Lesson video

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

This lesson is called Fieldwork: Presenting microclimate data, and it forms part of a unit of work called Weather and Climate: How do they vary? We're going to be thinking about microclimate data that you might have collected around your school site and how you could actually present that on the page so that it actually creates really good, meaningful interpretations of the data and we can create great conclusions from it.

Let's get started.

By the end of this lesson, you will be able to present microclimate data using a range of data presentation techniques.

Let's first of all begin by reviewing some of the keywords that we're going to be using in this lesson.

First of all, axis, you'll have heard this word before, I'm sure: a line in the structure of a graph against which data is plotted.

An independent variable is a variable that the geographer changes, and a dependent variable is a variable that changes as a result of a change to the independent variable.

Spatial influence is how distance from, or nearness to, a geographical feature influences data.

The lesson is in two parts.

We're first of all going to be looking at how we compare change in different variables, and then we're gonna be thinking about presenting data to find spatial patterns.

Let's start with that first one about comparing change in different variables.

So we're on the third part of our enquiry cycle, data presentation.

Different types of data are collected during a microclimate survey.

Here you can see some examples of the kind of data that was collected, things like land use, aspect, wind speed, and air temperature.

Land use and aspect are qualitative data.

This means they're text-based.

Wind speed and air temperature are quantitative data.

This means that they are based on numbers.

You can see the difference in the table there.

The sites were chosen by the geographer based on the land use and the aspect.

The only way the data in these variables can be changed is if the geographer chooses a different site.

So if you like, the geographer themselves actually chose that data in that part of the table.

Therefore, these are known as independent variables.

The wind speed and air temperature readings are measurements.

The values of these variables change as a result of the land use and as a result of the aspect.

This means that they are called dependent variables.

They depend on the data in the independent variable section.

In this field work enquiry, we want to see if there is a relationship between different data sets.

As we are trying to compare qualitative with quantitative data, this idea is best shown using a bar chart.

Here you can see an example of the bar chart.

It shows the average wind speed at four different sites that we used in our microclimate survey.

In a bar chart, the dependent variable will always occupy the y-axis.

So the wind speed data that we get is dependent on the type of place that we are surveying.

The independent variable will always occupy the x-axis.

So remember, the independent variable is the one that is chosen by the geographer.

It cannot change unless the geographer chooses it to.

So in this case, the actual sites are the independent variable.

Our first check for understanding.

True or false? It does not matter which set of data is presented on which axis in a graph.

Is that true or false? Hopefully this is an easy one.

Do pause the video and then get back to me.

Well, yes, hopefully you can see that that statement is false.

Now tell me why it is false.

Yes, the independent variable is shown on the x-axis while the dependent variable is shown on the y-axis.

Well done if you've got that.

Now, one way to compare data is to present it side by side.

So you can see on the left hand side of the screen here we've got the bar chart for the wind speed data, and then on the right hand side we can see you've got a bar chart for air temperature.

The x-axis is the same in both of them.

It's got field, hedgerow, artificial pitch and driveway.

Our four sites that we measured our microclimate data on.

But Laura says "It's not that easy to see if there's a relationship between the two dependent variables and the different sites." Can we easily see if there's a relationship between wind speed and air temperature here? I have to say, Laura is right.

I agree with her.

It's not an ideal way of presenting the data.

What else could she do to make the comparison easier to see? One solution is to add a second y-axis to the graph.

So you can see we've got our x-axis showing the four different sites, and we've got one y-axis showing air temperature.

We could put in a second y-axis here, showing the average wind speed.

This means that two sets of data can be presented on one set of axes.

Let's put our title in: A bar chart to show the wind speed and air temperature at the four different sites.

So we can put our air temperature in in one colour and then we can put our wind speed in in a different colour, but we can put the bars alongside each other, so they're now much easier to compare.

But these types graphs just take a little bit longer to read and take a little bit more care.

Jacob says here, "The wind speed at the hedgerow was 18.

1 metres per second." But what mistake has Jacob made? Yes, hopefully you can see that when using a graph with two axes, you need to double check that you're reading the data from the correct axis.

Jacob is trying to read wind speed, but he's actually read it from the axis on the left hand side of the graph, which is air temperature.

