Lesson video

Hello and welcome.

My name is Ms. Harrison.

I'm so excited to be landing with you today.

Today's lesson is called GIS Analysing Inequality at Different Scales.

Grab everything you might need for today's lesson and let's begin our learning.

By the end of today's lesson, you'll be able to use geographic information systems to analyse patterns of inequality at different scales.

Before we can begin this learning, we need to define the keywords that we'll be using throughout today's lesson.

The keywords in today's lesson are inequality, GIS, GDP per capita, healthy life expectancy, and deprivation.

Inequality, this is the uneven distribution of resources, opportunities, and living conditions.

GIS, this is a computer system that captures and displays geographic data to help understand spatial patterns.

GDP per capita, this is the total monetary value of all the goods and services produced in a country over a year divided by the population.

Healthy life expectancy, this is the estimated average number of years lived free from disability or disease burden.

Deprivation, this is a lack of basic needs and services that people need to live a safe and healthy life.

Now that we've defined these keywords, we can begin our learning.

To start our lesson, we're going to explore analysing global inequality using digital maps.

Data on inequality, how wealth and opportunities are shared between countries, can be shown in different ways.

One way is through a table like this.

It shows statistics from the World Bank, looking at something called GDP per capita.

The higher the GDP per capita, the wealthier the country tends to be.

The lower it is, the poorer the country usually is.

This table only shows a small part of it.

The full version has 189 countries, and that's a lot of data, but it helps us to compare countries around the world and understand global inequality.

How easy would it be to analyse inequality using this table? Are there better ways to display this data? Pause the video here whilst you decide and press play when you're ready to continue.

Excellent.

Izzy said "That's a lot of rows.

I don't know where some of these countries are." And she's right.

It would be quite hard to analyse inequality just by looking at this table.

There are so many numbers, and it's difficult to quickly spot patterns or compare countries.

A better way to show this data might be through a map, especially a choropleth map.

That's a type of map where countries are shaded in different colours depending on their GDP per capita.

This makes it much easier to see which parts of the world are richer or poorer at a glance.

As geographers, we don't like to just look at numbers, we want to see how things vary across space.

That means looking at where things happen and how they're different in different places.

One way to do this is by visualising data on a map, which helps us to spot patterns, like which parts of the world are richer or poorer.

It's much easier to understand data when we can see it across different countries or regions, rather than just reading it in a big table.

This map shows GDP per capita for different countries in 2023.

The colours show us differences around the world.

This is called a choropleth map where countries are shaded to show different levels of wealth, and we can spot clear differences here.

North America and Western Europe are shaded the darkest, which means they have a higher GDP per capita.

These are some of the wealthiest areas in the world.

In contrast, places like Sub-Saharan Africa and parts of Southwest Asia are shaded lighter.

These countries have a low GDP per capita, which shows lower levels of income and wealth.

Maps like this help geographers quickly understand and describe global patterns in inequality.

Which of these countries has a high GDP per capita? Pause the video here whilst you decide and press play when you're ready to continue.

Excellent, the answer is Australia.

Well done on this task.

Our World in Data provides a source of digital maps that helps us to visualise and analyse global inequality.

<v Instructor>In this video,</v> we're going to use the Our World in Data website to analyse inequality between different countries.

So global inequality.

Now, Our World in Data is an organisation which takes data that's been gathered by reputable sources and creates charts, so graphs and digital maps, which help us understand the different aspects of the geography of the world.

So in this, we're wanting to look at inequality between countries.

So one way we could do this is if we can come to the top of the page, we can click on where it says Browse by Topic.

And you can see there's lots of different topics here that I can choose.

And if I click on health, for example, if I choose any of these, like life expectancy or smoking or cause of death, it will create some graphs, or it will display, I should say, some graphs and some maps related to those things around the world.

So these are incredible ways of being able to visualise inequality between different countries.

So there can be a load of different social inequalities that I can investigate.

And maybe if I come down to poverty and economic development, I can find some economic inequalities that I can investigate.

So that's one way I can find these graphs and charts.

Another way is just typing what I want to find into here and seeing if it's got it.

So for example, if I type in GDP per capita, then we can see that it's actually got some information on this.

And we can see there's a map down here where it says GDP per capita 2023.

