EQI and more positive quality-of-life perceptions, while noting whether this reflects genuine improvement or the displacement of poorer residents (gentrification), directly answering whether regeneration has improved quality of life.
Why this scores
8 marks (AO3, levels-based, Section C — unfamiliar context). Level 3 (6-8): a clear hypothesis for THIS location, appropriate primary methods (EQI, Likert questionnaire, counts) with sampling detail, relevant secondary data (census/IMD/house prices/historical maps for change over time), suitable presentation/analysis (incl. a statistical test), AND attention to reliability/validity, linked back to a possible conclusion. Must be applied to the specific area in the resource (regenerated vs un-regenerated), not a generic method list. Naming gentrification/displacement shows strong application.
Question 9
Edexcel IAL Geography Unit 2 style8 marks
Q (8 marks). 'Because it relies on personal judgement, an Environmental Quality Index can never give trustworthy results.' With reference to an urban investigation, evaluate the reliability and validity of using an EQI. (8 marks)
Model answer
An Environmental Quality Index (EQI) turns environmental quality into a numerical bipolar score so places can be compared, but because scoring rests on personal judgement its trustworthiness is genuinely open to question. This answer evaluates how far an EQI can give reliable and valid results.

Why the EQI is open to criticism. The core weakness is subjectivity: deciding whether litter is '−2' or '−1' depends on the observer, so different people give different scores for the same street, weakening validity. If only one person scores each site once, there is no check on consistency, so reliability is low. A survey done on a single afternoon is only a snapshot — a market that has just packed up, roadworks or weather can distort the score, and the indicators chosen (and their weighting) are themselves value judgements. In these senses the statement has real force.

But the EQI can be made trustworthy. These weaknesses can be controlled. Using a clearly defined scoring sheet with a description for every score standardises judgements; having two or more people score independently and taking the mean reduces individual bias; repeating the survey on different days and times improves reliability; and triangulating the EQI with more objective data — pedestrian/traffic counts, census, IMD and house prices — checks whether the EQI pattern is real. When these steps are taken, an EQI can produce a consistent, defensible pattern (e.g. quality rising from the inner city to the suburbs) that agrees with independent data.

Judgement. On balance, the statement is too strong. An EQI is inherently subjective, so a careless, single-observer, one-day survey is indeed untrustworthy — supporting the criticism. However, subjectivity can be substantially reduced by defined criteria, independent repeat scoring and triangulation, at which point the EQI gives reasonably reliable and valid results that are good enough to compare places and support a conclusion. The most accurate view is that an EQI is not worthless but must be used carefully: its results are trustworthy only when the subjectivity is deliberately controlled and the pattern is confirmed against more objective evidence.
Why this scores
8 marks (AO3, levels-based). Level 3 (6-8): evaluates BOTH sides — why the EQI is untrustworthy (subjectivity/observer variation = validity; no repeats = reliability; snapshot; indicator choice) AND how it can be made trustworthy (defined sheet, independent scoring + mean, repeats, triangulation) — with urban specifics, reaching a justified judgement ('not worthless but must be controlled'). A one-sided 'the EQI is too subjective' answer with no counterweight, or no urban detail, caps at Level 2. The command 'evaluate' demands weighing and a conclusion.

Key Definitions and Keywords — Human Geography Research and Fieldwork

Definitions to memorise and the exact keywords mark schemes credit for human geography research and fieldwork answers — sharpened from recent examiner reports for the 2026 Pearson Edexcel International A Level XGE01/YGE01 sitting.

