Cambridge IGCSE Environmental Management Fieldwork 4: Tables, maps, graphs and spatial data

Field data become useful when they are recorded without ambiguity and presented in a form that reveals patterns. A graph is not decoration. Its type, axes and scale should answer the investigation question while keeping raw observations traceable.

This Eclat skills note develops Cambridge IGCSE Environmental Management 0680 requirements to record and analyse data, identify and process anomalies, translate evidence between forms and interpret graphical, numerical and spatial information.

Recording raw data

Design the table before fieldwork. Put the independent variable or sampling location in the first column and each measured variable in a separate column. Include units in headings, not in every cell. Use consistent precision for measurements made with the same instrument.

Keep raw and processed data distinct. Raw data are the observations recorded at the site. Processed data include means, totals, percentages, rates or indices calculated afterward. Preserving raw values allows calculations and anomaly decisions to be checked.

A field table may also need date, time, coordinates, weather, habitat notes and photograph numbers. Use defined categories. If pollution is scored from 0 to 5, state what each score means so different observers apply the same rule.

Do not create false precision. A ruler marked in centimetres does not justify several unexplained decimal places. Conversely, early rounding can distort a later mean or percentage. Retain the measured precision during processing and round the reported result appropriately.

Processing numerical evidence

Useful summaries include total, mean, median, range, percentage, percentage change and rate. Choose a calculation that fits the data and question. A mean can summarise repeat measurements but may be affected by an extreme value. A median can better represent skewed observations, while a range describes spread but uses only the highest and lowest values.

An anomaly is a result that does not fit the broader pattern or the repeat readings. It is not automatically an error. Mark it, check the original record and apparatus notes, repeat the measurement if possible and consider a field explanation. Calculate results with and without it only when this comparison helps, and justify any exclusion.

Choosing a graph

• Use a bar chart for separate categories, such as mean litter counts at named land uses. Bars are separated.
• Use a line graph when ordered or continuous values show change, such as temperature through time. Plot points accurately and connect them only when the sequence supports interpolation.
• Use a scatter graph to investigate association between two numerical variables, such as distance from a road and lichen cover. A trend does not prove that one variable causes the other.