Lesson video

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

Today's lesson is all about presenting geographical data that you might have collected during your own field work.

There's gonna be lots of new information.

So let's get started.

By the end of today's lesson, you will be able to present quantitative and qualitative data and use maps to present spatial data.

So there are some key words we're going to be looking at today.

Three of these here.

So let's start with the first.

Discrete data, data in which each field is unconnected to and does not affect the data in any other field.

In contrast to that, there's continuous data, and this is data that is connected to values before and after it in sequence.

I'm gonna go through some examples of that in a moment.

And then there's the type of data that geographers use probably more than any other, and that's spatial data.

Data that can vary according to where it is collected.

In terms of the structure of our lesson today, it's in three parts.

First of all, how do geographers present quantitative data? We'll then do the same but with qualitative data.

And then finally, we'll look at how geographers present spatial data.

Let's begin with the first one.

So presenting data helps geographers to see patterns and relationships in their data, and it's these patterns that will help them answer their inquiry question.

That's the real reason why geographers present data at all.

If geographers are taking part in successful data presentation, it means that they have to think carefully about what information they need to get from their data, as well as understand the nature of different types of data.

So for example, geographers would recognise the difference between quantitative data, data that is numerical and qualitative data, data that is based on text.

Geographers also distinguish between discreet and continuous quantitative data.

So discreet data can be sorted into exclusive categories.

Let's look at an example of that.

So here's Jacob, and he's conducted a traffic survey.

So he's got the different types of vehicles that he's seen on his survey and he's at the moment it's presented as a tally chart.

So you can see there are eight cars, there were two bikes, three lorries, and so on.

Now this type of data is discreet data because what Jacob's done is he's sorted the traffic by the type of vehicle.

So these categories are exclusive of one another.

So a bike cannot also be a lorry.

It can only be one of those things.

On the other hand, continuous quantitative data can appear in a sequence or along a continuum.

So here's some data that Jen has collected.

He's gone out on five consecutive days and he's collected some rainfall data.

You can see on the first day, there was one millimetre.

On the second day, 0.

2 millimetres of rain and so on.

So this is continuous data because the depth of the rain is a continuum.

In theory, any amount of rainfall can fall in that space of time.

Realistically, we know there's probably a limit to how much rainfall can fall, but actually if you think about rainfall as a piece of data, it could go to infinity in terms of the amount of rainfall that could come down.

So let's check your understanding on that first part.

True or false? Geographers present data to make it more attractive.

Have a think, pause the video, and I'll tell you the answer in a moment.

So the answer is false, but why is that? Well, geographers are far more interested in presenting data so that it becomes more meaningful.

It doesn't really matter whether it's pretty or not.

It's actually being done to make the data tell us something.

It allows geographers to see patterns and relationships in the data that will help them answer the inquiry question.

Hope you managed to get that right.

Well, let's actually look at some ways of presenting data now.

So a bar chart is one way of presenting discreet data.

You can see we've got an example here.

Again, we've got a traffic survey.

So number of cars, eight bikes, there's two of them, lorries three, and so on.

The height of the bars represents the amount of the thing that's being shown.

But here's the important bit.

The bars stand separate from each other.

You can see there are gaps in between the different bars.

And this is what shows that it is discrete data because it shows that the categories are exclusive of one another.

They cannot be confused with each other.

Now if we were to put the bars together without any gaps, it wouldn't be a bar chart, we would call it a histogram.

Now it's not just that it's been given a different name, it really does mean something different because the bars have been closed, put together, it means that we're actually showing now continuous data.

So in a histogram, the bars touch each other.

This is to show that the data on the x-axis, so the axis that runs along the bottom of the graph which that means it's continuous data.

So we know that the data was covered over five different days in sequence.

So there was never a point at which we were not in a day, if that makes sense.

So we went from day one to day two to day three, and so on.

So that data is continuous data.

The bars therefore have to be placed right next to each other.

There are other ways of showing continuous data and one of them is a line chart.

Individual plots are put onto the chart and we can see these are represented by the dots in this example.

And then the plots are joined point to point with straight lines.

In this example, we're looking at how noise levels change as one moves away from the town square.

And so you can see that the noise level is quite high to begin with.

Then it goes down and then it rises up again ever so slightly.

In this case, the distance is what is continuous and we know that because distance is on a continuum, it could go on forever.

There are other ways of presenting quantitative data.

Let's take a look at a couple of them now.

So first of all, a pie chart.

You've probably heard of one of those before.

This shows a relative proportions of different things in relation to the total amount.

And we would use different colours for the different sectors.

But one you might not have heard of is a radial graph or it might be called a radar chart, a radar graph, and this is quite an unusual thing to look at, but it's actually very, very clever.

