2024 CCEA AS-Level Geography 3910 模拟试题及答案详解

(a) Peak rainfall of 20mm occurs at hour 6; peak discharge of 45 cumecs occurs at hour 12.
(b) Lag time = time of peak discharge - time of peak rainfall = 12 - 6 = \( 6 \text{ hours} \).
(c) Any two of: impermeable geology or extensive urban/impermeable surfaces reduce infiltration, forcing more precipitation to travel as fast surface run-off rather than slower throughflow/groundwater flow, shortening the lag time; steep relief/basin slope increases the speed of overland flow into the channel, shortening the lag time; high drainage density (many tributaries) means water reaches the main channel via short, direct routes; a circular or fan-shaped basin means tributaries are of similar length, so run-off arrives at the main channel at a similar time, producing a short, sharp lag.
(d) Discharge before the storm (baseflow) = 10 cumecs. At hour 24, discharge (11 cumecs) is still slightly above this because slower pathways (throughflow and groundwater/baseflow contributions from the storm) continue to feed the channel after the fast surface run-off has already receded; discharge takes longer than 24 hours to fall fully back to its pre-storm baseflow level.

(a) [1] correct peak rainfall time (hour 6); [1] correct peak discharge time (hour 12). (b) [1] correct lag time (6 hours) with working shown. (c) [2] marks for each of two valid characteristics (1 mark identification + 1 mark explanation of how it shortens lag time), maximum [4]. Any other valid characteristic (e.g. drainage density, basin shape) credited. (d) [1] correct baseflow value (10 cumecs) AND a valid reason for it not yet returning to this level (e.g. continuing throughflow/groundwater input).

(a) The zoning approach places land uses that can tolerate flooding (washland/nature reserve, then playing fields/allotments) closest to the river, while permanent, high-value land uses (housing, commercial development) are set back furthest from the channel where flood risk is lowest; this creates a gradient of increasing land-use value/permanence with increasing distance from the river.
(b) Effectiveness - strengths: land-use zoning is a relatively low-cost, sustainable strategy compared with hard engineering (e.g. building flood walls); it reduces the risk of damage to property and danger to life, since the highest-value/most vulnerable uses (homes, businesses) are kept away from the highest-risk zone; the washland/nature reserve zone can also provide valuable habitat and temporary flood water storage, reducing peak downstream discharge. Limitations: it is difficult and costly to apply retrospectively where housing/development already exists close to the river; it does not reduce the amount or speed of run-off reaching the river, so flooding of Zone 1 and Zone 2 land uses will still occur regularly; it requires ongoing enforcement through planning permission, and pressure for new housing can lead to zoning being overridden. A balanced evaluation reaches a substantiated judgement.

(a) [1] mark for each valid descriptive point about the zoning pattern, up to [3]: e.g. flood-tolerant/low-value uses nearest the river; increasing value/permanence with distance; specific zone examples correctly used. (b) Level 1 (1-2 marks): basic, one-sided evaluation. Level 2 (3-4 marks): evaluation considering both strengths and limitations, with reference to the scenario, reaching a judgement.

Climate: the tundra has a very cold climate, with long, severe winters (temperatures well below 0°C) and short, cool summers (mean temperatures rarely exceeding around 10°C); annual precipitation is low, as the cold air holds little moisture, so the biome is technically a cold desert in terms of moisture input. Soils: tundra soils are thin, acidic and poorly developed due to the short growing season and low rates of biological activity/decomposition; a layer of permafrost (permanently frozen ground) lies beneath the surface, restricting drainage (causing waterlogging in the short summer thaw) and limiting the rooting depth of plants.

[2] for a valid description of climate (e.g. long cold winters/short cool summers; low precipitation); [2] for a valid description of soils (e.g. thin/poorly developed; presence of permafrost restricting drainage/roots). Any other valid point credited within the [4] maximum.

Impact 1: rising temperatures cause permafrost to thaw, which releases greenhouse gases (methane and carbon dioxide) that were previously locked in frozen organic material; this creates a positive feedback loop that amplifies further warming. Impact 2: warmer summers allow the treeline and shrub vegetation to migrate further north/to higher latitudes (a process sometimes called 'shrubification'), reducing the area of open tundra and altering habitats for tundra-adapted species such as caribou/reindeer, which rely on lichen-dominated ground cover. Other valid impacts: coastal erosion increasing as protective sea ice melts; changes to the timing of seasonal events (phenology) disrupting migratory species.

[1] identification + [1] explanation for each of two valid impacts, maximum [4]. Any other valid, accurate impact credited (e.g. permafrost thaw/methane release; treeline/shrub encroachment; coastal erosion; disrupted animal migration).

Plant succession is the process by which a plant community changes and develops over time on a previously bare or disturbed surface, in response to changing environmental conditions that the plants themselves help to create (e.g. by building up soil and organic matter). It proceeds through a series of seral stages, beginning with a pioneer community of hardy species tolerant of harsh conditions (e.g. marram grass on sand dunes), followed by a sequence of intermediate seral communities as soil depth, nutrients and shelter improve, allowing progressively larger and more complex species (e.g. shrubs, then trees) to colonise. Eventually, in the absence of further disturbance, the community reaches a climatic climax vegetation - a stable, self-perpetuating community (e.g. mixed deciduous woodland in the UK) that is in equilibrium with the local climate and will not naturally develop further. A named example (e.g. succession on the Umbra sand dunes, progressing from pioneer marram grass through to climax woodland/scrub) should be used to illustrate the stages.

