Geographical data handling overview: SQA Advanced Higher Geography

Geographical data handling is worth 20 marks of the question paper, the joint-largest skill area. This guide maps the techniques; the module dot points take data types, graphs, maps, descriptive statistics and the inferential tests in detail.

Data types and sampling

Data is nominal (categories), ordinal (ranked) or interval (equal-gap numbers), and the type decides which graph and test are valid. Sampling is random, regular or stratified, and a sound sample is the condition for trustworthy analysis.

Graphical and map-based presentation

The examinable graphs (bipolar, dispersion, kite, logarithmic, polar, systems, scattergraph, triangular) and maps (annotated overlay, choropleth, cross section, dot, flow line, isoline, proportional symbols, sphere of influence, transect) each suit a particular data structure. The skill is selecting and justifying the right one.

Descriptive statistics

Measures of central tendency (mean, median, mode) give the typical value; measures of dispersion (range, interquartile range, standard deviation, standard error of the mean, coefficient of variation) give the spread. Both are needed to describe data fully.

Inferential tests

Spearman's rank (ranked data) and Pearson's (interval data) measure correlation; chi-squared tests association between categories; linear regression models a relationship; nearest neighbour measures point clustering. All require significance to be checked, and none proves cause.

How to use this module

Practise selecting and interpreting graphs, maps and statistics on past papers and the specimen paper, and apply them to your geographical study data. Always match the technique to the data type and check significance before drawing conclusions.