So I'm going to click on this, and here we go.

Here's the map in front of us now.

Now, GDP per capita, remember, is all the goods and services produced in a country in a year divided by the population.

So it's kind of a measure of how wealthy a country is, how much income there is in a country.

It's quite a narrow measure, if we remember of development, but it is one measure.

So we can see, we can see the key at the bottom of the map and we can see where it has a lower figure.

So lower income countries are in the sort of lighter greens and the darker blues are the higher income countries.

Now a useful aspect of this map is if I hover over a colour, it actually just highlights those countries around the world.

So we can see here which countries are in the highest income bracket.

We can see that there's a concentration in North America, Western Europe, and then pockets in the Middle East, East Asia, and Oceania.

And again, if I hover over some of the lower numbers, then we can see that they're concentrated in sort of Sub-Saharan Central Africa, and there's Afghanistan there in Asia.

Now I was quite careful about where I said there in Africa, Sub-Sahara means south of the Sahara Desert.

And Central Africa is an important term.

And the reason why I'm saying that is if I keep going up these, I actually have to come right the way up to between 10,000 and $20,000 GDP per capita to highlight countries in southern tip of Africa and North Africa.

So there is a real difference between the wealth, the GDP per capita of different countries in Africa.

It's important to understand that.

So that can help us analyse patterns between different parts of the world.

Another useful thing I can do is if I actually hover over the country, it gives us the actual GDP per capita specifically and how much it's grown.

And that can be quite useful because if we've got two countries in the same category, they might be quite different.

If, for example, United States, they're both in the top category.

United States, they're nearly at $75,000.

Canada at around 55,000, so that's 56,000 really.

So you know, almost a $20,000 difference there.

So that is quite significant.

Now we know that GDP per capita is actually a fairly narrow measure of development.

It doesn't tell us how much income inequality there is within a country, for example.

It doesn't really tell us how happy people are.

So there's lots of different problems with GDP per capita.

So we would need to look at other maps here to start analysing inequality across the world.

So if I choose another map, this one's healthy life expectancy.

So if we look at this section here, it says the estimated average number of years lived free from disability or disease burden, we can then analyse the map again.

And again, I can highlight over one of the highest categories, and we can see again, there's a concentration in West Europe and North America.

You can also see Chile there.

You'll notice though that the United States is not highlighted.

I have to actually come down to the next category.

So if we highlight over to the United States just over 66 years, this is in 2019, remember, and Canada's actually over five years higher than that.

So it's a reversal from the GDP per capita figures.

And this is why it's important to look at different maps.

Again, I can come to the lower numbers, see the Lesotho there in the lowest category.

And then again, we're going into different parts of the world, Central African Republic, Somalia there, and we can start analysing these patterns about where people's healthy life expectancy is lower or higher.

So Our World in Data is a really useful website for us to analyse global inequality, both economic inequality and social inequality, environmental inequality.

As long as I use different maps, I'm able to do that.

So the question is, is this a geographic information system? And I think the answer to that is actually a little bit grey in that can we visualise geographical data? Absolutely, we can visualise geographical data over space here, spatial data.

Can we analyse it and interpret it? Yes we can.

So in some ways, that makes it a geographic information system.

However, there are certain functionalities that other GIS applications have that this doesn't.

So I can't upload data for example onto the system.

I can't put two layers of data onto one map and maybe toggle between the two or use a swipe or a transparency tool to be able to analyse the data in a little bit more detail.

So we sometimes have to just choose the GIS application or the way we're getting our digital maps depending on exactly what we want to do with it.

But Our World in Data is an incredibly useful tool that we can use to analyse global inequality, <v ->Geography isn't just about where things happen.

</v> It's about how things change over time.

Websites like Our World in Data help us to do this by letting us visualise change some data across the years.

By clicking the playtime lapse button, we can watch how things like GDP per capita change from year to year in different countries.

This helps us to see important trends like which countries are getting richer, which ones are falling behind, and how patterns of inequality shift over time.

Which of the following can be done using Our World in Data websites? Is it A, analyse patterns in GDP per capita around the world? B, use a swipe tool to compare healthy life expectancy and GDP per capita on the same map? Or C, use two maps to compare healthy life expectancy with GDP per capita around the world? Pause the video here whilst you decide, and press play when you're ready to continue.

