Lesson video

Hello.

My name is Mr. Conway.

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

The emphasis is going to be very much on how we use geographic information systems, otherwise known as GIS.

So let's get started.

This lesson is part of the units linked to urbanisation and urban change.

By the end of today's lesson, the intended outcome is that you'll be able to use GIS to visualise trends in urbanisation.

You'll be learning to use GIS techniques which can be applied to all kinds of spatial data that we use in geography.

And although some of the learning may be new to you, don't worry because I'll be here to help you along the way.

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

And the keywords for today's lesson are filter, pop-up, choropleth, and shapefile.

Let's look at each of those briefly now.

Filter is the selection of some data in a GIS layer which helps us to visualise spatial patterns more clearly.

And that can help us to declutter a web map.

Pop-up is something which is often used in GIS and it's there to manage further details about a place.

It's a read-only display of attribute information, that's information contained in the layer such as text, images, and charts, and it's usually linked to a specific location.

Choropleth is a very well-established cartographical method which is used to visualise data for what are called bounded areas or polygons and we use shading or colours to make the polygons distinct from each other.

Shapefile is an important tool used in GIS and it's a vector data storage format used for location, shape, and attributes of geographical features such as borders of polygons showing the outline or the area of a country.

Now the word vector means as you zoom in and out it doesn't pixelate so it always stays nice and sharp no matter what zoom level we choose to use.

There are two learning cycles for this lesson, looking at how we can use GIS to visualise global trends in urbanisation.

So we're going to look at the first of these now to see how we can use GIS to visualise world cities.

Take a moment to reflect on the message on this t-shirt.

You may have heard the expression been there, done that, got the t-shirt.

In a sense every human being on the planet could now wear this t-shirt because humanity has become mostly urban.

What does this mean exactly? A good question to ask first of all is when did humanity become urban? We know that the human race, homo sapiens, evolved around 300,000 years ago.

For almost all of that time that humans have existed we've lived mainly in countryside rural areas, not in urban areas like towns and cities.

So we used to be rural for almost all of our existence.

There's another satirical saying, what did the Romans ever do for us? To which the answer is, of course, quite a lot, including a lot of words we commonly use.

For example we get the word urban from the Romans whose Latin word urbs, U-R-B-S, meant city.

But although some earlier civilizations such as the Romans, Aztecs and Greeks did have some towns and cities, the vast majority of people even in those areas were still living in rural areas.

And by the way we also get the word rural from the Romans.

Ruralis meant of the countryside.

In fact living in towns and cities was so unusual that the modern word urban in English language wasn't really used at all until around 400 years ago in the 1600s.

Part of the answer to the question, when did humanity become urban, is to consider the pace of change from rural to urban.

Here's an animated line chart from our world in data to show the change in urban population going back to 10,000 years ago.

It would appear that it's been extremely slow for most of that time.

There was no change at all for most of our existence.

Almost all of the shift from rural to urban has happened very recently.

So what we've seen from the animation is that for most of the last 10,000 years things didn't really change much at all until the last 2,000 years of the Common Era and in particular urbanisation didn't really begin to take off until the 1600s onwards when the first use of the word urban appeared.

And that, of course, is when we, that's humanity, rapidly changed from being rural towards becoming urban.

So for almost all of our existence humanity's been rural.

And remember, this chart is just the last 10,000 years.

There are well over quarter of a million years of humanity before this chart.

So can we really nail an answer to the question, when did humanity become urban, by putting a date on the rural urban shift? Yes we can.

We're going to see another excellent animated line chart by Our World in Data which just shows the change from 1960 to 2023.

And it visualises the percentage of people living in rural areas as a red line and the percentage of people living in urban areas as a blue line.

Watch out for the moment of change.

So we see that the rural percentage was out in the lead until relatively recently.

Then the urban percentage caught up and took over.

Did you spot the year? Opinions differ a little bit about this, but only by a couple of years.

