Geographical skills

Field sketching - what to include

• location name

• labelled points of interest

• estimated height

EIA - environmental impact assessment

• looks at environmental, economic and social factors before development is done

• +3 (positive impact) to -3 (negative impact)

• doesn’t take into account importance of each factor - all even

flow line maps

show movement of people or goods

• width represents number (wider=more)

• arrowheads indicate direction

mean

add all numbers - divide by how many

• exclude anomalies

mode

most common number in a data set

median

middle value when all points are ranked

• if there is an even number of the results = add middle 2 and divide by 2

range

span of data within which all numbers lie

dispersion

how data is dispersed within a range

interquartile range

shows the middle 50% of data

1. find median

2. find 25% and 75% (by counting ¼ of the total number of values from the median)

3. find difference between upper and lower quartiles

variance

how far something varies from the average

1. find the mean

2. calculate how much difference each value is from the mean

standard deviation

shows by how much a value varies form the mean

• shows data dispersion from the mean

1. calculate mean

2. calculate variance for each value

3. square variance values

4. put into formula

standard deviation formula

stand. dev = sq. root | sum of (x- mean)²/pop. size

scatter graphs

shows correlation between 2 data sets

• don’t join dots

• positive, negative or no correlation

life of best fits

can be drawn on a scatter graph

spearmans rank correlation

tests strength of relationship between 2 variables

• gives figure between +1 (perfect correlation) 0 (no correlation) and -1(perfect negative correlation)

spearmans rank - calculation

1. rank values form highest to lowest

2. rank second data set from higher to lowest

3. subtract 1st rank from second

4. square each difference for each place

5. add up all differences²

spearmans rank formula

put sum of d² into formula

= 6xsum of d² / n(n²-1)

• n = number of different paired values

• will give a value for spearmans

spearmans rank - signficance testing

• paired data sets - 1 = degrees of freedom

• find confidence - 0.05

• significant = equal to or greater than the critical value

• insignificant = lower than critical value

Lorenz curve

shows and measures inequality in a graph form

• assumes that an equal world - 10%of people have 10% of income

• equality = straight line on Lorenz

• more curve bends away from straight line = more inequality

Lorenz curve - how to draw

1. draw graph - plot equality line

2. calculate ratios of advantage (what each 10% of poorness owns of the countries wealth)

3. plot lowest ratio - poorest 10%

4. plot each 10% in order

5. join all points to form a line

gini index/coefficient

measures the inequality of wealth distribution

• measures area between lorna curve and the line of equality

• shown as a value btwn. 0-100

• 0 = equality

• 100 = complete inequality

• coefficient is shown as 0-1

adavantages of gini index/coefficient

• measures inequality - doesn’t show uniform picture of whole pop.

• can compare inequalities of different kinds within a country

• can see trends over time

chi squared

statistical test that measures and analyses distributions of data

1. establish hypothesis

2. decide theoretical numbers - expected values = E

3. record observed values = O

4. find difference between 2 sets = O-E

5. square results = O-E²

6. divide by expected = O-E²/E

7. add up these results = X²

8. find degrees of freedom (data-1) and critical value (0.05)

9. if x² is less than/equal to crit. val = ACCEPT null hypothesis

null hypothesis = there is no significant differences

index of diversity

measures diversity by counting number of species in a community and the number of plants in each species

• higher figure produced = higher diversity

index of diversity - calculation

simpsons index

1. use formula

2. find N = total number of individual plants

3. Nx(N-1)

4. find n = number of individuals in each species

5. nx(n-1) - for each species

6. add up n(n-1) values

7. divide N(N-1) by sum of n(n-1)

t test formula - 1 sample

= mean - theoretical value/ standard deviation divided by square root of sample size

t test - hypothesis

finds the statistical difference between 2 groups

null = no significant difference between results

alternative = is a significant difference between results

t test - calculation

1. find mean of each sample

2. find difference between means

3. standard dev. for each sample

4. square standard dev.

5. divide standard dev.² by sample size of each group

6. add the 2 values

7. square root the number = standard error of the difference

8. divide the difference in the means by the standard error of the difference

9. find d.f. = sum of both sample sizes -2

10. find critical value

11. if t value is OVER critical value - reject null (is sig. difference)