Excellent, the answer is B, well done on this task.

I would now like you to open the link on your screen.

I would like you to complete the following tasks, search for GDP per capita, open a second tab and search for healthy life expectancy, and then open a third tab and search for one other map, which will allow analysis of global economic or social inequality.

You may find a map related to happiness, HDI, or access to services or resources.

Pause video here whilst you attempt this task and press play when you're ready to continue.

Fantastic.

Before we check our answers, I would like you to complete one more task.

I would now like you to work with a partner and choose one of the maps you have looked at and describe any patterns of economic or social inequality you can see.

I would like you to use the functions built into the digital map to help you find and describe evidence.

Pause video here whilst you attempt this task, and press play when you're ready to continue.

For the first task, the maps you use to analyse social and economic inequality may look like this.

Well done if you managed to identify those maps as well.

For the second task, your answer may sound a bit like this.

Jun said, "I chose to look at how satisfied people are with their life, which is a measure of happiness.

Countries in Oceania, Northern Europe, and Central America tend to have higher scores.

For example, Costa Rica in Central America has a very high score of 7.

27.

Countries in Central Africa, like South Sudan, have 2.

82, and Afghanistan, 1.

36, in Central Asia have very low scores as well." Well done if you managed to explain that as well.

We're now going to explore analysing inequality across a local area using GIS.

Inequality doesn't just happen between countries, it can also happen within a country or even within a single city.

On the left, we can see global inequality.

This map shows differences in wealth between countries using GDP per capita.

Some countries are much richer than others.

In the middle, we can see national inequality.

This map shows how parts of the UK where some regions are more developed than others, even though it's one country, there are big differences in income and opportunity.

On the right, we zoom in to see local inequality.

This map shows different neighbourhoods in and around Sheffield.

Even in one city, there can be areas that are much wealthier or poorer than others.

So inequality exists at different scales, from the whole world to a country to your local area.

The ArcGIS Geography Visualizer lets us explore real data on inequality in the UK.

One important data layer it includes is called the index of multiple deprivation, or IMD.

The IMD uses official government statistics to show which areas of the country are more or less developed.

It looks at things like income, education, health, and housing.

So it's not just about money, but about people's overall quality of life.

And each area gets a ranking which helps geographers understand where support is most needed and how different places compare across the country.

The index of multiple deprivation is A, helps measure economic inequality, B, helps measure social inequality, or C, helps measure both economic and social inequality? Pause the video here whilst you decide, and press play when you're ready to continue.

Excellent, the answer is C, well done if you've got that correct.

The ArcGIS Geography Visualizer can be used to analyse how deprivation changes across a city, town, or village.

<v Instructor>In this video,</v> we're going to use the ArcGIS Geography Visualizer to analyse inequality within a place.

Now to do this, I need to add some dataset, some layers of data onto the map.

Now there's two ways I could do this.

I could click on the toolbar in the Open Map button, you can click on this, or I can click on Add a Layer because actually this data, these layers of data can be found in both these areas.

I'm going to click on Add Layer, and you can see right at the top, it says English Indices of Multiple Deprivation.

Now deprivation is where we don't have access to the needs and services that we need to have a healthy life.

So we can see there that this indices of deprivation uses 39 different measures to decide on whether an area is considered deprived or not deprived.

So we're going to click on this, and once I clicked on it, you can see in the toolbar where it says map layers, a little one has been shown.

So it shows there's now one layer available to us on the map.

If I scroll down using the scroll wheel of the mouse, you'll see that there's actually another one here for Northern Ireland, Scotland, and Wales.

I'll click the Northern Ireland one as well so we can show both of these layers.

And once I've done this, I'm going to close this, and I can now see the map, and you can see where we've got the UK, you've now got some colours here.

We haven't got it on the rest of the map because the data only applies to the UK.

Now if I zoom into the UK, and I'm just doing that using the scroll wheel of the mouse, you can see England is now all coloured in.

If I go to the map layers, and I could click on the English layer, and that takes it away.

And if I change this base map, I'm gonna change it to a chartered territory map, that sometimes helps the other layers show up on the map.

So if I zoom into Northern Ireland now, you can see we've got all these different colours.

