Go through the following list of explanations that you will need for your Geography skills exam on Monday. Note down any you do not quite understand and we will go through them in revision tomorrow.

Happy Revising!!!

Mrs Matthew

Cartographic diagrams

Dispersion Diagrams

A graph where the data sets can be placed in one column with the variable on the vertical axes of the graph

Advantages:

• Shows the spread from the mean.
• Very visual.
• Gives an indication of the reliability of the data.
• Can work out mean, range, mode, median, lower quartile, upper quartile and interquartile range.
• Can compare graphs easily using the above ^ for analysis.
• Anomalies can be shown.
• Can work out standard deviation.

Disadvantages:

• Works better with lots of data.
• The standard deviation can easily be manipulated and can be bias.

Radial Diagrams

This shows how a variable e.g. wind direction changes due to an independent variable e.g. direction etc.

Advantages:

• Can compare multiple sets of data.
• Lots of data can be put on one graph.
• Visual.
• Individual variables within the diagram can be compared.

Disadvantages:

• No stats tests can be linked to them.
• Hard to spot anomalies.
• Hard to make a scale suitable.

Kite Diagrams

This is often used to show the abundance of particular plant species varies with distance as the wider the data points means that the more common they are.

Advantages:

• Clear and easy to interpret.
• Shows changes over distance.
• Shows density and distribution of variables.

Disadvantages:

• Not all data can be represented by these charts.
• Time consuming to plot by hand.

Proportional Pie Charts

Out of 100% these show a breakup of data into a percentage of the total which is then shown by segments on the pie chart.

Advantages:

• Allow fractional and percentage comparison.
• Display approximate proportions of variables throughout the area taken up by the pie chart.
• Visual- can see a general trend.

Disadvantages:

• Can’t use for exact comparisons.
• Impossible to extract specific data.
• Can’t represent more than one point at a time.
• May not always be accurate (especially if plotted by hand).
• Overlaps can cause issues if they are used on maps.

Line Graphs and Bar Charts

This is where a variable is plotted against an independent variable e.g. rainfall against location.

Advantages:

• Little background knowledge would be needed to understand the graphs.
• Comparisons can easily be made with other similar graphs or more than one line/chart can be plotted on one graph.
• Anomalies are quite clear.
• Give visual image- shows the general trend/correlation (giving basis for stats test to analyse.)
• Can plot the standard deviation.
• Bar Charts show cumulative data/discrete data which are common so they can be used for many purposes.
• Line Graphs use continuous data which is also common.

Disadvantages:

• Can be tedious and time-consuming to construct by hand.
• Can be difficult to read accurately.
• Can often require additional information for them to be useful.

Dot Maps

This shows distribution of data over an area, e.g. instances of disease over an area

Advantages:

• Effective in showing the spatial density.
• Shows variation and pattern.
• Easy to interpret.
• Purpose is easily understood.
• Easy to generate on a computer.

Disadvantages:

• Actual values can’t be seen.
• Dots crowded/can lead to clustering –  not very accurate.
• Time consuming if done by hand.
• Small countries aren’t represented accurately (imagine dots in the USA compared to the UK –  they may have the same amount of dots but the UK will look clustered and suggest lots of something whilst in the USA the dots may look sparse and suggest little of something).
• Easy to make a mistake/be subjective.

Triangular Graphs

This allows three proportional variables out of 100% to be plotted against each other.

Advantages:

• Easy to compare.
• 3 bits of data can be compared at the same time as they use the same scale (are always out of 100).
• By using lots of graphs, comparisons can be made.

Disadvantages:

• Difficult to construct.
• May be wrongly interpreted.
• Quite difficult to read - have to have the background knowledge of how to use the graph.

Proportional Circles/Symbols

This is where the size of a circle/ symbol shows the population of data often for one area.

Advantages:

• Very visual
• Can represent a large range of data.
• Not dependent on size of the area.

Disadvantages:

• Difficult to produce.
• Not accurate/ can’t extract exact data.
• Overlap can occur making it confusing and difficult to read/interpret.

Flow Lines, Desire and Trip Lines

Flow lines are where width of the arrow represents a flow rate also which direction the flow is moving often used for rivers discharge. Desire lines shows where a population moves from one area to another, e.g. animals migrating. Trip lines show where populations have visited an area e.g. shopping centre.

Advantages:

• Immediate impression- visual
• Can show movements easily such as traffic/migration etc.
• Desire lines show trends in migration of population.
• Gives clear sense of direction.
• Clear location component.

Disadvantages:

• Hard to draw.
• Flows can be in the same direction/overlap.
• May be difficult to show meeting point of the wide bands without overwhelming the map.

Choropleth Maps

Cholopleth maps uses colour overlay over a map to show how an area fits into a range of values, often with darker values representing the higher values and lighter for the lower values.

