Lesson video

Hi there.

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

This lesson is called "Fieldwork: Presenting air pollution Data," and it forms part of a unit of work called "Anthropocene: What impact have humans had on the world?" In this lesson, we're gonna be looking at how we might present air pollution data.

Most importantly, how we can use certain techniques to make it easy to spot the relationships that it represents.

Let's get started.

By the end of this lesson, you will be able to present qualitative and quantitative air pollution data.

Let's take a look at some keywords first of all.

Qualitative data is data that is made up of words.

Quantitative data is data that is made up of numbers.

Choropleth shading is a form of shading that uses one colour, where the greatest density of something is shown by the darkest shade of that colour.

Social shading is where we use colour conventions in data presentation to represent certain ideas.

This lesson is in two stages.

We're first of all going to be thinking about how we can present the actual air pollution data that was collected, and then thinking about how we can present people's opinions on air pollution.

Let's start with our actual air pollution data.

So here we are in the third stage of our inquiry cycle.

The data from the particulate matter capture cards can be summarised in a table.

You can see it here.

We've got the six sites, and then we've got the number of squares on our capture cards that contained particulate matter.

This data is quantitative, as it is a count of the number of squares on the capture cards that contained particulate matter.

The data from the questionnaire surveys can also be summarised in a table.

You can see here we've got six different opinions that people had about air pollution, 60 responses.

There's a tally chart as well, but you can see the totals in the final column two.

This questionnaire data is both qualitative, as it contains descriptions of emotions, worried, apathetic, sad, et cetera, and quantitative, as the frequency of the responses have also been recorded.

When we look at the second question in our questionnaire, it's a similar thing.

This questionnaire data is both qualitative, 'cause it contains descriptions of favorability, very favourable, favourable, neutral, et cetera, and quantitative, as the frequency of responses has been recorded.

So for example, 38 people felt unfavourable about no vehicle zones.

Now let's check our understanding of those ideas.

Complete the sentences with the missing words.

Do pause the video so you can have a look through the paragraph, and then come back to me.

Right, what words did you get in those gaps? Sometimes questionnaire data can be both qualitative, such as descriptions, and quantitative, such as how often certain responses may have been given.

Well done if you've got those ideas.

The actual air pollution data from the capture cards can be shown on a map using choropleth shading.

The locations of the capture cards are first of all shown on the map by circles of the same size.

These circles are then coloured according to the rules of choropleth shading.

So here's my blank colour palette, here on the right.

The capture card locations with the highest amount of polluted squares are shown with the darkest shade.

The capture cards located with the least are shown with the lightest shade at the other end of the colour palette.

Values in between these are shaded in a gradient.

So you can see now we're going from the least amount of pollution with the lightest shade in gradation, up to the areas that had the most pollution shown by the darkest shade.

Laura has decided to use social shading in her choropleth palate.

She explains this here.

She says, "I have chosen black and grey in my choropleth palate, as these shades are often associated with dirt and pollution." So here's an example of our choropleth map.

Now, Laura's not too happy.

She says, "I've made the circles too big.

They hide a lot of the features on the map." She's right.

The circles are way too large here.

They're actually obscuring our view of the map, so we can't really see what's going on there.

"That's better," she says, made them a lot smaller.

"I can now see the pattern and the map's features." And of course, that's the important thing about presenting data on a map.

We need to be able to see the data, but we need to be able to see the map very clearly as well.

Now, let's take a look at Lucas's map here.

What mistakes has Lucas made on his choropleth map of air pollution data? Is it A, he has chosen his locations randomly, B, his circles are too big, C, his locations are all in rural areas, D, his palette does not have enough shades to show variety in the data, and E, his palette is the wrong way round? Pause the video here, so you can have a really close look at Lucas's map, and then come back to me with the right answer.

Now, bear in mind there's going to be more than one correct answer here.

Right, let's look at each of these ideas in turn.

He's chosen his locations randomly.

Well, that's fine.

That's part of the data collection and the planning.

It's not part of the presentation.

His circles are too big.

Yes, I think we can agree that that one is a problem.

We've certainly obscured quite a lot of the map by the size of the circles.

His locations are all in rural areas.

Well, they're in green spaces, but that doesn't necessarily mean that's a bad thing, and it's certainly not something that's a problem with data presentation.

It possibly was a problem in the planning stage.

His palette does not have enough shades to show variety in the data.

Let's take a look at his key here.

Each of his bands is around 33 values.

Now, that's quite a lot.

It means that for the colours that he's chosen, a single colour could represent anything from 0 to 33.

There's a lot of variety in there.

So I would say, yes, his palette doesn't have enough shades to really show the variety of data.

And finally, his palette is the wrong way round.

Yes, you can see he's chosen the darkest colour as representing the least amount of pollution, and his lightest colour is in the 67 to 100 category.

So yes, he's got his colours the wrong way around.

So we've got three correct answers here.

His circles are too big, his palette doesn't have enough shades, and his palette is the wrong way round.

Well done if you've got all three.

Let's move on to our first practise task now.

Use a map of your data collection sites to create a choropleth map of your air pollution data.

Make sure your map contains all the elements that make it both readable and correct.

You're definitely gonna want to pause the video here, and do take your time over this task.

It is worth spending the extra time checking to make sure that your map is as good as it can be.

Do come back to me when you're ready.

Let's now take a look at your map.

Now, this is the kind of thing it might look like.

You can see here I've added on some extra information.

We have a North point to orientate the map.

We have a scale bar to show me how large the site is, and the key is very clearly labelled as well.

Let's look at some other elements of your map as well.

Your palette should have enough shades to show the variety in the data.

Five is normally about right, but you might have something different if your data needs it.