The wind speed at the hedgerow was actually closer to one metre per second.

So what is the value of the average wind speed in metres per second on the artificial pitch? Same diagram as we saw previously.

Can you read it? Is the answer A: one metre per second, B: 1.

5, C: 17.

2, or D: 17.

8? Pause the video here so you can have a really close look at the graph.

Make sure you're reading the right axis when you are trying to find wind speed and then come back to me with the right answer.

Right, take a look at your graph.

What's the right answer? Well done.

It's 1.

5 metres per second.

You can see there on the artificial pitch, the pink bar goes up to the 1.

5 mark on our y-axis, which is on the right hand side of the graph.

Now let's do a practise task based on that idea.

Produce a double axis bar chart of the microclimate data you collected around your school site.

Make sure your data presentation includes all the elements that make your graph both readable and correct.

You're definitely going to want to pause the video here to have a go at this.

Really check your graph carefully at the end.

Have you included all the correct conventions that make your graph readable? Okay, let's look at the kind of thing you could have drawn.

It should look fairly similar to this where you have two y-axes.

You've got a single x-axis with your field sites named on it.

You should have a title and you should have each of the bars representing the correct height in relation to the wind speed and the air temperature.

There's other things you need to make sure that your answer should include.

As I said, a title is essential.

It should read something like: A bar chart to show the wind speed and air temperature at the four different sites.

Your axes should be very clearly labelled.

As well as being labelled, they need to include a unit, so degrees centigrade for air temperature and metres per second for your wind speed.

And it's also important that you've used colour effectively to make the distinction between the two axes.

Now, you might include a key with it or you might, like I have done, actually make the labels on the axes the colour of the bars.

Now let's move on to the second part of the lesson, which is all about presenting data to find spatial patterns.

Our microclimate data includes data that has a spatial influence.

This means the locations where the surveys took place are spatially significant.

This means a map can be used to present the data.

Now what you could do is just simply put the values for each element of the microclimate survey on the map itself.

You can see here I've put a dot for the four different sites, and then I've said whether it's sunny or shady, in other words, the aspect.

I've given a value in metres per second, so that's the wind speed, and then I've given a value in Celsius, so that's our air temperature.

So I've literally just taken my data collection table and placed it on the right point on the map.

As Aisha says, though, "It's really difficult to see any spatial patterns here." Yes, you have to read an awful lot of information and try and digest it all and then see if there's a pattern.

So maybe this isn't the best form of data presentation.

How else could Aisha present the data? Have you got any ideas? Shaded symbols could be placed on the map to show the value of the data.

So Andeep says, "What kind of symbols should we use? We want them to be easily recognisable for what they represent." And that's really important.

If I'm having to constantly use a key to try and work out what symbol means what, maybe that's not the best way to use a symbol.

The symbol should actually be kind of looking like the thing that we're trying to represent.

Let's see what Jun says.

He says, "How about a sun to show how shaded a place is? And a billowing flag for wind speed and a thermometer for air temperature?" He's got some good ideas there.

So let's look at an example of that.

Here you see I've got my thermometer ready for representing air temperature, but I've also got a palette of colour there, ranging from light pink, all the way through to dark red.

For my wind speed, I've got my billowing flag, but now I've got blue, but I've representing it from my lowest wind speed as a very light, pale blue, all the way up to my highest wind speed being a much darker, deeper blue.

This is known as choropleth shading.

The darker the colour, the higher the value of the data for that particular variable.

So in a way, I almost don't actually need the numbers underneath my colours.

I can instantly see that if I've got a really dark coloured thermometer, it's gonna mean it's the hottest.

If I've got a really light coloured flag, it means my wind speed is going to be very low.

The brilliant thing about choropleth shading is that you can instantly see what the high and low values are simply by looking at the depth of the colour.

In this example, each stage of the choropleth palette is of an equal value.

So if you actually look at my temperature, I'm going up by half a degree with each change in colour.

In my wind speed, I'm going up by 0.

3 metres per second with each change in colour.

So there's an equal value between each point.

So here's a check for understanding now.

Complete the sentences with the missing words.

You're going to want to definitely pause the video here so that you can have a really good look at the paragraph and then decide on what words would fit perfectly in those three gaps.

