Fieldwork Key Terms & Process

What is the Enquiry process?

Asking geographical questions to investigate human & physical processes through fieldwork.

List the steps of the fieldwork process in order:

1. Enquiry question & planning

2. Data collection

3. Data presentation & processing

4. Description, analysis & explanation of data

5. Conclusion

6. Evaluation

What is a Hypothesis?

An educated guess or prediction about the relationship between two or more variables that can be tested.

What are variables:

Different factors or characteristics that can be measured, observed or controlled within a geographical study.

Primary Data:

Data collected by you, within your fieldwork, directly from the source.

Secondary data:

Data that has already been collected by someone else, for something else, but can still be used in the investigation.

Primary data examples:

• Measurements

• Interview info

• Tallies

• Photographs

Secondary data examples:

• Census results

• Weather data

• Maps

• Old photos

• Websites

• Newspaper articles.

Primary data strengths:

• Specific to research needs & enquiry question

• Control over data reliability, quality & validity

• You can collect as much data as you need

• Flexibility over process

• It is up to date

Primary data limitations:

• Time consuming

• Potentially costly if it needs specialist equipment/resources

• Sample size needs to be large to be accurate

• Potential bias

Secondary data strengths:

• Easy & quick to access

• Cost effective

• Time saving

• Large amount of data sources are available

• Historical analysis

Secondary data limitations:

• Isn’t specific to enquiry question

• No/limited control over data quality

• Data may be biased

• Data may be out of date

• Availability issues

Quantitative data:

Data that can be measured or counted and given a numerical value.

Qualitative data:

Data that is non-numeric and describes characteristics or qualities.

Quantitative data strengths:

• Possible to have a larger sample size

• More objective

• More reliable

• Easy to reproduce results

• Information can often be collected quickly

• Clear & precise

Quantitative data limitations:

• Data can oversimplify and issue

• Heavily dependent on collection accuracy

• Human error or equipment error can lead to mistakes in measurement

• Data often lacks depth

Qualitative data strengths:

• Gives contextual understanding into behaviour

• More nuanced & in depth insight into experience

• Human-centred approach

Qualitative data limitations:

• Data analysis can be complex, difficult to make comparisons

• Often a small sample size

• Subjective

• Low reliability

• Time consuming

What is bias?

Anything that can unfairly influence the result of a study, making results less accurate.

Why is bias an issue in fieldwork?

It can distort results, leading to inaccurate or unreliable conclusions.

What is the purpose of Sampling?

• Gives an overview of the whole feature/population sampled

• There isn’t enough time/equipment/access to measure whole area being investigated

• Sampling provides a representative & statistically valid sample of what is being investigated

What are the 3 types of sampling?

• Random

• Systematic

• Stratified

What is random sampling?

Collecting data from randomly selected subsets of a population.

How does random sampling work?

1. Create a sampling frame (a complete list of all possible sampling points), give each sampling point or person a unique number

2. Then use a method to generate random numbers - random number generator.

What is systematic sampling?

Collecting data at regular intervals - e.g. every 500m or every 10th person.

How does systematic sampling work?

1. Identify the boundaries of the area/population group you are studying

2. Select an interval for sampling (e.g. ever 10metres/ every 5th person)

3. Select a starting point, collect data at every point at the regular interval chosen

What is stratified sampling?

Dividing a population or area into smaller groups/sites, then selecting samples from each group.

How does stratified sampling work?

1. Identify the different groups/types within a study area

2. Decide how many samples you want to take from each group or site

3. Within each site/group choose to use another sampling method to collect the data

Advantages of random sampling:

• Least biased of all sampling - all possible sample sites have an equal chance of being selected

• Can be used with a large sample area/population

Disadvantages of random sampling:

• Representation of the overall population may be poor if the randomly selected sites miss large areas

• Some sites may not be accessible or safe

Advantages of systematic sampling:

• Easy & quick - more straightforward than random sampling

• Covers whole study area equally

Disadvantages of systematic sampling:

• Not all sites have an equal chance of being selected - increases bias

• There may be over or under-representation of a particular feature

Advantages of stratified sampling:

• Can be used alongside systematic & random sampling

• Comparisons can be made between sub-sets

Disadvantages of stratified sampling:

• Proportions of sub-sets need to be known & accurate

What are some sources of error?

• Measurement Error - mistakes made when collecting data (human error)

• Operator Error - differences in the results collected by different people (for subjective data)

• Sampling Error - where a sample is biased towards one group, not representative of a whole population

What is validity?

The sustainability of the method used to answer the enquiry question. Valid data is supported by accurate & reliable data obtained using an appropriate method.

What are the 2 types of data in terms of presenting it?

1. Continuous

2. Discrete

What is continuous data?

