AQA geography skills unit 2/4a

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Isoline graph

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.
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Scatter graph

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.
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Logarithmic graphs

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
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Radial diagrams

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.
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Triangular graphs

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.
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Kite diagram

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.
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Proportional pie charts

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.
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Dot map

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.
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Map showing movement - trip, flow and desire lines

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.
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Choropleth maps

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.
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Sketch maps

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.
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Cross sectional diagrams

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.
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Field sketch

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.
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GIS

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.
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ICT

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.
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Measures of central tendancy - mean, median, mode.

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.
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Inter-quartile range

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
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Standard deviation

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.
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Dispersion diagrams

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.
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Chi squared

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.
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Spearman's rank correlation co-efficient

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.
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Mann Whitney-U

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.
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