Let's see what answers you've got.

So first of all, in choropleth shading, the depth of the shading represents the values in the data.

The darker the colour, the higher the value of the data for that particular variable.

Well done if you got those.

But before we move on, just double check the spelling on choropleth.

It is often misspelt, so do check that it's C-H-O-R at the start of that word.

The shaded symbols have been added to the base map of the school site, so you can see now we've also got aspect included as well as our temperature and wind speed.

So for each site we've got three different symbols.

Each one of them has a degree of choropleth shading.

Our temperature from light pink to dark red.

Our wind speed from light blue to dark blue.

And our aspect is slightly different.

So here we've used a grey to represent full shade through to a pale yellow and then a very much darker yellow to represent full sun.

Even without the key, I think you can quite clearly see that, for example, the driveway has high temperatures, a medium level of wind speed, and it's in full sunlight.

Whereas behind our hedgerow, you can see it's much cooler.

The wind speed is much lower, and it's in almost in full shade.

Izzy is thinking about the elements that make data presentation successful.

She's saying, "My map feels somehow kind of incomplete." What kind of thing do you think she means? What else needs to be included? There's a few things that have been missed out from this map.

First of all, we need to give it a title, don't we? A map to show aspect, air temperature and wind speed at the four survey sites.

But we're not over yet.

There's other things that are missing.

So we need to include our prevailing wind.

We can't really talk about wind speed unless we actually know where the wind is coming from.

So it's really important that that's included on the map as well.

Really important that we put on a north point.

Again, if we're talking about aspect, we can't describe aspect unless we are using our compass directions.

So we need to know the orientation of the school building and the school site.

We also need to have scale.

It's a map.

We need to make sure how large different areas are.

This will affect air temperature and wind speed potentially.

Now, another check for our understanding.

When maps are used in data presentation, what elements should they always include? There are six options here, so you can probably guess there's going to be more than one correct answer.

Different colours, a key, a title, a north point, a scale bar and grid lines.

Which of those should always be included in a map-based data presentation? Pause the video here and see how many correct answers you can find.

Let's see which ones you got.

Hopefully you recognise that it should have a key, a title, a north point, and a scale bar.

Now, often maps will have different colours and they will have to have different ways of representing data.

We've used a choropleth map, so different colours are important, but in other maps that might not be the case.

So it's definitely true, in choropleth shading, we would need to be using different colours, but not in every type of map.

Likewise, you're likely to have used maps that contain grid lines, but when you're using data presentation, putting things like grid lines on might not be appropriate.

It might not be needed, so they're often left out.

They're not included in every part of spatial data presentation.

Now it's over to you to do some data presentation as part of your practise task.

Use a base map of your school site to present your microclimate survey data.

You should use symbols and choropleth shading to represent the data as accurately as possible.

Make sure your data presentation includes all the elements that make the map both readable and correct.

This is obviously going to take you a little while, so do pause the video and think carefully about all the things that make data presentation successful.

Come back to me once you've got your piece of data, and then we'll see how it compares with mine.

Let's take a look at your ideas here.

So you've got your base map.

Hopefully you've been able to use some symbols and choropleth shading to represent the data as accurately as possible.

Your answer should look similar to this, where you've got symbols on the map in the right places, and you've also got the key which tells you what that data actually means.

Then it's time to double check that it's got all the elements that make the map both readable and correct.

Your answer should include a title, a north point, a key, and a scale bar.

Hopefully you got all of those things.

Let's now summarise our learning for today.

Geographers often compare independent and dependent variables as well as qualitative and quantitative data.

When geographers are looking to find a relationship between data sets, it may be possible to present that data in a graph that contains two axes.

Choropleth shading can be used to show the value of spatial data in symbols on a map where the darker the colour, the higher the value for that variable.

Well done for having a go at those different data presentation techniques.

You might have seen things like double axes graphs, or you might have seen choropleth shading used in maps online or in different textbooks and resources that you might have been using in geography.

It's quite different to actually draw them for yourself though.

So really well done on giving it a go.

If it didn't quite turn out how you thought it might, don't worry.

With more and more practise, you'll become more confident at handling data and presenting it effectively.