[1] basic definition of succession as directional change over time; [1] reference to seral stages/pioneer community; [1] reference to climatic climax vegetation; [1] use of a named/illustrative example. Maximum [4].

A plagioclimax is a stable plant community that has been prevented from reaching its natural climatic climax vegetation by continued human interference, such as grazing by livestock, regular mowing, or deliberate burning. As long as this human activity continues, the plagioclimax community remains stable (e.g. heather moorland maintained by grazing and burning, which would otherwise succeed to woodland); if the human interference stops, succession can resume towards the climatic climax.

[1] basic idea of succession halted/diverted by human activity; [1] example of the human activity involved (e.g. grazing/burning/mowing); [1] reference to the community remaining stable only while the activity continues, or a named example (e.g. heather moorland). Maximum [3].

(a) Temperature drop = \( 6.5\,^{\circ}\text{C} \times \frac{1200}{1000} = 6.5 \times 1.2 = 7.8\,^{\circ}\text{C} \). Summit temperature = \( 18\,^{\circ}\text{C} - 7.8\,^{\circ}\text{C} = 10.2\,^{\circ}\text{C} \).
(b) The environmental lapse rate is an average value, so actual conditions can differ: for example, a temperature inversion can occur when cold, dense air drains into and becomes trapped in a valley on a calm, clear night, sitting beneath a layer of warmer air above, meaning the summit could be warmer than a valley location, contrary to the simple lapse-rate calculation. Other valid reasons: aspect (a south-facing summit receiving more direct insolation); local cloud cover reducing daytime heating; strong winds increasing heat loss beyond the average lapse rate.

(a) [1] correct method/working shown; [1] correct final answer (\( 10.2\,^{\circ}\text{C} \)) with correct units. Own Figure Rule (OFR) applies if (a) working is shown but an arithmetic slip is made. (b) [1] identification of a valid reason (e.g. temperature inversion, aspect, cloud cover, wind); [1] explanation of how it causes a difference from the calculated value. Maximum [4].

In the tri-cellular model, warm air rises at the equator (driven by intense solar heating), forming the equatorial low, and moves polewards at height as part of the Hadley cell; as it cools, it descends at around 30°N/S, forming the sub-tropical high pressure belt (an area of subsiding, warming, drying air). Some of this air returns towards the equator at the surface (the trade winds), while some flows polewards at the surface as part of the mid-latitude/Ferrel cell. This surface air meets cold air flowing from the polar cell at around 60°N/S; because these air masses have different densities/temperatures, the warmer, less dense air is forced to rise over the colder air, creating an area of low pressure - the sub-polar low (linked to the Polar Front).

[1] explanation of the Hadley cell/rising air at the equator; [1] explanation of subsiding air producing the sub-tropical high at ~30°N/S; [1] explanation of the Ferrel/polar cell boundary; [1] explanation of rising air producing the sub-polar low at ~60°N/S. Maximum [4]. Any other valid, accurate development credited.

Frontal precipitation forms at the boundary (front) between two air masses of different temperature and density. At a warm front, a warm, less dense air mass advances and rises gradually (gently sloping) over a retreating mass of colder, denser air; as the warm air rises it cools by adiabatic expansion, water vapour condenses, and this typically produces prolonged, steady, moderate precipitation ahead of the front. At a cold front, a colder, denser air mass advances and undercuts a warmer air mass, forcing it to rise more steeply and rapidly; this rapid uplift and cooling typically produces shorter, more intense precipitation (often with cumulonimbus cloud) along and just behind the front.

[1] basic idea that warm and cold air masses meet at a front; [1] explanation that the less dense warm air is forced to rise over/is undercut by the denser cold air; [1] explanation that rising air cools and condenses to produce precipitation; [1] valid distinction/detail on warm vs cold front characteristics. Maximum [4].

On a synoptic chart, a depression is shown by a set of roughly circular or oval isobars (lines of equal pressure) surrounding a central area marked with a low pressure value and the letter 'L'; pressure increases outwards from this centre. Closely-spaced isobars indicate a steep pressure gradient and therefore strong winds circulating (anticlockwise in the Northern Hemisphere) around the low. Depressions are also typically shown with warm and cold front symbols extending from the centre, marking the boundaries of the associated air masses.

[1] closed/circular isobars around a low pressure centre marked 'L'; [1] closely-spaced isobars indicating strong winds; [1] presence of warm/cold front symbols. Maximum [3]. Any other valid, accurate point credited.

