1 Introduction

Urban land serves multiple, often overlapping functions, which can change over time and vary between day and night, sometimes only apparent to local residents. Understanding these patterns is critical for monitoring population dynamics, particularly as tracking change over time can be complicated by inconsistent boundaries in successive Censuses. Accurately summarising population data supports a range of stakeholder applications, including planning for educational provision, allocating resources to schools, and assessing risk management needs such as emergency services, flood planning, or health interventions. By linking land use, population trends, and temporal patterns, city planners and policymakers can make informed decisions that improve service delivery, mitigate risks, and respond effectively to evolving urban demands.

1.1 Learning Aims and Objectives

This practical investigates spatial patterns of day and night-time populations in Liverpool, including variations across different age groups. It also examines how residential and employment populations are distributed across the city, using the Local Authority District as a case study. The analysis considers the clustering of different industry types and how these clusters attract workers from various parts of Liverpool.

The aim of the practical is to apply GIS to explore the socio-economic geography of the city. The analysis utilises data from Ordnance Survey (OS), the 2021 Census for residential populations, and 2011 Census data for workplace populations, enabling a detailed examination of population distribution, employment patterns, and urban spatial dynamics.

You will also be developing the following GIS skills and understanding:

  1. Basic GIS/spatial analysis
  2. Numeric data classification schemes
  3. Develop an understanding of daytime and night-time population structure through workplace zone statistics, ONS population estimates and employment data
  4. Basic cartography: using population and built environment data to create publishable maps

2 Practical 1 - Part I

2.1 Getting data

  • Create a new folder for this practical—ideally on the university’s shared M drive (for example, M:)—which will serve as your working directory for all related files and outputs.
  • Download the data folder Prac1_data from Canvas and unzip it to your working directory (on Windows machines right click the file > Extract All). This is important steps as you won’t be able to use unzipped data.

2.2 Displaying data in QGIS

  • Start QGIS by clicking on Start > QGIS Desktop 3.40 (or any other QGIS version available on your machine; please note that this instruction is written for QGIS 3.40, so other versions may slightly differ)

2.2.1 Adding layers

Let’s start by adding data to the QGIS display. First, we add the Census 2021 population data for Liverpool Local Authority District at LSOA level

  • Start by clicking the Add Vector Layer button AddLayer or Layer>Add Layer>Add Vector Layer…
  • Then click on the Browse button and navigate to your working directory. Open the LivPop_2021.shp file located in the Prac1_data folder
  • Click Open and then Add (if a window pops up, click on Cancel or just close it)
  • Change the name of the layer to Population by right clicking on it and selecting Rename Layer.

This will add a layer containing information about Liverpool’s Census 2021 population aggregated by Lower Super Output Area (LSOA).
QGIS has a number of different ways of navigating around spatial data.

  • Explore the data by using some of the navigation tools such as Zoom In, Zoom Full and Pan Map

  • The Identify Features tool Info allows you to select an LSOA on the map and see the attribute information stored in the shapefile about that LSOA.

  • Click on any LSOA, a window will appear, titled Identify Results with information about the LSOA you selected e.g. the LSOA code or total population.

2.2.2 Changing the display colour

When we add a shapefile, QGIS randomly assigns a colour. You already know from the GIS practicals in Year 1 how to change a layer’s colour, so take some time to experiment with different colours.

  • Right click on the Population layer in the Layers Panels and choose Properties > Symbology option Style_tab. Play with different options, explore various colours, opacity etc.

  • Once you’re happy with your selection, click OK and this will close the Layer Properties window and update the colour on the map.

The options you have in this layer will depend on what type of spatial data you are dealing with. The `Population’ layer is a polygon layer, so we can change the colour of the polygons and their borders. Point layers or line layers will have different options.

  • Also experiment with the more advanced Fill option by going to Fill > Simple fill > Fill color option and selecting Transparent fill. Then go to Stroke width and change the default value first to 1mm and click OK. Then adjust the value to 0.1mm, click OK. Finally go to Stroke style and select No line.
  • See what happens; when such styling could be useful?

2.2.3 Grouping layers

When working with a large number of datasets/layers it may be useful to group them in a logical way, so the Layers Panel appears less cluttered.

  • Using the same steps as previously, add more shapefiles to your QGIS project.
  • First, add files from the OS_data/OS_Liverpool folder: FunctionalSite.shp, Road.shp and Railway Track.shp (You may need to unzip the files first)
  • Then, highlight the newly added files, right click on them and choose Group Selected
  • The default name is group 1 so we need to change it to something more meaningful such as OS data.
  • Right click on it > Rename

Now is a good time to save the QGIS project file. It is highly recommended to save your work fairly often as QGIS can occasionally crash and your work may be lost.

