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Globalisation > Data Presentation > Flashcards

Data Presentation Flashcards

1
Q

What are the strengths of CHi-squared

A

• It tests whether the observed data is different from what might be expected.

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2
Q

What are the weaknesses of Chi-squared?

A
  • Can only be used when data is grouped into categories.
  • Must have 20 bits of data to make the statistics valid.
  • Can only be used in categories.
  • Very time consuming and can be complicated.
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3
Q

Chi squared was it a primary/secondary source and what were the outcomes?

A
  • Primary 2. Noise levels will decrease with increasing distance from the CBD.
  • Secondary 3. Land use will change from commercial to residential with increasing distance from the CBD
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4
Q

What are the strengths of the land use map?

A
  • Visual (clear), shows clear patterns of dominant land use
  • Able to compare to models (Burgess)
  • Compare with other groups – many variables can be layered to show correlation between data.
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5
Q

What are the weaknesses of land use zoning?

A
  • Time consuming
  • Areas not covered/generalisation – each square is not fully representative of all land uses
  • Same colours for each map (comparison is easier)
  • Comparisons can be difficult, due to using different colours.
  • Slight change of data between squares might be disguised.
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6
Q

Did we use land use mapping as a primary or secondary source and what were the outcomes?

A

Primary – dominant land use.

As the distance from the city centre increases land use changes from commercial in the CBD to flats, terraced and semi-detached housing. Detached housing was found in the outskirts furthest away from the CBD.

  1. Land use changes from commercial uses to mainly residential uses with increasing distance from the CBD.
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7
Q

What are the strengths of line graphs?

A
  • They are not as time consuming as other data presentation methods
  • They are easy to interpret – clear correlation
  • Can help to make predictions about the results of data not yet recorded
  • The line graph helps to determine the relationship between two sets of values.
  • Multiple continuous data sets can be compared.
  • Multiple locations can be plotted on the same graph which can make clear comparisons and show patterns in a location.
  • Anomalies are easily identifiable.
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8
Q

What are the weaknesses of line graphs?

A

Can often make trends look more definite than it really is.

A large range of data can be hard to read

Can only be used with continuous data.

Difficult to interpret with multiple variables

Will be complex with uneven class intervals

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9
Q

Did we use line graphs to represent primary or secondary data, and what were the outcomes?

A

Primary – environmental quality score

As distance from the CBD increases, environmental quality increases, with some fluctuations and anomalies. These could be due to the transect not being straight (pragmatic sampling).

Environmental quality will improve with increasing distance from the CBD.

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10
Q

What are the strengths of scatter graphs?

A

The scatter diagram is used to find the correlation between these two variables.

It is the best method to show you a non-linear pattern.

Observation and reading is straightforward

Plotting the diagram is relatively simple.

Multiple locations can be potted on the same graph which can make clear comparisons.

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11
Q

What are the weaknesses of scatter graphs?

A

Scatter diagram does not show you the extent of correlation.

Scatter diagram does not show you the quantitative measure of the relationship between the variable.

This chart only shows you the relationship for two variables.

Can make patterns appear more definite than they are.

Inaccurate if only one sample is recorded.

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12
Q

Did we use scatter graphs to represent primary or secondary data, and what were the outcomes?

A

Primary – environmental quality

Environmental quality will increase as distance increases from the CBD, in all of the three wards.

Ipswich road shows the greatest range, and therefore improvement.

Environmental quality will increase with increasing distance from the CBD.

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13
Q

What are the weaknesses of Spearmen’s rank, house price and environmental quality?

A

Requires a large sample to gain accurate results.

It is mathematically complicated and it is easy to make a mistake, giving you inaccurate results – prone to human error.

Calculating ranks can be difficult especially with a greater sample size.

Can be misinterpreted.

2 sets of variable data are required.

The difference is not explained.

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14
Q

Dis we use Spearmen’s rank, house price and environmental quality to represent primary or secondary data and what were the outcomes?

A

Secondary – house price collected form Right move

Primary - EQS

House price and distance – 95% significantly confident that the data has not occurred by chance.

  1. Environmental quality will improve with increasing distance from CBD.
  2. Land use will change from mainly commercial uses in the CBD.
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15
Q

What were the strengths of Mann Whitney – test of noise levels and distance?

A

Measures the significance of the difference between overlapping data sets.

Can compare sets of data that are different sizes, making the test versatile.

States whether difference in the sets of data is significant or has occurred by chance.

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16
Q

What were the weaknesses of Mann Whitney – test of noise levels and distance?

A

You can’t compare more than two sets of data.

You can only compare data that overlaps.

Prone to human error as there is two columns of ranking.

The difference is not explained.

More appropriate when the data sets are independent of each other (in category)

Accuracy decreases with smaller sample sizes.

17
Q

What are the strengths of Spearmen’s rank, house price and environmental quality?

A

Tests the strength of a correlation (link) between two variables, (in this case, price and distance).

Proves/disproves

Puts a numerical value on how strong correlation is.

Tells you whether the correlation is statistically significant.

Reliability can be increased with a lager sample size.