Indicative content: Formation - hurricanes form over warm ocean water (sea surface temperature above approximately 26-27°C) to a depth of at least 50m, which provides the energy (latent heat) needed to fuel the storm; warm, moist air rises rapidly, creating an area of low pressure that draws in more air, which is deflected by the Coriolis force to create the characteristic rotating structure; as air rises it cools and condenses, releasing further latent heat that intensifies the system, and a calm 'eye' forms at the centre surrounded by the eyewall of most intense wind and rain. Named example (e.g. Hurricane Katrina 2005, or a more recent example such as Hurricane Ian 2022): describe specific impacts on people (deaths, displacement, injuries), property (flooding, wind damage, infrastructure destruction) and evaluate management strategies used, such as: prediction and tracking using satellite technology giving early warning; evacuation orders and shelters; building codes/flood defences (e.g. levees); emergency response and aid coordination. Evaluation should weigh successes (e.g. accurate forecasting reducing loss of life through evacuation) against failures/limitations (e.g. inadequate levee maintenance, slow emergency response, poverty limiting some residents' ability to evacuate), reaching a substantiated judgement on overall effectiveness.

Level 1 (1-5 marks): Basic, generalised, descriptive account with limited case-specific detail; little or no evaluation; weak QWC. Level 2 (6-10 marks): Sound description of formation and impacts with some case-specific detail and partial evaluation of management strategies; clear QWC. Level 3 (11-15 marks): Detailed, accurate explanation of formation, thorough place-specific evidence for the named example, and a well-substantiated evaluation of the effectiveness of management strategies; sophisticated QWC and appropriate use of an annotated diagram to support the explanation of formation. Answers lacking a named case study or a diagram where relevant are capped at a maximum of Level 2 (10/15).

Indicative content: a strong answer selects two contrasting landforms from different sections of a river's course, e.g. a waterfall (upper course) and a meander or oxbow lake (middle/lower course). Waterfall formation: forms where a river crosses a band of resistant rock overlying softer, less resistant rock; the softer rock is eroded faster (by hydraulic action and abrasion/corrasion), undercutting the resistant cap rock, which eventually collapses, causing the waterfall to retreat upstream and leaving a gorge of recession; a plunge pool forms at the base through abrasion by swirled sediment. Meander/oxbow lake formation: in the middle/lower course, helicoidal flow concentrates erosive energy (abrasion, hydraulic action) on the outer bank of a bend (forming a river cliff) while deposition (of transported sediment, via traction and suspension) occurs on the inner bank (forming a slip-off slope/point bar) where velocity is lower; over time meanders migrate and become more pronounced, and during a flood the river may cut across the narrow neck of a meander, leaving the old meander loop abandoned as an oxbow lake once deposition seals off the ends. Evaluation: erosion processes dominate landform formation in the upper course (steep gradient, high energy, coarse load favours hydraulic action/abrasion), while a combination of erosion (outer bank) and deposition (inner bank) is essential to explain meander development in the middle/lower course, and deposition alone completes the oxbow lake formation; transportation processes (e.g. traction of coarse load in the upper course, suspension/solution in the lower course) link erosion to deposition throughout by moving sediment from areas of erosion to areas of deposition. A substantiated judgement on relative importance, tied to the specific landforms chosen, should be reached.

Level 1 (1-5 marks): Basic, generalised description of landform(s) with limited or inaccurate process detail; little evaluation; weak QWC. Level 2 (6-10 marks): Sound explanation of formation for at least one landform with accurate process detail and some evaluation of relative importance; clear QWC. Level 3 (11-15 marks): Detailed, accurate explanation of the formation of two contrasting landforms with correct process terminology throughout, and a well-substantiated evaluation of the relative importance of erosion, transportation and deposition; sophisticated QWC. Answers lacking an annotated diagram are capped at a maximum of Level 2 (10/15).

Indicative content: from Table 1 - the UK's census is more recent and enumerates a higher proportion of the population, suggesting more reliable, up-to-date data; Kenya's census is older and covers a lower proportion of the population, and relies on door-to-door enumerators rather than self-completion, which is more time-consuming and prone to error/omission over a large or remote population. From own knowledge - MEDCs like the UK generally have greater funding, more trained staff, and better infrastructure (postal services, internet access, addressing systems) to support accurate data collection; LEDCs like Kenya may have larger remote/rural populations that are harder to reach, lower literacy rates affecting self-completion, and less funding for frequent, thorough censuses, all reducing reliability; vital registration (of births/deaths) is also typically more complete and reliable in MEDCs than LEDCs, further affecting the overall quality of population data.

Level 1 (1-2 marks): Basic, undeveloped description with limited/no use of the table. Level 2 (3-4 marks): Sound description drawing on the table OR own knowledge. Level 3 (5 marks): Detailed, well-developed description drawing on both Table 1 and own knowledge to contrast MEDC and LEDC reliability.

Country X: the low and near-equal Crude Birth Rate (9) and Crude Death Rate (9), very low Total Fertility Rate (1.6, below the replacement level of ~2.1), and very low Infant Mortality Rate (4) are characteristic of Stage 5 (or late Stage 4) of the DTM - typical of a highly developed MEDC with advanced healthcare, family planning and an ageing population, where population growth is slow, static or beginning to decline. Country Y: the high Crude Birth Rate (38) considerably exceeding the Crude Death Rate (11), high Total Fertility Rate (4.8) and high Infant Mortality Rate (55) are characteristic of Stage 2 of the DTM - typical of a developing LEDC where death rates have begun to fall (due to improving healthcare/sanitation) but birth rates remain high, producing rapid natural population growth.

[1] correct stage identified for Country X; [2] justification using at least two data points for Country X. [1] correct stage identified for Country Y; [1] justification using at least one data point for Country Y (or [2] justification if fully developed and Country X justification uses only [1]). Maximum [5] overall, distributed across both countries' identification and justification.

