Lesson video

Hello, my name is Mr. Conway.

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

The emphasis is gonna be very much on the way we use geographical information systems, otherwise known as GIS.

So let's get started.

This lesson is part of the "Fieldwork" unit, and by the end of today's lesson, our intended outcome is that you'll be able to use GIS to visualise and analyse human geography fieldwork data.

You'll be learning to use some GIS techniques which perhaps you haven't used before, so I'll be here to help you along the way, and hopefully those techniques will have quite wide applicability.

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

These are the keywords for today's lesson: georeferenced, bearing, and attribute.

Let's look at each of them in turn.

Georeferenced refers to information which is tied to a particular location, or as we sometimes say, geolocated, using an agreed system such as latitude and longitude, which we usually measure in decimal degrees.

A bearing is any angle measured clockwise from 0 to 359 degrees which is used to orientate attribute symbols or to show direction.

It could be, for example, pedestrian or vehicle traffic flows.

An attribute is a data value associated with a feature or variable measure in a GIS layer, and we sometimes use the word field when referring to attributes as well.

There are two learning cycles for this lesson about using GIS for human geography fieldwork.

So we're gonna look at the first of these now, which is using GIS primary data for fieldwork.

Fieldwork is often conducted in human landscapes such as urban areas.

GIS can visualise and analyse human features and processes, including the way that people interact with the physical environment.

As we're gonna see in this lesson, GIS can use primary or secondary data, or both.

But for any data to be used in GIS, it's got to be georeferenced.

In case you're not sure what that means, we'll be looking at that very soon, but let's see our fieldwork context first.

Welcome to Portsmouth.

Portsmouth is a city on the coast of Hampshire in southern England.

It's got a population of around 900,000 in the whole of the metro area, and the most significant economic activities over the years fall into four sectors: primary activities such as fishing, secondary sector activities including shipbuilding and rope making, especially in the days of sailing ships when they needed a lot of ropes.

Tertiary activities, as Portsmouth's name suggests, its principle economic activities are linked very much to its status as a large port settlement.

It has a very, very long-standing military tradition as well linked with that as a naval base, and it acts as an important gateway for sea transport to France and to the nearby Isle of Wight.

Leisure and tourism are increasingly important, often linked to its heritage as a naval base.

Then we have quaternary activities, which are also growing partly due to Portsmouth's status as a university city and linked to activities in the creative industries.

Consequently, there's a wealth of geographical interest in the area for which fieldwork is required.

So why would we need GIS fieldwork data about such areas? Well, one reason is that it's used to improve our understanding of a city, informing decisions about how it may be managed.

Portsmouth is a very old city.

It was founded hundreds of years ago.

And old settlements grow and change, and the changes present challenges, and the challenges require management.

This means there have been a number of important initiatives to look into.

There's been urban regeneration and rebranding, such as Gunwharf Quays, which we'll look at later.

Big flagship projects, such as the iconic Spinnaker Tower you can see on the right.

Heritage tourism; for example, Portsmouth Historic Dockyard includes famous ships such as the Mary Rose and HMS Victory, which you can see on the left.

And the Dockyard is one of the top three visited paid tourist attractions in the UK outside of London.

Transport infrastructure is important to manage.

For example, prioritising walking, cycling, and public transport is part of the city council's.

And sustainable urban living, including the development of nature-rich green spaces.

One part of Portsmouth where significant regeneration and rebranding has taken place is in the area around Gunwharf Quays.

This area used to be a major military ordinance storage centre.

It was called HM Gunwharf, and later it was called HMS Vernon.

But it closed in 1995, creating a massive brownfield site.

The regeneration and rebranding was largely retail-driven around a large new shopping centre, and that was opened in 2001.

And that in itself was a flagship project, as was the building of the nearby Spinnaker Tower, which opened a little bit later in 2005.

Gunwharf Quays is close to Portsmouth Historic Dockyard, including Nelson's ship HMS Victory and the Mary Rose, Henry VIII's flagship which sunk off Portsmouth and then the wreck of it was lifted and preserved in the 1980s.

The Mary Rose Museum was rebuilt and reopened in 2017, so there's been a lot of investment in regeneration and rebranding.

Now let's pick up on this vital aspect of GIS called georeferencing.

It's the precise location of a place using decimal degrees of latitude and longitude so that it can be used in GIS.

So we can see the precise location of one of our fieldwork sites in Portsmouth at Gunwharf Quays.