And then an authoritative 2024 report by the United Nations states that the year of this major development in humanity was 2007.

So do you remember the message on the t-shirt? "I used to be rural".

That was all of humanity for most of our existence.

But since 2007 the message has changed to "I am urban".

So anyone at school now has only lived during a time when humanity has become essentially urban.

So this major event, this major shift is taking a bit of getting used to.

There are advantages and disadvantages, opportunities and challenges.

Many opportunities arise in the largest urban areas known as world cities.

Probably the biggest driver of urbanisation is the economic opportunities which are on offer in world cities.

Urban opportunities also include access to services such as high quality healthcare, education, retail, entertainment.

But urbanisation is also creating very profound challenges.

There's an acute shortage of homes in cities, which has given rise to widespread informal settlements in many cities and a transport infrastructure that often struggles to cope.

There are also significant environmental issues in many world cities including pollution and waste management.

There's an overwhelming demand for services especially healthcare, water supply and education.

And there are often just too many people for the housing and jobs available leading to homelessness and unemployment.

So where are all these large urban places? Let's use GIS to find out.

Many of these urban places are world cities.

On conventional maps like this one they can be a bit difficult to spot.

There's a lot going on.

Such maps contain a lot of data.

They include the names of countries, settlements, sea areas, latitude, longitude lines.

All of these are really useful but it can be a little bit overwhelming.

So is there a way that GIS can offer a solution to help us to find places such as world cities a bit more easily? Here's a GIS web map that only visualises world cities.

There are 2540 of them.

That's quite a lot to take in.

It's what we might call big data.

How can GIS help us to simplify big data sets like this? Here's the same map but all the smaller cities have been hidden to just visualise cities with over 4 million people.

That's cities that have more people, by the way, than the whole of Wales.

So you may well be asking a similar question to Sophia.

How can we make maps that do this? Let's find out.

Well, there are various GIS tools which can be used to improve the visualisation patterns of georeference data.

This can happen because a web map is a digital layer cake often putting together several GIS layers like sheets of tracing paper.

This means that each layer can be managed separately.

So for example layers can be switched on or off.

This is sometimes known as toggling, and you can see the layer here appearing and disappearing.

Or we can filter parts of a layer on or off.

What's the small L shape on the top layer here? Here's a guide to show how we can use a filter and pop-ups too in order to manage and visualise big data about world cities.

So to do this work we need to sign into ArcGIS Online and click map to open the map viewer which you can see here.

So we're going to build this map from scratch, really.

So our first step is going to be to add and browse layers and in the drop down for ArcGIS Online we're going to type world cities.

Now several layers appear with world cities in their title, but we suggest you choose the one that's created by Esri and then for that layer click the plus add button and the data will be added to your map.

Now the data has been loaded onto the map because we can see that in the legend on the right, but it's not appearing on the map, and the reason for that is that the creators of it have not made it visible at this world scale, probably to avoid cluttering.

We can fix that by going to visibility and you'll see a little slider here.

If we just move that slider just to the left of world, all of the cities appear.

And another thing that sometimes creators of the layers do is to change the transparency for various reasons, but we're going to reduce that to zero so that it makes the layer nice and bright and a little bit easier to see.

So our world cities are shown as proportional circles proportional to their populations and now we can start doing interesting things with the layer.

So we go to the layers panel on the left, click that and we can see the layer has appeared, world cities, and what we're going to do in that is just check and see that we can use the visibility buttons.

So you'll see a little eye here, if I click it once it makes the layer disappear but it's only temporary.

If we click it again, it reappears.

So just practise using that so you know how that works and then make sure you leave it on for the next step.

And that next step is to use the filter.

The filter is on the right, we're going to click filter to open the panel for filter and then we're going to click add new to add a condition for our filter.

And in the top panel of the condition we have a drop down showing fields we can select.

So we're going to look at the drop down there and scroll down till we see the one which says population.

