AS Geography Unit 2 + A2 Geography Unit 4a >>

Purpose of graphs:

Having collected data as part of an investigation it is useful to summarise it in a map, diagram or graph. This is because relationships between large numbers of figures can be identified more easily and direct comparisons can be made. Trends are easier to spot this way rather than from a list of figures.

There are many types of graph that can be used to show information. It is important that every graph used is appropriate, accurate, and has a title, labels and key. Computers (spreadsheets) are helpful in drawing graphs, but be careful that the graph is meaningful. It is a good idea to add graphs to a base map of the study area to show how the data varies over space.

Main Types of graph:

• Simple Bar; Compound Bar; Histogram
• Pie; Proportional Pie
• Scatter
• Line

1:125,000	1:50,000	1:25,000	1:10,000
GOAD Plans			Geology Maps
	Possible Questions: Suggest 2 ways you might use a map to aid a geographical investigation. How could a specialised map (not an Ordnance Survey) be used in a geographical investigation? Give 3 features of a well used map in a geographical investigation. State 2 drawbacks of using published maps in a geographical investigation. How might a sketch map be useful in a geographical investigation and give 3 features of a well-used sketch map. With reference to a geographical investigation you have carried out, justify your choice of map.

 All maps should have a clear scale, north arrow, title, relevant key, and preferably be annotated.

Sketch Maps	Dot Maps	Choropleth Maps
Sketch maps can be valuable to students completing a geographical investigation. They simplify what is shown on published maps (such as Ordnance Survey) by only showing the features that are of interest. As such unnecessary detail is ignored and the map is easier to interpret. Accurate sketch maps can be useful to locate the study area, summarize results, and serve as important base maps. Method for drawing sketch maps: (1) draw a box the same shape as the map area you are using; (2) sketch in the main relief features, main rivers, woodland areas, and main routeways; (3) mark and label (or add a clear key) the main features you want your map to show; (4) add a title, north direction, and use colour.	Dot maps use small dots of a fixed size to represent a variable, such as numbers of people, shops, etc. located on a base map. The value of each dot should be high enough to avoid extremes and low enough to avoid too many areas having no data plotted giving the false impression of emptiness. The dots should be drawn the same size and ideally not too large that they merge together. Different coloured dots could be used to represent different data for comparison. These maps are helpful to show distributions but they do have limitations: (a) it is very difficult to count a large number of dots so accurate reading of the map is not possible; (b) this mapping technique relies on the dots being plotted accurately on a map representing a specific location rather than a broad region - detailed knowledge of the data is therefore necessary.	In choropleth or density shading maps, areas are shaded according to a key representing a range of values. It is an easy presentation technique which gives a good visual impression of change over space. It relies on a suitable key and is limited by the following: (a) it gives a false impression of abrupt change at the boundaries; (b) variations within each area are hidden, particularly if a wide data range is used; and (c) reading exact data figures from the map isn't possible. To draw a good choropleth map a suitable base map divided into small areas is needed (this can be scanned into a computer). The number of categories can be worked out in a similar way to histogram classes. Between 4 and 8 classes is best, depending on the range of your data and what it is you are trying to show. Each class should be shaded from light (small change or low numbers) to dark (large change or high numbers). All areas should be shaded unless there is "no data" for an area. There are 3 ways the data can be grouped into classes: (i) divide the range of values into equal-size classes; (ii) rank the values in order and then divide the list into the number of groups you want; (iii) inspect the data values carefully and divide them up to reflect the distribution of the data (good with extreme values).
Topological Maps		Isoline Maps
Topological maps use area or distance to represent values. Actual distance and direction are disregarded but the relative position of places is retained. There are two types: (1) Maps of areas (e.g. countries) in which the area has been distorted to be proportional to some value (such as population, GNP etc.) (2) Maps of route networks, e.g. the London Underground Map. These maps retain the relative position of each station. They are much easier to read and understand than conventional maps.		Iolines are lines on a map that join points of equal value, e.g. contours on a relief map, isotherms of temperature, isobars of pressure, etc. The interval between the isolines should be consistent and the numerical values should be added to each line. They only work where there is plenty of data spread all over the study area and the changes across space are fairly gradual. They avoid the problems that boundary lines create on choropleth maps.s

Selecting the right method of presenting data
Your choice of technique will depend on the type of data you have collected and what it is you want to show. Whichever method you use, it should be helpful to get across a message which a table of data would not be able to do as well, be simple to understand, and be drawn clearly. Accuracy, titles, labels, keys, northing arrows, scales, etc. are crucial!

	Main Graph Types	Mapping Methods
Identifying relationships between data	Scattergraphs (with lines of best-fit)	- - -
Describing spatial patterns	- - -	Isoline maps; Choropleth maps; Flow/Desire lines
Identifying differences between data	Line graphs; Bar graphs; Histograms; Pie graphs; Long/Cross sections; Proportional symbols; Dispersion graphs	Any graphical method plotted on a suitable base map to show spatial variations across the study area.

 

Analysing Data: Statistics

All geographical investigations should involve detailed analysis of the data collected. Statistics can help by taking the analysis one stage beyond that which can ever be achieved with maps and diagrams. Inspecting the data mathematically provides greater precision and may give some information that might otherwise go unnoticed. It should be remembered that using statistics is only an aid to analysis and needs careful planning and interpretation.

