Geographical skills
Geographical skills
Planning an investigation
Data
Primary data

Unprocessed information that has not been analysed or interpreted in any
kind of way

Advantages- reliable, not biased or invalid

Disadvantages- time consuming, unreliable or invalid due to human error
whilst collecting and using poor equipment
Secondary data

Information that has been analysed and interpreted

Advantages- quick to collect, more reliable of from a well known source

Disadvantages- biased, can’t be certain if the reliability and accuracy of
methods used to collect it.
Sampling
Sampling size

Point- sample taken at given point such as coordinates on a map

Linear- given points along a line or transect that has something alike such
as along sand dunes

Areal- given area such as a quadrat to measure vegetation cover or on a
grid square on a OS map
Sampling method
Random sampling

Using number tables to randomly generate a number to select a sample

Advantages- statistically sound so could lead to further analysis

Disadvantages- same item could be picked more than once, easy to miss
something
Stratified sampling

Take account of underlying patterns in data and ensures all are sampled

Advantages- ensures no significant aspect is missed

Disadvantages- data collection is biased, can’t make valid statistical
differences
Geographical skills
Systematic sampling

Item is selected at regular intervals

Advantages- easy to do, quick

Disadvantages- interval may coincide with in the data or location, can’t
make valid statistical inferences
Pragmatic sampling

Sample where you can get access and where changes are observed such as
troughs and crests of a sand dune system.

Advantages- safe realistic

Disadvantages- can’t make valid statistical inferences, not a fair sample
Location

The exact boundaries for your location need to be specified in the
planning stage

Location needs to be accessible, safe, and realistic in order to improve
accuracy of results.
Time

Enough time must be provided to complete investigation accurately

Creating a time plan of the day of investigation will help to know how
much time you can spend collecting data at each transect.
Risk assessment

It is important to think of any potential risks that could be involved in
collecting your data and what strategies you could use to prevent them or
reduce the risk of them occurring

To calculate the total risk you should identify the likelihood of the risk
occurring and the severity of that risk. Likelihood and severity are rated
from 1-5, 5 being the worst and these are multiplied together to calculate
the risk involved.

A pilot study should be used to allow you to visit the location before the
day of investigation to help identify potential risks.
Geographical skills
Hypothesis features
Features of a hypothesis

Simple- single question not multiple questions

Measureable- contains units that can be measured

Achievable- can be investigated with the location chosen, time, resources
and equipment available.

Realistic- can it be done? Avoid ‘what if’ questions as they are hard to
prove

Timed- how much time is needed for the investigation? Research best
carried out over a short time frame with short intervals.
Other features

Suitable scale- small scale is better than large scale as they are more
accessible

Readily researched- some topics can’t be researched effectively due to:
 Time needed
 Number of researchers needed
 Data being unavailable

Clearly defined- terms understood to avoid confusion

Clear geographical nature- spatial or locational

Based on wider geographical theories, ideas, concepts or processes

Clear aim- single purpose and focus on research allowing clear conclusions
to be stated.
Geographical skills
Data analysis- Methods to display a basic description of data
Measures of central tendency
These are used to calculate the average in a set of data. They can be very
useful when trying to identify simple differences between the sets of data.

Mode is the simplest method which shows the most frequently occurring
value.

Median is the middle value of the data when all items have been placed in
order from lowest to highest value.

Mean is the most useful method of central tendency and the only one that
requires a calculation. The mean is calculated by adding all the values in a
data set together and dividing by the number of values there is in that
data set.

Standard deviation involves a calculation and looks at the variation of all
the data from the mean. The larger the standard deviation the larger the
variation around the mean. This is useful when comparing two data sets
with a similar mean, but is more complex and requires a formula and a
calculation:
Measures of dispersion
Data sets vary in terms of how they are scattered around the average.
Dispersion is the various different measures which indicate the extent to which
data is grouped around the mean.

Range is the simplest way to look at the spread of values in a data set.
This can be misleading if the highest and lowest values are extreme.

