(1.1 - 1.6) Geographical Enquiry Process

List the geographical enquiry process.

Posing questions

Collecting evidence

Processing + Presenting data

Analysing- trends, patterns, knowledge + understanding

Concluding

Evaluating

Questions should be SMART.

What does that stand for?

Simple

Measurable

Achievable

Relevant

Timed

What is an aim?

A general statement of what we are trying to find out.

What is a research question?

An aim can be posed as a SMART question

• with sub-questions that focuses the data collection

What is a hypotheses?

A statement that can be tested

What is a null hypothesis?

A special hypothesis suggesting there is no relationship between investigated factors

• a negative hypothesis

What is primary data? Give examples.

Data collected first-hand, from the sources being investigated

• interviews

• measurements taken

• counting footfall

What is secondary data? Give examples.

data collected second-hand

• books

• reports

• newspapers

What is qualitative data? Give examples.

non-numerical data

• interview answers

• photo analysis

What is quantitive data? Give examples.

numerical data

• census data

• ‘rate — out of 10.’

What is a control gorup?

the standard to which comparisons are made in an experiment

What is sampling?

Investigating a small number of the parent population

Why do we sample?

Studying whole parent population is too time consuming

would be too expensive if we were working

impractical

What is systematic sampling?

Pros and Cons

Data collected at set intervals

• no subconscious bias

• simple to implement

• even distribution

• could cause under/over representation

• sample may be unrepresentative

What is stratified sampling?

Pros and Cons

Proportionally chosen sample to ensure an representative investigation

• results are more representative of population

• ensures no significant group is missed out

• proportions of sub-sets need to be known

• time-consuming + not always easy to apply

What is random sampling?

Pros and Cons

Each member of the total population has an equal chance of being selected

• statistically the least biased

• quick + simple

• may be unrepresentative of parent population

What is pragmatic sampling?

Pros and Cons

Data based on convenience

• quick and easy

• most biased as choices are solely convenience based

What can be measured with quantitative data?

flows

scales

spatial patterns

temporal change

What are some examples of flows that can be measured?

discharge

traffic

infiltration

What are examples of scales that can be measured?

river width

pebble size

gradient

What are examples of spatial pattern that can be measured?

retail land use

sediment sorting

What are examples of temporal change that can be measured?

temperature

rainfall

pressure

What are some secondary sources of evidence?

satellite images

aerial photographs

databases like National Statistics

GIS (geographic info system)

Name the strengths and weaknesses of bar charts.

strengths:

• simple to construct + read

• effective in showing discrete data

weaknesses:

• can be difficult to represent wide range of data

Name the strengths and weaknesses of line graphs.

strengths:

• simple to construct + read

• effective in showing continuous data

weaknesses:

• can be difficult to construct/read if values are large and variation is small

• not suitable for discrete data

Name the strengths and weaknesses of pie charts.

strengths:

• visually effective

• good for comparisons between 2 or more pie charts

weaknesses:

• difficult to read accurate values

• not effective when that are lots of sectors

Name the strengths and weaknesses of scatter graphs.

strengths

• visually effective in showing correlations between bivariate data

• proximity of scatter points + LoBF gives visual indication of degree of dependency/strength of correlation

weaknesses:

• can be difficult to determine whether correlations are positive or negative if the points aren’t close to LoBF

Name the strengths and weaknesses of proportional circles.

strengths:

• useful for representing absolute (raw) data with large range of values

• show both percentage value + absolute value

weaknesses:

• difficult to construct and read the scale

Name the strengths and weaknesses of chloropleths.

strengths:

• effective way to represent groups of data + their changing patterns

• use of density shading is visually effective

weaknesses:

• spatial patterns concealed if too few groups of data chosen

• patterns can be generalised if too few areas are available

Name the strengths and weaknesses of flow-line maps.

strengths:

• effective way to show flow patterns over space

weaknesses:

• difficult to construct + read scale

Name the strengths and weaknesses of located bar/pie charts.

strengths:

• effective way to show absolute values of discrete data/percentage data over space

weaknesses:

• position of located symbol may obscure important data on base map

• may start in one area and end in another- confusing

What is analysis?

• identifying trend/patterns in graphs

• using maths to find averages, ranges, dispersion

• describing and explaining

• link to with data in other graphs

• identifying and suggesting reasons for anomalies

PEELA

How can dispersion be measured? Evaluate.

Range

good- quick and easy to calculate

bad- only considers extreme values

Interquartile range

good- simple to calculate, more representative, extremes not considered

bad- not all data considered

How does using dispersion methods improve geographical understanding?

• allows us to see whether data is clustered around mean

• standard deviation is often used to measure confidence in statistical conclusions

What is the conclusion?

Direct answer to the key question, using data and theory.

Short and concise.

What is the evaluation stage?

identifying limitations of evidence and critical reflection of sources

What are the limitations of geographical evidence? Define them.

accuracy

• how close a measurement is to the true value

reliability

• reliable is repeated methodology = consistent results

• improved by repeated collection of data

bias

• inclination/prejudice for/against a view

What are some sources of error?

measurement error

• mistakes made when collecting data

operator error

• differences based on people i.e. scores

sampling errors

• local differences leading to slightly different results

What is a random error?

error causing results to be spread about the true value

mitigated by repeated measurements

What is a systematic error?

error causing results to differ by a consistent amount each time the measurement is made