Before attempting any statistical analysis, you should be clear what it is that you hope to achieve by using it, and be certain that the data type is appropriate. For your results to have any relevance, your data collection and sample size needs to be sound (put rubbish in, guess what comes out...!) This is why you should plan which statistical test you wish to use early in the planning stages of your project.

ForAS geography, you are expected to be familiar with 3 types of statistical technique:

• Measures of Central Tendancy (to compare and summarise data): mean, median, mode;
• The spread of data: range, interquartile range, standard deviation;
• Test for relationships/correlation/association: Spearman's Rank
• PLUS AT A2 LEVEL: Test for difference: Mann Whittney U Test & Chi Squared Test

1. Measures of Central Tendancy
When there is a lot of data it can be useful to find an average to summarise it, particularly when comparisons between data sets are desirable.

Measure	Method	Evaluation
Mean	All the data values are added together and then the total is divided by the number of values in the data set.	(+) It takes into consideration all the data. (+) The result can be used for further mathematical processing. (-) It can be misleading if there are a small number of very high or very low values which may distort the mean. The mean is best quoted with reference to the standard deviation.
Median	The central value in a series of ranked values. If there is an even number of values, the median is the mid-point between the two centrally placed values.	(+) It is not affected by extreme values. (-) It cannot be used for further mathematical processing. The median is best quoted with reference to the interquartile range.
Mode	The most frequently occurring number in a set of data values.	(+) It is very quick to calculate. (+) It is not affected by extreme values. (-) It can only be identified if the individual values are known. (-) The result cannot be used for further mathematical processing.

 

2. The Spread of the Data
The mean, median and mode give a useful summary value for a set of data but give no information about the spread of values around the "average" figure. As such, this summary value can be misleading and give an untrue picture of reality. The spread, or deviation, from a central value can be measured giving a fairer picture about the set of data.

Measure	Method	Evaluation
Range	The difference between the highest and lowest value. Regularly used when describing climate figures.	(+) Quick and easy to calculate. (-) A crude measure as it only considers extreme values and doesn't make any reference to any other values.
Interquartile Range	The interquartile range is the difference between the 25th and 75th percentiles. The higher the interquartile range, the greater the spread of values around the median.	(+) Although it is more complicated than the range, it is still quite simple to calculate. (+) The result represents the spread of the middle 50% of values and is therefore more representative of the entire data set. (+) Extreme values are not considered and so the result is unlikely to be skewed. (-) Not all the data is considered.
Standard Deviation	The standard deviation indicates the degree of clustering of each data value about the mean. It is calculated by measuring the difference (deviation) of each value from the mean; these results are then squared and then added together. This total is divided by the number of values in the data set, and finally the square root is taken from this result. A low SD value indicates that the data is clustered around the mean, whereas a high value indicates that the data is widely spaced with some much higher and lower figures than the mean value.	(+) The best way to measure the spread of data around the central value as it involves all the data. (+) Allows useful comparisons of the distribution of values in a data set to be made. (+) Gives results that can be used in further mathematical calculations for further analysis. (-) Reasonably complicated to calculate, although calculators and spreadsheets can help.

 3. Test for Relationship/Correlation/Association
When two things vary together (e.g. land values decreasing with distance from the CBD) there is a correlation, i.e. as one variable changes, there is a change to the other variable.

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 Geographical Investigation Report

 Example AS Investigation

Aim: "To investigate the effect of longshore drift on beach dimensions at Beesands".

Background Information:

• Locate the study area using appropriate scale of Ordnance Survey maps & aerial photographs and justify the suitability of the study site;
• Identify the physical and human features of the area which are relevant to the aim (e.g. rock types, prevailing wind direction, wave refraction, destructive and constructive wave locations);
• Define key terms & identify geographical concepts relevant to the aim (e.g. beach dimensions are affected by sediment size, beach width and cross sectional area).

Hypothesis: - a clear statement which can be tested. It provides a narrow focus to the study.

• Sediment Size: Smaller sediment is found at the Northern end of Beesands beach.
• Beach width: Beach width increases towards the Northern end of the beach.
• Cross-sectional Area: Beach cross sectional area increases towards the Northern end of the beach.

Kit Needs & Sampling Details (size & techniques):

• Tape - to measure beach width;
• Clinometer - to measure beach angle (and therefore calculate cross-sectional area);
• Bag for sediment sample;
• Phi sieve;
• Balance;
• Oven;
• Recording sheet;
• Identify the sample size;
• Identify the sampling technique.

Risk Assessment:

Hazard	How risks can be minimised

 

2.Data Collection

·Draw a summary methods table listing the quantitative and qualitative primary & secondary data collected;

·Annotated diagrams & photos describing the field techniques used;

·Pilot Study (practice run)

3.Data Presentation & Analysis

·Data processing - summarise the data in a spreadsheet

·Data presentation - draw appropriate graphs (profile; scattergraphs; located pie charts)

·Data description - trends, patterns, anomalies

·Data explanation - refer to theories to give reasons for your findings. Could "other factors" not previously considered be relevant?

·Data analysis - use a statistical method (descriptive statistics or a statistical test) to analyse results further

4.Conclusions

·Draw your findings together and relate to your main aim or question

·Has the investigation achieved what you set out to do?

·How valid are your conclusions?

5.Evaluation

·What would you do differently next time to make your results more reliable & conclusions stronger?

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