Hello, my name is Mr. Conway.

I'm very pleased to have the opportunity to guide you through today's geography lesson.

The emphasis in today's lesson is really gonna be very much on GIS, that's geographical information systems. So let's get started.

This lesson is linked to GCSE units about development and gaps or inequalities in development.

So by the end of today's lesson, we've got a strongly linked intended outcome that you'll be able to use GIS maps to analyse links between inequality and migration.

You'll be learning some GIS techniques, which can be applied to all sorts of spatial data along the way.

So if anything's new to you, I'm here to support that learning.

To help us achieve the outcome, we need to learn or remind ourselves about a few keywords.

Keywords for today's lesson are "HDI," "benchmark," and "pop-up." If we look at each of these individually, HDI is the Human Development Index.

It is one of the most important measures of development because it combines three measures into one.

So it's a composite measure of development.

The three measures it combines into one are about income, life expectancy, and education, and the resulting index ranges between zero and one.

Benchmark is a data value which supports comparison within a data set.

So it could be, for example, a category boundary or the highest or lowest value for measures of development in different countries, such as HDI, life expectancy, or average incomes.

A pop-up is something we use in GIS, which is a read-only display of attribute information.

And it could be text, or it could be images or charts, all of those things.

It can carry a lot of information, and it's often linked to a particular point or location on a web map.

There are two learning cycles for this lesson, looking at GIS maps to analyse links between inequality and migration.

So we're gonna look at the first of these learning cycles now.

How can GIS maps visualise inequality? Now I promise you'll be looking at some GIS maps during this learning cycle; this is geography, after all, but first, we're going to consider some key ideas about the causes of migration.

Migration is caused or driven by all sorts of factors, and they operate in different complex ways.

So it's helpful to categorise these drivers.

We can categorise them as economic factors, social factors, political factors, and environmental factors.

There can be negative drivers and positive drivers in these categories, which are often direct opposites.

The negative drivers are called push factors, which may encourage or even force people to leave a place or country.

Here are some examples.

I'll give you a moment to read them.

Then there are positive drivers of migration, which we call pull factors.

They act like a magnet, drawing people in their direction.

Here are some examples of pull factors, and I'll give you another few moments to read them.

Here's a historic example of push factors at work in one particular country.

We see here a very moving memorial in Dublin to a series of famine events in Ireland in the 19th century.

The statues depict starving people leaving their homes seeking a better life elsewhere.

They were actually inspired by eyewitness reports at the time, including engravings published in newspapers at the time, such as the Illustrated London News, so they're very accurate.

The push factors in the example included low hazard resilience and colonial links, when crop disease caused a series of famines, which became known as the Great Hunger.

This drove vast numbers of Irish people to migrate to other countries such as the UK, Canada, and the USA.

Ireland's population still hasn't recovered to the level it was before the Great Hunger.

Here's a well-known example of pull factors at work.

It's commemorated by another evocative sculpture, this time at Waterloo Station in London.

It's known as the Windrush Monument, and it was unveiled in 2022.

It symbolises a sequence of migrations from countries in the Caribbean to the UK.

And although there were some push factors in the Caribbean, most of the migration was driven by strong pull factors.

In particular, the pull factors were related to job opportunities and long-established colonial links.

The migration started in the late 1940s and became really significant in the 1950s onwards, because the UK's recovery from World War II created unprecedented workforce demand.

So there was substantial immigration from the Caribbean to the UK, and that carried on for many years.

There's a strong link here with inequalities in measures of development.

The inequalities, or development gaps, can actually become drivers of migration.

The gaps can be measured by inequalities in income, differences or gaps in life expectancy, and gaps in education and other single measures of development.

But the most comprehensive measure of development is HDI, the Human Development Index, which is this composite index that combines measures of income, life expectancy, and education to make a single index, and it ranges between zero and one.

We can use GIS to map many of these measures, including HDI.

If we want to map measures of development, such as HDI, it can be helpful to use what are called benchmarks for the data.

And these are used by the United Nations Development Programme, for example.

Their benchmarks create categories based on HDI, and it's important to appreciate that such categorization can be controversial, so you might want to consider this in your lessons.

Anyway, we can consider how the UNDP categories use benchmarks for HDI by looking at how they play out on this Gapminder scatter graph, which plots countries' HDI against life expectancy, and the bubbles are proportional to each country's population.

So the categories, and you can see two dividing lines here.

Countries with HDI equal to or greater than 0.

8 are categorised as developed or having very high development, or known as higher-income countries.

Countries with HDI between 0.

55 and 0.

799 are categorised as emerging, having medium to high development, or sometimes known as newly emerging economies.