There are various ways to find out this, but what we've used here is the location tool in ArcGIS Online and selected the option which gives us decimal degrees, abbreviated to DD, which GIS can use very easily.

The value of this is that we can then use the GIS to link to any data collected at Site 1 to that precise location.

So GIS attaches the data to that place.

How accurate is this? Well, using six decimal places, as we can see here, it's sufficient to locate anywhere in the planet to 111 millimetres, or just over 11 centimetres.

Such precision becomes very important when comparing places which are close to each other, which we often do in fieldwork.

So how can we georeference our primary data? When collecting primary data in an urban area, we need to record each site's precise location, and there are different options for doing so.

One way is to use an automated GPS-enabled device, like the one the student is using in the photo on the left, to record observations about traffic in an urban area using an app such as Survey123, which records the decimal degrees of latitude and longitude alongside the observations being made.

Or another way is to mark the location on a conventional map and use GIS later on to georeference the location.

As well as the georeferenced location, it's important to record other information which can be used by GIS.

For example, the time of day, the date, the day of the week, all of these things should be recorded because our results may be linked to those attributes.

Also, it's often used to record the compass bearing in degrees for the data involving orientation, such as the direction of traffic flow.

When we visualise georeferenced data, it makes it much more useful and powerful to enable us to address inquiry questions, test hypotheses, or compare places.

Here's an example of an inquiry question for an urban area, "What impacts do regeneration and rebranding have?" Could these impacts be direct? Could they be indirect impacts, such as a halo effect where places close to the regenerated area are impacted, either positively or negatively? Which impacts are measurable? We can link hypotheses to that inquiry question.

For example, we might hypothesise that environmental quality improves around a regenerated area, and then we obtain data to test that hypothesis.

We're going to use GIS to process and visualise some real data for Portsmouth.

It was collected by small teams of students from 15 sites along a two-kilometer urban transect.

That's a pre-planned line along which the data was collected.

They collected at each site quantitative data, including measurable attributes including environmental quality survey, vehicle counts, pedestrian counts, and as recommended earlier, they also recorded the compass bearing for the direction of traffic and temporal data such as the time of the observations.

Furthermore, they collected qualitative data at each site, including photographs and the origins of visitors.

How does this attribute data need to be set out in a spreadsheet for GIS? Well, here's an example.

We have columns for georeferenced data.

We have site details, that is the site number and a short description of the site, the key attributes for observations recorded, the bearing, showing the orientation of vehicle flow at each site, the date and time of the observations 'cause that could be important, and some URLs for the photographs taken and then uploaded to the school ArcGIS Online account.

Here's a video guide which is going to show how to use GIS primary data for human fieldwork.

We're going to use a ready-made web map called Fieldwork H, where H stands for human.

And what you'll notice is that there are some layers already loaded onto the map to support our learning about GIS.

The first thing we're going to do is just click the Time slider here because we're gonna use that later, and by default, it switches on every time you open the map.

So we're just gonna switch that off for now.

And you can see some orange dots have appeared, and those orange dots are about the data that we're going to manage.

Before we do that, you may just look at these features here.

First of all, we have Gunwharf Quays marked, and you can see the extent of it here.

And we can also see Portsmouth Historic Dockyard nearby.

So what we want to do first is to visualise the primary data as proportional, orientated symbols.

I'm going to zoom in just a little bit so you can see them better.

These orange dots, they will become clearer in a moment.

In Layers, we click the three dots for the options to open the Properties panel on the right.

Then you'll see a section called Symbology, where you click Edit layer style, and after that, there's a panel.

Number one, Choose attributes.

In that panel, you're going to click the word Field, which refers to the different attributes in the data set, and you're going to find Vehicle count.

And when you've ticked that, you click Add, and you'll see the symbols change to become proportional symbols.

Now, these proportional symbols are circles.

It would be good to use something a little bit more meaningful.

So we can do that if we go to the second panel called Pick a style.

And within that, we go to the section called Counts and Amounts, size, and click Style options.

Then in the panel that says Symbol style, we can click the pen, and that will open another panel.

And in that panel, click Basic point, and then Basic shapes.

And you can scroll down to Arrows and click that, and then scroll down just a little way until you find a two-way arrow.

And I think I'm right in saying there's only one two-way arrow in the selection.

So click on the two-way arrow, and then click Done.

You'll notice the symbols have become two-way arrows.

