1 Introduction

Land within cities is used for multiple, often overlapping purposes, the configuration of which is likely to have evolved over time. These uses may vary between day and night and may only be apparent to those familiar with these locations. Additionally, tracking population change over time is vital, though not always straightforward, as successive censuses have used inconsistent boundaries. Nevertheless, being able to summarise such information is highly beneficial for a range of stakeholder applications within cities.

1.1 Learning Aims and Objectives

This practical examines how employment, residential geography and population change map onto physical space within the case study Local Authority District of Liverpool. These analyses will assess the extent to which different industry types cluster within Liverpool, and how these attract workers from different areas of the city. Liverpool has also suffered for decades from population decline, however this trend has been reversed recently. This practical enables the analysis of both current and past trends in population change, which vary substantially within the city. The objective of this practical is to use GIS to analyse the socio-economic geography of Liverpool. You will take on a role as an economic advisor who has been tasked with assessing potential areas where a financial services company may wish to relocate within the city.

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

  1. Basic GIS tools and analysis
  2. Understanding of daytime and night-time population structure through workplace zone statistics, ONS population estimates and employment data
  3. Understanding of population change in GB and Liverpool
  4. Basic cartography: using built environment data to provide publishable maps

1.2 Assignment

Building on this practical exercise, you will conduct a similar piece of research focused on the Liverpool City Region. Your task will be to explore both day-time and night-time populations as well as population change between 1991 and 2011 (you can explore more recent data too).The analysis should consider various employment areas across LCR and identify regions of both positive and negative population growth. You are required to create several maps, tables, and graphs to effectively illustrate your findings and document your GIS analysis. In addition to the visual elements, you will write a concise report of no more than 1,250 words, explaining your findings and discussing some implications of the data. (Please note that maps and tables will count towards the remaining 1,250 words). You will receive higher marks for identifying hotspots of population growth, incorporating additional data (e.g., employment), providing evidence of further reading, and outlining the study’s limitations and the potential implications of your findings. Get your data from: Canvas, https://data.cdrc.ac.uk/geodata-packs, http://reshare.ukdataservice.ac.uk/852498/ and https://www.nomisweb.co.uk/default.asp

2 Practical - part I (week 2)

2.1 Getting data

  • Create a new folder for this practical (e.g. M:Prac2) - this will be your “working directory”
  • Download the data Practical 2 from Canvas and unzip it into your working directory.

For the assignment data go to https://data.cdrc.ac.uk/geodata-packs and download the Census Residential Data Pack 2011* or Mid Year Population Estimates and Census Workplace Data Pack 2011 for Liverpool City Region (Geography Type - Combined Authorities). You will be required to register if you are using this data repository for the first time. 2021 Census residential population data for Liverpool City Region is available from Canvas.

2.2 Displaying data in QGIS

  • Start QGIS by clicking on Start > QGIS Desktop 3.38 (or any other QGIS version available on your machine; please note that this instruction is written for QGIS 3.38, so other versions may slightly differ)

2.2.1 Adding layers

Let’s start off by adding data to the map display. First, we add the ONS population data - Annual Mid-year Population Estimates, 2013 for Liverpool. Then, familiarise yourself with the ONS methodology for population estimation provided in the link below: http://www.ons.gov.uk/ons/rel/pop-estimate/population-estimates-for-uk--england-and-wales--scotland-and-northern-ireland/2013/stb---mid-2013-uk-population-estimates.html. The Pop data 2013-18 also contains mid-year population estimates for a number of years (2013-2018). In this practical, we will use the population estimates for 2013 and the latest actual Census data for 2021, but feel free to explore other years as well.

  • Start by clicking the Layer>Add Layer>Add Vector Layer button AddLayer
  • Then click on the Browse button and navigate to your working directory. Open the Practical_2_data/Res_data/Population folder and then go to shapefiles. Select the file with .shp extension
  • Click Open and then Add
  • Change the name of the layer to Population_2013 by right clicking on it and selecting Rename Layer.

This will add a layer containing information about Liverpool’s 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 display colour

When we add a shapefile, QGIS randomly assigns a colour; you already know from the previous practical how to change it, so play around with different colours for a bit.

  • Right click on the Population_2013 layer in the Layers Panels and choose Properties > Symbology option Style_tab

  • 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_2013’ 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 go to Stroke style and select No pen.
  • See what happens; when such styling could be useful?