  • Select Project/Save and save the GIS project to your working directory.

2.2.4 Classifying data

To visualise population patterns in Liverpool, we will use the population layer. At present, the map simply shows the LSOA boundaries, with no information on how the population is distributed across the city. To make the map more informative, it is useful to display spatial patterns such as population distribution or density at a small-area level. Drawing on the skills you developed in Year 1, you should be able to symbolise and classify the population data to reveal these patterns. As the population dataset contains numerical data, a function called Graduated is typically used, but for categorical data the Categorized function is more appropriate.

In case you’re not sure how to proceed, follow the steps below:

  • Right click on the Population layer
  • Go to Properties > Symbology
  • From the drop down menu on the top select Graduated
  • Use Total Pop as your Value to classify the population dataset
  • Choose 5 Classes and select Equal Count (Quantile) as your Mode
  • Click on Classify and then OK

At this stage, the Symbology window should be filled in as on the picture below:
Classify

Now, we are going to change the default colour and adjust the LSOA boundaries, so the map looks more professional.

  • Go to the Properties > Symbology window and choose Blues for your Colour ramp
  • Then navigate to Symbol > Configure Symbol.
  • Click on Simple fill and select 0.1 as your Stroke width
  • Click OK twice

Your map should now look something like the one below:
pop1

  • Go back to the Properties/Symbology window

  • Click on the Histogram button and then Load Values

  • Adjust Histogram bins value to 25

  • What does it tell you about the population distribution, is the data normally distributed or skewed?

  • Can you describe spatial patterns of population distribution in Liverpool?

  • Is this more likely to be a day-time or night-time distribution - why?

  • How different classification modes (Jenks, St Dev) change the population distribution

  • Lecture 2 and additional reading will help you learn more about various classification schemes

2.2.5 Basic statistics and field calculator

  • You can get a summary statistics on the population data by going to Vector > Analysis Tools > Basic Statistics for Fields

  • Select Population as your Input layer and Total Pop as the Field to calculate statistics on, click Run

  • Now, create a copy the Population layer by right clicking on it > Duplicate Layer

  • Rename the new layer to Working_age

  • Right click on the Working_age layer then Open Attribute Table > Field calculator and create a new field called Work age where you calculate the total number of those between 16 and 64 years old

  • In the expression window, type the following formula: “16 to 24 y” + “25 to 34 y” + “35 to 49 y” + “50 to 64 y”, click OK

  • A new field, showing the total number of working-age population, should be added to your Attribute Table

  • Save the edits (right click the layer > Toggle Editing > Save)

  • Then following the above steps in Section 2.2.4 to create a map of Working_age population distribution in Liverpool (Use Work age as your Value to classify the data)

  • How does the distribution of the Work age vary from the entire residential population? Check the histogram too.

3 Practical 1 - Part II

3.1 Workplace Zones and joining tabular data

The Census 2021 residential population data pertain to the so-called night-time distribution, as during the day many people commute to their work places. The concept of the daytime population refers to the number of people, including workers, who are present in an area during normal business hours, in contrast to the residential population present during the evening and night-time hours. So let’s have a look at the day-time population in Liverpool, captured by the so-called workplace zones data. Start from loading up the relevant data:

  • Go to Workplace Zone/shapefiles folder, select WPZONE.shp and drag and drop it to the Layers Panel
  • Add the WP102EW.csv table from the Workplace Zone/tables folder
  • Open Attribute Table of the WPZONE layer and explore it
  • What data does it contain and what does that mean?

There is only one field called WZ11CD, which is a unique code for UK Workplace Zones (note: these are different to LSOAs). Importantly, there isn’t any population data attributed to the layer, so we need to add this information. As you may remember, this can be done by joining a numeric table to the shapefile. In order to join tabular data, it is necessary that both of your datasets have a common attribute (e.g. a name, unique reference or code). Can you identify the common field for the WPZONE layer and WP102EW table? If you open the WP102EW table you will notice that the first field is called WZ11CD, so yes - our join will be based on that field.

  • Right-click on the WPZONE layer, select Properties and click on the Joins option

  • Click on the plus button to create a new join.