In this example, you've got five different axes coming out from the centre and the plots have been placed on those axes.

So what you then end up with is a polygon shape in the middle to represent how that place has scored.

So in this case, the place has a score of two for noise, one for green space, two for traffic, one for access, and three for litter.

And what you would then do is take a different place, plot those ideas again, and you would see how the shape of the polygon changes.

Other ways is things like a scatter graph.

So this plots two different variables against each other and there's often a line of best fit running through the middle to highlight the general trend.

And you always put the line of best fit in the position that appears best.

And normally, so you have the equal number of plots either side of the line.

So you can see in this example, there are seven plots below the line and seven plots above it.

So we know that the line of best fit is in the best place, or you might have already used a pictogram.

So this is where you use a symbol or a picture to show the amount of something.

In this example, it's a pedestrian count again and we've got 10 pedestrians being represented by our little stickman.

So at site number one, we would look at maybe 32 different pedestrians.

And at site number two, there's nine, and so on.

So we can actually read off the number of pedestrians by using the symbol that's been ratioed like that.

Let's check your understanding so far.

So what types of data do a line chart and a scatter graph both show? So let's see what your options are.

It's what they both show.

Is it quantitative data, qualitative data, discreet data or continuous data? And I think you'll already be able to see that it's going to be more than one answer that is correct here.

Okay, let's look at our answers.

Yes.

So it's quantitative data.

So we can see that we are dealing with numbers here and both of them are showing continuous data.

In the line chart, it's the distance which is continuous.

And in our scatter graph, it is both the depth and the velocity, which would be continuous.

Let's do our first practise task of today's lesson.

Laura has drawn the following graph to present her traffic survey data.

Okay, what mistake has she made? And then part two, suggest a data presentation method that Laura could have used.

So look carefully at the graph, what mistake has Laura made? And then tell us what she should have done instead.

Pause the video, have a think, and then come back to me.

So let's see what Laura has done.

If you said right, it shouldn't be a line chart at all, you are right, the data is discreet data, but we would use a line chart if it was for continuous data.

So Laura has kind of muddled the two types of data up, but what could she have done instead? Well, there's a few different options.

You might have recognised that she could have done a bar chart, she could have done a pictogram.

It had little pictures of cars and bikes and so on.

She could have done a pie chart.

Hope you got one of those.

Let's move on to the second part of today's lesson which is about how we present qualitative data.

So we've done the numbers, now we're moving on to the text.

So again, just like with quantitative data, there's lots of different types of qualitative data as well.

We're gonna look at two in particular.

The first is stakeholder data.

And this is data that comes from sources that have an insider perspective on something.

The sources could be an expert, it could be a local person with local knowledge and a viewpoint.

So it's somebody who really knows a lot about the issue kind of from the inside.

In contrast to that are observers.

So observer data comes from sources that have an outsider's perspective on something.

So this is normally people who are visiting a place or who don't know the big history of an issue, something like that.

So you yourselves, geography students visiting an area are usually sources of observer data.

You're normally going to replace that you don't know that well.

Now stakeholder data could be quotes from an interview or a questionnaire and these could be presented in what we call a quote bank.

This is where quotes of similar opinion are grouped together.

Now there's different ways you could kind of present this on the page.

One of them is a Venn diagram, which we would normally only see in maths, but actually you can use the same principles for people's quotes.

So for example, the ring on the left hand side is gonna contain all the quotes which are showing somebody is in favour of something and the ring on the right hand side would show all the quotes where someone disagrees or doesn't really like whatever you are talking about.

So in this case, we're talking about some river flood defences.

So you can see on the like side, someone is saying that, "Oh, the defences are great 'cause they allow them to access a new fish fishing spot." On the other side, you've got an opinion where they're not in favour of the flood defences.

They look ugly and unnatural.

And actually there was probably gonna be quite a lot of people who would sit in the middle where they like some aspects and dislike others.

So this example here, "I do feel safer," which is a positive thing, but they cost so much money.

So they're also seeing the negative side as well.

So that statement would appear in the middle of the Venn diagram.

Now in this example, of course there's only three quotes.

What you would be doing if you had an interview or a questionnaire? You'd have lots and lots of quotes.

So you'd be writing lots and lots of times within those circles.

It doesn't have to be a Venn diagram.

You could place your quotes into a sector.

And this is where the quotes showing the strongest agreement with something are closest to the centre.

So like the bullseye and those with the strongest disagreement are in the outermost ring.

So again, let's look at an example here.

So this is talking about maybe whether a town needs regeneration, something like that.

So we can see that the quotes which is closest to the middle is kind of defending the town.

