Skills

Question 1

Cranedale aim

Answer 1

To investigate downstream changes in the river channel on the River Derwent to see if they fit Bradshaw’s model

Question 2

Crandale hypothesis

Answer 2

Load particle size decreases with distance downstream

Question 3

Helwath Beck

Answer 3

110m
3km from source
Drainage basin 10km2
2nd order stream

Question 4

Jugger Howe Beck

Answer 4

100m
5km from source
Drainage basin 28km2
3rd order stream

Question 5

Broxa

Answer 5

60m
12km from source
4th order stream

Question 6

Cranedale location

Answer 6

North York Moors, W of Scarborough
9km stretch dropping 50m in height
High moorland plateaus in the north, coniferous forest in the east and grassland in the south
Deep V-shaped valleys
Very little human interference
Permeable sandstone overlain by impermeable peat
Drainage basin has a high rainfall - river flows all year round > large
Risk assessment > safe and accessible
Landowner’s permission to access
Can all be visited in one day

Question 7

Graded profile shows…

Answer 7

How a river changes downstream

Height/distance from source

Question 8

Bradshaw’s model

Answer 8

All rivers follow this model
Based on a few USA rivers
River variables in proportion > we are testing this
Bed load size decreases downstream and becomes more rounded due to erosion - abrasion and attrition
Velocity increases downstream as tributaries added and less friction with bed and banks - greater efficiency and reduced channel bed roughness

Question 9

Data collection method and sampling

Answer 9

Sites chosen using satisfied sampling - situated progressively downstream from source > OS map used, stream orders
Cross-section sampled using systematic point sampling
1. Peg and line to work out width - 90 degrees and 30m tape measure
2. Width/10 to give 11 sample points across river (including bank)
3. Metre ruler placed down at each sample point and stone touching bottom of ruler picked up and measured along its B-axis using a mm ruler
4. Stone placed back - repeated at all three sites

Question 10

Data collection strengths and weaknesses

Answer 10

STRENGTHS
Systematic point sampling - representative of entire channel so bias reduced
Easy to replicate (reproducible) so can be directly compared > greater accuracy, conclusions more reliable
Easy and quick - all 3 sites in one day
11 points - sufficient for Mann-Whitney U stat test
WEAKNESSES
Difficult to measure B-axis accurately with a rule > sides not straight
Easier to pick up larger stones that hit ruler - skew in favour of larger stones
Not representative of smaller stones underneath

Question 11

Data presentation method

Answer 11

Pebble size at sample points bar chart
Mean pebbles size on bar chart
Located bar chart - on cross-section/wetted perimeter
Located proportional circles of velocity (radius)

Question 12

Data presentation strengths and weaknesses and justification

Answer 12

STRENGTHS
All in one clear graph - patterns can be made out and links between different variables
Ease of comparison between each site
Too much data for scatter graph etc.
Valid conclusions can be drawn
Suggests link with pebble size/ velocity and cross section
WEAKNESSES
Difficult to extract data, especially circles
3 needed
Not comparing directly with different sites
Can’t compare values easily - objective
JUSTIFICATION
Relate to aim, graph shows, illustrates conclusion clearly
Better than a single graph - one axes/comparisons between variables
Better than a scatter/line graph as more than one data points per sample point
Better than a pie chart - only averages and 3 sites
Better than a dispersion graph as difficult to read and only shows one variable

Question 13

Data analysis method

Answer 13

Mann Whitney U test
Null hypothesis and research hypothesis
Rank and calculate U values and critical value
U1 = 49.5
U2 = 71.5
CV = 30
If either U value higher than CV then reject RH and accept NH - no significant difference at 0.05 significance level
Less than 5% probability that this happened due to chance

Question 14

Data analysis strengths, weaknesses and justification

Answer 14

STRENGTHS
Can be used on different sized data sets
Determines significant difference and chance
Quantifies a perceived relationship
WEAKNESSES
Calculation long - prone to human errors
Does not explain why difference occurred
Cannot be applied to categorised data
JUSTIFICATION
Determines whether two sets of data of the same variable are significantly different
Tests between medians - outliers not used
Test proved no significant difference as U values higher than CV
Subjective view on data
Adds statistical validity to my investigation
Appropriate as measured in mm - continuous - can be ranked

Question 15

Cranedale Conclusions

Answer 15

No significant difference between stone size at Helwath Beck and Broxa

Question 16

How could your results be of use to geographers?