And then in Colour, we're going to change the colour of the arrows by clicking Pen.

And if we click Pen, and then in this panel here, we're going to select a shade of red, and its colour code is ff0011.

You can choose something else if you prefer.

Then we click Done.

And then in Symbol style, we're going to close that panel and then scroll to something called Size range, just here.

What we're going to do now is adjust the size of the symbol.

So, we want to do that manually.

Untick Adjust size automatically, and then we're gonna change the size range so that the lower one is 15 and the largest one we're gonna have is 80.

Next, we're gonna scroll down to the very bottom of this panel where it says Rotation by attribute.

At the moment, you can see the arrows are all pointing in the same direction, but it'll be much better if we could show direction of travel of the traffic along these roads.

So in Rotation by attribute, we switch that to on.

And you'll see the arrows have moved, but that's because they're using the site number, they're not using the bearing data.

So if we click this dropdown and then we select Bearing, and you'll see the arrows change to show the direction of traffic flow on those roads, that's a much better visualisation than we had before.

So we're going to click Done twice.

If we click Legend, we'll see that that appears as a key on the left-hand side, and we're now ready to save our map.

So it's already got a title, but being as you're logged onto ArcGIS Online, what you can do is save it with your own name.

For example, we could just save, we're saving it for the first time, so we click Save as, and we're just gonna call it Fieldwork H 1.

I'm going to type DEMO after mine, you don't have to do that, just to show that that's my copy.

Our next step is to configure the pop-ups for the vehicle count data.

If we click on the pop-ups at the moment, they're very long and not terribly meaningful, but we can improve those.

So we click the Layers panel.

Make sure that we're looking at the properties.

Show properties for vehicle count.

And then we find the Pop-ups button on the right-hand side.

And when we click that, you'll notice it opens the pop-ups so we can see what progress we're making as we configure the pop-up.

So in the panel on the right, you'll see a Fields list.

We're actually going to delete those and customise those in a moment.

We're also gonna change the title, so just delete what's there and type this formula of words, which will pick up the site number and site name from the data set that sits behind the map.

And it's gonna pick up photos for all the sites.

If we just click on a couple, you can see as we click on the pop-ups for each symbol, it's got photographs based on what was taken by the students at each of these locations.

Now, another interesting thing we can do with this data is configure the time because the time was recorded for the observation.

So if we click Time and put that Time slider back, we can then click on this little cog at the bottom of the page here where we've got Time slider options.

So we go to Time slider options, and in Time slider mode, there's a small dropdown menu.

Click that, and click Show data progressively.

Then move across to the Time intervals tab.

And we could leave this as it is, but we're gonna just change it to perhaps every quarter of an hour.

So, we do that, and then to finish that process, we click the cog again.

And then we can visualise the sequence if I just scroll it back to the start, and then we can step through either using play or fast forward or going backwards.

So I'm just gonna step forward now to show you what that looks like.

So this reveals the sequence by which the data was collected along the urban transect.

So if we show all that data in one go and then compare that data with another layer because one of these has been pre-prepared, which is Pedestrian count.

If we click that, we can then see another set of data underneath it.

So if we just remove the Properties panel here for a moment.

And then what we can do is click this layer on and off, and this layer on and off, and we can get some idea to see if there's a link between the data.

So, for example, we see that there's a lot of traffic on this road here, and on the other hand, the pedestrian count is relatively low.

If we look for somewhere where the pedestrian count is quite high, such as here, what's the vehicle count like? It seems to be very low.

We can't see it all that clearly, so we'll just toggle the Pedestrian count there, and you can see that that seems to be the case, particularly, as might be expected, in and around Gunwharf Quays itself.

Soon you'll have the opportunity to use GIS primary data yourself, but let's just check up on a couple of points from the video demonstration.

So the first check is, in GIS, what type of bearing is needed for direction or orientation to be visualised by rotating a symbol such as an arrow? You may wish to pause the video here and restart it when you've selected your answer.

Well done if you selected B, the number of degrees clockwise, which of course can be any number between 0 and 359 degrees.

Now for a second check.

Which two data attributes you can see here are essential to enable data to be georeferenced to a precise location? Take a moment to pause the video if you wish, and restart it when you've selected your two answers.

The two correct choices are A and D, A, decimal degrees of latitude, D, decimal degrees of longitude.

Well done if you selected those.

Now for the task which will help you to use GIS primary data yourself.