2.2.3 Grouping layers

When we work 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 the Layers Panels.
  • First, add files from the OS_data/OS_Liverpool folder: FunctionalSite.shp, Road.shp and Railway Track.shp
  • 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 display the population of Liverpool we will initially use the Population_2013 layer. At the moment we are displaying a map of Liverpool’s LSOAs and there is no visible information about the population distribution. However, it would be interesting to see some spatial patterns, such as population distribution at a small area level. Based on the skills gained in Practical 1, you should be able to display such information by creating a choropleth map. Have a go at classifying the population data. 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_2013 layer
  • Go to Properties > Symbology
  • From the drop down menu select Graduated
  • Use all_ages as your Value to classify your 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 as on the picture below:
Classify

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

  • Go to the Properties/Symbology window and choose Blues for your Color ramp
  • Then navigate to Symbol and click on the Change.. button
  • 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 tab (next to Classes) and then Load Values

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

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

  • Select Population_2013 as your Input layer and all_ages as the Field to calculate statistics on and click Run

2.2.5 Census 2021 population

The population data we have used so far is from 2013. This data is somewhat outdated and prone to inaccuracies as it consists only of estimates. More reliable population data are collected through the Census, which occurs every 10 years and provides a comprehensive picture of all the people and households in England and Wales. So let’s explore the Census 2021 data.

  • Go to your Practical_2_data>Census 2021 folder and then drag and drop the Liverpool_pop 20212.gpkg data into your QGIS Layers Panel. Please note: this dataset has a different extension from the previously used .shp files. This time, we are using the GeoPackage (GPKG) format, an SQLite database format that is well-supported by QGIS. If necessary, it can also be saved as a .shp file.
  • Name the layer Population_2021
  • Next, classify the total population using the steps outlined in section 2.2.4. Once you have classified the data, evaluate the differences and similarities between the spatial patterns for 2013 and 2021. pop21
  • Right click the Population_2021 layer and then Open Attribute table and see how many LSOA’s there are. How does it compare to the 298 LSOAs in 2013. How can you explain the difference? See more info on this issue here: https://ukdataservice.ac.uk/app/uploads/censusgeography2022-10-18.pdf
  • In addition, check the values for each quantile in 2013 and 2021— do they match?
  • Can you reassign the values manually?

2.2.6 Vulnerable population (Optional)

  • Now, copy the Population_2013 layer by right clicking on it > Duplicate Layer

  • Rename the new layer to Over_65

  • Go to the Attribute Table > Open field calculator and create a new field. Call it Over_65 and calculate the total number of those 65 and over by creating a simple equation in the Expression window (something like: “age_65” + “age_66” + …)

  • Then following the above steps on classifying data create a new map showing this time the Over_65 population distribution in Liverpool - Experiment with various classification schemes and answer the question below:

  • How does the distribution of the Over_65 vary from the total population for 2013, are there any clusters of high concentration of the ageing population? Check the histogram.

  • Finally, examine the distribution of the elderly in Liverpool as per Census 2021.

  • This dataset displays population by age bands, making it easy to filter for those aged 65 and over. Give it a try, but keep in mind that you may need to begin by converting the text field into a numeric one…

2.3 Workplace Zones and joining tabular data

Both, the Mid-Year Population Estimates and the Census 2021 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 resident population present during the evening and night-time hours. So let’s have a look at the day-time population in Liverpool, the so-called workplace zones. Start from loading up the relevant data:

  • Go to Res_data>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?

Yes, there is only one field called WZ11CD, which is a unique code for UK Workplace Zones. Importantly, there isn’t any population data attributed to the layer, so we need to add this information. As you probably remember, this can be done by joining numeric tables. 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 on the left-hand side

  • 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. By now you should know how to make a Join layer permanent, so go ahead and name the permanent layer WZ_population (if you experience any problems with it, go back to Practical 1, section 2.4.1). 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/Workplace Zone.

  • Once you create the permanent layer, it will be added automatically to the Layers Panel
  • You can delete now the WPZONE layer and the WP102EW table from your Layers Panel

To rename the variables follow the steps below:

2.3.1 Renaming variables, field calculator (Video 2.1)

There are two or more ways of renaming variables in QGIS. In Practical 1, we used a tool called Refactor fields, however, in GIS there is normally more than one way of doing the same thing. So now, we will use a different method (using Field calculator), where we will create new columns and name them appropriately.

The Field calculator is often used to interrogate spatial data and perform calculations on the basis of existing attribute values or defined functions, e.g. to calculate length, perimeter or area of spatial features. The results can be written to a new attribute column or they can replace already existing values. 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(64bit) 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 very similar
  • 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_2 column too - name it Density (use Decimal number (real), set Precision to 2, if you can change it)
  • Repeat this again for the WP102EW_1 column - what would you call 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

2.3.2 Optional

  • You can also use Field calculator to calculate night-time population density at LSOA level, which at times is more useful than just simple population counts. All you need is population count and the area for each LSOA.