Answer 16

Suggests Bradshaw’s model is not applicable to this/British rivers
Derwent is glaciated/rejuvenated

Question 17

Secondary data used

Answer 17

From previous fieldwork by other groups
Weather reports
OS maps
Bradshaw’s model

Question 18

Evaluation: Improvements and extensions

Answer 18

Use callipers instead of a rule - more accurate measure of B-axis
More sites measured for more representative data = more reliable conclusions
More points along river width - reduce error margin = reliable
Measure A,B and C axes and work out VOLUME of each stone - more accurate
Power’s’ scale of Roughness to look at pebble shape downstream (chi squared)
Compare at different rivers or in different weather conditions/seasons for comparison/different order streams - same conclusions?
Other variables measured eg velocity
Land use compared

Question 19

Cranedale success

Answer 19

Found no sig difference
May be due to rejuvenation of River Derwent or redirection after glaciation period
Other variables may influence
Understanding has further developed - links between variables etc
How accurate/reliable?

Question 20

Scatter graph uses and examples

Answer 20

Used when investigating the relationship between 2 continuous variables
Used in combination with Spearman’s rank
Velocity/dist from source = positive
Pebble size/ dist from source = negative

Question 21

Scatter graph advantages

Answer 21

Clearly show positive, negation or no correlation - line of best fit
Shows strength of correlation - distance from line
Useful when carrying out Spearman’s Rank - gives a more precise and objective expression of the strength and reliability of the relationship
Graph allows analysis on which conclusions can be based - line of best fit
Lots of points in a small space
Patterns identified quickly and easily and anomalies identified
Relatively easy to construct
Shows data spread clearly

Question 22

Scatter graph disadvantages

Answer 22

Line of best fit is a subjective judgement and can give a subjective judgement
Only 2 variables can be displayed
Too few data points can produce skewed results - incorrect graph analysis
Impossible to label data points - hard to ascertain exact values
Too many data points can quickly make graph unreasonable

Question 23

Bar graph uses and example

Answer 23

Used to display categoric or grouped data
Absolute values and contrasts between areas and places
Simple show a single series of data eg temp/month
Comparative show 2 or more sets of dad side by side eg temp/month different locations
Compound show how the total in each bar is divided up into a number of subtotals eg traffic - cars/vans/lorries
Divergent where data is spread on either side of x or y axis eg population pyramids (y axis)
Histograms show frequency of occurrence of data eg pebble sizes in a river/frequency
Pictographs show data in the form of pictures (key) eg world pop/yrs

Question 24

Bar graph advantages

Answer 24

Relationships can be easily perceived and compared
Original data can be easily extracted
Many potential uses - versatile
Can show pos and neg values
Pictographs make it very easy to see data
Good visual representation of statistical data - general trend
Simple to construct and easy to understand
Clear to see anomalies

Question 25

Bar graphs disadvantages

Answer 25

Histograms can be confusing as area of bar represents data - cacluclations needed Pictogrpahs have no actual scale - not very accurate and only a limited amount of data can be dispalcyes Only small data sets can be plotted or patterns can be missed Wide range of data - loses impact/difficult to read Graph categories can be reordered to emphasise certain effects Use only with discrete data Limited space for labelling with vertical bar graphs Reading accurately can be difficult Time consuming

Question 26

Line graph uses and examples

Answer 26

Use continuous data Simple show a single series of data eg rainfall Comparative show 2 or more sets of data on the same graph using the same scale eg DTM Compound have several different components eg worl d popn/ time split into continents

Question 27

Line graph advantages

Answer 27

Can suggest trends and estimate future patterns eg 1941 no UK census, but using 1931 and 1951 popn data, 1941 popn can be estimated (interim data can be inferred) Can be combined with bar graphs to show more info eg climate graphs, storm hydrographs Log and semi log scales can be used Can compare multiple continuous data sets easily

