Lesson video

Hi, my name is Chloe and I'm a geography field studies tutor.

This lesson is called Constructing graphs in geography and it forms part of the Geographical skills unit of work.

It's all about taking raw data and presenting it in graphical ways so that we can understand the geographical meaning behind it in greater detail.

Let's get started.

By the end of this lesson, you will be able to accurately construct graphs of frequency and proportional data.

There's some keywords to review before we begin.

First of all, discrete data.

This is data that can be arranged into exclusive categories.

Continuous data is data that sits on a continuum from low to high values.

Frequency data is data such as counts or the actual amount of something.

And proportional data is data that is relative amount of something when considered as part of a whole.

This lesson is in three parts.

First of all, we're going to ask the question, why do geographers use various data presentations? We're gonna think about how geographers present frequency data and then how geographers present proportional data.

Let's start with that first one about why do geographers use various data presentations? There are many different types of graphs, and it's kind of tempting to think of them a bit like a menu that geographers can use when they want to present some quantitative data, something that we can just choose any method to go with any type of data.

In fact, geographers look for data presentation methods that are appropriate for the data.

This means they will look for techniques that will work with the type of data they are handling and the geographical meaning that they are trying to showcase in the data.

For example, geographers will look carefully at their data to see it if it's discrete data, that's data that can be sorted into exclusive categories, or continuous data that sits on a continuum from low to high values.

Graphs for discrete data need to be able to show those distinct categories in the data.

So something like a bar chart does this because it separates the different bars, so it's very clear that the data is sitting in exclusive categories.

Graphs for continuous data need to be able to show how the data changes along the continuum.

A line chart does this by having the data joined point to point by a line.

So let's check our understanding so far.

True or false? Any data presentation method can be used with any data set.

Is that true or false? Pause the video, have a think, and then come back to me.

So, well done if you recognised that it is false, but why is it false? So, data presentation methods need to be appropriate for the type of data and what it hopes to show.

Data presentation needs to add value to the geographical understanding of the data.

If the data tells the whole story when the geographer looks at it in its raw form, then there's little to be gained by presenting it.

So if we look at the data here in the table about the questionnaire responses and we see that there's eight people who strongly agree with something and then one in each of the other categories, I have the question, is there any point really creating the bar chart? Because I can see straight away from the data in the table what the outcome is going to be.

Geographers can try to find meaning by presenting graphs that show relationships between data variables.

These may compare one variable of data with another or show how one variable influences another, and one example of a graph that can show this is a scattergraph.

Scattergraphs are all about relationships between data.

Geographers who are interested in the actual amounts of things will want to use graphs that display frequency data.

One example of this is a pictogram, and you can see one in the example here.

Geographers who are interested in the relative amounts of different things will want to use graphs that display proportional data, such as a pie chart.

Another check for our understanding here.

Complete the sentences with the missing words.

Pause the video so you can have a good read of the paragraph and then come back to me.

Right, let's see what you got.

The purpose of data presentation is to allow geographers to understand the data more effectively.

They may look for relationships between variables or try to show the actual amount of something through frequency data and the relative amounts of something through proportional data.

I hope you got those correct.

Our first task of this lesson.

Sofia has presented some data on volcanoes.

So let's take a quick look at the bar chart here.

We've got the number of active volcanoes since 1950 and then we've got five different countries, and we can see the amount of volcanoes that are active in each of those countries according to the height of the bars.

Task one, state whether the data is discrete or continuous and how you know this.

And task two, state whether the purpose of the graph is to show a relationship, frequency, or proportions.

Pause the video and have a think based on everything we've learnt so far and then come back to me.

Okay, so let's have first of all this idea about whether it's discrete or continuous.

It's discrete data, and we know this because the bars are drawn in separation, there's gaps between them.

We also know that data can only apply to one country at a time.

You cannot have, for example, a volcano that is active in Russia and in Chile.

Our second task, state whether the purpose of the graph is to show a relationship, frequency, or proportions, and yes, hopefully you can see that it's all about frequency.

It's about the number of volcanoes, the amount of volcanoes, so it has to be about frequency.

Let's look at the second part of today's lesson now, how do geographers present frequency data? There are some conventions that geographers need to follow when they're drawing graphs.

Now, here we've got a line chart, so let's look at some of the conventions that geographers need to use.

All graphs should have a title, and there is almost always the same pattern to how this title is written.

It's A, then the name of the graph, to show, and then a brief description of the data.

So you can see in this example it's A line chart to show how noise levels change with distance from the town square.