It's saying, "Oh, it's got such a rich history." Perhaps they don't want anything to change.

The quote that's on the furthest part of the sector says, "I can't understand why anyone would want to come here.

It's a dump." So clearly they're in favour of complete change.

And then there's the kind of nuance in the middle.

"The town is past its best, but it's still functional." So they're kind of seeing both sides of the argument.

And again, in this example there are three quotes, but if you were to draw a sector diagram with lots of information from questionnaires and interviews, you would have lots of quotes to be handling.

Right, let's check your understanding of what we've just learnt.

Complete the sentences with the missing words.

Have a read of the paragraph and then come back to me with your missing words.

So let's take a look.

What kind of data comes from insider sources? Yes, it's stakeholder data.

These are usually local people or experts with knowledge or viewpoints about something.

Now if it's not stakeholder data, it's going to be observer data, comes from outside sources who might not know as much about the geography of the area.

I hope you got those correct.

Now there are again other ways of presenting qualitative data.

One way is an annotated field sketch.

You could also do an annotated photograph with the same principles.

This is where you would present observer data.

So somebody coming to a place from the outside and describing it from their point of view.

The geographer could draw a simple sketch of the area and you can see an example of one there.

And then write detailed labels.

We would call these annotations if they've got a lot of detail which discuss their opinion of features in the field sketch.

So in this example we've got a coastline and we've got quite a steep cliff.

And the observer has said there's evidence of recent erosion of the cliffs which could affect future access to the beach.

They've been in quite a lot of detail there about what they're seeing and what they think it means geographically.

Qualitative data does not have to be opinions that come directly from interviews.

It can also come from other sources, secondary sources.

So it could come from social media, it could come from newspapers, adverts.

There's lots of different places that you could get qualitative data from.

These sources might use a lot of adjectives or describing words to describe a place.

And one way you could present these is in a word cloud.

And you've probably seen a few of these.

And they allocate the most commonly used adjectives, the largest font size.

So whichever word in your word cloud appears the biggest.

That means it was the most frequently used word in the source of information.

Other words are then presented as their relative sides according to how frequently they were used.

So here's an example.

So Sam has created this word cloud based on adjectives she found on social media describing her town.

Which word was mentioned more than any other on social media? So you're looking for the word which is largest in that word cloud.

Yes, I think you're right.

It is definitely going to be pretty.

It is the one that's right in the middle and it's the biggest.

And then if you were going to take this further, you could say which one was the least mentioned, possibly scenic or busy.

They look to be the smallest word.

So we'd know how people are using different words on social media to describe Sam's town.

Right, let's look at a practise task for this section of work.

People were asked how they felt about a new food market opening.

Use the letter codes to place the quotes in the correct place in the Venn diagram.

So the top ring are for quotes which are in favour of the new food market and the bottom ring is for those which are not in favour of it.

So you've got three quotes there.

Read them really carefully and think about what was the person trying to say in terms of their opinion when they gave these answers in their questionnaire.

And then place the right letters in the right place in the Venn diagram.

Right, let's look at your ideas here.

Our first quote, "Markets can create a lot of mess that someone else has to clean up." This doesn't sound like someone who's going to be in favour of the market.

So if you placed a in the bottom ring, you have got the right answer.

Second quote, "I would definitely come into town more if there was a food market." This sounds like someone who's really going to welcome it.

So yeah, they're in favour.

They're gonna go in the top ring there.

And then our final quote, "It would bring more money into the town." So they're in favour in one way, but also more traffic congestion.

So they're seeing that there's an issue as well.

So yes, they're going to be in the middle, they're in favour in one way, but they're not in favour in another.

Right, the final part of today's lesson, how do geographers present spatial data? And this is really exciting because this is what makes us geographers.

Geographers deal a lot with spatial data.

Spatial data is shown using a map.

The map can be drawn or shaded in special ways to show different forms of data.

So here's an example.

It's one of the most simple ways and that's a dot map.

You can see why it's called that.

It's a map (chuckles) that's covered in lots of little dots.

A dot is placed on the map to represent every position of a particular geographical feature.

And this allows geographers to see where those features are clustered.

So let's look at the example here.

We've got a dot map that is showing features that improve accessibility in the town.

So the geographer has walked around the town and every time they've seen an accessibility feature, they've noted it down on the map and we can see that there's a particular road in this town which seems to have a lot of accessibility features.

And then there are some areas of the map that don't have any at all.

So it's quite an easy quick way of seeing where things are clustered.

Then you've got choropleth maps.

These show the relative density of something by using a graded colour system.

It's a very clever way of doing data presentation.