And countries with HDI less than 0.

55 are categorised as developing, with low development, or sometimes called low-income countries.

Quite often, individual countries are used as benchmarks, like this.

If we look at the range of HDI from zero up to one, and then we consider each country's rank out of a total of 191 countries, we see Switzerland at the top of this ranking, the UK a little bit behind that, ranked 18.

Then we see another group: Sri Lanka ranked 73, Indonesia ranked at 114.

Then we see a couple of countries: Ethiopia ranked at 175, and the lowest-ranked country in the world in this particular category is 191, which is South Sudan.

Now, no countries in the world have HDI below 0.

385.

So we can look at the categories with these countries as follows.

The benchmarks are shown.

Then we have the HICs, or developed countries; the newly emerging economies, or NEEs, or emerging economies; then we have the LICs, lower-income countries, or developing countries.

Now let's find out how we can visualise and analyse how inequalities may influence migration and trends in migration using GIS.

This is a video guide showing how to visualise and analyse how inequalities may influence migration and trends in migration.

We're going to use a ready-made map created in ArcGIS Online to complete some tasks to show how this is done.

So the first thing you need to do is to click the layers panel and make one of the layers visible.

That layer is called "HDI World 2021." Just gonna make it visible now by clicking the visibility icon.

That will change it, and you'll see that the data has appeared on the screen.

There are a couple of ways to interpret the data on the map.

One of which is to check out the legend on the left-hand side, and we can see that there are four categories of countries.

There's an extra category, which is for areas with no data or countries with no data.

We can ignore that for the moment.

And what we're looking at is four categories for HDI based on the categories used by the United Nations Development Programme, where countries with highest HDI are any which are 0.

8 or more, and the countries with the lowest HDI are those that are less than 0.

55.

And there are countries in between those two categories.

The other way we can interpret the data is by clicking on the countries.

So, if we click on any particular country, if we click on the UK to start off with, it reveals information about the HDI of the country as it is now.

We can see the UK is 0.

929.

We can also see the rank of the country out of 191 countries.

UK's ranking 18.

And we can also see the percentage change of HDI between 2011 and 2021.

Perhaps more usefully, we can see the trend over a longer period of time.

So if we float our cursors over the line chart, we can see that in 1991 the UK's HDI was 0.

809, and then we can go through the years to 2001, 2011, and 2021, where we see it is 0.

929.

So we can use a country like the UK as a benchmark to compare with other countries.

And we know it's a country that fits into the very high HDI category 'cause it's well above 0.

8.

And that means we can then look at countries in other parts of the world.

So, a good suggestion would be to look at countries, maybe in South America or Africa, or Asia, to compare the HDI.

We're just gonna look at one particular example, which is Mali in Africa.

So if we click on Mali, we see that it's a bit of a different picture.

What we've gotta make sure of is we take account of what's going on with the vertical axis here so that we don't sort of make any errors in making judgments between countries.

But fortunately, we've got pop-up information, so we can look at Mali's data over the same period of time as for the UK.

What we can see is that back in 1991, it was very low indeed.

It was 0.

245.

And then, as the years went by, it has increased somewhat.

So 2001, it was 0.

329.

If we move forward to 2011, we see it 0.

409.

And if we move up to near the present time, in 2021, we can see it at 0.

428.

Its rank compared to other countries of the world is quite low.

It's 186 out of 191.

Having said that, its HDI has increased from quite a low base by 4.

6% since 2011 to 2021.

So, a good question to ask ourselves is: well, to what extent might this data suggest that migration to the UK is likely? We can see that there are ongoing and very significant gaps in HDI between Mali and the UK since 1991, but the gap is narrowing slightly.

1991, the gap was 0.

564.

By 2021, the gap was down a bit to 0.

501.

But that's still a very large gap.

The HDI gap would suggest that there would be potential for strong push factors from Mali and, correspondingly, strong pull factors towards the UK.

So migration to the UK would be likely.

That doesn't necessarily mean to say it's going to happen, but it would be likely to occur because there's a strong migration gradient, if you like, a big difference between the two countries.

But we need to consider other factors that could be important in influencing migration.

You could have things like migration controls, and you could also have colonial links, which may or may not influence migration to the UK.

Mali is actually a country that was part of the French colonies, so maybe they wouldn't be quite the same colonial link pressure or driver for migration between Mali and the UK.

You'll soon have the opportunity to visualise and analyse how inequalities can influence migration using GIS yourself.

But let's just check up on a couple of points from the video demonstration.

Our first check asks you: which of these statements you can see, A, B, and C, is an example of a benchmark? You may wish to pause the video here and restart it when you've selected your answer.