Then we have the actual condition itself, we're going to leave that as it is, is at least, we're going to change the number here to four million, four with six zeros after it, and then we save that condition.

Watch what happens.

The filter has hidden all the cities that are under four million people, so we can only see the ones that are at least four million.

This means that we have decluttered the map and managed it so that we're just seeing the very biggest cities in the world.

We can also change the visualisation by looking at base maps.

So if we look at the contents panel on the left and click the base map gallery, we can look for a base map that will improve the visualisation still further.

I'm going to suggest you look at this one called Dark Grey Canvas.

Watch the difference.

So the darker base map really helps the proportional circle symbols to stand out quite a lot more because the contrast is greater.

So you might want to experiment with some different base maps to see which ones provide the most effective visualisations, and having done some good work here we don't want to lose it so let's save our map by clicking save and open.

We're saving the map for the first time so we click save as and let's call this one urbanisation, then we click save and it will save the map for us into our content.

With our map saved we can then check out the legend and we can see we've got the legend on the left or key to help us interpret the proportional circles.

We can also click the pop-ups by clicking these symbols and we'll see the names of the cities.

We can tidy up these pop ups and we'll see how to do that in a moment.

If you get a whole group of them together, as you can see, around China, a whole lot of cities over four million.

You can click through the pop-ups with the small arrow at the top of the pop-up or you can zoom down to have a closer look.

Now you may have noticed when you're clicking the pop-ups it was showing you all the attribute data associated with that city, but we may want to be selective in the same way as we've been selective using the filter.

The way to do that is to click the layers panel so that we can tell the GIS which layer we want to configure.

We click the three dots representing options so that we can see the properties panel on the right, and we move across to the other side to the settings toolbar and we click pop-ups, and as well as the pop-up panel on the right we also see a progress panel to show how we are changing the pop-ups which is quite useful.

So the first thing we're going to do is alter the title, at the moment it's showing city name and admin name.

Let's change that and we just literally can type a different formula of words to pick up the different fields, they have to be in curly brackets, you can choose them from the curly bracket dialogue here but to save you time just going to suggest you type city name, country name, that's abbreviated in the panel, and then we're going to check the fields list and first of all we've got all the fields showing so we're going to delete them and then we're gonna add the ones we want, we're gonna customise what we show.

And this is what we do, we click add content and we're going to add some text so we click the text box, and in the dialogue box we can type this formula of words, so we're putting population equals, the curly brackets pop refers to the population from the database behind the map, then we type semicolon gap Status equals, and we're gonna find the status of the city.

Watch what happens to the pop-up over here when we click OK.

So the pop-ups are configured to show the population of all the world cities and their status, so for example Mexico City is the national and provincial capital.

We're now going to save our work so we go to save and open and click save.

We've already saved our map once so we just need to click save, very simply, we don't need to give it another name.

Let's see what the spatial patterns on our map are showing, so we just close the dialogue boxes so they're not so much in the way and we can start to examine those patterns.

Perhaps the most striking aspect of the pattern of world cities over 4 million is this massive concentration of them in southeast Asia.

We can click on the pop-ups to see where they are, we just flick through a few of these now, we have Beijing, Guangzhou, Wuhan, Shanghai, and then we also see a similar sort of cluster in the Indian subcontinent, so we have cities such as Karachi in Pakistan and Mumbai, Delhi in India.

We also see after that probably the next biggest concentration would be in Africa, so cities like Lagos, in South America we have cities like Buenos Aires, but by contrast we see very few of these largest cities in North America, in Europe and in Australasia.

Soon you're going to have the opportunity to use filters and configure pop-ups for yourself, but let's just check on some points from the video demonstration.

The first check is which one of the following icons is used to access the filter tool in ArcGIS Online? You may wish to pause the video here and restart it when you've selected your answer.

The correct answer is B, the funnel icon, and they use that because it sort of shows that you're only allowing selected data through to the map, well done if you remember that.