Region Q is overpopulated: despite having the highest population density (420 people/km2), it does not have enough resources to support its population at an adequate standard of living, requiring food/energy imports - this is the defining feature of overpopulation (population exceeds available resources), regardless of the absolute density figure. Region P is underpopulated: despite having the lowest population density (15 people/km2), it has more resources (abundant surplus) than are needed to support its current population, meaning it could support a larger population at the same standard of living without depleting resources - the defining feature of underpopulation. Region R shows optimum population: its population size (90 people/km2) is balanced with the resources available, meaning the maximum standard of living is being achieved for the current level of resources and technology, without surplus or shortfall.

[1] correct identification of overpopulated region with reasoning; [1] correct identification of underpopulated region with reasoning; [1] correct identification of optimum population region with reasoning; [2] for overall recognition that over/underpopulation depends on the population-resource balance rather than density alone. Maximum [5].

The rural-urban continuum describes the gradual transition in land use and character from the city centre, through suburbs and the rural-urban fringe, to fully rural areas. Suburbanisation is the process by which the built-up area of a city expands outwards, as residents and businesses relocate from the crowded inner city to newly developed housing and commercial areas at the edge of the urban area (the suburbs/rural-urban fringe), often attracted by more space, lower costs and access to greenfield sites. Counterurbanisation is a distinct process in which people and businesses move beyond the urban area altogether, relocating to smaller towns, villages or rural areas outside the city, often driven by a desire to escape urban problems (congestion, pollution, cost) while retaining connections to the city through commuting or remote working; unlike suburbanisation, counterurbanisation involves leaving the urban area's built-up extent entirely rather than extending it.

[2] accurate definition of suburbanisation (outward growth of the built-up urban area); [2] accurate definition of counterurbanisation (movement away from urban areas to smaller towns/rural areas); [1] clear point of distinction/contrast made between the two. Maximum [5].

Indicative content: MEDC inner cities (e.g. Belfast) can experience economic and social deprivation, including higher unemployment following the decline of traditional manufacturing/dock industries, lower incomes, and poor-quality or ageing housing stock; this can be linked to wider issues such as higher crime rates, poorer health outcomes and lower educational attainment concentrated in these areas. Some inner-city areas undergo re-urbanisation and gentrification, where investment, new residents (often young professionals) and businesses return to previously declining areas, raising property prices and improving the local economy, but this can also displace lower-income original residents (a negative consequence) who can no longer afford to live there. A named example should be used to illustrate specific issues (e.g. redevelopment of Belfast's former dockland/Titanic Quarter alongside continued deprivation in some surrounding inner-city wards).

Level 1 (1-2 marks): Basic, generalised points with little/no named example. Level 2 (3-4 marks): Sound explanation of at least one issue (deprivation OR gentrification/re-urbanisation) with some use of a named example. Level 3 (5 marks): Detailed explanation of both deprivation and re-urbanisation/gentrification, well illustrated with a named example.

Indicative content: rapid rural-urban migration in many LEDC cities outstrips the capacity of formal housing and infrastructure, leading to the growth of informal settlements (slums/squatter settlements) characterised by poor-quality, self-built housing, lacking secure land tenure; these settlements often lack adequate service provision, including reliable clean water, sanitation, electricity and waste collection, contributing to health problems; formal employment opportunities may be limited, so a large proportion of residents rely on informal economic activity (e.g. street vending, informal recycling, small-scale services) which is often low-paid and insecure. A named example should be used (e.g. an informal settlement in Nairobi such as Kibera) to illustrate the scale and specific nature of these challenges.

Level 1 (1-2 marks): Basic, generalised points with little/no named example. Level 2 (3-4 marks): Sound explanation of at least one issue (informal settlements, service provision OR informal economy) with some use of a named example. Level 3 (5 marks): Detailed explanation covering informal settlement growth, service provision AND economic activity, well illustrated with a named example.

One key problem is that 'development' is a broad and multi-dimensional concept, encompassing not just economic wellbeing (income, industrial output) but also social factors (health, education, quality of life) and increasingly environmental sustainability; this makes a single, universally agreed definition difficult. A related problem is that purely economic measures (such as GNI/GDP per capita) can be misleading on their own, since they say nothing about how income is distributed within a country (inequality) or about non-monetary aspects of wellbeing such as life expectancy, literacy or access to clean water - a country could have relatively high average income yet very poor social development for much of its population.

[1] identification of a valid problem (e.g. multi-dimensional nature of development; limitations of economic measures alone; issue of averages hiding inequality); [1]-[2] further development/explanation of the problem. Maximum [3]. Any other valid point credited (e.g. cultural bias in defining 'development' from a Western/economic perspective).

Economic measure - GNI (Gross National Income) per capita: this measures the total income generated by a country's residents (including income from abroad), divided by the population, giving an average income figure; a higher GNI per capita is generally associated with greater economic development, though it does not show how income is distributed. Social measure - literacy rate: this measures the percentage of the adult population able to read and write; a higher literacy rate generally indicates a more developed education system and greater human capital, and is linked to wider social and economic opportunities. Other valid measures: economic - GDP per capita, % employed in tertiary/quaternary sector; social - life expectancy, infant mortality rate, access to clean water; composite - Human Development Index (HDI), Multidimensional Poverty Index.