So if you can always use that pattern within your title, you know you will have written a good descriptive title.

Where your graph or chart has axes, these should be really clearly labelled with a description of what the data is.

The values along each axis should be at regular intervals.

So here you can see the distance is every 100 metres and the noise level is every 20 decibels.

There's no variation on that.

It's also really important that you include the units.

So in this case, distance is measured in metres and the noise level is in decibels.

A bar chart is one of the most simple ways of showing frequency data.

We've got one here.

Each bar is drawn so that its height is aligned with the correct value on the y-axis.

And the y-axis should always start at zero.

That's because we are showing the total amount of something, so we have to show it from zero to its full value.

The bars should be the same colour if they're showing amounts of the same thing.

Now, in this example, it's showing the amount of world natural gas reserves by the different regions, so by continent.

And because it's natural gas in each of the columns, each of those columns needs to be the same colour.

Also note that they're the same width.

That's also important.

A histogram shows frequency data that is continuous.

It is constructed in exactly the same way as a bar chart, except you can probably see that the bars are touching, and that's to show that it is continuous data.

Continuous frequency data might also be shown using a dot chart, and there's an example there.

Each dot represents a single occurrence of the thing that's being measured.

Now, strictly speaking, that means there's no need to have values on the y-axis.

You might sometimes see them, but really, because each dot is a value of one, all you need to do is count up the dots.

Let's check our understanding about frequency data.

So Jacob has drawn this bar chart.

What error has he made? Let's take a look at it.

We've got A bar chart to show the average height of trees in five different plots of woodland.

So, is there an incorrect title? Is it that the bars have been drawn in a separate way? Is it that the y-axis starts at zero? Or is it because there are no units? Which is the error that Jacob has made with this chart? Pause the video and have a good look at it and then come back to me.

Okay, hopefully you can see that Jacob has missed off the units.

So although we can probably guess that the height is going to be metres, we don't know for sure.

They could be really young trees and that could actually be centimetres, they could be saplings that have just been planted.

So really important that Jacob remembers to put the unit values in there.

Continuous data can also be shown by a line chart.

Here's a good example of one here.

The plots are joined by straight lines, exactly from point to point.

The first line starts at the first point, not at zero.

It might be that in this instant, a zero distance from source is not going to be zero in terms of metres width of the river, so we can't assume that.

So the first point is the first part of the line, not the zero.

The values and units may be needed on both axes, and you can see we've got those added in here.

Now, a dotted line represents projected data or estimated data.

It data that we're not really sure about.

It's data that we are assuming to be true rather than data that's been collected.

You will often find this in relation to climate change data, where a dotted line is used about projections into the future.

Pictograms mimic bar charts and histograms, but they use stacked pictures or symbols instead of bars, and they can be used for discrete data and continuous data.

Here we've got some data about world fertility rates.

The key tells the reader the value of every symbol, so it's really important that you look at the key first before starting to interpret these types of graph.

Normally, you don't need to include additional values, as the reader is expected to use the key.

So you wouldn't normally have a value on the alternative axis.

In this case it's the x-axis, but it could be the y-axis indeed.

So let's check our understanding there.

True or false? A line chart can have both solid and dashed lines.

Is that true or false? Think about what we've just done, pause the video, and hopefully come back to me with the right answer.

All right, let's see what you thought about that.

Yes, it's true.

Well done.

So why is it true? Well, hopefully you can remember that the solid line represents the data that is known while a dashed line represents projected data or estimated data.

Well done.

Our second task.

Izzy has started to draw a pictogram showing annual carbon dioxide emissions for different countries.

So we can see here she's got a little cloud symbol that she's using in her pictogram.

She's done the UK, she's done China, and she's done USA, but we've still got Saudi Arabia to go.

Complete the pictogram for her.

The raw data is in the table there.

So Saudi Arabia needs to be represented as 22 tonnes per capita of carbon dioxide emissions.

Remember to include all the elements that make a pictogram complete.

So it might not just be that you have to think about the Saudi Arabia value.

Pause the video, think about the kind of scale that you're going to be needing on your graph as well, and then come back to me.

Right, here's what the graph should look like.

You should have Saudi Arabia having 1, 2, 3, 4, 5 1/2 clouds in your pictogram, but you should also include a title.

And hopefully you've managed to follow that standard way of writing a title, A pictogram to show the annual carbon dioxide emissions for different countries.

Check your own work, and hopefully you've got both of those key things included.

We now move on to the third part of the lesson, how do geographers present proportional data? One of the most effective ways of showing proportional data is a pie chart.