Interquartile range is an improvement on the range as it takes the range
of the middle half of the data range either side of the median. It is quick
and easy to calculate and avoids the distortion caused by extreme values.
Geographical skills
Frequencies
Frequency diagrams are useful to plot the distribution of values in your data
set. They give a good visual representation of whether the 2 sets are different
and allow frequencies to be compared.
Skew is a term used to describe the distribution of data. Data is skewed if the
distribution isn’t symmetrical. If the mode is lower than the mean it is
negatively skewed and if it is higher than the mean then it is positively skewed.
Kurtosis describes the amplitude of the data or its shape. Distributions can be
peaked, flat or in between.
Measurement of patterns
These tests are used to measure whether a pattern exists and what type of
pattern it might be.
Nearest neighbour
This involves comparing the observed spacing of a set of points and the spacing
that would be expected if the pattern had been random. This technique relies
on measuring the distance between neighbouring points usually on a map. The
observed spacing is expressed as the distance of all points from their nearest
neighbour in straight line measurement.
Lorenz curve
This technique is a measure of the degree of concentration of points. These
curves are used in economic geography, on data linked to areas and involve the
comparison of percentage frequencies.
Geographical skills
Presentation
Types of data
 Discrete data- data with distinct separate parts
 Ordinal data- data that has an order
 Continuous data- data where there are no breaks but instead something
happens continuously.
 Areal data- data that applies to an area rather than a point or an
individual
 Time series data- data where something occurs at intervals
 Period data- data where a phenomenon repeats it’s self at intervals
Factors to consider when selecting a technique to present your data
factors
Scale/interval
location
Size of symbol
shading
colour
lettering
dimension
key
Legend or
caption
scale
Comment
 If the scale is too big it will hide patterns
 If the scale is too small, it will take too long to make
and could produce complex patterns that are hard to
interpret.
 Figures have a clear scale with no overlapping values
 Scale always starts at 0
Where should the figure be placed to represent the area it
applies to best? Make sure comparative diagrams are the
same scale
Symbols shouldn’t be too large or else they can overlap or
hide information on the map. They mustn’t be too small or
else they can be hard to see or may be read incorrectly
Should always get darker as the value increases, white will
apply to their being no data. The shade should always be of
the same colour.
Colour should be avoided as it is difficult to compare colours.
However, colour can be useful when comparing patterns
Varies in alignment, size, spacing and style. Shouldn’t obscure
data and should be able to be reads easily, similar sizes
should be used on one technique of presenting data
3D can cope with higher values, but the value is proportional
to its value so this involves careful calculation and it may
mislead visually by looking less than it actually is
All maps and diagrams should have a clear key, and should be
located in a clear location on your map or diagram
All diagrams and maps should have a title and figure number
All maps should have a scale
Geographical skills
Data presentation- non spatial data

Tables- effective, simple, good for showing a large amount of data in a
concise way. However, they have little visual impact.

Diagrams- use images. Pictograms use symbols to represent values.
Display data in a simple and visually stimulating way. Can be exaggerated
or distorted and give vague displays of data.

Charts- more precise than tables or diagrams and are available in a vast
variety of forms. They are forms of proportional symbols as their length,
area or volume is proportional to the value of the data. They are easily
available on computer programs and are visually stimulating.

Bar chart- used for discrete or time series data. Simple, quick and give an
instant visual impression. Can be vertical or horizontal. Length of the bar
is proportional to its value. Width of bar can be misled over the value
represented by the bar. Gaps should be left between the bars or else the
chart is called a histogram.

Pie chart- used with percentage data to show parts of a whole set of
data. Visually stimulating. Too many sectors can make it look
unprofessional. Hard to use colours as they distract from the sector size.
Difficult to label sectors so they are easy to read. Only useful for
percentage data.

Divided bar chart- used to show constituents of a whole. Must keep the
divisions in the same order if you want a comparison. Too many sections
makes it look unprofessional. Difficult to use lots of colours, not always
easy to compare.

Rose or star- used to show directions. Length of bar reflects frequency
and width. Time consuming to draw and takes time to read as 2 aspects
are shown.

Proportional circle, square, triangle- area of shape is proportional to the
square root of the data value. Can cope with large numbers. Time
consuming to calculate and draw. Not easy to compare accurately, scale
complex to draw.

Proportional sphere, cube or pyramid- area of the symbol is proportional
to the cube root of the data value. Copes with very large numbers and
gives a good visual appearance. Time consuming to calculate and draw. Not
easy to compare accurately, scale complex to draw.
Geographical skills

Graphs- ideal of continuous data or when looking for patterns between 2
or more variables. The X axis has the independent variable and the Y axis
has the dependent variable. The graph shows to what extent X causes or
influences Y. Types of graph include: line graph, scatter graph, multiple
line graph, triangular graph, compound graph and positive or negative
graph.
Geographical skills
Data presentation: spatial data
Maps - relevant features and data are marked on a map. Need a suitable scale,
title and north to be indicated.
Isolines - uses point data by drawing a line to join up the points of the same
value, the lines represent the same value at the points in an area through which
it passes.