The shades in the palette should run from darkest to lightest, so that's from the most polluted down to the least.

And the size of the circles should not hide the map's features.

You should very clearly be able to see the colour of the circles, but you should also be able to see the features of the map too.

Now let's move on to the second part of this lesson where we're going to be thinking about how we can present those opinions on air pollution that we had in our questionnaires.

The first question in the questionnaire could be represented by a word cloud.

A word cloud presents the emotions people feel about air pollution as the words themselves.

The frequency of each response is shown by the size of the word as it is presented on the page.

So here are two responses that people had to our questionnaire.

They were worried and ashamed, but you can see the size of the words has been presented differently.

Worried received a high number of responses.

So that word has been presented much larger than ashamed, which had a low number of responses.

Let's look at what Jacob and Sam are now doing.

So Jacob says, "Seven people said they were angry about air pollution, so I've drawn the letters seven millimetres high." He's actually scaled his word according to how many people responded in that way.

Sam says, "If we use the same scale for each word, it will mean that they are proportional to each other.

We can easily see which is the most and least popular words." The words should be arranged so they form a cloud-like shape, with the largest word in the centre, and the smallest on the outside.

Let's check our understanding about word clouds.

Have a look at Aisha's word cloud here.

What was the most common way that her questionnaire responders described their high street? Was it A, busy, B, clean, or C, unique? Pause the video, have a look at Aisha's word cloud, and then hopefully you can come back to me with the right answer.

Did you get it right? Yes, it's clean.

Clean is shown as the largest word in the word cloud, so that was the most common way that her questionnaire responders responded.

Well done if you got that.

The second question in the questionnaire also shows the frequency of certain responses.

So let's remind ourselves of that question.

We asked our questionnaire responders, "To what extent are you in favour of these ways of reducing local air pollution?" The different levels of favorability can be associated with different social shading.

Which colours would you choose to represent each level? So remember, our options were from very favourable through to very unfavourable.

What colours would you choose to represent each of those words? Let's listen into this conversation between Andeep, Jun, and Izzy now.

Andeep says, "If I think about being favourable for something, I think of the colour green." Jun says, "And when I think about things I don't like, I think of the colour red." So Izzy says, "It would make sense, therefore, if we chose the colour yellow to represent something being neither favourable nor unfavourable." It makes sense when you think about those colours and how we use them socially.

These ideas give us a social shading colour palette for us to work with, and here it is: very favourable, being green, going through yellows and oranges, through to very unfavourable, being red.

Shading that follows social conventions helps geographers to quickly see patterns in their data.

This colour palette can be used to create a stacked bar chart for each of the air pollution management options.

We start by drawing a set of axes.

The height of the Y axis is the total number of questionnaire responses.

In our case, this was 60 responses.

So you can see we've gone up to 60 on our Y axis there.

Starting with very favourable, the frequency of responses are drawn using a bar.

So you can see here for no vehicle zones, there's just a few people who said that they were very favourable of that, and that's been represented by that small green bar there.

The bars follow the order of the palette, from very favourable to very unfavourable.

So you can see the order of the colours is exactly the same as the order in our key.

What's really important is how we actually draw these bars.

Each block should be positioned according to the cumulative value of the previous categories.

So there were five people who said that they were very favourable of a new ULEZ.

Two people said they were favourable of it.

This means that the favourable category sits on the seven line, the five plus the two.

So we're stacking them one on top of the other, so eventually we will reach our maximum of 60 responses.

With all the bars drawn, one can compare how people feel about each of the different air pollution management strategies, and you can see it's really quite obvious now what they're in favour of, and definitely what they're not in favour of.

Let's check our understanding of that.

True or false? The colours geographers use in their data presentation are always randomly chosen.

Have a think about that statement, pause the video, and then come back to me.

Yes, hopefully you recognised that's a false statement, but tell me why.

Well done.

Yes, the colours geographers use are often associated with social conventions, such as red being negative and green being positive.

Let's do our practise task now.

Draw a word cloud to show the emotions that your questionnaire respondents say they felt about air pollution.

Then draw a stacked bar chart to show how your questionnaire respondents say they felt about different air pollution management strategies.

For both pieces of data presentation, remember to check that they contain all the elements that make them both readable and correct.

Now again, pause the video and do take your time over this.

It is worth spending those extra few minutes checking that everything makes sense, and everything is neat and tidy in your graphs.

Right, let's start with the word cloud.

Let's see what you came up with.

It should look something like this.

So you've got the cloud shape, you've got the words being proportionally sized to each other, according to how people responded, with the largest word in the middle, going out to the smallest word on the outside.

Then we have our stacked bar chart to show how our questionnaire respondents say they felt about the different air pollution management strategies that we suggested.

So your stacked bar chart may look something like this.

You will have your bars rising up to the top number of responses, you'll have them very clearly labelled according to the different management strategies, you'll have a key which shows, in social shading, from very favourable to very unfavourable, and importantly, you will have a title, so something like this, "A stacked bar chart to show how people responded to the question, 'To what extent are you in favour of these ways of reducing local air pollution?'" So it's really obvious what it all means.

Do check also that you have labelled your axes, and that your data is accurate.

Let's now summarise our learning.

Questionnaire data can often contain a mixture of qualitative and quantitative data, which needs to be considered in the data presentation stage of the field work inquiry.

Geographers often use certain colour palettes to make their data presentation effective.

This may be choropleth shading to show the amount of something, or social shading to aid the reader's interpretation of the ideas.

Well done.

Producing two pieces of data presentation is not easy, but you did really well giving them a go.

Remember, your data presentation is not about making everything look pretty in your inquiry.

Colour is used for a purpose and a function, and geographers have become experts in really thinking about how colour influences how we interpret data.