Now for a second check, when using the filter tool which of the conditions you can see here, A, B, C or D, will succeed in only showing the largest cities? You may wish to pause the video again if that helps.

So the correct condition to show only the largest cities is B, population is at least 4 million.

Well done if that was your choice.

Now for the tasks which are going to help you to use the filter tool and to configure pop-ups to manage and visualise big data about world cities.

For these tasks you'll need to open the link provided which directs you to ArcGIS Online and its map viewer and sign in.

In task 1a you're going to visualise world cities using a filter and for task b decide which base maps provide the best contrast for visualising the data.

Then for task 2 you'll configure pop-ups for world cities and for task 3 describe the spatial pattern of the largest world cities.

So pause the video now to take some time to undertake the tasks and when you're ready press play to obtain some feedback on these tasks.

Hopefully the task went well for you, here's some feedback.

For task 1a your web map should look something like this, and for task 1b the base maps which provide the best contrast for visualising this data would be the likes of Topographic, Light Grey Canvas Dark Grey Canvas or the Charted Territory Map.

For task 2 your pop-ups for world cities should look something like this.

For task 3, perhaps your points about the spatial pattern of the largest world cities were similar to those of Alex, who found that most of the world's largest cities are in Asia, such as Shanghai in China, Karachi in Pakistan and Delhi in India, or Aisha, who noticed that after Asia the continents with the most world cities over 4 million are in Africa, for example in Nigeria, or in South America we see Argentina.

Or were you like Sofia, surprised to find that the continents with the fewest of the world's largest cities are in Europe, Australasia and North America.

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

In our second learning cycle we'll be learning how to use GIS to visualise trends in urbanisation, and we'll be using GIS generated choropleth maps.

Choropleth maps are a long established cartographical method which is used for visualising data in bounded areas or in other words polygons, that can include whole countries or counties or any other administrative areas.

Choropleth maps are nothing new, they've been around for two centuries.

The first choropleth map which you can see here was produced in 1826 by Baron Pierre Charles Dupin.

And he invented the method to visualise the availability of basic education across France.

He used the bounded areas for departments of the country.

Then this technique became used more and more widely to communicate spatial variations.

But the actual term choropleth map wasn't used until a geographer called John Kirtland introduced it in 1938.

And in the following decade, that term rapidly found widespread use among mapmakers.

At that time, such maps would have been largely hand-drawn and shaded.

And that was a painstaking process.

But the development of GIS makes this so much easier.

How can we apply the use of GIS choropleth maps to visualise data about urbanisation for bounded areas such as whole countries? Here's a GIS choropleth map created by Our World in Data, and it shows the share of population living in urban areas in each country in 2023.

Like Jacob, you'd be entitled to wonder, how do GIS choropleth maps actually work? Well, what GIS can do to create choropleth maps is use geo-referenced shapefiles.

Shapefiles are a storage format used for the data about location, shape, and attributes of geographical features.

And that includes bounded areas such as country shapes.

The shapefiles will contain place identifiers.

Attribute data can then be matched or joined using the place identifiers in the shapefiles, provided they're the same.

Here's a blank shapefile map of world countries, which is commonly used in ArcGIS Online.

The shapefile contains polygons for each country, and they're bounded by their borders and geo-referenced.

As an example, we have a glimpse here of place identifiers for the polygon of Canada, such as country name and the International Organisation for Standardisation Codes.

They're known as ISO codes.

Canada's two-letter ISO code is CA in this instance.

Then the attribute data contained in a spreadsheet can be joined by linking the place identifiers.

And that means that the values for each particular country are correctly assigned to that country for the GIS to visualise them.

For example, the data for urban percentages of Canada are shown for Canada, but not for another country.

This, of course, means that other work we ask GIS to do will correspond, such as creating the legend or configuring the pop-ups to visualise attribute data for each country.

Now let's see a GIS guide showing how to manage and visualise big data about trends in urbanisation using choropleth maps and pop-ups.

