Data Sampling in Geography

This section explains the data sampling used in GCSE Geography fieldwork. In geographical fieldwork, collecting data is a crucial task that allows geographers to analyse and draw conclusions about the environment. Data can be classified into two main categories: quantitative and qualitative. Understanding how to collect data effectively, through various sampling techniques, is essential for ensuring accuracy and reliability in geographical investigations.

Types of Data in Geography

Data in fieldwork can be split into two main types: quantitative and qualitative.

Quantitative Data

Quantitative data consists of numerical information that can be measured and counted. This type of data is particularly useful in geography as it provides objective facts and figures that can be used for statistical analysis and to identify trends.

Examples of Quantitative Data:

• Velocity of a River: The speed at which water flows in a river, usually measured in metres per second.
• Number of Cars: The count of vehicles passing through a particular area, for example, during a traffic survey.
• Amount of Litter: The weight or count of litter found in an area during a clean-up event.

Quantitative data can be further divided into different types based on how they are measured:

• Nominal Data: Data that appears as categories or labels with no specific order. For example, when asking respondents whether they support a new park project, answers might be recorded as 1 = Yes and 2 = No.
• Ordinal Data: This type of data has a rank or order, but the differences between each rank are not necessarily equal. An example could be the ranking of cities by population size. We know that City 1 has more people than City 2, but the exact difference in population is not specified.
• Interval Data: This type of data has a meaningful order, and the difference between each value is equal. An example is temperature, which can be measured in degrees Celsius or Fahrenheit.
• Ratio Data: Like interval data but with a true zero point, meaning ratios can be made. For example, the number of people per doctor in a region.

Qualitative Data

Qualitative data is descriptive and non-numerical. It is based on opinions, experiences, and observations, making it more subjective than quantitative data. Despite this, qualitative data is still crucial in geography as it provides context and deeper insights into geographical phenomena.

Examples of Qualitative Data:

• Questionnaires: When conducting fieldwork, geographers often ask people about their opinions or experiences. For example, a questionnaire might ask visitors to a park how they feel about the facilities available.
• Field Sketches and Photographs: A field sketch is a drawing of a landscape or feature observed during fieldwork, providing a visual record of the area. Photographs also capture detailed information about the environment, helping to analyse changes over time.
• Maps: Maps are used to present spatial information about an area. These can be hand-drawn or sourced from digital platforms like GIS (Geographical Information Systems). Maps help geographers visualise the location of features and analyse spatial relationships.
• Satellite Images: Satellite imagery offers a bird’s-eye view of landscapes, allowing geographers to assess changes in land use, vegetation cover, and urban growth.
• Interviews and Focus Groups: Collecting qualitative data through interviews or focus groups allows geographers to explore local perceptions, experiences, and attitudes about a particular issue, such as environmental change or urban development.

Primary and Secondary Data

Data used in fieldwork can be categorised as either primary or secondary:

• Primary Data: This is data that you collect directly during your fieldwork. It could include measurements, observations, tallies, photographs, and surveys. For example, if you are studying river characteristics, you may measure the river’s width, depth, and flow velocity yourself.

Example: A survey where you ask people to rate the cleanliness of a park on a scale of 1 to 5 would provide primary data.

• Secondary Data: This is data that has already been collected by others and is available for use. Secondary data can be accessed from sources such as books, government reports, websites, or databases. An example might include historical climate data or census statistics.

Example: If you are analysing traffic trends in a city, you might use secondary data such as traffic flow records from the local council.

Sampling in Fieldwork

Sampling is a method of selecting a subset of data from a larger population. It is essential to use sampling techniques to avoid bias and ensure that data collection is representative of the area or population being studied.

Random Sampling

Random sampling involves selecting data points, locations, or people randomly. This method ensures that each individual or site has an equal chance of being chosen, reducing bias.

Example:

If you are conducting a survey in a park and want to understand the general opinion of park visitors, you could randomly select individuals to interview. This avoids focusing on any group and gives a more balanced view.

Systematic Sampling

Systematic sampling involves collecting data at regular intervals. This approach is useful when data needs to be collected in an organised, consistent manner.

Example:

If you are surveying vegetation along a transect, you might take measurements every 5 metres. This ensures that data is collected in a regular pattern, providing a clear overview of vegetation changes along the transect.

Stratified Sampling

Stratified sampling divides the population into subgroups, or strata, based on specific characteristics. Then, samples are taken from each subgroup. This method ensures that the sample represents different categories or groups within the population.

Example:

If you are studying the types of buildings in a city, you could divide the city into different zones (e.g. residential, commercial, and industrial areas). You would then select samples from each zone to ensure that your data reflects the full range of building types.

• Stratified Random Sampling: In this case, you randomly select samples from each subgroup. For example, if you are surveying people in a town and want to ensure you represent different age groups, you could randomly select individuals from various age categories.
• Stratified Systematic Sampling: This combines systematic sampling with stratified sampling. For example, if you want to study traffic patterns in a city, you could take measurements at regular intervals (systematic) from different districts of the city (stratified).

This video explains the types of sampling in more detail.

Data Collection Sheets

Designing a good data collection sheet is key to gathering clear and accurate data. These sheets should be simple, well-structured, and designed to capture all the necessary information without confusion. The easier it is to record data, the more reliable your results will be.

Key Considerations:

• The sheet should clearly list the type of data to be collected (e.g. measurements, opinions).
• It should include space for notes and observations.
• It should be designed to minimise mistakes, with easy-to-understand headings and categories.

For example, if you are collecting data on the amount of litter in a park, your data collection sheet might include columns for the type of litter (e.g. plastic, paper, glass), location (e.g. near a bench, in the playground), and the quantity found (e.g. number of pieces or weight).

By carefully selecting and applying the appropriate sampling techniques and ensuring that both quantitative and qualitative data are collected in a systematic, unbiased manner, geographers can produce reliable, well-rounded analyses of the environments they study. Understanding how to handle and interpret data is a fundamental skill in geography, crucial for drawing meaningful conclusions and making informed decisions.