Geography 4A Skills

  • Created by: MrMeeb
  • Created on: 04-02-17 16:31

Line Graph

Aim: Method to show continuous data


- Can easily show trend over time

- Can be used to predict future trends

- Can be used to compare data

- Can be used to estimate for points we don't have data for


- Scale must be done carefully, and can distort results, making comparison misleading

- Can only be used with continuous data

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Compound Line Graph

Can be used to show multiple pieces of information, and generally needs two vertical/horizontal axis - climate graph

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Divergent Line Graph

Can go into positive and negative values - either size of axis - temperature variation

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Bar Graph

Represents the number within a set of data, like a category. Can be used to compare different data sets of categories within a data set


- Visually strong - easy to use

- Allows easy comparison

- Easy to draw

- Shows trends better than tables


- Can be reorganised to emphasis certain things - may impact conclusion

- Only uses discrete data (data that can only take certain values)

- Only shows patterns

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Divergent Bar Graph

Bars can go either side of the axis - population pyramid

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Scatter Graph

Shows two sets of data plotted against each other - shows a relationship between two variables. The line of best fit represents the direction and nature of the correlation


- Lots of data can be plotted into a small space

- Patterns can be identified quickly and evenly

- Retains exact data values

- Anomalies easily seen

- Shows what type of relationship exists


- Data needs to be continuous

- Can only compare two sets of data

- Mistakes easily made with large data sets

- Line of best fit can show misleading relationship

- Doesn't show how strong the relationship is - relies on Spearman's Rank

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Pie Chart

Displays data components of a whole data set, shown as segments and percentage values


- Good visual representation

- Shows percentage total for each category

- Can be easy to compare

- Can be used effectively on a base map


- Too many categories make them hard to interpret

- Too few make them too simplistic

- No exact numerical data

- Use only with discrete data

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Triangular Graph

A graph with three axis. Figures of each data set must be percentages


- Useful in showing patterns of clustering between three variables

Can plot many data points


- Very limited use - have to be three data sets

- Does not have a spatial element - can't be placed on a graph

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Radial Diagrams

Radial Diagrams     

Plot data in a circular fashion around a central point


- Specific technique and effective way of presenting data on wind direction frequency

- Polar graphs - effective at showing change over time


- Can only be used on limited types of data - change over time or direction

- Polar graphs distort higher values, making it slightly harder to interpret

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Box and Whisker Plot

Allows you to see the spread of data. Can be used to compare data if the same scale is used


- Useful visual representation of dispersion in data

- Can compare different data sets


- Time consuming to plot more than one data set

- Does not give a numerical value of dispersion

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Logarithmic Scales

Specialised version of a line graph - the scale increases in multiples of ten


- Good way of displaying a large range of data

- Shows the rate of change in data


- Zero cannot be plotted

- Negative and positive values can't be on the same graph

- Very easy to make mistakes on a large graph

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Kite Diagrams

Good at displaying change in percentage over a distance


- Visual representation of plant coverage at various stages - width of kite represents the amount

- Allows easy comparison between multiple kite diagrams


- Very specific and works with a limited data range

- Can be easily affected by anomalies

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Remotely Sensed Data

Use of technology to gain data - photographs, digital satellite images


- Can remind you of an area when you leave

- Good overview of satellite images


- Can crop out things accidentally

- Photo can be overcrowded

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Use of Databses

Using things like a census as secondary data


- Large amount of data available in various resources


- Time consuming to find data needed

- Need to be clear about the data you're trying to find, or you'll get vague results

- Census data is gathered every 10 years - large gaps

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Integrates hardware, software and data in a presentable fashion good for analysis


- Allows recognition of patterns

- Can access data regarding these patterns - maps can hold data


- Technology required is very expensive - digital cameras and satellites and software

- Time consuming to input data

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Interquartile Range

To calculate lower quartile (when n is the number of values)

(n + 1)/4

To calculate the upper quartile

((n + 1)/4) x 3

To get interquartile range, subtract the upper from the lower


- Gives a mathematical figure to show dispersion

- Simple to calculate

- Not affected by anomalies


- Works alongside median - not the best at finding central tendency of a data set

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Standard Deviation

Calculates a figure that shows the extent that the data is clustered around the mean - a larger figure means a wider spread around the mean, and less reliable.