Enquiry question
Examiner keyword
The overarching geographical question a fieldwork investigation sets out to answer (e.g. 'Has regeneration improved quality of life in area X?'); it is broken down into a testable hypothesis.
Hypothesis
Examiner keyword
A precise, testable statement predicting a relationship or difference (e.g. 'environmental quality improves with distance from the CBD') that data can support or reject.
Null hypothesis (H0)
Examiner keyword
The statement that there is no relationship or no difference between the variables; a statistical test (e.g. chi-squared or Spearman's rank) decides whether to reject it.
Primary data
Examiner keyword
Data collected first-hand in the field by the investigator, e.g. an EQI survey, pedestrian and traffic counts, questionnaires or land-use mapping.
Secondary data
Examiner keyword
Data that already exists and is collected from another source, e.g. census data, the Index of Multiple Deprivation, house-price data, council regeneration reports or historical maps.
Systematic sampling
Examiner keyword
Selecting sample sites at a fixed, regular interval (e.g. every 200 m along a transect); gives even coverage and is quick and repeatable, ideal for testing change with distance from the CBD.
Random sampling
Examiner keyword
Selecting sample sites or respondents using random numbers so every one has an equal chance of selection; removes bias but can cluster points and leave gaps.
Stratified sampling
Examiner keyword
Dividing the study area or population into sub-groups (strata) and sampling each in proportion to its size or importance; ensures each sub-area (e.g. CBD, inner city, suburb) or group (e.g. age band) is fairly represented.
Environmental Quality Index (EQI)
Examiner keyword
A bipolar survey scoring chosen urban indicators (e.g. litter, greenery, building quality, noise, graffiti) on a scale such as −2 to +2; totalled to compare environmental quality between sites, but partly subjective.
Bipolar survey
Examiner keyword
A survey with opposite statements at each end of a numbered scale (e.g. 'clean +2 … dirty −2'); the observer scores each indicator, and the scores are added to give an overall value such as an EQI.
Likert scale
Examiner keyword
An ordered rating scale (e.g. strongly agree to strongly disagree, scored 5 to 1) used in questionnaires to turn people's opinions or perceptions into numerical, comparable data.
Index of Multiple Deprivation (IMD)
Examiner keyword
A secondary dataset that combines measures of income, employment, health, education, crime and housing to give each small area a deprivation score/rank; used to compare quality of life across a city.
Choropleth map
Examiner keyword
A map that shades areas (e.g. wards) by class of value, so darker shading shows higher values (e.g. greater deprivation); ideal for area data but implies each area is internally uniform.
Chi-squared test
Examiner keyword
A statistical test comparing observed frequencies with expected frequencies using the formula chi-squared = sum of (O − E)² / E; the result is compared with a critical value at the chosen degrees of freedom and significance level to accept or reject the null hypothesis.
Example
Pedestrian counts of 90/70/50/30 against an expected 60 each give a chi-squared of 33.34; with df = 3 this exceeds the 95% critical value (7.82), so the null hypothesis is rejected.
Spearman's rank correlation (rs)
Examiner keyword
A statistical test measuring the strength and direction of a relationship between two ranked variables, from −1 (perfect negative) through 0 (none) to +1 (perfect positive); compared with a critical value to test significance.
Example
rs = +0.81 for EQI against distance from the CBD means a strong positive correlation; if it exceeds the 95% critical value the null hypothesis is rejected.
Reliability
Examiner keyword
The extent to which a method would give the same result if repeated; improved by larger samples, repeated measurements, independent scoring and a standard procedure.
Validity
Examiner keyword
The extent to which the data actually measures what the hypothesis needs; weakened by subjectivity (e.g. of an EQI), bias, snapshot conditions or measuring the wrong variable.
Anomaly
Examiner keyword
A data point that does not fit the expected trend (e.g. a regenerated inner-city site with an unexpectedly high EQI); should be checked for error, then explained rather than deleted, as it often reflects real processes.

Common Mistakes and Misconceptions — Human Geography Research and Fieldwork

The traps other students keep falling into on human geography research and fieldwork questions — taken from recent Pearson Edexcel International A Level XGE01/YGE01 examiner reports and mark schemes — and how to avoid them.