Well done if you selected B, a statistic to enable useful comparison with other countries.

Now for a second check.

Which of these HDI values is used as a benchmark by the UNDP to categorise countries as HIC, or very high, or developed? You may wish to pause the video here while you have a think about that.

Well done if you remembered that the HDI benchmark used by the UNDP is this one: that is, greater than or equal to 0.

8 to categorise countries as HIC, very high, or developed.

Now for the task, which will help you to use GIS maps to visualise inequality.

For these tasks, you'll need to open the link provided, which takes you to a ready-made map called "Migration 1." Then you can sign into ArcGIS Online.

In task one, you are going to visualise and analyse pop-up data.

As a follow-up, task two, you're going to find and record data for one country using the UK as a benchmark and choose another country with a contrasting level of development, such as Mali.

Then task three challenges you to consider the extent to which the data suggests that migration to the UK is likely and try to use data to support your answer.

So, pause the video now to take some time to undertake the tasks.

When you're ready, press play to obtain some feedback on the tasks.

Hopefully, you were able to undertake those tasks effectively.

For task one, your work to visualise and analyse HDI pop-up data should look something like this map.

It's got the legend showing.

Your pop-up data should include line chart showing the trend in HDI between 1991 and 2021, although for some countries the data may be missing for the earlier years.

For task two, here's a check to see how you got on with recording data for one country in comparison with the UK as a benchmark.

So we use the example of Mali.

Take a moment to review these figures to see how they compare with yours.

Finally, for task three: to what extent might the data suggest that migration to the UK is likely? These are some points you might have made.

There are ongoing significant HDI gaps between Mali and the UK ever since 1991.

However, we see that the gap is narrowing very slightly.

So the 1991 gap was 0.

564.

By 2021, the gap was slightly smaller, 0.

501.

It may well be significant that HDI suggests strong push factors from Mali and strong pull factors to the UK.

So migration to the UK would be likely, and we might call this a strong migration gradient.

We may also consider that other factors could be very important, such as colonial links and migration controls.

So if your answers were very different to these or you recognise some errors, take another look back at the video demonstration.

Our second learning cycle is gonna help us to dig a little deeper into the migration story with the help of GIS visualisations.

But let's just consider a little bit of background first.

Why does the UK need migration? The UK population pyramid gives us some important clues.

It gives us clues about crucial demographic factors.

For example, the UK's population structure is ageing, with over a quarter of the UK population now over 60, and this is increasing.

One consequence of this is that the proportion of people in the working age groups is declining, and this is showing an ongoing trend towards an ageing population.

At the same time, the birth rate is falling.

Here's another chart showing these demographic issues and how they're likely to play out over the first half of the 21st century.

It runs from the year 2000 up to 2050.

So we see trends such as the ageing population, and that's likely to develop further.

We also see a reduction in the proportion of people who are in the working age groups, and we also see a fall in the birth rate.

Conversely, we can look at the population structure for a different country.

Here's the population pyramid for Mali, which is very different to that of the UK.

There are key demographic factors here, which are likely to be drivers of migration.

For example, only a very small proportion of Mali's population is in older age groups.

In stark contrast with the UK, its population structure is youthful, with over 2/3 of the population under 30.

In a low-income country like Mali, this creates a surplus in the workforce.

There are many young adults looking for work, but with a corresponding shortage of job opportunities there.

There is further reinforcement of Mali's youthful population with a very high birth rate.

Let's take a quick look at the balance between immigration to the UK and immigration from the UK.

That's what we call net migration.

In these government figures, we see that the balance was fairly steady until the 1990s, but net migration then steadily increased, with a significant jump in recent years.

A key drive for this is demographic change in the UK, meaning the country needs net migration to fill gaps in the workforce.

There may be other factors which affect UK migration as well.

One example would be historical or colonial legacy, and we see that often with links between former colonies that are in the Commonwealth.

There are longstanding relationships there which can affect immigration.

And the size of population may be a factor as well.

For example, one of those former colonies is India, and that has the biggest population in the world.

As in many other countries, we must also consider how government controls affect and filter migration.

The government gives priority to potential migrants at graduate level and in critical shortage occupations such as the medical professions.

This will tend to encourage immigration.

The government also tries to increase training and participation rates.

This is intended to lower demand for immigration.

The government will also use barriers to some potential migrants.

For example, they restrict immigration of so-called lower-skilled workers and their dependents.

That's their partners and children, and this is intended to discourage immigration.

Another key driver of immigration and migration in general is trading relationships between countries.

UK's biggest trading partnerships are with countries in Europe.

The EU remains a very important trade partner for the UK, with around half of all exports and imports, and trade often means that people are moving from one country to another.