[1] valid economic measure named with an accurate brief explanation of what it shows; [1] valid social measure named with an accurate brief explanation of what it shows; [1] for a clear, accurate comparison/summary of how both measures indicate development. Maximum [3].

The Millennium Development Goals (MDGs, 2000-2015) set eight specific targets for the international community, including halving extreme poverty, achieving universal primary education, reducing child mortality and combating disease, aiming to make measurable progress in reducing the development gap by 2015. The 2030 Agenda for Sustainable Development, and its 17 Sustainable Development Goals (the 'Global Goals'), built on and expanded the MDGs, broadening the focus to include environmental sustainability (e.g. climate action, clean water) alongside continued economic and social targets (e.g. no poverty, quality education, gender equality), with a 2030 target date and application to all countries (not only LEDCs). Both frameworks aim to coordinate international aid, policy and investment towards common, measurable development targets.

[1] accurate reference to the MDGs and/or 2030 Agenda/Global Goals by name; [1] identification of at least one specific aim/target area (e.g. reducing poverty, education, health, environmental sustainability); [1] explanation of how the framework helps narrow the development gap (e.g. coordinating international aid/policy towards measurable targets). Maximum [3].

An emerging market is a country that is undergoing rapid economic growth and industrialisation, moving away from a primarily agricultural/low-income economy towards greater manufacturing and service-sector output, increasing trade and investment links with the rest of the world, and rising income levels - but which has not yet reached the level of development typical of an MEDC. Well-known groupings of emerging markets include the BRICS countries (Brazil, Russia, India, China, South Africa) and the MINT countries (Mexico, Indonesia, Nigeria, Turkey).

[1] accurate definition of 'emerging market' (rapid growth/industrialisation, not yet MEDC-level); [1] mark for each of two valid named emerging market countries, up to [2]. Maximum [3].

Reason 1: many emerging markets have attracted large amounts of foreign direct investment (FDI) into manufacturing and industry, as multinational companies are drawn by relatively low labour costs and large workforces, boosting industrial output, employment and exports. Reason 2: governments in many emerging markets have pursued policies to liberalise and open up their economies to international trade and investment (e.g. reducing trade barriers, encouraging exports, joining international trade organisations), integrating them more fully into the global economy and allowing them to benefit from growing global demand for manufactured goods. Other valid reasons: large and growing domestic populations providing both a large workforce and a large consumer market; substantial natural resource wealth generating export revenue; significant government investment in infrastructure and education.

[1] mark for identifying each of two valid reasons, up to [2] (e.g. FDI/low labour costs; trade liberalisation; large workforce/domestic market; resource wealth; infrastructure investment); [1] further mark for a clear explanation of how one of these reasons drives economic growth. Maximum [3].

Indicative content (e.g. using Uganda as a named LEDC case study): Globalisation - increased foreign direct investment and integration into global trade networks can bring new industries, jobs and infrastructure investment (e.g. growth of export-oriented agriculture or textiles), and access to global markets and technology; however, globalisation can also expose LEDC economies to volatile global commodity prices, create dependency on foreign investment, and mean profits are repatriated to foreign-owned multinational companies rather than reinvested locally, sometimes worsening inequality. Aid - can provide crucial short-term humanitarian relief (e.g. following famine or disaster), fund long-term development projects (schools, hospitals, clean water infrastructure, e.g. supported by international donors and NGOs), and build capacity through skills/technology transfer; however, aid can create dependency, may be misused through corruption or mismanagement, may be tied to conditions that primarily benefit the donor country, and top-down aid projects can fail to address local needs effectively. A well-balanced essay evaluates both globalisation and aid together, using specific, place-specific evidence from the named LEDC, and reaches a substantiated overall judgement on their relative importance/effectiveness for development, potentially also referencing progress (or lack of it) against Millennium Development Goals/2030 Agenda targets.

Level 1 (1-5 marks): Basic, generalised points on globalisation and/or aid, with little case-specific detail; weak QWC. Level 2 (6-10 marks): Sound coverage of both globalisation and aid with some case-specific detail and partial evaluation; clear QWC. Level 3 (11-15 marks): Detailed, well-developed and balanced evaluation of both globalisation and aid, with substantial place-specific evidence for the named LEDC, and a well-substantiated overall judgement; sophisticated QWC. Answers omitting a named LEDC case study are capped at a maximum of Level 2 (10/15).

Indicative content: Arguments for - a designated National Park would provide stronger, statutory protection for landscapes of outstanding natural beauty (building on existing measures such as AONBs and ASSIs/SSSIs), helping to conserve habitats and biodiversity from inappropriate development; it could boost the local economy through increased tourism revenue, visitor spending and associated employment; it can provide a coordinated management framework balancing conservation, recreation and local community/farming interests; other UK/Ireland regions with National Parks (e.g. the Mourne Mountains has long been proposed) benefit from national recognition and dedicated funding. Arguments against - stricter planning controls within a National Park could restrict local landowners' and farmers' ability to develop or diversify their land, potentially harming rural livelihoods and causing local opposition; increased tourism could itself cause environmental problems (footpath erosion, litter, traffic congestion) in sensitive areas if not carefully managed; the cost of establishing and running a National Park authority is significant, and some argue existing protections (AONB/ASSI status) are already sufficient without an additional layer of designation and bureaucracy; disagreement between stakeholders (landowners, conservation groups, local councils) about park boundaries and rules can create prolonged conflict. A balanced essay weighs conservation/economic benefits against restrictions on landowners and additional costs, reaching a substantiated judgement, ideally referencing a specific proposed area such as the Mourne Mountains.