Data that can be any value within a range & can be divided into finer & finer increments. e.g. temperature.

What is discrete data?

Data that is distinct and made of only separate values that cannot be divided further.

Is continuous or discrete data infinite or finite values?

Continuous data = infinite, Discrete data = finite

Is continuous or discrete data measurable or countable?

Continuous = measurable, Discrete = countable

Examples of ways continuous data is represented:

• Bar graphs

• Pie charts

Examples of ways discrete data is represented:

• Line graphs

• Histograms

Bar Graph use:

Used to show absolute values.

Bar graph strengths:

• Summarises a large set of data

• Easy to interpret & construct

• Shows trends clearly

Bar graph limitations:

• Requires additional information

• Does not show causes, effects or patterns

• Can only be used with discrete data

Line graph use:

Used to show continuous data.

Line graph strengths:

• Shows trends & patterns clearly

• Quicker & easier to construct than a bar graph

• Easy to interpret

• Requires little written explanation

Line graph limitations:

• Does not show causes or effects

• Can be misleading if scales on axis are altered

• If there are multiple lines on a graph it can be confusing

Pie chart use:

Used to represent percentages.

Pie Chart strengths:

• Clearly shows the proportion of the whole

• Easy to compare different components

• Easy to label

• Info can be highlighted by separating segments

Pie chart limitations:

• Don’t show changes over time

• Difficult to understand without clear labelling

• Hard to compare two sets of data

• Can only be used for a small number of categories or segments become confusing

Scatter graph use:

Used to compare 2 sets of data.

Scatter graph strengths:

• Clearly shows data correlation

• Shows the spread of data

• Makes it easy to identify anomalies & outliers

Scatter graph limitations:

• Data points can’t be labeled

• Too many data points can make it difficult to read

• Can only show the relationship between two sets of data

Proportional symbols map use:

Used to show located data.

Proportional symbols map strengths:

• Illustrates the differences between many places

• Easy to read

• Data is specific to particular locations

Proportional symbols map limitations:

• Not easy to calculate the actual value

• Time-consuming to construct

• Positioning on a map may be difficult, especially with larger symbols

Isoline map use:

Used to connect data points of equal value

Isoline map strengths:

• Good visual representation of data

• Easy to show patterns/trends

Isoline map limitations:

• Can be difficult to construct

• Small lines & numbers can be difficult to read

Triangular graphs use:

Used to show the relationship between 3 pieces of data.

Triangular graphs strengths:

• 3 pieces of data can be compared at once

• Easy to compare data

Triangular graphs limitations:

• Data must be in percentages

• Can be difficult to read & construct

Chloropleth maps use:

Used to show values for different locations

Chloropleth maps strengths:

• Gives clear visual impression of the changes over space

• Shows a large amount of data

• Groupings are flexible

Chloropleth maps limitations:

• Makes it seem as if there is an abrupt change in the boundary

• Distinguishing between shades can be hard

• Variations within value sets are not visible

Photograph strengths:

• An accurate record at the time

• Can represent things more clearly than numerical data

• Can be used to show data collection techniques

• Can be used next to historical photos to show changes over time

• Helps recall key features

Photograph limitations:

• Not all photos are relevant

• Can be subjective & biased as student selects what is photographed

• Photographs can sometimes contain too much info

• Are 2D so judging depth is difficult

Field sketch strengths:

• Things can be left out of sketch if not relevant to enquiry

• Smaller important details can be more detailed

• Gives broad overview of features

• Helps recall of key features

Field sketch limitations:

• Scale in sketch may be inaccurate

• Important details may be missed

• Sketch may contain inaccuracies which affect analysis

Define Evaluation:

An honest reflection of the data collection process & consideration on how to improve your practice moving forward.

What to evaluate in your fieldwork:

1. Relevance

2. Accuracy

3. Bias & Error

4. Scalability

What is relevance?

Whether method aligns with research question.

What is accuracy?

The chance of error when collecting data.

What is evaluation of bias & error?

Evaluating what potential sources of bias & error there were in the data collection.

What is scalability?

Whether the method can be applied to a larger sample if needed.

What are spatial patterns?

Patterns that reveal how variables or phenomena are distributed across a geographic area or spatial dimension.

Examples of spatial patterns:

• Clustered

• Dispersed

• Random

What are linear patterns?

Describe relationships between variables where changes in one variable correspond to changes in another - in a consistent, linear manner.

Examples of linear patterns:

Direct Relationships.

What is accuracy - associated with data collection?

What the chance of error was when collecting data - student error or equipment failure.

What is reliability - associated with data collection?

Whether the data was typical or reproducible. Would it be different on a different day? Was it a suitable sample size? How recent was the data?