f764 geography skills

hypothesis

null (H0) testing for no relationship / alternate (H1) relationship in sample does accurately reflect total statistical population

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primary data

unprocessed / not anaylsed / not interpreted / geographer in direct control

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secondary data

published documents that have been analysed / interpreted / processed census data / textbooks / research papers / published maps

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quality of data

Nominal (lowest quality) 1 or 0 - mode + chi squared / Ordinal (medium quality) ranked - spearmans + median / Inteval (high quality) discrete scale + magnitude - pearsons + mean + SD

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GIS Adv

AEGIS 3 with piot study / large amount + variety of data / change scale / dynamic so can cope with change / spatial data to show patterns + trends

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Maps (adv + disadv)

plan view with scale + direction / shows what exists visually spatial / flexible scales / can be dated / can be overly detailed

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Photos (adv + disadv)

many different views (aerial, ground level, satellite) / captured at specific time / shows variety of data / only at one specific point /

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risk assessment 4 stages

1. identify risk 2. evaluate severity 3. control measures to reduce risk 4. re-evaluate severity

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accuracy (description + errors)

level at which data is exact and free from error - potential errors include measurement error (repeats) operator error (ind.climate.equip)

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reliability (description + errors)

extent to which the sample data reflects the greater total statistical population - potential errors include sampling error (inappropriate frame + too small size)

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pilot study (description + adv/disadv)

small scale, preliminary to eval success / weaknesses + improvements of study before full scale is performed - ADV - is time achievable / location appropriate / effectiveness / risk / equip . DISADV - takes time

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random sampling

'random number tables to select sample point - every individual having equal chance' / adv - stat. tests + not bias / disadv - not represent total SP (miss points) + long time

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Stratified sampling

'underlying patterns in total SP taken into account + proportionally sampled' / adv - no points missing / disadv - possibly bias (false info used), possibly no stat tests, needs info, groups under/over represented

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Systematic sampling

'points are selected at regular intervals can be spatial + non spatial' / adv - easy, quick, even coverage / disadv - bias, possibly no stat, interval coincide with data pattern / location, missed variations, regularities not typical

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Pragmatic sampling

'sampling is taken where access is available and changes are observed' / adv - realistic + risk assessment based / disadv - bias + possibly not stat test

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Units of sampling

point / grid (quadrat) / line (transect) / belt (wider transect)

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spatial - choloropleth map

'ratio values are expressed as densities/% with use of areas' / method - standardise values - classes - colour shading - key,title,direction,scale / adv - easy + visual / disadv - concealed in whole area + abrupt at boundary + large dominate

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spatial - dot map

'ratio values + use of area' / find base map - decide on dot value + size - key, title, scale, direct / adv - info in areas, visual + spatial, stat analysis / disadv - dots merge

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spatial - proportional symbols

' ratio data in specific location' / base map - symbol + scale - size of symbol - key,title,scale,direction / adv - visual + spatial, data recovered, stat tests / disadv - difficult to produce, scale + symbols misread

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spatial - isoline / isopleth map

'ration data with points' / base map - number + value of isolines - draw by interpolation (between know values) - number isolines - KTSD / adv - spatial patterns, gradual change, guess points / disadv - assume change constant, need lots of points

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spatial - flow line

'ratio data use lines (routed or non routed) to show flow paths' / suitable scale - classes + scale of line (width) - KTSD / adv - visual, quick + easy, shows different variables / disadv - generalised, no stat test, scale carefully chose width

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non spatial - table

nominal (names) , interval (%), can use freq distribution for histogram / adv - backbone, clear grouping, stat tests / disadv - no visual

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non spatial - bar chart

interval (discrete) width always constant + length varies / adv - visual with temporal + spatial, stat tests, comparable / disadv - only non continuous

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stacked/divided bar chart

quantity divided into components of absolute values or proportions / adv - easy to draw, visual, stat tests / disadv - too many divisions confuse

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pie charts

% data in segments of whole sample / adv - visual, easily comparable, stat tests with given % / disadv - don't always give %, no temporal change, can confuse, order of division standardised

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rose/star charts

show direction and length.width of bars show frequency / adv - shows direction + spatial changes, visual pattern, if accurate + scale can recover stat / disadv - hard to produce width/length scale, not always exact direction

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line graph

variations absolute/% X independent and Y dependent / adv - shows trends + anomalies / disadv - only continuos

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scatter graph

overall trend (not just line) + simple to show relationship / adv - correlation suggested, anomalies, stat tests / disadv - few points skew data, too many confuse, only 2 variables

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triangular graph

3 variables add up to 100% each side of triangle, spatial + temporal / adv - compare 3 variables / disadv - only%, difficult to read + construct

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Lorenz curve

shows inequalities (deviation) in distribution of phenomena, lorenz = actual distribution, more concave = greater inequality / adv - visual, multiple distributions, Gini coeff. = degree of inequality / disadv - time for c.freq, no exact quant. value

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kite diagram

how phenomena's occurrence changes over distance (bradshaw's) / adv - visual, stat tests, relative phenomena compared / disadv - % change + no absolute values

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sketch map

rough map that shows basic info - add essential info, KTSD / adv - visual + easy to interpret / disadv - needs quality TKSD to be effective

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mean / median / mode

average of all values - included anomalous data / middle value / most common value

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standard deviation

measures central tendency around mean

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kurtosis

talled / steeper curve = lower SD compared to normal distribution

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distribution

normal (bell shaped curve) / +ve skew = mean greater than mode (upwards hill) / -ve skew = mode greater than mean (downwards hill)

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Spearman's rank correlation coefficient

used to show the how strong relationship between two ranked variables / adv - easy + simple, not normal distributed / disadv - poor as no magnitude, no explanation for causes

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spearman's rank significance testing

use degree of freedom (classes - 2) and standard significance 0.05 (95%) / adv - are results representative of total SP, whether they are significant + to what degree, reduces thoughts of bias + chance, 95% sure

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Pearson's product moment correlation coefficient

show relationship / adv - statistically better, more specific, uses magnitude / disadv - only normally distributed data, hard to construct, no reason given

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Chi squared

differences between comparable sets of data / null hypothesis put forward (H0) no correlation / significance test for deviation due to chance / > critical value can reject H0

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nearest neighbour analysis

statistically describe distribution of spatial data / 'central place theory' / CLUSTERED (H1) = 0 RANDOM (H0) =1 REGULAR (H1) =2.15 / disadv - changes with measurements, as crow flies? or along roads, services affect

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