Objectives: By the end of this subtopic learners should be able to:
Construct diagrams, tables, charts, graphs, and models to represent different types of spatial geographical phenomena.
Read and interpret diagrams, tables, charts, graphs and models that represent various geographical phenomena.
Data
Data are pieces of information that are presented as numbers, values or characters.
It may be defined as discrete pieces of information that need interpretation in order to become meaningful.
Types of data
There are two types of data: qualitative and quantitative.
Qualitative data
Qualitative data is data that is descriptive; meaning that it is not measured.
This data includes description of things like tastes, smells, textures, appearances, slopes, colours and many more.
This type of data is very difficult to analyse.
Quantitative data
This is data that is measured.
It can be individual figures (discrete) or continuous.
Discrete data is data that assumes particular values or number.
Examples are numbers of males or females, buses, animals and so on.
One is either male or female, there is no half female.
Continuous data on the other hand takes any infinite value.
Their measurement is, therefore, given in a range.
Temperatures, height, flow and so on, take infinite values and hence they give us continuous data.
Methods of presenting geographical data
There are many methods of presenting geographical data and some of them such as maps, sketch maps and photographs have been covered already in previous map reading topics.
This section will look at tables, bar graphs, line graphs and pie charts.
Flowline diagrams, topological diagrams are adequately covered under transport studies.
Tables
Tables are a means of arranging data in rows and columns.
The table below shows monthly temperature and rainfall figures of an area.
In the case below, total rainfall and average temperature figures are presented on monthly basis.
The table is the primary means of presenting numerical data even before development of other ways of presenting data.
First row or column of a table is normally that of the independent variable.
An independent variable is that aspect of the data that does not change because of the other.
In the table below, months of the year are independent of rainfall and temperature.
The dependent variable is the variable whose value or distribution depends on the other variable.
In the table below, rainfall and temperature depend on the month of the year and so each one of them is a dependent variable.
Month
January
February
March
ToC
26
25
22
Rainfall
132
140
108
TASK: determine dependent and independent variables from the following pairs; Name of pupils and their grades; deaths per year and hospital name; yield and farm size; mealie meal type and price; rent and room size
Line graphs
Line graphs show relationship between two variables on the X and Y axis.
A line graph is especially suited for data that is of continuous nature.
The independent variable is plotted on the X axis while the dependent variable is on the Y axis.
Data is first of all tabulated and then plotted on an X and Y graph.
The months of the year are on the X axis while temperature is on the Y axis in the graph below.
On the measured variable (usually the dependent variable) there is need to use scale.
Our example is 1 centimeter represents 5° on a temperature graph.
The example below shows the outcome.
A line graph gives better view of trend than a table.
The line graph can be either straight line or curved depending on the choice of the person who draws it (the cartographer).
Line graphs can also be multiple where different variables can be compared at the same time.
In the line graph above transport costs of different transport modes are compared over distance.
The aim of such a graph is to see which mode is cheaper in which distance range.
An example is road transport (C1) which is cheapest on short distances but becoming the most expensive on greater distances.
Bar graphs are best suited for discrete data.
Usually, they are plotted in just the same way as line graphs.
On bar graphs the independent variable is plotted on the X axis and the dependent variable on the Y axis.
There are instances when bar graphs are horizontal and in such instances independent variables happen on the Y axis.
Construction of bar graphs involve, first, tabulating data.
Independent variables are plotted on the X axis.
In the case of rainfall graphs, months are the independent variable and so are plotted on the X axis.
A scale is chosen for the measured data so that 1 centimeter represents 20mm.
Below is rainfall data of a place and the bar graph that can be drawn from it.
Simple bar graph showing rainfall
Multiple bar graph showing sales
Another multiple bar graph showing sales
Compound or Combo line and bar graphs
Line and bar graphs can be used together to show relationship between variables.
Temperature and rainfall are examples of geographical phenomena that are closely related and are used to determine types of climate.
Using data that we originally introduced on temperature and rainfall the resultant graphs would look like the one given below.
Combo line and bar graph
The combo line and bar graph enables us to typify climates.
The above combo graph typically shows a tropical continental or savanna type of climate.
Describing and interpreting data presented in tables and graphs
Data is presented on tables in discrete form and needs to be interpreted.
Description
High/low
Description includes identifying whether a phenomenon being looked is high or low.
In a temperature graph, one needs to comment on whether temperatures of a place can be generally regarded as high or low.
In line or bar graphs that are compound, comparisons can be made indicating which is higher than the other, for example, modes of transport and cost per distance.
Peak times and spread
Peak or low times also need to be mentioned if there are any.
Mention of spread is also needed, that is, if rain falls throughout the year or in certain months of it.
Temperature graph for Station X shows highest monthly temperature of 27°C being experienced in October while lowest monthly temperature of 16°C being experienced in June.
