3.4 Geographical skills checklist

Competence in geographical skills should be developed during study of the course content, in an integrated way and not as a separate theme or topic. While the relative balance of quantitative and qualitative methods and skills will differ between each of the core elements and the options, students must be introduced to a roughly equal balance of quantitative and qualitative methods across the specification.

During their A-level course students should:

  • understand the nature and use of different types of geographical information, including qualitative and quantitative data, primary and secondary data, images, factual text and discursive/creative material, digital data, numerical and spatial data and other forms of data, including crowd-sourced and 'big data'
  • collect, analyse and interpret such information, and demonstrate the ability to understand and apply suitable analytical approaches for the different information types
  • undertake informed and critical questioning of data sources, analytical methodologies, data reporting and presentation, including the ability to identify sources of error in data and to identify the misuse of data
  • communicate and evaluate findings, draw well-evidenced conclusions informed by wider theory, and construct extended written argument about geographical matters.

Students at A-level are required to demonstrate all the skills and approaches detailed below.

3.4.1 Qualitative skills and quantitative skills

Students should develop the following with respect to qualitative data:

  • use and understanding of a mixture of methodological approaches, including interviews
  • interpretation and evaluation of a range of source material including textual and visual sources
  • understanding of the opportunities and limitations of qualitative techniques such as coding and sampling, and appreciation of how they actively create particular geographical representations
  • understanding of the ethical and socio-political implications of collecting, studying and representing geographical data about human communities.

Students should develop the following with respect to quantitative data:

  • understanding of what makes data geographical and the geospatial technologies (eg GIS) that are used to collect, analyse and present geographical data
  • an ability to collect and use digital and geo-located data, and understand a range of approaches to use and analyse such data
  • understanding of the purposes and difference between the following and to use them in appropriate contexts:
    • descriptive statistics of central tendency and dispersion
    • descriptive measures of difference and association, inferential statistics and the foundations of relational statistics
    • measurement, measurement errors, and sampling
    • understanding of the ethical and socio-political implications of collecting, studying and representing geographical data about human communities.

3.4.2 Specific skills

The following sections identify specific qualitative and quantitative skills to be developed.

3.4.2.1 Core skills

  • Use and annotation of illustrative and visual material: base maps, sketch maps, OS maps (at a variety of scales), diagrams, graphs, field sketches, photographs, geospatial, geo-located and digital imagery.
  • Use of overlays, both physical and electronic.
  • Literacy – use of factual text and discursive/creative material and coding techniques when analysing text.
  • Numeracy – use of number, measure and measurement.
  • Questionnaire and interview techniques.

3.4.2.2 Cartographic skills

  • Atlas maps.
  • Weather maps – including synoptic charts (if applicable).
  • Maps with located proportional symbols.
  • Maps showing movement – flow lines, desire lines and trip lines.
  • Maps showing spatial patterns – choropleth, isoline and dot maps.

3.4.2.3 Graphical skills

  • Line graphs – simple, comparative, compound and divergent.
  • Bar graphs – simple, comparative, compound and divergent.
  • Scatter graphs, and the use of best fit line.
  • Pie charts and proportional divided circles.
  • Triangular graphs.
  • Graphs with logarithmic scales.
  • Dispersion diagrams.

3.4.2.4 Statistical skills

  • Measures of central tendency – mean, mode, median.
  • Measures of dispersion – range, inter-quartile range and standard deviation.
  • Inferential and relational statistical techniques to include Spearman’s rank correlation and Chi-square test and the application of significance tests.

3.4.2.5 ICT skills

  • Use of remotely sensed data (as described above in Core skills).
  • Use of electronic databases.
  • Use of innovative sources of data such as crowd sourcing and ‘big data’.
  • Use of ICT to generate evidence of many of the skills provided above such as producing maps, graphs and statistical calculations.