To do this, we're going to use the same map, but we're going to toggle the layer of world cities off for the moment.

And then we're going to search for another layer, a new layer.

So we go to Add, Browse Layers.

In the dropdown, we select ArcGIS Online.

And we're going to do a particular search this time for a layer called percentage of populations living in urban areas.

So you can type those words into the search and it will find that layer.

We're then going to add the layer to our map.

And we see immediately that we've got a different type of map here, choropleth map, which is showing the shading for particular bounded areas, in this case countries.

In layers, we find that new layer, click Options, Show Properties for the panel on the right.

And then we're going to edit the layer style.

What we want to do is to show the data that sits behind this map, be selective about it.

So we click Edit Layer Style.

Then we go to the first panel, which is Choose Attributes, and we're going to select one of the fields.

So we click Field.

And then we're going to scroll down till we see a series of fields which are called urb percentage.

And you'll see there's a year after each one.

And they go from 1955, that's the urban percentage in 1955, right through to 2015, this is actual data, and then a prediction for 2035.

So the most recent factual data we can look at is 2015.

So we're going to select that and then add that to our map and watch what happens.

We then go to the second panel, pick a style, and we're going to click Style Options.

Now, one thing that sometimes happens with choropleth shading is it can be the wrong way round.

So we have perhaps lighter colours here for the more urban areas, but darker colours for the less urban areas.

And it would be good to be able to switch those round.

And we could do that very easily by clicking the button here called Flip Ramp Colours.

And you'll see the colours are switched and are much more intuitive.

Next, we're gonna try and capture a bit more of the variation between the urban percentages in different countries by doing something called classifying our data.

So we scroll down to the part of the dialogue that allows us to do that and we switch on Classify Data.

We're going to select a method for classifying the data, which is going to be not natural breaks, but equal interval.

We click equal interval and then gonna change the number of classes, these are the number of classes you will see that are currently four.

And we're gonna try equal intervals of 10%.

That means we're gonna have to have 10 classes.

So we increase the number of classes to 10.

And then hopefully our data presentation is gonna be a little more fine grained.

When you've done that, click done twice and then click the legend over on the left to display the meaning for those choropleth shadings.

Then click save and save your map.

Now, if we click any of the countries, we get a whole lot of information, some of which is useful, some of which is not.

So what we're gonna do is simplify the pop-ups as we did before.

So to do that, we're going to click on the new layer in layers here and click options, properties.

When we've done that, we're going to go to the toolbar on the right hand side and click pop-ups.

And once you got to pop-ups, we see the pop-up dialogue and we also see a panel to show the progress of our work with the pop-ups.

We're going to first of all configure the title, which currently shows percentage of population living in urban areas.

We're going to delete that and instead type this formula of words, country, colon, and I'm gonna pick up name, which is the name of the country from the data that sits behind the map.

And you can see that's already changed in the pop-up over here.

Then what we're going to do is delete the fields list because it's an awful lot of information there, some of which we don't need.

So we're going to delete that list and you'll see that disappear in the pop-up.

And instead of that, what we're going to do is to add a chart.

So we click add content.

We're going to click chart.

And in configure chart, we want to choose a line chart.

We're going to show the trend in urban population over the years.

So because that's what we're going to do, in the title for the chart, we can change that by typing that into here.

And you will see that title has appeared already, although the chart hasn't.

And then we went to select the fields we want to show in our chart.

So we click select fields and you'll see, if you remember, several of the fields are about urban percentage of population over the years.

So it's quite straightforward.

We click the urban percentage for 1955, for 1975, for 1995.

Scroll down a little bit.

2015 and then the prediction for 2035.

And we have a very useful line chart which has appeared in the pop-up.

And we can click on different countries to see what they show.

And if you float the cursor over the line chart, it gives you the percentage urban for 1955, for 1975, 1995, 2015 and then the prediction for 2035.

In this case, that's for Mali.