Range
When describing data on line and bar graphs one also needs to take note of range.
Range refers to the difference between maximum and minimum.
If it is temperature of Station X the temperature range is 27°C - 16°C = 13°C.
This temperature range is considered or described as high for a place.
Rainfall range for Station X is 140mm — 0mm = 140mm.
Again for a place that has average monthly rainfall of 67mm a range of 140mm is very high and it means there are periods of extreme dryness.
Trend
General trend is also important and this looks at whether there is general increase or decrease throughout the period of observation.
If there are periods of general increase and periods of general decrease these can be also be highlighted.
Averages
A student can use arithmetic mean or mode.
Modes are those values that appear most frequently within some data range.
Indicating averages, especially using modal values, shows of a good student.
A student can say average temperatures are around 22°C for Station X.
Interpretation
Interpretation of the data involves making inferences and relationships between variables.
This then allows the process of identifying/anticipating potential problems or challenges and offering solutions possible.
Making deductions from data
From data given such as that of temperature of Station X inference can mean that vegetation has to be deciduous, meaning that it should be able to survive hot and wet as well as cool and dry periods.
Irrigation is needed for crops to survive the May to July cool-dry season.
We can also infer that temperatures decrease a bit in November and December because of rainfall activity.
Relationships
Data on Station X shows that when temperatures are high rainfall is also high and when temperatures are low rainfall is correspondingly low.
This is description of relationship between variables.
Such relationships focus on trend, peaks, lows, range and duration/spread.
Pie charts
Pie charts are graphs drawn in circular form with segments.
Each segment is a proportion of the whole circle.
Data represented on pie charts thus proportional.
Pie charts are more suited to describing data that has totals, data that is about volumes or data that is concerned with contributions.
The fewer the variables being looked at the more suitable they are for presentation on pie charts.
This means pie charts are not suitable for many variables because too many sectors make it difficult to get any impression on.
Up to about five variables are thus ideal.
To obtain the size of each region in the pie we divide sales by total and multiply by 360°.
The Eastern region will occupy a sector of the size of 72°.
The process is done for all regions and the different sectors are segmented.
The resultant pie chart is as shown below.
The advantage of a pie chart is its visual impact on generalizing contribution of sectors.
The more the number of sectors the less suitable a pie chart becomes in giving comparisons.
Also, when figures are not markedly different sectors look almost equal rendering the pie chart less suitable as a graphic tool.
When constructing a pie chart we tabulate data first.
Description and Interpretation
Pie charts provide opportunity to make descriptions based on comparisons and contribution of sectors towards a whole.
The pie chart is therefore effective in bringing out this comparison.
Choropleth maps
Choropleth maps are maps that use different shades or colours to show measurement and distribution data.
The darker that shade or colour the higher the measurement being shown.
It is normally used to show geographical aspects such as density or intensity.
Choropleth maps show phenomena like, income per capita, population density, vegetation, and so on.
Dot maps
They are also known as dot distribution maps or dot density maps.
They use dots to show/indicate the presence of a feature/phenomenon.
Dot maps are usually used to show population distribution.
Each dot as a value.
The first map below shows distribution of quality hotels for tourists in Zimbabwe.
A student should be able to note the concentration of the aspect being shown on a dot map.
The dot map of Africa below shows density of a type of animal tick called Acari.
The highest density of this tick is in Zimbabwe.
Dot maps are also usually shown together with proportional symbols.
Proportional symbols
Proportional symbols are symbols used on a map (usually circles and squares) to show data values in proportion at their location.
The proportional symbol maps have a scale that helps to give values to the different symbols.
It is important to note that proportional symbols are usually used together with choropleth maps.
Field Studies
This is also referred to as field work or field research.
It involves collecting data from natural settings or environments outside of an experiment.
Methods of data collection in field studies vary depending on the nature of the aspect being studied.
These include identifying, observing, interviewing, measuring, counting, sampling, photographing, mapping, and so on.
Tips on conducting field studies
Identify aspect/phenomenon to be studied.
Locate the area where the phenomenon is to be studied. (This might involve obtaining a map).
Delineate the actual area to be studied.
Determine data that needs to be collected.
Determine methods of data collection to be used.
Identify equipment and personnel needed to conduct the field study.
Conduct the field study.
Write field notes.
Analyse data that has been collected and describe findings.
Relate your findings to theory to establish differences and similarities.
Explain these differences and similarities.
Importance of fieldwork
It helps to arouse a student's interest in geography.
It develops in students the ability to look at the environment with ‘a geographical eye.'
Students are able to relate theory learnt in the classroom to the actual phenomena in the environment.
Develops a systems approach to studying of geographical phenomena.
The involvement of practical work ensures that the students retain all knowledge learnt for the rest of their lives. Chances of forgetting are minimal.