✕Justifying a method with 'because it is easy/quick' instead of why it suits the hypothesis
Edexcel IAL Geography Unit 2 examiner reports
Why it happens
Students remember that a method was convenient rather than why it produces the right, representative data for the enquiry.
How to avoid it
Justify every method against the hypothesis and data needed: 'systematic sampling every 200 m gave even coverage of the transect so the change in EQI with distance from the CBD was fairly represented'. Convenience alone is a Band 1 answer.
✕Giving invented or generic fieldwork with no real place, figures or detail
Edexcel IAL Geography Unit 2 examiner reports
Why it happens
Students who did not engage with their own investigation fall back on textbook-style, place-less descriptions.
How to avoid it
Use your real investigation specifics in Section B — the actual city/transect, number of sites, interval, indicators and results — and apply methods to the specific resource in Section C. Specific detail (who/where/how many) is what reaches Band 3.
✕Describing what you did when the question asks you to evaluate or assess
Edexcel IAL Geography Unit 2 examiner reports
Why it happens
Recounting the method feels safe, so students narrate the fieldwork instead of judging its reliability and validity.
How to avoid it
For 'evaluate/assess', weigh strengths against weaknesses using the words reliability and validity, give specific limitations (EQI subjectivity, sample size, snapshot) and reach a judgement with targeted improvements.
✕Writing a conclusion that does not link back to the original hypothesis or enquiry question
Edexcel IAL Geography Unit 2 examiner reports
Why it happens
Students treat the conclusion as a general summary of the trip rather than a direct answer to what they set out to test.
How to avoid it
Always return to the hypothesis: state whether the data supported or rejected it, quote the key result (e.g. the chi-squared / rs value and significance), and answer the enquiry question explicitly.
✕Listing vague limitations like 'human error' or 'the weather' without saying how they affected the data
Edexcel IAL Geography Unit 2 examiner reports
Why it happens
Students know limitations are expected but reach for generic phrases rather than method-specific problems.
How to avoid it
Name a specific limitation and its effect: 'scoring the EQI alone (one observer) reduced validity because the scores reflected one person's opinion of what counts as littered'. Then give a targeted improvement.
✕Treating EQI, bipolar or Likert scores as fully objective facts
Edexcel IAL Geography Unit 2 examiner reports
Why it happens
Because the scores are numbers, students assume they are as objective as a measured length or count.
How to avoid it
State that EQI/bipolar and Likert scores are based on personal judgement or perception, so they are partly subjective; reduce this with defined scoring sheets, independent scoring and averaging, and triangulate with objective data (counts, census, IMD) before drawing firm conclusions.

Human Geography Research and Fieldwork

Detailed Study Notes

At a glance

What you’ll learn

Quick recap

Memorise this

How it’s examined

Take this whole topic with you

1Turn an aim into a testable urban hypothesis (3 marks)

2Choose and justify a sampling strategy (4 marks)

3Design an Environmental Quality Index survey (4 marks)

4Design a Likert-scale questionnaire (5 marks)

5Match urban data to the right presentation technique (5 marks)

6Carry out a chi-squared test (8 marks)

7Interpret an urban EQI dataset and spot an anomaly (6 marks)

8Evaluate reliability and validity of an urban method (8 marks)

Enquiry question

Hypothesis

Null hypothesis (H0)

Primary data

Secondary data

Systematic sampling

Random sampling

Stratified sampling

Environmental Quality Index (EQI)

Bipolar survey

Likert scale

Index of Multiple Deprivation (IMD)

Choropleth map

Chi-squared test

Spearman's rank correlation (rs)

Reliability

Validity

Anomaly

✕Justifying a method with 'because it is easy/quick' instead of why it suits the hypothesis

✕Giving invented or generic fieldwork with no real place, figures or detail

✕Describing what you did when the question asks you to evaluate or assess

✕Writing a conclusion that does not link back to the original hypothesis or enquiry question

✕Listing vague limitations like 'human error' or 'the weather' without saying how they affected the data

✕Treating EQI, bipolar or Likert scores as fully objective facts