Question 28

Line graph disadvantages

Answer 28

Too many lines can be confusing | Only continuous data

Question 29

Triangular graph uses and examples

Answer 29

Scatter graphs showing 3 sets of variables to see interrelationships 3 variables that each total 100 eg employment structures where employment is divided into primary, secondary and tertiary sectors as a percentage of working population

Question 30

Triangular graphs advantages

Answer 30

Varying proportions can be seen - relative importance Dominant component can be identified Shows clusters

Question 31

Triangular graphs disadvantages

Answer 31

Only work with a limited range of data - 3 variables in % form Can be difficult to read and construct Can be wrongly interpreted

Question 32

Kite graphs uses and examples

Answer 32

Used to display changing characteristics of flora/fauna across and area eg different species across a dune transect - changes over a distance Kites represent presence or absence or individual species along line of transect Thickness of kite shows number of % of each Represents number or percentage surface of each species

Question 33

Kite graphs advantages

Answer 33

Can be interpreted to reveal relationships between the organisms and the physical character of the surface Dune zones can be identifies by characteristic species Often plotted with height and gradient of surface. This allows explanations to be attempted for any interrelationships between species or between species and surface characteristics Width of kite, representing a single species, enables a visual comparison to be made of a distribution of vegetation at any point on section Sees trends in statistics in a visual way Visually effective - changes between species over distance easily identified Any species/combinations - distinguish one from another - competition Quick and specific % read off Sections for large scale Density shown

Question 34

Kite diagrams disadvantages

Answer 34

``` Only works with a limited range of data Visually subjective Dominant species over estimated Scale = some species not shown Data discrete but lines show continuity Can be tricky to construct and analyse correctly Time consuming ```

Question 35

Radial diagrams uses and example

Answer 35

Used to plot data in a circular fashion around a central point Used to show orientations as given by the points on a compass eg wind rose Used to show continuous cycles related to change over time of change in direction - polar graphs eg daily/annual progressions of temp/traffic flows Good when one variable is a directional feature

Question 36

Radial diagrams advantages

Answer 36

Allow you to display several independent variables Visual Compare multiple data sets

Question 37

Radial diagrams disadvantages

Answer 37

Only useful with a limited type of data - scale must be continuous around the edge Polar graphs slightly distort higher values - difficult to interpret Anomalies difficult to spot Scaling difficult

Question 38

Pie charts and proportional divided circles uses and examples

Answer 38

Show how data is split (total) into separate components Proportional circles used when size of 2 or more totals is being compared Area of circle represents totals - can also be split eg energy sources Useful for %s and statistical data

Question 39

Pie charts and proportional divided circles advantages

Answer 39

Clear visual comparison of relative proportions of components and statistical data - general trend Comparisons between charts - show changes in distribution or total Simple to construct and easy to understand Clear anomalies Cumulative/discrete - many purposes Easy to draw Shows % of each segment Can represent a wide range of statistical data and are visually very effective - contribution to each section is easy to see Comparison between percentage components

Question 40

Pie charts and proportional divided circles disadvantages

Answer 40

Very difficult to extract original data unless stated If the amount of data is complex, the interpretation of patterns is challenging Best used with a wide range of values within categories - difficult for smaller values Too few/many categories is simplistic and difficult to interpret Scales difficult for proportional circles Calculation of amounts is difficult Only 1 point at a time Accuracy in drawing Overlapping issues - maps

Question 41

Dispersion diagrams and box and whisker plots uses and examples

Answer 41

Visual investigation of spread of data Show a range of values in a data set Data in one column - variable on vertical

Question 42

Dispersion diagrams and box and whisker plots advantages

Answer 42

Range becomes apparent and clustering identified Box and whisker analyse further - remove extreme values and focus on IQR Dispersion diagrams of more than one data set can be constructed and compared - same scale Skewing becomes evident eg pebble size at different sample site on a river Often used with statistical techniques such as standard deviation Visual representation of dispersion in a data set Useful for making comparisons between ahead or at the same location over a period of time Shows spread from mean Indication of reliability Mean, range, mode etc can be calculated Anomalies seen

Question 43

Dispersion diagrams and box and whisker plots disadvantages

Answer 43

Usually only display one data set Time consuming to compare many Data must be in a form which can be placed along a number line Works better with lots of data

Question 44

Sketch maps uses and examples

Answer 44

Display certain features of an area for a clearer understanding - rough map of study site eg follow a river along its course

Question 45

Sketch maps advantages

Answer 45

Good memory tool - accompanied with detailed annotations

Question 46

Sketch maps disadvantages

Answer 46

May not be accurate

Question 47

OS base maps uses and examples

Answer 47

Show direction, distance, relief, routeways and recognisable features of lank marks eg car parks eg useful when understanding why river channel variables are different at different parts of a river > stream orders

Question 48

OS base maps advantages

Answer 48

Universally recognised | Proportional and accurate

Question 49

OS base maps disadvantages

Answer 49

Too much data

Question 50

Maps with located proportional symbols uses and examples

Answer 50

Used to investigate spatial patterns and compare characteristics of different places Located symbol maps eg location of river study sites Located proportional bars eg world population current/future Proportional divided circles eg showing proportion of work force in P/S/T industry eg show spatial patterns well - earthquake maps show plate boundaries well Anomalies can be identified and begin to explain (hot spots)

Question 51

Maps with located proportional symbols advantages

Answer 51