So that ongoing migration of people between European countries is very significant.

Let's see how we can configure and analyse pop-up data about immigration to the UK in the following video clip.

This guide shows how we can use the ready-made Migration 1 map to configure and analyse pop-up data about immigration to the UK.

The first thing we're going to do is to switch off the HDI World layer so that we can do some other tasks.

So we just switch that to invisible, and then we're going to click and make visible one of the other layers, and that's the layer called "Immigration UK 1990 to 2020." So we're gonna make that visible by clicking the eye, and you'll see a whole lot of symbols will appear on the map.

Now, the symbols are by default all pointing north, so it would be good to change that.

In order to do that, we're going to click in the layers panel and go to the options at the end of the layer that we're concerned with to show properties.

So that's the properties for the Immigration UK 1990 to 2020.

We click that, and the panel opens on the right-hand side.

We then move to symbology and click edit layer style, and then go to pick a style.

Don't click the number two, click where it says, "Style options." Then there is an option to rotate by attribute, which is what we're interested in.

If we open that panel and drop down here, we have to switch the rotation by attribute on.

And you can see the arrows have all changed direction, but they rotated according to the year 1990, which is not what we want at all.

So we're gonna choose this column of data here called "Bearing to UK," where I've put in roughly the compass direction using magnetic bearings from zero to 360 to show the direction to the UK.

So if we click that, and see what happens to the arrows.

Now they all change their orientation so they're pointing more or less to the UK.

If we click done, we can take a better look at that.

We have to click done twice.

So the visualization's been improved by showing the direction of travel that people might take if they're on their way to the UK.

Now let's do something else in the properties for this layer.

So we go to the options once again for that layer.

And this time we're going to choose another aspect of the options, which is the pop-ups.

So the pop-ups option enables us to control what we see if we click on different points of the map.

So if we click on that, now we can open the panel, and it happens to show one of the countries, and we can see a list of fields.

Now, that's rather a lot of information to take in.

So if that does appear, you can always just delete them because we're gonna control that ourselves.

So we just delete those lists, and you'll see it disappears.

Then, what we want to do is to add a title for the pop-up.

So we're going to add that in the title panel here by typing this formula of words.

So we're looking to pick up the name of the source country from the database, that's in the curly brackets there.

What we've put in front of it is: "Immigration to the UK 1990 to 2020 from.

." and it picks up the name of Egypt.

But it hasn't just picked up Egypt; it's picked up every single country.

You can see if I click around, you can see the names of the countries are appearing.

No matter where we are, it's picked up all the names automatically.

But we're going to make the pop-ups even more powerful by doing something called adding content.

So we're gonna add some content, and we're going to add a chart.

The chart we're going to add is going to be a line chart, and you'll see it developing in this panel here, the progress panel.

So we're not gonna give it a title or a caption or alternative text or anything like that.

What we're going to do instead is select fields.

We want to tell the line chart what data to pick up.

So I'm gonna click that now, and if you scroll down, you can see years appear.

And I'm going to choose the years, and you'll see them appearing in the progress panel.

So we start with 1990, 1995, year 2000, year 2005, it instantly picks them up and charts them.

2010, 2015, and 2020.

We don't want the rank because that's not actually a value of immigration over the years.

So we can see our chart there is complete, and we can click done and done twice, and then we're in a good position to save our work.

So we go to the save panel here and save the map.

If you need to "save it as," you can just change the name by adding your initials at the end or a number, and you can see the progress of the save appears at the bottom as well.

We can use this data in the pop-ups to analyse migration from all of the countries to the UK.

But what we've gotta be careful about is looking at the vertical axis here because that does change depending on the country that we're looking at, and depends on factors such as population.

So I'm gonna close that one for the moment, and we're going to look at one particular country, which is Poland.

Now, in case you don't know where Poland is, you can always use the search panel here to find countries.

I'm gonna show that to you now.

So it goes to Poland, and we can see, sure enough, that's Poland.

Well done.

And if you click the chart for Poland, you can then analyse its migration over that period of time.

So we're just gonna look at Poland's migration from 1990, first of all, and we see it's 71,908.

It would be a good idea to record that down, make a note of that.

Then, if we look at their migration by 2020, it's gone up significantly.

It's 835,975.

Now I'm gonna do this one for you.

The difference between 2020's value and the 1990 value is over 3/4 of a million people: 764,067 people.

What we can now do, if we just zoom out, is have a look at the layer we looked at before, which is the HDI World, to see if there's any correspondence.

So if we zoom back into Poland over here.

Let's just check that's Poland.

Yes, it is.