Advantages- drawn easily on computers, can see areas of equal value, can
see gradual changes, avoids problem of boundary lines

Disadvantages- don’t show discontinuous distributions, only work where
there is enough data spread over the area and when the changes are
gradual.
Choropleths - density maps where areas are shaded to represent the average
number per unit area.

Advantages- visual impressions of change over a space, general anomalies
can be identified, easily done by computer or hand, doesn’t breech data
protection, good for data that involved density reading, easy to interpret
via a key.

Disadvantages- general, gives false impression of an abrupt change at
boundaries, variations within each area are hidden, reading exact figures
is impossible
Located symbols- dot maps

Advantages- effective in showing spatial density, shows variation and
pattern, easy to interpret, purpose is easily understood, easy to generate
in a computer.

Disadvantages- actual values can’t be seen, dot crowding can lead to
clustering which is not very accurate, time consuming if done by hand,
easy to make a mistake
Located symbols- proportional symbols

Advantages- can deal with larger numbers than dots

Disadvantages- size of circles (too small and they can be hard to see, too
large and they can cover the map), scale (need a scale proportional to the
square root of the data value), location of circle (hard to be precise so it
can be inaccurate).
Geographical skills
Located symbols- combined dots and circles

Advantages- good where low and high values are concerned

Disadvantages- confusing and difficult to read exact numbers, needs
complex scale, location of symbols
Located symbols- located bars

Advantages- simple and quick, easy to compare, can subdivide the bars to
show other features

Disadvantages- size of bar can hide detail on the map, bars can extend
over boundaries causing confusion, and location is hard to see as the bar
is large.
Flow lines

Advantages- immediate visual impression, show movements easily, gives
clear sense of direction, clear spatial component

Disadvantages- hard to draw, can be in the same direction or overlap,
hard to show meeting point of wide bands without overwhelming the map
Trip lines

type of flow line that shows direction and volume of movement but are
straight line drawings
Sketches

Advantages- give a good visual impression and shows main features clearly

Disadvantages- must be used effectively to be of any use or else they are
a waste of time, must be correctly annotated
Photographs

Advantages- set context of investigation, analyse area in detail after day
of investigating- more detail can be found when analysed in a lab

Disadvantages- must be annotated to be effective
Geographical skills
Analysis of data- qualitative data and quantitative data
Qualitative data subjective description in words influenced by an individual’s
opinions and beliefs. A scatter graph is used to see if there is a link between 2
variables. A correlation is either positive or negative. If there is no correlation
then the variables aren’t linked.
Quantitative data statistical data consisting of numbers. To establish clear links
statistical analysis is used to identify trends, groups and anomalies. They show
if a relationship really exists and the strength of that relationship.
When doing statistical analysis...

Keep it simple- use simple methods of analysis to start with as they often
show results without the need of complex calculations

Remember your hypothesis and choose a correct method to suit this.

Work towards a 95% significance level. This means that you can be
confident that anything above or on 95% is significant and anything below
is not significant.

Check you have the formula correct as most errors are made through
small mistakes in calculations.
Analysis of data
Qualitative data
Data is a subjective description in words which varies depending on who is
recording it (it is open to interpretation) such as a description of housing quality

In its simplest form this could be an analysis of some graphs. This is
quick, visual and shows the obvious anomalies.

One of the simplest approaches to qualitative analysis is to see if two
variables are related by using a scatter graph. A line of best fit will show
the correlation between the two as either a positive or negative
correlation. If there is no correlation then the two variables aren’t linked.
Geographical skills
Quantitative data
Statistical data which consists of numbers. It is objective and should be value
free.

When there is no clear trend or link between two sets of data the
analysis of data can be taken into further detail with statistical analysis.
They can identify trends, groups and anomalies as well as predictions
from larger sets of data that is too complex to be analysed by using
qualitative analysis. Statistical analysis is useful as it shows whether a
relationship really exists as well as the strength of the relationship. It
can also simplify large sets of data into a single result that can give a
measure of its level of accuracy and reliability.
A few things to think about when doing statistical analysis:

Keep it simple- don’t do statistical analysis for the sake of it and start
off with simple methods of analysis as often the result is clear without
the need for a mathematical calculation.