Now because these are two different layers, they've actually got a different key, a different legend.

So if I click on the legend here at the bottom, we can see that where it's dark, these are the least deprived neighbourhoods.

So the most affluent, the least deprived.

And the darker browns are the most deprived.

So sort of darker greens, greens, least deprived, browns, most deprived.

And you can see in Northern Ireland, we can start maybe picking out general patterns.

So maybe this eastern central area is potentially the least deprived area of Northern Ireland.

We've gotta be careful with general patterns because if we don't, if we ignore specific details, sometimes really important information about people's lives are lost.

So if I zoom into Belfast for example, which is within that east central area, you can see there's definitely a lot of areas which are actually in the most deprived neighbourhoods of Northern Ireland.

So this is useful for picking out national pictures, but actually here, we're going to look at a place, a local area, and look at deprivation over local area.

So to do this, I'm going to click on the English indices.

I'm going to delete the Northern Ireland indices.

And I'm going to go to the left hand side to the search button, and I'm going to type in Sheffield, press Enter, and it takes us to Sheffield.

And this is the area that you can see.

Now a quite useful thing to do here is to change the base map to imagery hybrid, and you can start seeing the labels come on the map.

And if we take the data layer off, that might be quite useful to see the satellite imagery.

And I think we're going to have to do that because although we can see the data on the map, it's quite difficult to see where Sheffield is exactly, where does Sheffield start or where does it stop.

It's very hard to see with this data, this layer of data on it.

So what I'm going to do, I'm going to go to the map layers.

I'm just going to, I don't want to delete it, I don't want to remove it, but I want to toggle the visibility.

So I'm going to untick that.

And now we can see a bit more clearly where Sheffield is because I can see these green fields, which are out of the Sheffield city really, and we can see where the sort of built up area is within Sheffield.

It's helpful if you know the area, I know for example the M1 running down here is quite a good dividing line between Rotherham and Sheffield.

So what I can do is I can click on Sketch, and if I go to the line button, I'm going to make that a little bit bigger.

I'm going to double click here on the colour to make it a red button.

And this doesn't have to be precise, but what I will do is I'm just going to draw a very rough outline around the outskirts of Sheffield.

It doesn't have to be precise, it just has to be the rough outline.

I'm going to double click there.

And what that allows me to do is when I go back to the map layer and click on the deprivation, it gives me a much better idea of where Sheffield is so I can pick out the patterns.

And if I click on the legend now, we can see on this layer, the purple colours are the least deprived net deprived neighbourhoods, and the brown and orange colours are the most deprived.

And I definitely can see a pattern in Sheffield towards the southwest.

If I just draw a line, I'm just gonna use this line button here.

I could draw a line down here, and I can just hold it like this for a second.

And we can see that towards the southwest, the west of this line, these are the least deprived areas.

I just put the legend back on.

And towards the east of this line are the more deprived neighbourhoods.

And again, it's a generalisation, but it gives us a clear picture.

Sheffield's quite unusual with this east west split.

Often we have the more deprived areas in the centre and the least deprived areas all around the outskirts.

And it's worth comparing to other places, but Sheffield does have this east west split.

So that's a good way of being able to see a pattern over a specific place.

What we can also do, just going to get rid of this line here, is we can use what we call a transect.

Now a transect is a straight line where we make observations or we study regular intervals, in this case, along the line.

We do this because it's a really good way of showing changes over geographical space.

So I'm going to click on the Sketch button again and I'm going to draw a line through Sheffield.

So going to start here, and I'm gonna come all the way down.

So from the northeast to the southwest.

If I want to make that line a little bit bigger, I can use this arrow edit button, click on it, and I can pull the size up.

I could have done that straight away.

It would work and I could change the colour.

That's just a way of being able to see it a little bit more clearly.

I can then add some symbols.

So I'm gonna add some symbols on here.

I'm actually gonna change the colour to black.

I think that might be a bit easier to see.

So I'm gonna just choose five areas roughly equal distance apart.

If I wanted to measure these to make sure they were accurate, then I could do this.

I could really think about which areas I wanted to study.

But you can see what I've done there.

I've taken a transect line across Sheffield, and now I can study each area.