A colour palette is chosen where the lightest shade represents the least dense area and the darkest shade represents the most dense.

So in other words, the darker the colour, the more there is of something.

And the lighter, the least there is of something.

Now this is really clever because instantly I can see on the map where there is the most of something without necessarily needing the key.

What is important is that it is generally one colour and it's grading within that one colour band.

We don't go from one colour to another generally.

So in this example we've got a choropleth map that's showing the density of buildings that have retail use.

The darkest colour is appearing in the top right part of the map.

So we can see that that must be where there's the most retail outlets, the most shops.

Whereas if we go to more towards the bottom left, you can see there's a sector there which is the lightest colour.

So we know there's probably very few shops there.

Let's check our understanding of those two ideas first.

This map shows an area of woodland.

Which of the following statements is correct? Right, let's have a look at the map first.

We've got a dot map showing the location of pine trees in the forest.

So it's a forest with lots of different types of trees, but this is where each pine tree is.

The centre of the map has the highest density of pine trees.

The pine trees are evenly spread across the woodland.

There are more pine trees in the west of the map than the east.

Pause the video, read the statements again, and decide which one of those is correct.

Right.

What do you think? Which one? Well, the centre of the map has the highest density.

Mm, not really.

Doesn't seem to be very much in terms of pine trees in the centre of the map.

The pine trees are evenly spread across the woodland.

No, definitely not.

There's definitely a couple of clusters there.

There are more pine trees in the west of the map than the east.

Let's look at our north point to check.

Yes, there are definitely more pine trees in the west than the east.

I hope you got that one right.

So more different types of maps.

So counts of data can be presented on a proportional symbol map.

You might sometimes see them as a proportional shape map.

The size of the symbol represents the size or quantity of the feature that's found there.

A symbol style will often be chosen that's appropriate for the data.

So we can see in this example, we're looking at a pedestrian count.

So it kind of makes sense that we've got a little footprint to represent the number of people that have been counted in that pedestrian count.

The larger the foot, the more people were counted.

Again, it's really clever style of mapping 'cause I don't necessarily need the key in order to see what the general pattern is.

If I do want to know exactly how many people there were in different areas, then the key becomes more useful.

One of the most sophisticated forms of data presentation in terms of spatial data is a situated chart map.

Individual charts are created and then placed in the correct place on the map.

And this means that the reader can see patterns in the nature of the data and also where it is found.

So we're looking at many different ways of interpreting this data.

Here we have a situated pie chart map which shows the relative amounts of different types of litter in the town.

So each individual pie chart is selling us their information, how much plastic, how much metal and textile and so on there is, but then I can see which bits of the town are generating the most amount of litter of certain types.

So I can see for example, that if I wanted to know where the most plastic litter is, I can see that it's in my pie chart and it's been placed onto the map.

So a really clever way of presenting data.

It wouldn't necessarily have to be a pie chart, you could do bar charts, you could do other forms of data presentation and just make a small version of them and place them on top of the map.

Right, another check for understanding.

Complete the sentences by finding the missing words.

Read the paragraph, have a think about which words would fit best in those gaps and then come back to me.

Okay, let's take a look at this.

So in a something symbol map, proportional symbol map, the size of the symbol represents the size or quantity of the geographical feature found there.

The something the symbol, the greater the size or quantity of the feature.

So the larger the symbol or the bigger the symbol, the greater the size or quantity of the feature.

A symbol will often be chosen that is appropriate for the data.

So it's looking like the thing that we are trying to present.

We're onto our final practise task of the day.

Sofia has collected the traffic count data in the table.

Using the blank map, create a form of spatial data presentation for this data.

Remember to include a title and a key.

There are lots of different ways of presenting spatial data.

Look carefully at the type of data that Sofia has there and have a really careful think.

How could she present this? Have a look at the blank map as well to think about how much space you've got and how well this data could fall onto it.

Pause the video and I will come back to you with one of my ideas.

So here's the map that we are going to be presenting on, and there are lots of different ways you could have presented this data.

This is just one example.

I've used a situated bar chart map, so I've made small little bar charts for each of the three sites and then placed them on the map in the relevant place.

You could have used something like a situated pie chart map as well, that would work here or a situated pictogram chart, that would work here as well.

So we've covered a lot of things today.

Let's do a summary.

Quantitative data can be presented using a variety of graphs and charts such as bar charts, line graphs, and scatter graphs.

Qualitative data can be presented using a variety of text-based graphics such as quote banks and word clouds.

Different styles of maps such as choropleth maps and proportional symbol maps can show how data varies spatially.

There really was a lot of new information there, so well done.

But do remember, if you need to remind yourself of anything, you can always go back and have another look at the video.