AQA geography skills unit 2/4a

How:

• Data points on the map are joined up with data points of equal value. 

Use:

• Temperature/atmospheric pressure/gradient e.g. contour lines.

Pros:

• Shows gradual changes - avoids abrupt change.
• Can clearly see boundaries.
• Can see areas of equal value.

Cons:

• Assumes a gradual change exists.
• Small numbers/units may be difficult to read.
• Only works with a large quantity of data.

How:

• Independent variable goes on the x-axis e.g. distance downstream.
• Dependent variable goes on the y-axis e.g. discharge.

Uses:

• Any two variables with a relationship.

Pros:

• Anomalies are easily identifiable.
• Uses bivariate data which enables you to see whether there is a relationship between the variables - aids interpretation.
• Strength of correlation can be confirmed using a statistical test e.g. spearman's rank.
• Line of best fit - predict future data sets.

Cons:

• Doesn't show cause and effect.
• 'Overplotting' can be an issue with lots of similar results.
• Have to have continuous data.

How:

• Full log/log-log - both axis are logarithmic scales.
• Semi-log - one axis is linear and the other is logarithmic.

Use:

• Population graphs - used with very large ranges.

Pros:

• Allows you to work with/plot a large range of numbers.
• Shows overall trend/previously unseen patterns that normal graphs do not show.
• Smaller values are given greater priority due to the nature of the logarithmic scale 1-10.

Cons:

• Postitive and negative values can't be plotted on the same graph.
• Zero can't be plotted

How:

• May show info like a bar chart.
• May show orientation e.g. compass points.
• May show continuous cycles e.g. time.

Use:

• Environmental quality survey (if using as a radial bar chart).

Pros:

• Visual representation of data.
• Displays multiple variables.

Cons:

• Suitable for only continuous data - limited use.
• May only show general trends if based on averages.
• Can be difficult to read/interpret.

Use:

• Employment structures (tertiary, secondary, primary)/ethnicity.

Pros:

• Visual representation of the relationship between three variables.
• Percentages are plotted - especially easy to compare/contrast.
• Shows clusters of data.

Cons:

• Raw data must be converted into %.
• Can be difficult to interpret, especially if there is a lot of data plotted.

How:

• Thickness shows number/percentage of each special at a point in time.
• Thickness is balanced equally below and above the line.

Uses:

• Distribution of plant species along a transect of a sand dune. 

Pros:

• Visual representation of change and progress over a specific distance.
• Uses raw data and percentages.
• Comparisons can be made between different species - can identify zones.

Cons:

• Limited to the transect lines.
• Only suitable for specific data with a specific purpose.
• Visually subjective - the scale used can affect the diagram.

How:

• x/y x 360 = number of degrees, when x = variable and y = total. 
• Proportions = square root total. 

Use:

• Use of services in a town/amount of crops grown in a certain area.

Pros:

• Clear, visual representation of data. 
• Able to compare easily.
• Relatively easy to construct.

Cons:

• May not show numerical data.
• May get crowded if there are too many divisions.
• Categoric data only.

Use:

• Population of a city/country/region.

Pros:

• Shows spacial distribution and density.
• Anomalies shown if there is a lot of data.
• Clustering and patterns identifiable.

Cons:

• Large amount of data may lead to overcrowding.
• Areas may seem empty if the data is lower than the scale.
• Large dot values may be inaccurate.

How:

• Flow - width of arrow represents the flow rate and direction the flow is moving in e.g. migration.
• Desire - where a quantity moves from orgin to destination e.g. migration
• Trip - shows regular trips e.g. where people shop.

Pros:

• Strong visual impression of movement.
• Clear sense of direction.

Cons:

• Can be hard to interpret if map becomes obscured.
• Can be difficult to draw.
• Difficult to show the meeting point of wide bands.

Uses:

• GNP per country/levels of extreme poverty per country/region.

Pros:

• Visual representation of data.
• Can easily identify patterns/clusters.
• Anomalies identified if cells are an adequate size.

Cons:

• Assumes abrupt change at boundaries - no gradient shown.
• Can hide anomalies within an area. 
• Shows one variable only.

Pros:

• Simplistic view of sample site and main features. 
• Can pick out features to annotate/comment on.

Cons:

• Qualitative - based on obervation and personal perspective - may be bias.
• May be hard to interpret if skill of drawer isn't good.

Pros:

• Shows what the area looks like before you have been.