``` Located proportional bars easy to read Good visual representation of data Large ranges Not dependent on size Adds data/location ```

Question 52

Maps with located proportional symbols disadvantages

Answer 52

``` Show only a limited number of points Large range looks messy/cluttered Small range makes patterns hard to identify Wide range of extreme values - scale? Scale needs to be right so scale fits Difficult to produces Can't extract data Overlap = confusing ```

Question 53

Maps showing movement: flow, desire and trip line maps uses and examples

Answer 53

Flow line represent movement along a given route - variable width along a given route. Width of arrow is proportional eg traffic Desire lines show movement of a population from one place top another eg migration Trip lines show where people have visited - central point eg supermarket

Question 54

Maps showing movement: flow, desire and trip line maps advantages

Answer 54

Good to show direction and size of movement (flow) Good visual impression of movement (flow) Clear sense of direction Location compared Only concentrate on origin and destination and number on route - generalised (desire) Possible to identify sphere of influence (trip)

Question 55

Maps showing movement: flow, desire and trip line maps disadvantages

Answer 55

Best used on large scale maps - % or rate plotted (trip) Must use same scale (trip) Ignores actual route taken (desire) Non-density (trip) Maps lack precise interpretation unless statistical data is added - overlap (flow) Desire and trip lines only interested in source and destination areas Hard to draw Difficult to show meeting point (lots of lines)

Question 56

Chloropleth maps uses and examples

Answer 56

Show spatial distributions, using shadings of different densities to represent different densities of population or different %s of land of a particular crop etc.

Question 57

Chloropleth maps advantages

Answer 57

Very effective at displaying distribution and spotting patterns Easy to read Show which areas have similar/different densities - compare Visually effective Shows density General trend and anomalies identified

Question 58

Chloropleth maps disadvantages

Answer 58

Do not show total values of distribution they represent Suggests abrupt changes, but usually more gradual Consistent values implied by shading - don't reflect densities Size of administrative area affects size Only applied where clear boundaries exist Hides variation within each zone - actual data at a point not shown Class intervals chosen carefully to see patterns Can't extract data from a point Large variation = lots of colours and confusing

Question 59

Measures of central tendency uses and examples

Answer 59

Calculate 'average' > representative of all data - midpoint = typical Mean, median and mode Stone sizes

Question 60

Measures of central tendency advantages

Answer 60

Can compare with other averages MEAN: useful in small ranges MEDIAN: not affected by extreme values MODE: shows skewing (highest frequency)

Question 61

Measures of central tendency disadvantages

Answer 61

MEAN: heavily influenced by extreme values MEDIAN: not based on figures, but rankings; not arithmetically sound MODE: not useful with data with no representative figures; useful with only large data sets

Question 62

Measures of dispersion uses and examples

Answer 62

Analyse spread of data Stone sizes IQR: rank order; median is accompanying central tendency STANDARD DEVIATION: shows how the data is spread about a mean value if there are fixed independent variables and a frequency of these variables eg pebble size at one site in a river - often shown in a histogram with IV on horizontal axis and frequency on vertical axis. Higher value = more spread out from mean. Parametric test as it assumes normal distribution pattern - bell shaped curve

Question 63

Measures of dispersion advantages

Answer 63

IQR: removes outliers STANDARD DEVIATION: average amount by which the values in a data set vary from the mean - low = few extreme values and a more reliable representation of mean Shows how much data is clustered around a mean value; gives a better idea how the data is distributed

Question 64

Measures of dispersion disadvantages

Answer 64

SD: doesn't give you the full range of the data; it can be hard to calculate; only used with data which can be plotted on a histogram so where an independent variable is plotted against the frequency of it; it can be affected by outliers in data; assumes a normal distribution pattern