Once calculated, add the value to the mean and also subtract it from the mean to find the two values that 68.2% of results sit between. The smaller the range, the better

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Spearman's Rank

Common technique for measuring correlation strength. The ideal data set is more than 10 and less than 30 pairs of data


- Shows the strength of a relationship

- Can test its statistical significance (how likely the result you got is random (lower chance is better))


- Illogical variables can still show a correlation

- Not reliable with less than 10 values

- Hard to do with more than 30

- Does not imply a casual relationship - a change in one WILL result in a change in the other

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Examines what you expect to find in your study and what you actually find, and also tests the significance of the results


- Can be applied to large data sets

- States whether the difference is statistically significant

- Easy to interpret and use the figure


- Data must be displayed as frequencies, not percentages

- Does not tell you the nature of the difference between expected data and observed data, just that there is one

- Must have a fairly large data set

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Mann Whitney U Test

Assesses the possibility of the difference between the two sets of data being as a result of chance or fluke. The data is assumed to not be normally distributed and can be ranked. There must be fewer than 20, and more than 5 samples 


- States whether the difference is the result of chance

- Can be used for smaller data sets than Chi-Squared

- Easy to interpret and use


- Not good with large sets

- Does not tell you nature of difference, just that there is one

- Must have a small-ish data set

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Chloropleth Map

Use a system of colours/shades to show patterns on a map that may be otherwise hidden in numerical data


- Can show a clear pattern that may not be clear with just numbers


- Does not show regional variation

-Assumes there is an abrupt change at boundary lines, when it's probably gradual

- Too large a class interval means a lot of places will be the same shade, even if values aren't too similar

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Dot Maps

A dot is placed for each feature on a map of relevance e.g arable farms in the South of England. Can be used with a scale


- Can show exact location of feature

- Can easily show patterns and clusters


- Dots don't show exact values

- Can very easily become overcrowded

- Scale of map is important

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Isoline Maps

Isolines are lines that represent the same value along their whole length, like contour lines or isobars


- Shows a clear pattern

- Actual values can be identified


- Anomalies can affect map and have to be removed, meaning it isn't a direct reflection

- Can become overcrowded if overly detailed

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Desire Lines

Lines that show the quickest path from origin to destination (as the crow flies). Thickness is proportional to how many people/things travel from origin to destination


- Show a clear pattern

- Can determine approximate values


- Hard to ensure exact values

- Does not show the actual path travelled

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Flow Lines

Used to show the actual flow and direction of something. The lines are proportional to the number traveling along the route


- Can calculate exact values

- Can use percentages

- Shows direction and volume


- Need at least a local scale map - can't be done on a large scale

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Trip Lines

Similar to desire lines, except there is no thickness as the amount of people that travel from origin to destination is not presented


- Shows a clear pattern

- Easier to draw as lines don't need scale


- No exact values

- Doesn't say how many times journey is taken

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OS Maps

Can be used as a base map or on its own. Shows how the land is being used, and the presence of human or physical features

Maps with proportional symbols   

Base maps with symbols added that proportionally represent values at the location they are linked to


- Allows comparison of areas

- Allows recognition of patterns


- Can become too cluttered easily

- Too little information can make it less useful

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Sketch Maps

Maps drawn quickly but carefully at the location


- Can be used to get a general idea of the area when leaving it

- Can be used as a base map


- Can be misleading if not drawn correctly

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Town Centre Plans

Very detailed diagrams of central area land use


- Gives a good, accurate presentation of land use in a central area


- Elements can be vague, like labels

- Can get out of date very quickly

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