Allows further analysis of data.

Does not assume normal distribution.

18
Q

Did we use Mann-Whitney to represent primary or secondary data and what were the outcomes?

A

Primary – noise levels in decibels

The results support hypothesis 2 by showing that Town Close and Mancroft and Town Close and Eaton are significantly different.

Cannot test Eaton and Mancroft as the results were on-overlapping, meaning they were significantly different.

Noise levels will increase with increasing distance from the CBD.

19
Q

What are the strengths of the isopleth map?

A

Visualises the change, so it is easy to interpret. Clear to observe gradual changes.

You can see areas of rapid change indicated by the lines being closer together

Shows patterns over a large spatial area.

GIS can be superimposed onto the base map to show correlations.

It has fixed intervals so that change can be easily identifiable.

20
Q

What are the weaknesses of the isopleth map?

A

Time consuming

Only works well with a clear trend

If different combinations of the dots are joined different patterns are produced.

The selection of intervals is subjective, and may not be clear or on the other hand not include enough information.

Does not show how conclusive the data is.

The reliability is dependent on the sample size.

Difficulty in showing anomalies.

Some variations at the location of each isoline may not be apparent.

The shading implies there are equal values which may be misleading.

Data is required for a number of locations over a large area.

21
Q

Did we use the isopleth map to represent primary or secondary data and what were the outcomes?

A

Primary – decibels recorded in a number of locations.

Noise levels peak in the CDB and decrease with increasing distance from the CBD, showing a clear pattern.

2.Noise levels will decrease with increasing distance from the city centre.

22
Q

What are the strengths of the Triangular Graph of housing types in the three wards?

A

Includes three sets of data, showing greater correlations and patterns

A large sample size can show a distinct pattern

It is effective at identifying patterns

23
Q
A
24
Q

Did we use the Triangular Graph of housing types in the three wards to represent primary or secondary data and what were the outcomes?

A

Secondary – Rightmove data on housing type

There is a mixture of housing types in the outer and inner suburbs however there is a clustering of detached housing types in Eaton.

  1. Land use changes as the distance from the CBD increases.
25
Q

What were the strengths of Nearest Neighbour Analysis total area calculated?

A

It shows whether settlements are clustered (0), random (1), or regular (2).

26
Q

What were the weaknesses of Nearest Neighbour Analysis?

A

It can be difficult to work out the centre of the settlement for the measuring procedure.

It can be difficult to decide which settlements to include. (small hamlets)

The spread of settlements may be affected by geographical boundaries such as rivers.

Does not explain the patterns shown.

For comparisons the size of the area is critical, areas will need to be of a similar size.

27
Q

Did we use Nearest Neighbour to represent primary o secondary data?

A

Secondary – OS map data

28
Q

What are the strengths of GIS (Geographical Information Systems)?

A

Can process a huge amount of data. Can map accurately and effectively to show patterns

29
Q

What are the weaknesses of GIS (Geographical Information Systems)?

A

If there’s a large range of data it is difficult to choose an appropriate scale. Maps can become overpopulated with data so patterns are difficult to identify. Expensive, time consuming, can go wrong

30
Q

Did we use GIS to represent primary or secondary data and what were the outcomes?

A

Primary

To show how pedestrian and vehicle numbers change with varying distance from the CBD in SW Norwich.

4 - vehicle and pedestrian numbers

31
Q

What are the strengths of the choropleth map?

A

It is easy to read and is visual. Quick and easy to complete.

32
Q

What are the weaknesses of a choropleth map?

A

If the range of data is large it is difficult to shade in correctly. Too many shades makes it difficult to tell the difference between boundaries. It implies the density of areas data changes as you cross over boundaries. It implies the density of the data you find is the same for the whole of an area.

33
Q

Developing a plan.

A
  • identify data to support hypothesis (scale, quality, ease, qualitative or quantitive, stat test)
  • appropriate strategies & collected methods
  • limitations - time and resources (amount of data, equipment, people)
  • potential
34
Q

collect and record data

A
  • noise - one reading at each point
  • EQS along a linear stratified transect
  • pedestrian and vehicles (pragmatic sample sites, time - one reading not reliable)
  • land use - obscured 100m3 overall
  • accuracy and reliability - random systematic stratified spatial)
35
Q

reliability and data collection

A
  • EQS small sample size and subjetive
  • Land use 100m3 subjective
  • census data - office of national statistics
  • Noise
  • decibel meter - accurate reading - only on reading = unreliable
  • Traffic and pedestrians = interpretation of traffic
  • collected once on one day
36
Q

enviro quality

A
  • scatter and line graphs
  • repeat in different locations, day/week/year
  • Increase sample size
  • Improve sampling strategy
  • further variables
  • more sources of data
  • *
37
Q

suitable geographical question OR hypothesis for investigation

A
  • Suitible scale - practical and suitable SW 3 transects - main roads - all land use zones = accurate
  • Capable of research - problems of measurement, inaccessibility or lack of
  • Cleary defined - differences or relationship between variables
  • Geographical nature
  • wider theories concepts or processes

Globalisation (11 decks)

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