Now a good thing I can do here, I can zoom in, I can find the area.

If I want to look at the area in more detail, I could toggle the visibility.

There we go.

So I can look at the area and what's around it if I would like.

But if I click on this area, it gives me a bit more information.

So you can see that this area is in the 10% most deprived areas in England.

So this is fairly deprived, and we can see these different reasons why that is.

The lower the score, the more deprived it is according to this data.

So we can see there are issues maybe with crime, education, employment, health, income, so on and so forth.

But also areas where maybe there's less deprivation.

So it's important to kind of think about kind of everything, every aspect of an area.

So it gives us some clear information there.

And I can go through and I can look at each of these different areas, and I'll just go right towards the end.

So I could go all the way along here, but let's take this end one and I could click on this area.

I can find out the information.

This is actually, as it says, the 100% most deprived areas in England.

And that it's a bit of an odd way of saying it, but that essentially means it's within the least deprived areas in the country within England.

So this is, I'd expect very affluent area, fairly wealthy, people with good incomes, pretty good health, employment, education access, low crime, and low barriers, relatively low barriers to housing services.

So that is a really effective way of looking at inequality across a place and finding out some secondary data, which can be really valuable in trying to understand how lives are different across a town or a city or even a village.

<v ->A straight line along which observations</v> or measurements are made is called a? Pause the video here whilst you decide, and press play when you're ready to continue.

Fantastic, a transect, well done.

Once you've explored the map and spotted patterns of inequality, you can start adding your own notes to help explain what you found.

To do this, you need to click on the Sketch in the toolbar, then choose the edit arrow in the popup, and click on the symbol on the map where you want to add your note.

Finally, click Add in the popup window, and you can use the notes to describe what the data shows.

For example, you might say an area has a high deprivation or low income, or explain why a place might be better or worse off than others.

I would now like you to open the link on your screen and complete the following tasks.

Click on Add Layer in the bottom toolbar, select English Indices of Multiple Deprivation IMD 2019 or the equivalent for Scotland, Wales, or Northern Ireland, search for a place of your choice, and your teacher may have a specific area for you to choose.

Click on a map layer and hide the deprivation layer.

Make sure the base map is imagery hybrid, and then click on Sketch in the toolbar, select line from the popup menu, and draw around the area of your chosen place.

Make sure your line is in a visible colour and thickness.

Pause the video here whilst you attempt this task, and press play when you're ready to continue.

Fantastic.

Before we check our answers, I would like you to complete one more task.

I would like you to click on a map layer and make the deprivation layer visible, click on Sketch in the toolbar, and use the popup menu to draw a transect line across your chosen area.

Add three or four symbols of equal points along the transect.

And then I would like you to click on the area each point along the transect and analyse the level of deprivation.

Remember that higher numbers mean less deprivation.

And then I would like you to describe how income, health, and crime varies across the different areas along the transect.

You might want to add this information on your map by adding notes to each symbol along your transect.

If you're logged in, you can save your work as well.

Pause the video here whilst you attempt this task, and press play when you're ready to continue.

Fantastic.

Let's check our answers.

For the first task, your completed map will look similar to this for your chosen place.

For the second task, if you added your data to the map correctly, you can click on each symbol along your transect and reveal your notes.

Well done on this task.

You've done brilliantly.

We've now comes the end of our learning on GIS, Analysing Inequality at Different Scales, and you've done fantastically.

Before we end this lesson, let's summarise everything we've learned today.

Digital maps and GIS are powerful tools to help geographers explore the world and understand important issues like inequality.

We can use digital maps to visualise inequality both globally, between countries, and within the UK, between regions, cities, and neighbourhoods.

GIS or geographic information systems lets us layer different types of data onto a map.

This helps us to see patterns in things like income, education, health, and housing.

For example, we can use GIS to find out which areas are more or less deprived.

One way geographers use GIS is by drawing a transect, a line across a place like a city, and then analysing how deprivation changes along this line.

This helps us to understand how one area can be very wealthy while another just a few kilometres away faces more poverty and fewer opportunities.

So digital maps and GIS are not just about location, they help us ask deeper questions about how people live, what resources they have, and how fairly those resources are shared.

Well done on this lesson, you've done fantastically, and I look forward to learning with you again very soon.