We can see that if we then click the HDI, we get the pop-up from that, and it gives us some information about Poland.

And we can use that to see: has there been any particular issues with HDI with regard to migration? So we'll just make sure we can see the UK down there.

So the UK, remember, is 0.

929, and what we see is that by 2021, the HDI in Poland was 0.

876.

Now, it puts it in the very high category, but it's less than the HDI for the UK.

So that could be a factor; it's a possibility, but the difference isn't all that marked.

So what would be good for you to do is to explore different countries to see what the differences is between immigration over the years between 1990 and 2020, calculate what the trend is, and then compare that with the HDI for that country in the most recent time.

Soon, you'll have the opportunity to configure and analyse pop-up data about immigration to the UK.

But let's just check up on a few points from the video first.

When adding content to a pop-up to show changes in migration over time, which of these icons you see here, A, B, C, and D, should be selected? You may wish to pause the video here and restart it when you've selected your answer.

The correct choice is C, the icon for a line graph.

Here's our second check.

There are three line charts here: A, B, and C.

In these line charts showing the trend in numbers of immigrants to the UK over the years, which country has the highest source in 2021? You may wish to pause the video here and restart it when you've selected your answer.

In this case, the correct choice is Poland.

So well done if you chose that.

It's important to check the numbers on the vertical axis because the scales differ quite a lot, and you can make mistakes unless you look at the numbers carefully.

And a third quick check.

Is it true or false to say that inequalities in HDI are the main cause of migration? So you may wish to pause the video here and restart it when you've selected your answer.

The correct answer is false.

Pause the video again to consider possible reasons why that's the case.

So here are some possible reasons.

HDI inequalities are just one driver of migration, but there are other key factors.

For example, demographics: does a country have an ageing or youthful population? Government policy: data is often skewed by immigration controls and filters.

The size of population in source countries can be a factor as well.

And finally, you could consider historical and colonial links, because they often establish patterns of migration that go on for many, many years.

Now for the task, which will help you to configure and analyse pop-up data about immigration to the UK.

As for the tasks in learning cycle one, you'll need to access the same ready-made web map called "Migration 1." The link is here in case you don't have it open already.

In task one, you are going to configure pop-up data to visualise immigration to the UK.

In task two, you're going to analyse data in pop-ups showing immigration to the UK and then record your findings in the table provided.

Task three asks you to what extent may there be differences in HDI which are linked to high immigration to the UK? And task four invites you to suggest other data that might help us to understand immigration to the UK.

So pause the video now to take some time to undertake the tasks, and when you are ready, press play to obtain some feedback.

Hopefully, the task went well for you.

For task one, your web map, in which you configured pop-up data to visualise immigration to the UK, should look something like this, with one of the pop-ups shown offset from the map.

For task two, in which you analysed data in pop-ups showing immigration to the UK, your data table and calculations should look something like this.

You might want to pause the video now to look at these in a little bit more detail.

For task three, which asks you to what extent are differences in HDI linked to high migration to the UK, we see quite a complex picture.

HDI can drive migration, but there are anomalies, and they're indicated by examples such as: India and Pakistan have lower HDI, but Poland has high HDI.

Romania has relatively high HDI but rapidly rising migration to the UK.

Ireland's HDI is higher than the UK, and immigration to the UK is actually going down.

Germany's HDI is higher than the UK, but immigration to the UK is going up.

So it's a complex picture.

For task four, you are invited to suggest other data that might help us understand immigration to the UK.

Here are a few good ideas.

Demographics in UK and the source countries can be a factor.

So we could have, for example, ageing versus youthful populations.

Immigration data shows that the balance of migration is quite a useful factor.

Government policy is often skewing immigration using controls and filters.

Size of population in source countries could be a factor, and we may need to adjust that to the proportion of population.

Historical and colonial links, established patterns of immigration, and cultural family links are all important.

And then, finally, proximity of source countries and, of course, trading relationships.

Really well done.

What we've been doing this lesson is learning some GIS skills and then applying them in different situations, and it's that kind of deliberate practise that can help make our GIS capabilities become fluent.

If we just summarise our learning here today, we can say that GIS maps can be used to visualise and analyse inequalities in measures of development, such as HDI.

Benchmarks are often used and can be useful to compare inequalities in measures of development, such as HDI.

GIS visualisations can be used to support analysis of reasons for migration or drivers of migration.

And GIS can also be used to visualise and analyse migration trends over time, supported by configured pop-ups.

So in this lesson, we've learned about various powerful ways that GIS can support the story of migration.

Excellent work.

Hopefully, you found this learning useful, and I look forward to the next time we can meet for some learning.

Until then, all the best.

Bye for now.