Cons:

• Only show a snapshot in time.
• Suseptable to external influences e.g. weather.
• Only show a small section of the area.

Pros:

• Identifies the most important features.
• You can add as much/as little data as you'd like.
• Shows your interpretation of the area.

Cons:

• Qualitative - may be bias.
• Only shows one view at one point in time.
• May lack detail.
• May be difficult to interpret depending on drawer.

Uses:

• Cartographic modelling/determining land use, soil, vegetation, elevation, land ownership, characteristics.

Pros:

• Bypass the mechanical processes of mapping.
• Higher quality.

Cons:

• Expensive - can't be used all over the world.
• Time consuming
• Needs regularly updating.

Uses:

• Databases/census data/capture/store/analyse data.

Pros:

• Easy to make comparisons over time.
• Saves time.
• Can be converted into graphs - visual representation.

Cons:

• Can be expensive.
• Not available everywhere.

Mean - Average of all the values. Total number of data sets/(divided by) the number in the sample.

Median - Middle value. If there are two middle values, add them together and divide by 2.

Mode - Most common value.

Pros:

• Clear and simple.
• Mode can me used with non-numerical data.
• Median - very large and small numbers do not affect result.
• Mean - useful in making measurements more accurate.

Cons:

• Can't use continuous data. 
• Median and mode do not account for whole spread of data.
• Mean is easily distorted by very large/small values and anomalies.

How:

• Count the number of values. 
• Work out the LQ ranking: 3(n+1)/4
• Work out the UQ ranking: (n+1)/4
• Find out the data set/value that matches the ranking for each quartile.
• Minus the LQ value from the UQ value to find the interquartile range.

For box and whisker, the mediam - (n+1)/2

Pros:

• Shows spread of data around the mean.
• Not influenced by extreme/outlying data sets.

Cons:

• Not all data is considered
• Complicated to calculate

How:

• Calculate the mean and minus it from the value in the column.
• Square each answer and add up the totals.
• Submit into equarion - this will give you your standard deviation number.
• + and - the standard deviation number from the mean, this shows you the range of data around the mean.
• Work out how many of the data sets are within the range.
• Number of data sets in the range/total number of values x100 = %
• If answer is above 68% then the data is close to the mean. Lower than 68% - 2nd SD needs to be taken by doubling the SD score and + and - that from the mean to find the new range - the answer needs to be 95% confident.

Pros:

• More accurate than the range as it uses all of the data - more accurate.
• Low S.D score means small range so the mean is more reliable as there is little variation.

Cons:

• Can be affected by anomalied/outliers.

What:

• Display the main pattern in the distribution of data.

Pros:

• Visually effective - full range of data is seen together.
• Useful for making comparisons.

Cons:

• Data must be in a form that can be placed along a number line.
• Lots of values may lead to clustering - difficult to interpret.

How:

• Identify null hypothesis - no significant difference between observed an expected.
• Subtract observed frequencies from expected and square the result.
• Divide this by the expected value for that group.
• Compare with degrees of freedom: on the critical values chart, the degree will be one less than the total number of observed values.

Use:

• To assess the degree of difference between observed and theoretical data e.g. number of pebbles along a river. 

Pros:

• Statistical significance of results can be tested.

Cons:

• Doesn't explain why there is a pattern.
• Does not give the strenght of the relationship.
• Percentages cannot be used.

How?

• Formulate a null hypothesis.
• Individually rank the values of each variable. 1 = highest value.
• Find the difference between the two.
• Square the differences and sum the values.
• Input into the formula.

Appropriateness:

• Appropriate for data with 10-30 values with 2 variables that are believed to be related.

Pros:

• Indicates the statistical significance of a result - rules out chance.
• Gives numerical value to the strength and direction of a correlation.

Cons:

• Does not show if there is a casual link
• Too many tied ranks affect the validity of the test.
• Subject to human error.

How:

• Select null hypothesis.
• Rank the data sets across the two columns. 1 = lowest value.
• Treat as two seperate columns. Add ranks in first column to get your R1 value then add ranks in the second column to get your R2  value.
• Input int the formula.
• Choose the smaller U value of either U1 or U2.
• Compare to the critical values table: less than the critical value means you should reject the null hypothesis at 95% confident. Greater than the critical value - accpet the nul. 

Use:

• Used to show if there is a statistical difference between two sets of data e.g. size of rocks in upper course and lower course.

Cons:

• Does not explain cause and effect.