For these tasks, you're going to need to open the link provided, which takes you to a ready-made web map called Fieldwork H.

That's H for human.

In task 1, you're going to visualise primary data as proportional, orientated symbols.

In task 2a, you'll be configuring pop-ups for Vehicle count data.

Task 2b involves configuring time, that's temporal data for Vehicle count data.

And in task C, you're asked to compare Pedestrian count and Vehicle count and suggest possible links.

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 1, your web map for Vehicle count should look something like this, using proportional, orientated symbols, and you can see how the arrows, the double arrows, have been orientated using the bearings data.

For task 2a, your configured pop-ups for Vehicle count may have looked similar to this.

For task 2b, your time-enabled data should look something like this, and we can see an animation of how that may have developed.

For task 2c, your comparison of Pedestrian count and Vehicle count should look something like this, here's an animation.

And some possible links between these attributes could be that Vehicle count is higher where Pedestrian count is lower.

Vehicle count is lower where Pedestrian count is higher.

And the higher Vehicle count may contribute to lower environmental quality.

Your answers may have been rather different to those, but if you recognise any particular errors, take another look back at the video demonstration.

Our second learning cycle will focus on how we might use GIS secondary data for human fieldwork.

How can secondary GIS data support fieldwork inquiry? There are various ways.

For example, we can use georeferenced historical maps, maps from the past, or we can use georeferenced maps by the government or non-government organisations, or we can use aerial or satellite images georeferenced as media layers.

We're gonna take a quick look at how these might work for us.

The UK Government's Office for National Statistics, or the ONS, shares georeferenced data such as the Index of Multiple Deprivation.

The georeferenced ONS data is published for small areas.

That is, for example, Lower Super Output Areas, or LSOAs.

Then mapped using GIS choropleth techniques.

Georeferenced IMD data can be loaded to Map Viewer to enable comparison with primary data.

Such data sets are very large.

We call this big data.

So, filters can be used to select specific areas or specific attributes.

There are 32,844 LSOAs.

That's the Lower-layer Super Output Areas in the country, and they're ranked from the most deprived to the least deprived.

How is the Index of Multiple Deprivation calculated? The IMD is a composite index.

That means it's made up of different components, and there are seven of these.

They're called domains, including one called Living Environment Deprivation.

That domain contributes 9.

3% of the total IMD.

It appears in data sets in two ways, as Environmental Score or Environmental Rank.

And arguably, Environmental Rank is probably the most useful of the two.

With our sort of fieldwork, the easiest way to collect similar data to the IMD is to look at the environmental quality surveys we can do, or EQS.

Although it's assessed differently, there can be some links made with Living Environment Deprivation.

It may be helpful to visualise the weighting for each domain using a pie chart.

And you can see Living Environment Deprivation here.

Living Environment Deprivation must be interpreted carefully.

Here's an example comparing Lower-layer Super Output Areas in Portsmouth.

Lower ranks mean lower levels of environmental deprivation.

We can see LSOA 024E ranks 14,973 out of the total in the country of 32,844.

Higher ranks, on the other hand, mean higher levels of environmental deprivation.

So, on the other hand, LSOA 016B ranks 4,375 out of the total of 32,844.

So, the sad face suggests that that LSOA is more deprived, at least environmentally.

The visualisation of georeferenced data can then be used to test hypotheses or make comparisons with secondary data.

Let's just refer back to our inquiry question for an urban area, "What impacts do regeneration and rebranding have?" So a second hypothesis might be about possible links between our primary data and our secondary data, such as EQS data aligns with ONS data for Living Environment Deprivation.

That is, the Environmental Rank.

We're going to see a second video clip now providing a step-by-step guide to see how we might use GIS secondary data for human fieldwork.

To do this, we're going to visualise one of the other attributes measured by the students, and we're gonna compare it with some secondary data from the government.

So as before, we're going to toggle the Time slider off, and then we're gonna move to the Layers panel.

And for the Vehicle count layer, we're going to actually use it again, recycle it if you like.

So I'm gonna click the options.

This time, I'm going to click Duplicate.

So that means we've got a copy of that same layer.

You can see it's Vehicle count- Copy.

I don't wanna get muddled up, so I'm going to rename the new layer and call it EQS because that's the data we want to show in that layer.

Click OK to do that.

Then we're going to just hide the original Vehicle count layer.

And the duplicated layer's got time-enabled data in it, so we just switch off the Time slider again.

Now we're ready to configure the new layer.