And what we see with Mali and several other countries is that they've undergone rapid urbanisation from single figure percentages of urban population in 1955 to a prediction of over 50% by 2035.

And we can follow the trends for any of the countries in the world.

Here's Brazil.

It starts with just over 40%.

By 2035 it's expected to be just under 90%.

And the curve of change is much different.

You can see that the increase, the pace of increase for urbanisation is actually levelling off as it reaches the higher numbers.

The useful thing we can do to improve the visualisation is to change the base map.

And we're gonna perhaps select something more appropriate to provide better contrast, such as topographic.

Having done that, we can shut down the different panels, make sure that we can then see the legend.

So in just a few minutes, we've managed to visualise the urbanisation levels in the countries across the world.

We've also got pop-up information which shows the trends over an 80 year period.

So it just remains to save our work.

Soon you'll have your own opportunity to use GIS choropleth maps and the pop-ups to visualise urbanisation data.

But let's just check up on some of the points from the video demonstration first.

Which two of the following apply to the visualisation by choropleth maps? You may wish to pause the video here and restart it when you selected two answers.

The correct choices are A, polygons and B, shapefiles.

Well done if you got those.

Here's our second check.

When using Map Viewer to configure a line chart in a pop-up, which is the correct icon sequence to use? So look carefully at your choices here of the sequences in A, B and C.

And then pause the video and restart it when you've selected your answer.

In this case, the correct sequence to choose is A, which starts with the pop-ups icon, then the chart icon and then the specific line chart icon.

Now for the task which will help you to use GIS choropleth maps and pop-ups to visualise urbanisation data.

As for the task in Learning Cycle 1, you'll need to access your saved web map, Urbanisation.

In task 1, you're going to visualise levels of urbanisation using GIS choropleths.

In task 2, you're going to configure pop-ups for the urbanisation data.

And finally, in task 3, you're going to explain what line charts visualise about urbanisation trends.

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

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

Hopefully you were able to undertake those tasks effectively.

Here's some feedback.

For task 1, your visualisation of urbanisation levels should look something like this.

And it's used the topographic base map to provide effective contrast for the data.

For task 2, here's an animation to show what the pop-up line charts should look like.

And we see a selection of countries with their pop-ups appearing showing data about their urban trends.

So for each country, taking Japan as an example, you should be able to float the cursor over the line chart to see the urban percentages from 1955 to 2035.

For task 3, I wonder if the points you made about urbanisation trends were similar to Laura, who found that the trends are easy to spot using the pop-up line charts.

The urban population of most countries seems to have increased rapidly from 1955 to 2015, and is forecast to continue to 2035, but not as fast.

Or Jun, who spotted that the urban population in many countries of the global south is growing very rapidly, but from a low base.

Good point.

For example, Mali and Chad in Africa or Vietnam in Asia.

But in South America, the increase is slower.

Or Izzy, who concluded the urban population in countries of the global north is growing, but not very fast.

However, it was already quite high.

It was starting from a higher base, for example, in Canada and North America, France in Europe or Japan in Asia.

If your answers were very different or you recognise any errors, take another look at the video demonstration.

Let's summarise our learning with these key points.

We began by learning that for the first time ever, humanity became urban around 2007, which is quite a dramatic and major event in human development.

Then we learned that GIS can help us to manage and visualise big data about world cities using filters and pop-ups.

It can also help us to manage and visualise big data about trends in urbanisation using choropleth maps and pop-ups.

We also found out that GIS choropleth maps use shape files, which are joined to attributes using place identifiers in the shape files.

And finally, we learned how GIS pop-ups can be configured to visualise trends in big data about urban population using line charts.

Well done.

What we've been doing in this lesson is learning some really useful and powerful GIS skills, and we've applied them to the topic of global importance.

This kind of deliberate practise that can really help your GIS skills to become fluent.

Hopefully you found the learning interesting and useful.

I look forward to learning with you together again in another lesson.

So all the best and bye for now.