  • The Add Vector Join dialogue box will open

  • Make sure that WP102EW is selected in the Join layer dropdown box

  • WZ11CD should be selected in the Join field dropdown box

  • WZ11CD should also be selected in the Target field dropdown box (as per picture below)
    JoinTables

  • Hit OK twice

  • Open Attribute Table of the WPZONE layer and you will see that several new fields have been added to the Attribute table! Please ask for help if this is not the case

So, now we should map the Workplace zones population, but there are two things that we need to do before. First, the join that we have made is not permanent, which means that it’ll be lost once you close the project/QGIS. By now you should know how to make a join/layer permanent, so go ahead and do it. In case you’re not sure, follow the steps below:

  • Right click the layer then Export > Save features as choose ESRI Shapefile as you format,
  • Click on the Browse button (three dots) and navigate to your M: drive; name the permanent layer WZ_population
  • Click Save then OK
  • The WZ_population newly created layer will be added automatically to the Layers Panel
  • You can delete now the WPZONE layer and the WP102EW table from your Layers Panel as they are redundant

Second, we need to rename the columns in the Attribute Table as the existing ones do not make much sense. You can get the relevant column names from the table called variables_description.csv, saved in your working directory/data/Workplace Zone. The top three variables are the ones you need.

To rename the variables follow the steps below:

3.1.1 Renaming variables, field calculator

There are various ways of renaming variables in QGIS. A tool called Refactor fields can be used, however, in this instance we will use a different method (using Field calculator), where we will create new columns and name them appropriately. This methods also changes the type/format of the variables from text to numeric which is essential for any .csv data.

We have already used the Field Calculator; however, we will now explore some of its other functions. This tool is widely used for interrogating spatial data and performing calculations based on existing attribute values or defined functions. For example, it can be used to calculate the length, perimeter, or area of spatial features, as well as carry out a range of other operations such as creating or deleting fields. The results can be written to a new attribute field or used to update existing values.

Important
Note: text files cannot be displayed using the ‘Graduated’ scheme in QGIS - text variables won’t be simply visible when you try to classify the data. Please follow the steps below:

  • Go to Attribute table of the WZ population layer
  • Select Toggle editing modeToggleEdit
  • Click on Open field calculator button
  • In the dialog box that opens check the Create a new field box, type WZ_pop in the Output field name box and choose Integer (64 bit) as your output field type
  • In the expression window specify the name of the column that need to be renamed, in our case it is the "WP102EW_WP" or something similar (this may vary depending on the version of your QGIS)
  • Go to Fields and Values and double click on the "WP102EW_WP"
  • Click OK and the new column should be added automatically. If this is not the case please ask for help.
  • Save your edits by clicking the Toggle editing mode button
  • Repeat the above steps for the WP102EW_WP_1 column too - name it Density (use Decimal number (real) as your output field type, set Precision to 2, if you can change it)
  • Finally, delete the redundant columns by clicking on Delete field Delete_column button in the Attribute table
  • Click again Toggle editing mode and save the changes

3.2 Mapping Workplace Zones population in Liverpool

Now you are ready to map the workplace zones population in Liverpool:

  • Create a map of WZ population by following the steps from Section 2.2.4

  • Use WZ_pop column to classify the data and apply Greens as the Colour ramp

  • Your output map should look something like the one below:
    WZ_pop1

  • How similar the spatial distributions of the day and night-time population in Liverpool are?

  • Can they be directly compared?

  • Also, can you think of a location of the major employers in Liverpool such as the Universities, Hospitals, Airport or major retail areas? Is there any relationship?

3.3 Basic GIS analysis

In order to identify the major employers in Liverpool we will use the FunctionalSite layer from the OS dataset. We will also explore the relationship between the major employers and day-time population, Our basic analysis will comprise two steps. Firstly, we will select those areas that have the highest numbers of WZ population count - let’s say above the 5th quantile, which in our case is 658 people. Then we will use this information to extract the Functional Sites that are located within the selected Workplace Zones.

  • Open Attribute Table of the WZ_population layer

  • Click on the Select features using an expression button Select_query

  • In the expression window type this simple query “WZ_pop” > 658 and click Select features (note: it may not work if you do simple copy > paste)

  • Now, you should have all the workplace zones (WZ) with the population above 658 selected - 86 WZ in total

  • To select the Functional Sites that intersect with the selected WZ go to Vector > Research Tools > Select by Location

  • Select features in Functional Site that intersect with WZ_population

  • Check the box Selected features only

  • Click Run
    Check your output against the map below: Select_Location

  • Now, save the selected functional sites as a new layer

  • Right click the FunctionalSite layer and export the selected features as a new shapefile and name it WZ_FunctionalSites (Right click FunctionalSite > Export > Save Selected Faetures As… )

Note: If you are getting an error saying: Feature has invalid Geometry… This can be fixed by enabling the Processing Toolbox (right-click the top anywhere and toggle on) > Fix geometries (type in the search box ‘Fix geometries’).
Fix geom

  • Put WZ_population as the Input layer.

