Glaciated Landscape Skills (AQA A Level Geography)

Glaciated Landscape Skills

• Geographical skills are working skills essential to developing a synoptic approach to answering questions but also observing the 'bigger picture' in geography

• It is important to be confident with a mixture of numerical quantitative skills and qualitative written communication skills 

• Many of the skills are already outlined elsewhere in the revision notes

Understanding data

• In data analysis, variables are the amounts that have been measured - the number of drumlins or the size of erratics

• For each variable, a value is noted against each sample - drumlin a, drumlin b etc

• The data set is the collection of total values and is then analysed further

• The sample size and type of data, influence the choice of statistical test to use 

  • Spearman's rank would be used to test for correlation between two variables etc. 

• Types of data can be grouped into:

  • Nominal

  • Ordinal

  • Interval

  • Ratio

• Nominal
  

  • Also known as categorical data, its main purpose is to classify or group information

  • Data is organised into distinct categories, but the categories have no numerical or quantitative meaning

  • Examples of categories can include things such as dog, cat, blue, male and female etc.

  • Or they can be labelled with numerical codes such as 1 for glacial 2 for periglacial etc.

  • They can be summarised using percentages or frequencies e.g. 40% of periglacial pingos are open system

  • Remember they have no order or mathematical relationship and performing statistical analysis is pointless

• Ordinal

  • Ordinal data is a type of data that can be ordered or ranked into categories

  • Examples include:

    • Primary school, secondary school, sixth form or college, and university

      • The categories show a clear progression/order on levels of possible education 

    • Very satisfied, satisfied, neutral, dissatisfied and very dissatisfied

      • These categories show levels of satisfaction, but intervals between them may not be equal

  • Ordinal data allows ranking and comparing of vales, but doesn't provide information on size of the differences  between the categories 

  • Statistical analysis and interpretation can be used such as calculating median, mode, or Spearman's rank; but ordinal data doesn't allow for mathematical calculations such as adding or subtracting

• Interval

  • Interval data is the same as ordinal data, but the intervals between the categories is constant

    • For example pH values of water; scale of temperature or time on a clock

  • Interval data is therefore, a more precise measurement compared to nominal and ordinal data, but it does not include a true zero point

  • Interval data allows for various statistical operations such as calculating mean, median mode, standard deviations, and conducting tests such as t and u-tests 

• Ratio

  • Unlike interval data, ratio data includes a true zero point, which allows for a more comprehensive analysis of the data

  • The difference between any two consecutive values is the same throughout the entire range of the data

  • The true zero point indicates a total absence of the value at that point

  • This makes ratio data the highest level of measurement in terms of precision and mathematical operations

  • Examples include:

    • Weight: an object is weighed in kilograms and grams

    • If a value of zero is recorded it means there was no weight to the object

    • Distance: the distance travelled by a glacier for one day is recorded in centimetres 

    • A recorded value of zero indicates that the glacier travelled no distance

  • Ratio data allows for a wide range of mathematical operations, including addition, subtraction, multiplication, and division

  • Statistical analysis techniques applicable to ratio data include measures of central tendency (mean, median, mode), measures of dispersion (range, variance, standard deviation), and parametric tests

Evaluative skills 

• Being asked to assess the impacts or causes of a range of factors is a common exam question

• When deciding if something is significant consider four things: 

  • Time - how long will it take for a strategy or impact to take effect?

  • Scale - how many people will be affected?  

  • Cost - What will the cost be and to whom? 

    • A cost can be human or environmental - what benefits the environment may come at a cost to human activity

    • Rather than considering whether something is expensive or cheap, think about whether it is worth the cost because of the benefits it will create

    • It is important to remember that just because something is expensive that doesn’t mean it is the worst option

  • Ethics - Does the strategy ensure dignity for local people and other stakeholders? 

• This will allow for a well-rounded and substantiated argument in 9 mark and 20 mark questions

Photo analysis

• This is an important observational skill 

• Look at the foreground, midground and background

• Consider the impact of the colours

• Think about what has not been included in the picture, what might be just out of frame?

Percentage and percentage change

• To give the amount A as a percentage of sample B, divide A by B and multiply by 100

  • In 2020, 25 out of 360 homes in Catland were burgled

  • What is the percentage (to the nearest whole number) of homes burgled?

• A percentage change shows by how much something has either increased or decreased

• In 2021 only 21 houses were burgled. What is the percentage change in Catland?

• 

• There has been a decrease of 16% in the rate of burglaries in the Catland area

• Remember that a positive figure shows an increase but a negative is a decrease

Mann-Whitney U test

• Also known as the Wilcoxon rank-sum test, it is a nonparametric test used to compare two independent groups, population or samples to determine if there is a significant difference between their distributions

• It makes no assumptions of the data being normally distributed

• The test works by assigning ranks to the observations from both groups combined and considers all the values as a single pool

• Then, it compares the sums of the ranks for each group

• The test looks at whether the distributions of the two groups differ significantly based on the ranks

• A general outline of how the Mann-Whitney U test works:

  • Combine the data from both groups into a single dataset

  • Rank the combined data, assigning a rank to each observation (identical data are given an average rank)

  • Then calculate the sum of the ranks for each group

  • Use the U statistic (the smaller of the two sums of ranks) to determine the test statistic

  • Compare the test statistic to the critical values in the Mann-Whitney U distribution or use a significance level to determine if the difference between the groups is statistically significant

  • If the p-value is below the chosen significance level (often 0.05), the test concludes that there is a significant difference between the groups

• The Mann-Whitney U test does not make any specific distribution for the data and is effective in comparing ordinal or continuous variables between two independent groups