Level 1 (1-5 marks): Basic, one-sided or generalised points; weak QWC. Level 2 (6-10 marks): Some relevant points on both sides with limited development/place-specific detail; adequate QWC. Level 3 (11-15 marks): A wide range of well-developed, balanced points on both sides with specific, place-specific evidence (ideally referencing a proposed NI location such as the Mournes) and a substantiated overall judgement; sophisticated QWC. Answers which fail to address both sides of the argument are capped at a maximum of Level 2 (10/15).

Factor 1 - Safety: the student must plan a safe route between counting sites, which may involve busy roads or built-up areas; this could include working in pairs or small groups, choosing sites with safe pedestrian crossings, carrying a mobile phone, and informing a teacher/responsible adult of the planned route and expected return time. Factor 2 - Reliability/comparability of timing: for the data to validly test the hypothesis, pedestrian counts at each site must be taken under comparable conditions - for example, at the same time of day (since footfall naturally varies through the day) and on the same day of the week (avoiding market days, weekends or special events that would distort 'normal' footfall), so that distance from the CBD is genuinely the variable being tested rather than time or day effects. Other valid factors: weather conditions on the day; choice of an appropriate, justified sampling technique for selecting count locations/times; obtaining any necessary permissions.

[1] identification + [1] explanation for each of two valid planning factors, maximum [4] (e.g. safety; reliability/comparability of timing; weather; sampling method; permissions).

Reason 1: stratified sampling divides the river into distinct sub-groups or 'strata' (e.g. upper, middle and lower course) and ensures sites are selected from each stratum, so the sample is representative of change along the entire river's course rather than being clustered in just one section, which could happen with purely random sampling. Reason 2: because the investigation's hypothesis concerns how channel characteristics change with distance downstream, stratified sampling lets the student deliberately ensure adequate coverage of each part of the course (e.g. two sites in each of the upper, middle and lower course), making it easier to identify and analyse the trend the hypothesis is testing, compared with a method that might, by chance, undersample one section.

[1] identification + [1] explanation for each of two valid reasons, maximum [4] (e.g. even/representative spread along the course; deliberate coverage of relevant sub-groups relevant to the hypothesis; reduces risk of bias/clustering from purely random selection).

(a) A scattergraph is the most appropriate graph type, because the investigation involves two continuous numerical variables (distance and building height) and a scattergraph (ideally with a best-fit line, ranked-correlation could also be applied) is the standard technique for displaying and analysing the relationship/correlation between two such variables.
(b) The data shows a clear negative relationship: building height is greatest close to the CBD (18 storeys at 50m) and decreases steadily/consistently as distance from the CBD increases, falling to just 2 storeys by 1000m; the rate of decrease appears to be more rapid over the first 400m (18 to 8 storeys) and then slows at greater distances (8 to 2 storeys between 400m and 1000m).
(c) Building height is not a perfect indicator of CBD extent because it can be affected by other factors unrelated to distance from the CBD, such as local planning/height restrictions, land value fluctuations, redevelopment history, or the specific function of an individual building (e.g. a single tall residential tower built outside the CBD); relying on this single indicator alone could therefore give a misleading picture of where the true CBD boundary lies.

(a) [1] correct graph type (scattergraph); [1] valid justification (two continuous/numerical variables being compared). (b) [1] mark for each valid descriptive point, up to [3]: e.g. overall negative/decreasing trend; correct use of start/end data values; comment on rate of change (e.g. steeper decline nearer the CBD). (c) [1] identification of a valid limitation; [1] development/explanation. Maximum [7].

(a) A bar chart is the most appropriate graph, because the data (pedestrian counts) is grouped into discrete, non-continuous time-period categories (rather than being continuous data), which a bar chart displays more clearly than a line graph.
(b) The data shows two distinct peaks in pedestrian activity: the highest count is 120 people during 1-2pm (the lunchtime period), and there is a second, lower peak of 95 people during 5-6pm; counts are lowest in the early morning (45 people, 9-10am) and late evening (30 people, 7-8pm), with a dip to 70 people during the mid-afternoon (3-4pm) between the two peaks.
(c) The lunchtime peak (1-2pm) likely reflects workers, shoppers and students leaving offices, shops and schools to buy food or run errands during a lunch break, increasing footfall in a commercial/town-centre location; the second peak (5-6pm) likely reflects the end of the traditional working/school day, as people travel home, pass through the area to access public transport, or stop to shop on their way home.

(a) [1] correct graph type (bar chart); [1] valid justification (discrete/categorical time-period data). (b) [1] mark for each valid descriptive point using specific data values, up to [3]: e.g. identifying the main peak (120, 1-2pm); identifying the secondary peak (95, 5-6pm); identifying the lowest value(s)/troughs. (c) [1] identification of a valid geographical reason; [1] development/explanation linking it to the observed pattern. Maximum [7].