Advantages:

• Visual impression of change over a space- gives general impression.
• General anomalies can be identified.
• Easily done by hand or on the computer.
• Doesn’t breach data protection.
• Good for data which involves density reading.
• Easy to interpret via a key.

Disadvantages:

• General
• Gives false impression of abrupt change at the boundaries.
• Variations within each area are hidden.
• Reading exact figures is impossible.

Isoline Graphs

Isoline graphs are where data points on a map are joined up with data points of equal values, e.g. contour lines on a map.

Advantages:

• Drawn easily on computers.
• Can see areas of equal value.
• Can see gradual changes.
• Avoids the problem of boundary lines.

Disadvantages:

• Do not show discontinuous distributions.
• Only work where there is plenty of data spread over the study area and the changes are gradual.
• Small lines and numbers on graphs can be difficult to read.

Statistical tests

Spearman’s Rank

Spearman’s rank is the most common statistical test as it assumes no distribution pattern in the data so is non- parametric. It is used to find if there is a correlation between two variables. It shows there is a positive correlation if the value calculated is close to 1 and it shows there’s a negative correlation when the calculated is close to -1, there is limited correlation when the value calculated is close to zero. To calculate the degrees of freedom for this test is just the number of paired measurements. You reject the null hypothesis if the value calculated is higher than the value from the table of critical values. The null hypothesis for this test is always: there is no relationship between the two variable sets of data.

Advantages:

• Shows the significance of the data.
• Proves/disproves correlation.
• Allows for further analysis.
• Doesn’t assume normal distribution

Disadvantages:

• Can be difficult to work out.
• Quite a complicated formula.
• Can be misinterpreted.
• Need two sets of variable data so the test can be performed

Chi Squared

A chi square test is used to investigate whether distributions of categorical variables where the answer are in a fixed range e.g. gender, differ from one another by using expected and observed frequency. To calculate the degrees of freedom use (n-1) where n is the number of categories of data, this would then give you a value of the table then if the calculated value is higher than this you can reject your null hypothesis and accept the alternate. The hypothesis for this type of test is always: data is distributed randomly.

Advantages:

• Can test association between variables.
• Identifies difference between observed and expected.

Disadvantages:

• Can’t use percentages.
• Data must be numerical.
• Categories of 2 are not good to compare.
• The number of observations must be 20+
• The test becomes invalid if any of the expected values are below 5
• Quite complicated to get right- difficult formula again!

Standard deviation:

This shows how the data is spread about a mean value if there are fixed independent variables and a frequency of these variables e.g. pebble size at one site in a river, this are often shown a histogram (right) with the independent variable on the horizontal axis and frequency on the vertical axis. The higher the value calculated the more the data is spread out from the mean. This test is parametric as it assumes normal distribution pattern for the data as shown in the graph as a “bell” shaped curve.

Advantages:

• Shows how much data is clustered around a mean value
• It gives a better idea of how the data is distributed

Disadvantages

• It doesn’t give you the full range of the data
• It can be hard to calculate
• Only be used with data which can be plotted on a histogram so where a independent variable is plotted against frequency of it.
• It can be affected by outliers in data
• Assumes a normal distribution pattern

Mann Whitney

This test uses the median values between sets of data to see if there is any correlation between a set of data. For this test we take the lowest calculated value and if this is smaller than this critical value we can reject the null hypothesis and accept the alternate hypothesis. This test is best when there is between 5 to 20 sample sizes. The null hypothesis is always: there is no significant difference in the two sets of data.

Advantages:

• Shows the median between 2 sets of data.
• Good with dealing with skewed data so data doesn’t need to be normally distributed
• You can decide the boundaries of the 2 groups.
• Only needs one variable in a set of data

Disadvantages:

• More appropriate when the data sets are independent of each other.
• More appropriate when both sets of data have the same shape distribution.
• Have to have equal sample sizes.
• Becomes less accurate when the sample size is below 5 for above 20

Here are some useful websites:

http://www.s-cool.co.uk/a-level/geography

http://getrevising.co.uk/resources/areas/geography/levels/as

http://revisionworld.co.uk/a2-level-level-revision/geography

http://freespace.virgin.net/i.ford/revision/skills.htm

http://web.aqa.org.uk/qual/gce/humanities/geography_materials.php

Happy revising!!!

Anyway…. I think I have to go now as I've got more things to do like 2 past papers, and 2 classes worth of cupcakes to bake for end of school year class parties (:
It's gunna be both really nice and very sad to leave school for the final time. I'm sure that Friday's adventures will be a great way to finish my time at Secondary School.
(whole year group picnic lunch, year group photo, leavers assemboly then the MAY BALL! yay!) Must remember to ask ppl to sign my year book tomorrow!