Remember what your hypothesis is in your investigation and choose the
correct statistical methods to suit this.

Remember that you need to work towards a 95% significance level. This
means that you can be confident that anything above or on 95% is
accurate. Anything less is inaccurate.

Check you have the formula correct, as most errors are made by small
mistakes in the actual calculation.

If you don’t understand how to do a technique or what the technique
shows then don’t use it.
Geographical skills
GIS Geographical information system
GIS is a computer tool used for storing, organising, displaying and analysing
geographical information. It allows data to be located to any spot on the earth’s
surface. It can be used for things such as field samples, aerial photography,
land surveying, GPS, population census and administrative records.
Advantages

Can cope with large amounts of data

Can cope with wide variety of data

Can cover large study areas

Can change scale of an area

Dynamic so can cope with frequent changes

Easier to keep up to date so findings are current

Fast and efficient

Avoid political boundaries and other practical boundaries to research
GIS can be used in geographical investigations:

Stage 3 collecting and recording data: can be used in the field to measure
and record information as it can be linked to a specific point. E.g. weather
monitors can collect information and transfer them to GIS. Also used to
measure distances in a straight line more accurately than on a map.
Secondary data can be accessed from several sites through GIS.

Stage 4 analysing and interpreting data: allows you to ask questions such
as “where is?” and “what if?” Possible to measure distances between
locations hence giving exact measurements. Raster analysis: GIS can
store data as a grid.
Geographical skills
Statistical testing
These tell you if there is a difference or correlation between 2 sets of data as
well as saying the degree of the difference or correlation.
Mann Whitney U test
Tests the difference between medians of 2 sets of data and can be used when
the data is in an awkward form such as with samples of very different sizes.

Advantages:
 Shows the median between 2 sets of data
 Good at dealing with skewed data
 You can decide the boundaries of 2 groups

Disadvantages
 More appropriate when the data sets are independent from each
other
 More appropriate when both sets of data have the same shape
distribution
 Have to have equal sample sizes
Spearman’s rank
Used when both sets of data can be easily ranked, when a quick and easy
measure of correlation is needed and where exact values may be uncertain. It
indicates if 2 sets are related. To be significant the end value must be at or
above 95% significance level.

Advantages
 Shows the significance of the data
 Proves/disproves correlations
 Allows for further analysis

Disadvantages
 Can be difficult to work out
 Quite a complicated formula
 Can be misinterpreted
Geographical skills
Chi squared test
Used when data is in categories or when it can be grouped into categories i.e.
when it is nominal. It tests the difference between the observed pattern and
the expected pattern from chance events.

Advantages
 Can test associations between variables
 Identifies differences between observed and expected variables

Disadvantages
 Can’t use percentages
 Data must be numerical
 Categories of 2 are not good to compare
 The observations must be 20+
 Quite complicated to get right due to difficult formula
Geographical skills
Testing for relationships and correlations
There are three types of correlation or relationship, these are; positive
correlation (when one value increases so does the other), negative correlation
(as one value decreases so does the other) and no correlation (there is no link
between the two values, so they do not influence each other). Statistical tests
are used to find out the type of correlation but also the strength of that
correlation. To be confident of a significant relationship you must be at least
95% or above certain that the null hypothesis can be rejected.
Spearman’s rank
Used when both sets of data can be easily ranked, a quick and easy measure of
correlation is needed and where exact values may be uncertain. This calculation
can indicate if two sets of data are related. A scatter graph can be drawn first
to give a rough idea f the expected result. These can show you if the
correlation is positive or negative.
Firstly data must be ranked, and must be ranked in an appropriate way to suit
your null and alternative hypotheses so from either smallest to largest or vice
versa.
The result will lie between +1 (a prefect positive correlation) and -1 (a perfect
negative correlation) with 0 representing no correlation. The exact significance
of your result is checked by looking up the result in a table of significance.
However spearman’s ranks shouldn’t be used when there are a lot (more than 4)
sets of tied ranks; where there are a limited number of data sets as usually at
least 8 are needed and where two data sets are unequal in number.
Chi squared test
This is most useful when your data has been collected in categories or when you
can group it into categories (when your data is nominal). It tests if there is a
difference between the observed pattern and the expected pattern from
chance events. For the test to be valid, the total number of observations should
be more than twenty.