  • Go to the Population layer > Open Attribute Table > Toggle editing mode > Open field calculator

  • Create a new field (name it Area, output type - decimal number, precision 2) > Geometry > $area, click OK

  • Now, to create population density column, divide values from the all_ages column by the Area using a simple spatial query in the Field calculator
    Tip: use 'ha' rather than 'sqm' to calculate the Area field; you can adjust this in Project properties > General > Measurements, accessed from the Project menu

  • This method can be used with any count data including working population, employment, protected species and so on. So now create a map of residential population density at LSOA level in Liverpool

2.4 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 Color ramp

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

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

  • 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 retailers? Is there any pattern?

2.5 Basic GIS analysis - Select by Attribute and Select by Location queries (Video 2.2)

In order to identify the major employers in Liverpool we will use the FunctionalSite layer from the OS dataset folder. Our basic analysis will be comprised of 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. The steps below provide some help:

  • 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 (it may not work if you do simply copy > paste) and then click Select features

  • Now, you should have all the WZ with the population above 658 selected - 86 WZs 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

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

  • Export the selected features in the FunctionalSite layer as a new shapefile and name it WZ_FunctionalSites (Right click FunctionalSite > Export > Save Fatures 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) click on Toolbox to open it and then type Fix geometries.

  • Put WZ_population as your 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.

2.5.1 Optional

  • By implementing the above steps, experiment with different values than the 5th quantile (e.g. above the mean value) - what difference does it make?
  • How does that relate to how different industries cluster within Liverpool, and how these clusters attract workers from different areas of the city?

2.5.2 Labelling (advance functions)

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, are not 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 lables you want to display.

  • Under Rendering>Label options, scroll down to the Data defines>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 the concept of SQL queries (the commands used to interact with and manipulate databases), and in particualr explore ‘SQL LIKE’ operator and ‘SQL Wildcards’ 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%’ (Why did we have to use the % sign?)

  • Then 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 Label toolbar is active
  • Then click on this button Move label
  • Drag and drop any label wherever you wish

2.5.3 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

  • Add Liverpool_boundary shapefile (you can download it from https://osdatahub.os.uk/downloads/open/BoundaryLine ; the layer you need is called: district_borough_unitary_region under the Boundary-Line section).

  • Alternatively,(perhaps an easier option) you can create your own boundaries by dissolving the Population_2013 layer. So click on Vector > Geoprocessing Tools > Dissolve… but make sure you create a copy of the Population_2013 layer beforehand.

  • To clip the desired layer, 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

  • Now, load the ImportantBuilding layer and check if there is any relationship between the Workplace zones population and concentration of the Important buildings

  • Carry out a similar spatial analysis to the one above and describe the findings to your colleague next to you.

  • Finally, identify the remaining big employers in Liverpool using a background map. Simple turning a layer on and off and comparing the shading against a background map can be helpful.

2.5.4 Employment data (optional)

As you have noticed the direct comparison between the day and night-time populations isn’t really possible due to the incompatible boundaries. Workplace zones have completely different boundaries to LSOAs that are used to display the residential population. One way of dealing with this issue may be analysing the ONS employment data at LSOA level provided by https://www.nomisweb.co.uk.

  • Go to the Nomis website > Query data > Business Register and Employment Survey: open access (2009 to 2015 or 2015 to 2019)
  • Use the guidance provided to select employment data for Liverpool at LSOA level, so in step 1 you choose 2011 super output areas - lower layer and tick all check boxes; in step 2 you choose a date e.g. 2019, then choose Employment in step 3; broad industrial groups in step 4; count in step 5 and Microsoft Excel or a csv table in step 6 (include area codes as your option)
  • Once you get your data, sum up all the columns and join the employment data to your Population_2013 layer
  • Now you can directly compare the residential population against the employment data using the steps from 2.2.4 section

3 Practical - part II (week 3)

3.1 Raster data

So far, we have been using vector data only. Now we will explore population change in Great Britain based on raster data. A raster dataset consists of a matrix of cells (or pixels) organized into rows and columns (or a grid) where each cell contains a value representing information, such as population count in our case. The raster data we are going to explore now, contain publicly available Census data for 1971, 1981, 1991, 2001 and 2011. These are population surfaces computed for 1km grid using kernel estimation. They are estimates of counts of people for regular grids (1km x 1km) and as such these can be directly compared between Censuses. The development of 100m grid, which is much more suitable for comparison of smaller areas, is still in progress. So in this exercise, we will look at the population change in Great Britain and then in Liverpool between 2011 and 1991.