So we go to the options to click Show properties for the new layer.

And of course, it's still showing the Vehicle count data, and we don't wanna do that.

We want to show the EQS data.

So we go to Edit layer style, and in Choose attributes, we click the X so that we can show another attribute instead.

We go to the list of fields or attributes, and this time, we want the one that's about EQS.

It says EQS 5-50 because that's the range.

So we tick that and add it.

The default, as we saw before, is proportional circles, but it'd be good to choose something more distinctive.

If we go to Pick a style and Counts and Amounts, size, and click Style options, we can choose a different symbol.

So in Symbol style this time, we click the pen, and then we're going to go to Basic point.

And this time in the dropdown, we click Basic shapes.

Let's pick a simple shape this time, and then we can configure it.

We click Done.

So we go to Fill colour again, and we're going to choose a shade of green, which seems appropriate for EQS.

00bf16 is the code.

Click Done, and we have green diamonds instead of the proportional circles in orange.

And in Symbol style, we click the X to shut that little panel.

Then we scroll down to where we can configure the size range.

We want to configure the size range manually, so we untick Adjust size automatically.

I'm gonna change the lower size to 20.

I'm gonna change the upper size this time to 60.

May have noticed that these symbols have been rotated because you've inherited from the Vehicle count layer.

So we go to Rotation by attribute and very simply switch that off because direction of flow isn't applicable with this attribute.

And then we can click Done.

To complete this step, let's have a look at the legend.

We can see that the EQS data is now displayed, and we can save our work.

There's one thing more we need to do with this layer, which is configure the pop-ups for it.

So if we click the Layers panel, and we look at the EQS layer this time to show its properties, and click the pop-up icon to reveal its dialogue box and the progress panel, which is still showing the vehicles number.

So we need to change that.

So we use the Text dialogue again.

Edit text.

Remove the words that were there before and replace them with something about EQS.

So very simply, EQS = and we're referring to the attribute title just there.

So we use that formula of words, click OK, and now we can see the EQS value is showing for each of the sites.

So we can just check that.

We've got the photographs for each one.

EQS for that site, EQS for that site, and so forth.

So this seems like another good time to save all our good work.

Now, having plotted out EQS data, it's gonna be interesting to see if we can make links with secondary data.

So we're gonna try and make links with the ONS data for Living Environment Deprivation for this area.

In particular, we're gonna use the measure of Environmental Rank, remembering that higher ranks are not particularly good news because that means a higher level of environmental deprivation.

Now remember, this is a ready-made map, so we already have the IMD Environmental Rank data built in.

So we switch that visibility button to make it visible, and we can see each of the areas is shown here, each of the LSOAs or Lower Super Output Areas.

If we click on any particular LSOA, we'll see its Environmental Rank number.

So in this case, we have the 022B LSOA is ranked 4,946.

But let's move along our transect to see what the differences are.

You could do this as well, but I'm just gonna demonstrate it now.

So if we go near to Gunwharf Quays, I'm just gonna move along the transect trying to click on the LSOA so we can look at the differing numbers.

So we have one there that's ranked 14,970.

That's almost 15,000th.

That's out of a total of 32,844 in the whole country.

So it's round about halfway in terms of deprivation.

Definitely not the worst, definitely not the best.

If we have a look at the adjacent ones, we just click through here.

This is ranked lower, so that's probably a better thing for that area.

Then we get one that's ranked even lower.

But then we get one that's ranked much higher, in the top 5,000.

And finally, if we just go to this area here, it's on the edge of this one here.

This ranked about 9,550.

And this one here, which also touches on that, is ranked about 9,953.

So they're both in the top 10,000.

So the question is, can we see any alignment between these two sets of data? This is quite a difficult thing to do, but we can see some patterns.

If we go to one of the highest ranked LSOAs for deprivation, do we also see that our EQS is low? Perhaps that's the case.

If we go to another area with a low rank in the top 5,000, which means a high level of deprivation, what do we see? This is probably a fairer test because we've got several EQS measures there, and they all seem to be on the low side, below 25.

So perhaps there is some correspondence between our data and the secondary data.

Conversely, do we find that the LSOAs with the more desirable low ranks also have higher EQS? Well, let's have a look.

We have a couple of LSOAs ranked below 14,000, and they do seem to be attracting the higher EQS scores.

We have another one there that's 18,976, and its scores are quite high.

That would suggest that there is correspondence between our data and the secondary data from the ONS.