  • Click the (…) next to [Create temporary layer] to save to your directory. Choose where to save the output and name it WZ_population2.

Continue the rest of the instructions with the newly saved (WZ_population2) shapefile.

3.3.1 Labelling

Labels can be added to a map to show some information about an object/spatial entity. Any vector layer can have labels associated with it. These labels rely on the attribute data of a given layer for their content. In this practical, we will display labels for the WZ_FunctionalSites layer. Follow the steps below:

  • Go to the layer and right click, then Properties > Labels and select from the drop-down menu Single labels
  • Use ‘distname’ for the Label with field
  • Click OK

As we have labelled all the sites, the labels tend to overlap and as such, may not be very useful. Ideally’ we would show only those labels for the major employers such as hospitals, universities and the airport.

  • This can be executed by using the Rendering function within the Labels window. You need to create a simple SQL query that will select the above mentioned sites. This method gives you full control over the labels you want to display.

  • In Label options, scroll down to the Data defined > Show label button

  • Click on it and select Edit.

  • In the window that opens up, write a simple SQL query that will select the required three features

  • Familiarise yourself with SQL queries here: https://www.w3schools.com/sql/sql_wildcards.asp and try doing it yourself. However, if you get stuck, the following query should be helpful “distname” LIKE ‘%Hospital’ OR “distname” LIKE ‘%Airport’ OR “distname” LIKE ‘%University%’

  • Experiment a bit more with your labels:

    • Change your font to Arial and font size to 12
    • Try different placement options
    • Add buffer and drop shadow if you wish

One of the most useful options is to be able to move labels manually as the default placement, offered by QGIS, may be far from ideal… The build in option of Label Placement is very limited and it may be very tricky to get your labels in a desired place, so the solution is here:

  • Make sure that your Labels toolbar is active (if not, then right-click the top anywhere and toggle on)
  • Then click on this button Move label
  • Drag and drop any label wherever you wish

3.3.2 Clipping

  • Now, clip the WZ_FunctionalSites layer to the boundary of Liverpool. At the moment the largest functional site: Port of Liverpool extends well beyond the city boundary
  • Create a Liverpool_boundary shapefile by going to Vector > Geoprocessing Tools > Dissolve
  • Choose Population as your Input layer > Click Run
  • Remember where you have saved this layer as you’ll need it in the following practical

Then

  • Go to Vector > Geoprocessing Tools > Clip…
  • Use the WZ_FunctionalSites as the Input layer and Liverpool_boundary as the Overlay layer
  • Click on Run and save the output to your working directory, name it WZ_FSites, click OK
  • Right click the WZ_FunctionalSites layer > Styles > Copy Style
  • Right click the WZ_FSites > Styles > Paste Style (this step saves you time as you can simply copy > paste all styling work you have done so far)
  • Remove the WZ_FunctionalSites layer from the Layers Panel
  • Save the project
  • If you have done it correctly, your map should look similar to the one below:
    WZ_Functional Sites

Optional

You may also need to use various Census 2021 data (including workplace population) for any other research project (e.g. your dissertation) so the short instructions below demonstrate how to download Census 2021 data from the Nomis website.

Download Census 2021 data from Nomis
- Go to https://www.nomisweb.co.uk/ and click on Query Data under Data Downloads
- You’ll see a list of all datasets available from Nomis. Select Census 2021, then for example the ‘Number of usual residents in households and communal establishments’
- On the left-hand side click on Geography and then select 2021 super output areas – lower layer
- From the drop-down menu select Liverpool and click on the ’tick all’ button next to it
- Check the box next to ‘Tick to select columns’ under ‘residence Type’
- Then under ‘Format/Layout’ make sure ‘Comma separated values (.csv)’ is selected and check the box next to ‘Include area codes’ under ‘Other options’, then hit the ‘Download Data’ button
- Once your data is ready for Download, click on the file and then open the downloaded Excel table
- Delete rows 312 and then rows 1-7 – these are rows with additional information that you won’t need it
- Type LSOA CD in the unnamed column and save the table to you M: drive. Name it: Census2021-population

4 Practical 1 - Part III