It's the one you're probably most familiar with.

The size of each segment is proportional to the amount of the thing that's being measured.

Here's an example here, pie chart to show the proportion of population in different age groups.

The largest segment starts at zero degree point and extends clockwise.

So it doesn't matter that it's not the nought to 19 segment that starts at zero.

It's the one that's the biggest that starts at zero.

Each subsequent segment is ordered from largest to smallest, so it will always be your smallest segment which arrives back at the zero point.

To know the size of different segments in degrees, we calculate the segment value divided by the total value times 360, because there's 360 degrees in a circle.

You can then use a protractor to draw each segment, remember, starting at the zero mark.

It's important not to round up or down, as then the final total may come out actually to be more or less than 360 degrees.

As the purpose of a pie chart is to show relative rather than actual values, these values tend not to be included.

You might see them sometimes in other formats included, but generally geographers will say no, it's about the relative amount, not the actual amount.

A stacked bar chart places bars on top of each other to show relative percentages of things in different categories.

Here's an example here again.

The top of the stack needs to sit exactly on the 100% line.

There is no need to state the value of each bar, as this can be read off the y-axis.

The largest part of the bar should be nearest the x-axis.

And just like in a pie chart, you go from largest to smallest, this time from the bottom up to the top to the 100% point.

Right, let's check our understanding there.

Complete the sentences with the missing words.

Pause the video so you can have a read of this paragraph and try to find those missing words.

Right, let's take a look.

In a pie chart, the size of each segment is proportional, you can have relative as well, to the amount of the thing being measured.

The largest segment starts at zero degrees and extends clockwise from there.

There can be more sophisticated ways to present proportional data, and one way of doing this is where you take a number of stacked bar charts and place them next to each other, and this allows them to then be compared.

To support this comparison, the largest segment no longer has to be placed next to the x-axis.

You always put the segments in the same order so you can very clearly see the variation between the different stacks.

Now, a Sankey diagram uses a split arrow to show how flows of something vary proportionally, and it will always flow from the left-hand side to the right.

The width of the arrows shows the relative amount of the thing flowing.

So you can see at the start, on the left-hand side you've got 100% of whatever it is that is moving, and then as it moves towards the right things branch off to show the relative amounts of different things.

In this case, it's about migrants, and it's showing the proportion of emigrants who move to each of the top five UK emigrant destinations.

So where people from the UK emigrate to.

Conventionally, the flow of the largest size continues straight from the source.

So in this case, Spain, you can see it moves straight out from left to right.

So that's gonna be the largest one, and then from there, subsequent flows kind of curve away from that arrow.

Another check for our understanding.

In what way is a series of stacked bar charts useful to geographers? They highlight any difference in the total amount of data, they allow the size of segments to be compared, they merge two data sets together, or they create an opportunity to present discrete data.

Which is the correct answer? Pause the video, have a think, and then come back to me.

Right, hopefully you recognised that it allows for comparison.

So if you've got two stacked bar charts next to each other, it means you can compare their situations.

So, our final task of today.

Using the raw data given in the table, draw a stacked bar chart to show the relative amount of energy consumed by different sectors.

So we see we have the four different sectors there, domestic, services, industry, and transport.

We've got the percentages already worked out for us.

Remember to include all the important aspects of the graph that make it complete.

Pause the video, this is gonna take you a little while, and then come back to me and we'll compare what our graphs look like.

Right, let's see what your graph looks like compared to mine.

Hopefully, your answer will include the idea of four different sectors and you will have a key to show how each one is coloured differently.

Now, I've just used different colours of blue, but you can use any colours that you like.

Very importantly, the bottom sector is going to be transport because that's the one which is the largest.

That should then be followed by domestic, followed by services, followed by industry in that order.

The top of the bar should be at 100%, and I should also have a nice title.

In my case, I've got A stacked bar chart to show the relative amount of energy consumed by different sectors.

You might have something slightly different, but it should follow that standard format.

Let's summarise our learning.

Before presenting data, geographers consider the type of data they are using and the overall purpose of the data presentation.

Frequency data shows the amounts of something and can be presented using bar charts, histograms, dot charts, line charts, and pictograms. Proportional data shows the relative amounts of something compared to the whole.

This can be presented using pie charts, stacked bar charts, and Sankey diagrams. There really was quite a lot there, wasn't there? Lots of different types of graph, lots of rules that you've got to follow.

But once you get into the practise of drawing more and more graphs, those rules will melt away because you'll just be doing it naturally.

Best of luck.