But how reliable are these comparisons? We probably need to be a bit cautious.

EQS and Environmental Rank are calculated in different ways, so comparability is limited.

The EQS, after all, is based on a very small sample taken at a very particular time, and it's quite subjective, so the comparisons may lack validity.

But it seems clear that the capacity of GIS to visualise these attribute patterns really has something to offer in terms of helping us to answer our inquiry questions, to test our hypotheses, and make links with secondary data.

Soon you'll have the opportunity to use GIS secondary data for human fieldwork, but let's just check up on some of the key points from the video demonstration.

In the Index of Multiple Deprivation, or IMD, what weighting is given to the Living Environment Deprivation? You may wish to pause the video here and restart it when you've selected your answer.

The correct choice is C, 9.

3%, so it contributes just under a 10th of the IMD.

Our second check.

Look at the map carefully.

It's a GeoGif, which means it's animated.

The question is, which Lower-layer Super Output Area, or LSOA, in Portsmouth has the most or worst environmental deprivation? You may wish to pause the video here and restart it when you've selected your answer.

Okay, the correct answer, we'll look at A, it could be that, but it isn't because that is a relatively less deprived area, hence the smiley.

The other one is the correct answer, B, because it's ranked higher, which means that there's more deprivation in that area.

So it's ranked 4,375 out of 32,844.

Now for the task which will help you to use GIS secondary data for human fieldwork.

As for the task in learning cycle 1, you'll need to access the same ready-made web map called Fieldwork H, H being short for human.

The link is here in case you don't have it open already.

In task 1, you're going to visualise other attributes for fieldwork data.

We're going to map EQS, in fact.

In task 2, you're going to configure pop-ups for Portsmouth's EQS data.

In task 3a, you're going to link secondary ONS data with Portsmouth's EQS data.

Then in task 3b, you'll need to consider to what extent do the two data sets align? And in task 3c, reflect more generally on how effectively GIS can present data and inform our conclusions.

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

Hopefully the tasks went well for you.

Now for some feedback.

For task 1, your proportional EQS symbols should look something like this.

We've used green diamonds.

You may have used something a little bit different.

Your configured EQS symbols should look something like this, and we can see they've been configured with the site number, a site detail, and a photograph for each site, plus, most importantly, the EQS value of 39 for that site or indeed another site.

Then for task 3a, your EQS symbols and secondary ONS data should look like this.

And we've clicked on one of the LSOAs; you could have clicked on others as well.

Then for task 3b, to what extent do the two data sets align? We had some prompts.

So the first one was do the higher ranked IMD 2019 - Env Rank also have lower EQS? Well, the two LSOAs with the highest ranks, that's in the top 5,000 in the UK, generally have the lowest EQS scores.

So that sort of makes sense.

The second prompt, do the lower ranked IMD 2019 also have high EQS? Well, the two LSOAs with the lowest ranks generally have the highest scores, of over 30, which is an interesting alignment.

How reliable are those comparisons? Well, EQS and Env Rank are calculated in different ways, so compatibility is a bit limited.

And EQS is based on a small sample taken at one particular time and is quite subjective given the small sample, so comparisons may lack validity.

Finally, for task 3c, how effectively can GIS present our data and inform our conclusions? Well, were any of your points of view similar to these? Alex points out that georeferenced data enables GIS to visualise attribute patterns, such as Vehicle Count and EQS, which both seem to improve along the transect near the regenerated area.

So maybe there's a possible halo effect close to that area.

Aisha comments that configuring proportional symbols and pop-ups is an effective way to visualise site attributes.

Bearings are also useful if we want to show vehicle count direction.

And the time slider shows the sequence of our data collection.

Sofia said that although they're not the same, it's interesting to make links between our EQS and GIS secondary data attributes, such as Env Rank, because we can compare with more reliable data.

But careful interpretation is needed, absolutely.

Well done, we've developed very good GIS knowledge today and skills that will be useful for your human geography fieldwork.

So a summary of our learning would be these points here.

GIS can be used to visualise and configure primary georeferenced data attributes from human geography fieldwork.

The visualisations from GIS can use attributes including bearing data to create proportional, orientated symbols.

These can also be time-enabled using temporal data attributes to enhance the visualisation.

And we can use GIS visualisations of secondary data, such as government data about deprivation, that can be used to support our inquiry.

Hopefully you found the learning interesting and useful.

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

All the best, and bye for now.