Question 65

Spearman's rank correlation test uses and examples

Answer 65

Measures correlation (linear relationship) between the similarity of 2 different variables Scatter graph drawn first to test relationship -1 > 1 -ve > 0 > +ve Check against table/graph critical values eg distance downstream and speed of flow of river Assumes no distribution pattern in the data so is non-parametric Degrees of freedom = number of paired measurements Reject NH if value calculated is higher than value of CV

Question 66

Spearman's rank correlation test advantages

Answer 66

Shows the significance of the data (or due to chance) Proves/disproves correlation Allows for further analysis Doesn't assume normal distribution Tests strength of relationship Does not imply a causal relationship ie change in 1 variables leads to change in other

Question 67

Spearman's rank correlation test disadvantages

Answer 67

Not reliable with fewer than 10 pairs of data More than 30 pairs is difficult Compares ranks not actual data 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

Question 68

Chi-squared test uses and examples

Answer 68

Examines spatial distributions Compares data that have been collected (O) against a theoretical random distribution of those data (E) Compares means How dissimilar to expected? eg provision of GP services in a city evenly spread? or concentrated in wards with high average incomes? eg Spread of ethnic groups within wars of a city Degrees of freedom = n-1 Calculated value higher than CV then reject NH

Question 69

Chi-squared test advantages

Answer 69

Can test association between variables Identifies difference between observed and expected Objective statistical significant results

Question 70

Chi-squared test disadvantages

Answer 70

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 expected values are below 5 Quite complicated to get right - difficult formula Large data sets - categoric Comparison of O/E is a preliminary analysis More complicated when O data evenly spread

Question 71

Mann-Whitney U test uses and examples

Answer 71

Do 2 sets of data come from the same or different populations? Assumes distribution for same population data If lowest U value is less than CV (found in table), NH rejected Assesses degree of overlap between 2 distributions more than would be expected by chance Median values compared to see if there is any correlation 5-20 points best used

Question 72

Mann-Whitney U test advantages

Answer 72

Shows the median between 2 sets of data Good with dealing skewed data so data doesn't need to be normally distributed You can divide the boundaries of 2 groups Only needs one variable in a set of data Distribution can be uneven ie unpaired Rid of outliers/not swayed by anomalies Compares 2 data sets not normally distributed - data capable of being ranked Point-dispersal graph used Non-parametric

Question 73

Mann-Whitney U test disadvantages

Answer 73

Best used with 5-20 small samples (less accurate) More appropriate when 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

Question 74

What do statistics do?

Answer 74

Interpret and analyse data collected in fieldwork investigations Aids improved understanding of geographical phenomena under investigation Bigger sample size = more reliable conclusions Hypothesis/null hypothesis established Results tested for significance levels against tables - 5%/1% exceeded significant and null hypothesis Therefore reliable and can be explained and justified to increase understanding If results not significant, further geographical explanations sought Can be used with presentation techniques to further develop understanding Allows you to quantify a perceived relationship

Question 75

How do maps improve understanding?

Answer 75

Aid interpretation and analysis of data to aid improved understanding of phenomenon under investigation Basic tool of a geographer Show spatial patterns - identified

Question 76

How do graphs improve understanding?

Answer 76

Show data in continuous form Give a clear visual representation of data so that patterns can be identified and anomalies found Often used before stat tests to see if there is a relationship as often test relationships or allow a visual comparison between variables