Indicative content: Limitations - a single day's count is only a snapshot and may not be representative of 'typical' footfall, since counts can be affected by that day's weather, whether it was a weekday or weekend, or whether a local event (e.g. a market, sports fixture or public holiday) was taking place, all of which could make the count unusually high or low; counting by eye can also be prone to human error, especially at busy times when it is hard to count accurately (undercounting), reducing precision. Improvements - repeating counts on multiple different days (including both weekdays and a weekend) and at different times of year, then using an average, would produce more reliable, representative data; using an automated pedestrian counting device (electronic sensor) rather than manual counting would reduce human error and allow continuous, more accurate data collection.

Level 1 (1-2 marks): Identifies a limitation or an improvement with little development. Level 2 (3-4 marks): Identifies and explains at least one limitation AND suggests a valid, relevant improvement to increase reliability.

Indicative content: Limitations - classifying land use by eye is subjective, as different students (or the same student on different occasions) may categorise a mixed-use or ambiguous building differently, reducing the reliability and repeatability of the results; it also only captures land use at the specific moment of observation, which may not reflect a building's use throughout the whole day (e.g. a building could be used differently at different times) or account for upper floors not visible from street level. Improvements - using a standardised, pre-agreed land-use classification key (with clear category definitions) that all members of a group apply consistently would improve reliability; cross-checking observations against secondary sources (e.g. planning records, online business directories) or GIS/satellite imagery could verify or supplement the visual survey and reduce subjectivity.

Level 1 (1-2 marks): Identifies a limitation or an improvement with little development. Level 2 (3-4 marks): Identifies and explains at least one limitation AND suggests a valid, relevant improvement to increase reliability.

(a) Ranking distance (1 = smallest) and pedestrian count (1 = smallest), both ascending:
Site 1: distance rank 1, count rank 6, d = 1-6 = -5, d² = 25
Site 2: distance rank 2, count rank 5, d = 2-5 = -3, d² = 9
Site 3: distance rank 3, count rank 4, d = 3-4 = -1, d² = 1
Site 4: distance rank 4, count rank 2, d = 4-2 = 2, d² = 4
Site 5: distance rank 5, count rank 3, d = 5-3 = 2, d² = 4
Site 6: distance rank 6, count rank 1, d = 6-1 = 5, d² = 25
\( \sum d^2 = 25+9+1+4+4+25 = 68 \)
\( r_s = 1 - \dfrac{6 \times 68}{6(6^2-1)} = 1 - \dfrac{408}{6 \times 35} = 1 - \dfrac{408}{210} = 1 - 1.943 = -0.94 \) (to 2 d.p.)
(b) The calculated \( |r_s| = 0.94 \) is greater than the critical value of 0.886 for \( n=6 \) at the 5% significance level, so the result is statistically significant. This means we can be more than 95% confident that the strong negative relationship (as distance from the CBD increases, pedestrian count decreases) is a genuine relationship and not simply due to chance, so the null hypothesis (that there is no relationship between the two variables) can be rejected.

(a) [1] correct ranking of both variables shown; [1] correct d and d² values shown for all six sites; [1] correct final \( r_s \) value (-0.94, accept -0.93 to -0.95 for minor rounding) with correct substitution into the formula shown. Own Figure Rule (OFR) applies to (b) if an arithmetic slip is made in (a) but sound significance reasoning is applied to the candidate's own (incorrect) value. (b) [1] correct comparison of the candidate's \( r_s \) value against the critical value (0.886); [1] correct conclusion that the result is significant, with valid reasoning (e.g. reference to the 5% probability / rejecting the null hypothesis). Maximum [5].

(a) Distance downstream is already ranked 1 (nearest source) to 8 (furthest downstream). Ranking pebble size (1 = smallest), ascending:
Site 1: distance rank 1, pebble rank 5, d = 1-5 = -4, d² = 16
Site 2: distance rank 2, pebble rank 8, d = 2-8 = -6, d² = 36
Site 3: distance rank 3, pebble rank 3, d = 3-3 = 0, d² = 0
Site 4: distance rank 4, pebble rank 7, d = 4-7 = -3, d² = 9
Site 5: distance rank 5, pebble rank 1, d = 5-1 = 4, d² = 16
Site 6: distance rank 6, pebble rank 6, d = 6-6 = 0, d² = 0
Site 7: distance rank 7, pebble rank 2, d = 7-2 = 5, d² = 25
Site 8: distance rank 8, pebble rank 4, d = 8-4 = 4, d² = 16
\( \sum d^2 = 16+36+0+9+16+0+25+16 = 118 \)
\( r_s = 1 - \dfrac{6 \times 118}{8(8^2-1)} = 1 - \dfrac{708}{8 \times 63} = 1 - \dfrac{708}{504} = 1 - 1.405 = -0.40 \) (to 2 d.p.)
(b) The calculated \( |r_s| = 0.40 \) is less than the critical value of 0.738 for \( n=8 \) at the 5% significance level, so the result is NOT statistically significant. This means the apparent (weak, negative) relationship between distance downstream and pebble size at these sites could plausibly have occurred by chance, so the null hypothesis (that there is no relationship between the two variables) cannot be rejected; this could reflect real-world factors such as a tributary confluence or local variation in rock type introducing new, differently-sized sediment partway down the river.

(a) [1] correct ranking of pebble size shown; [1] correct d and d² values shown for all eight sites; [1] correct final \( r_s \) value (-0.40, accept -0.39 to -0.41 for minor rounding) with correct substitution into the formula shown. Own Figure Rule (OFR) applies to (b) if an arithmetic slip is made in (a) but sound significance reasoning is applied to the candidate's own (incorrect) value. (b) [1] correct comparison of the candidate's \( r_s \) value against the critical value (0.738); [1] correct conclusion that the result is NOT significant, with valid reasoning (e.g. inability to reject the null hypothesis, or a plausible geographical reason for the weak relationship). Maximum [5].

Systematic sampling involves selecting respondents according to a fixed, pre-determined interval (e.g. every 10th person passing a point), rather than the researcher choosing who to approach; this removes the risk of the researcher unconsciously introducing bias by selecting people who look more approachable or willing to answer (which could happen with a purely opportunistic approach). It also ensures the sample is spread evenly across the whole data collection period, rather than being clustered, giving a fairer cross-section of the shoppers present.

[1] basic identification of systematic sampling reducing researcher bias/subjectivity; [1] development, e.g. reference to an even spread of respondents across the data collection period; [1] further valid development or an accurate worked example. Maximum [3].

One limitation is that questionnaire data relies on respondents' self-reported answers, which may not always be accurate - respondents may misremember, exaggerate, or give answers they believe are more socially acceptable rather than their true opinion or behaviour, introducing bias into the results. Related to this, response rates can be low (many people approached may refuse to take part), which can reduce the sample size and mean the final sample is not fully representative of the target population, further limiting the reliability of conclusions drawn from the data.

[1] identification of a valid limitation (e.g. respondent honesty/bias; low response rate; small/unrepresentative sample); [2] development/explanation of how this affects the reliability of the data. Maximum [3].

One improvement is to pilot the questionnaire - testing it on a small sample of people before the main data collection - to identify any questions that are ambiguous, leading, or difficult for respondents to understand, and to refine the wording accordingly; this increases the likelihood that all respondents interpret questions in the same way, improving the consistency and reliability of the data collected in the full survey. Other valid improvements: using closed/multiple-choice questions rather than open questions where possible to allow easier, more consistent recording and analysis of responses; increasing the overall sample size to reduce the impact of any individual unusual response.

[1] identification of a valid improvement (e.g. piloting the questionnaire; using closed questions; increasing sample size); [2] explanation of how this improves reliability. Maximum [3].

Consideration 1 - informed consent: respondents should be briefly told the purpose of the study and how the data will be used before being asked to take part, and must be free to decline to answer or to stop partway through without pressure; this respects their right to choose whether to participate. Consideration 2 - confidentiality and anonymity: any personal information or opinions collected should be kept anonymous (e.g. not recording names) and confidential, used only for the purposes of the investigation, and no individual respondent should be identifiable in the write-up of results, protecting respondents' privacy. Other valid considerations: ensuring the safety and comfort of respondents (e.g. not approaching people in an intimidating way, being mindful of vulnerable individuals such as children); being honest about the purpose of the research and not misleading respondents.

[1] identification + [1] explanation for each of two valid ethical considerations, maximum [4] (e.g. informed consent; confidentiality/anonymity; respondent safety/comfort; honesty about research purpose).

(a) Land-use types with a distinctive visual signature from above, such as woodland (dense, textured green cover), water bodies (rivers, lakes - typically dark, smooth areas), or clearly built-up urban areas (dense grid-like patterns of roofs and roads), can be reliably identified directly from a satellite image.
(b) GIS and satellite imagery allow a student to view, measure and map land use across a much wider area than could be covered on foot, including locations that might be difficult, unsafe, or too time-consuming to visit in person; this allows the student to plan a more efficient, representative sampling strategy (e.g. identifying appropriate transect lines or site locations) before committing time to fieldwork, and to add layers of data (e.g. land use zones, transport routes) that can be directly compared with data collected in the field.

(a) [1] any one valid, visually-distinctive land-use type (e.g. woodland; water; clearly built-up area). (b) [1] identification of a valid advantage (e.g. covering a larger area; efficiency; safety; pre-fieldwork planning); [2] development/explanation of the advantage. Maximum [4].

A key limitation is that satellite imagery shows land cover (what is visible from above, such as a rooftop or area of tarmac) rather than land use (the actual function of that space); for example, a satellite image cannot reliably distinguish between a building used as a shop, an office or a house, since they may look similar from directly above. This means satellite imagery alone is insufficient to fully classify land use, and needs to be supplemented or verified by ground-based fieldwork observation, secondary sources (e.g. business directories), or more detailed street-level imagery. Other valid limitations: imagery may be out of date, so does not reflect recent changes; cloud cover can obscure parts of an image; free/lower-resolution imagery may not show enough detail to distinguish smaller land-use differences.

[1] identification of a valid limitation (e.g. shows land cover not land use/function; image may be out of date; cloud cover; resolution limits); [2] development/explanation of the limitation. Maximum [3].