  • Open up Internet Explorer and navigate to: http://reshare.ukdataservice.ac.uk/852498/

  • Scroll down and first familiarize yourself with the Coverage and Methodology section

  • Download the total-population.zip folder and unzip the data to your working directory

  • Now let’s add the datasets to the map display

  • Click the Add Raster Layer button AddRaster

  • Choose Browse and navigate to your working directory

  • From the 1991 folder > select the 5a_ascii_grid1991_Total_Population_TP91Bs.asc file and click Open

  • QGIS will ask what coordinate system the files are stored in (if this is not the case, go to Layer Properties > General and select the coordinates from there)

  • Type 27700 into the filter box and select OSGB 1936 / British National Grid and press OK
    OSGB1936

  • Click Add
    This will add a layer which shows Great Britain, with a series of values in black (0) to white (max value of 9262.14). Can you think of what these values mean?

  • Then add the 2011 raster data (use the 5a_ascii_grid2011_Total_Population_URPopAll.asc file) by clicking the Add Raster Layer button again

3.1.1 Changing the Symbology

Explore the map for the entire country, what spatial pattern can you see? The default symbology for a raster grid in QGIS is black to white. This is OK, but it does not show the changes particularly well. We can change this to a red or blue colour scheme, which shows the data more efficiently.

  • Right click on the 1991 raster layer and choose Properties
  • Select Symbology
  • Change Render type from Singleband gray to Singleband pseudocolor
  • From the Color ramp drop down menu select Spectral
  • Click Classify, then OK
  • Repeat these steps to change the symbology for 2011 data

3.1.2 Raster calculator (Video 2.3)

This map shows a much clearer picture of the GB population distribution in 2011. However, it would be interesting to see what are the differences in total population, let’s say over the past 20 years (1991 dataset). Such comparison can be easily done for raster data, as we have population counts for both years. Using the grid cells rather than ONS boundaries is really useful because they are consistent over the time. QGIS calculates the difference between the two datasets using Raster calculator. It is a very flexible tool so it can be used to calculate not only differences between various datasets but also more complex formulas such as Townsend score etc. (https://census.ukdataservice.ac.uk/get-data/related/deprivation).

  • To do this we use Raster calculator (similar to the field calculator, used for vector data).

  • Go to Raster > Raster calculator

  • Click the (…) by the Output layer

  • Choose where to save the output (you can call it: Grid 2011-1991 change), click OK

  • In Raster bands window, double click on the 2011 layer

  • Single click the (-) sign button

  • Double click the 1991 layer
    RastCalc

  • Click OK

  • Adjust the colours and classification values in the Symbology section – these could be adjusted based on the Histogram values (see Step 3.1.3).

  • You could exclude the outliers (highest and lowest values) to highlight the areas with positive/negative population growth. Under the Min/Max value settings, you can choose the Cumulative count cut from 2% to 98%. This means that the data range is set from 2% to 98% of the data values; this can be further adapted manually.

  • After adjusting, your map should look something similar to the one below

PopChange

  • Experiment with different Symbology options to see what works best. Add a background map to see what areas of the UK have population change.

  • Explore the results and make comments on the population change between 2011 and 1991

  • Where are the major positive/negative changes in population can be observed?

3.1.3 Histogram

Within QGIS we can look at the properties and histogram of raster data too. This can tell us some useful information about the values within the raster, which might be useful for further analysis.

  • Right click the Grid 2011-1991 change > Properties > Histogram
  • You can see that the data is slightly skewed, although most of the values are around 0
  • We can Zoom In on this - draw a box around the 0 value, QGIS will then zoom in to that section

3.2 Clipping to a vector polygon (Video 2.4)

Finally, we’d like to produce a raster map of population change for Liverpool, so we could compare the trends against the day time and night time population distribution within the City. We will use an extraction tool to clip the GB boundary to the spatial extent of Liverpool City. Follow the steps below:

  • Go to Raster > Extraction > Clip Raster by Mask Layer…

  • Select the Grid 2011-1991 change as your Input layer

  • Ensure that you choose Liverpool_boundary as your Mask layer

  • Set the output directory and give it a sensible name

  • Set both the Source and Target CRS to British National Grid

  • Click Run

  • Copy the style from the Grid 2011-1991 change layer

Note If you get an error massage, make sure that both layers have the same coordinate reference system (CRS). Change the Source CRS and Target CRS to be same projection (e.g. EPSG:27700) and resave the layers, then clip the Raster layer.

This will create a clipped layer for Liverpool Local Authority boundary. Comment on the accuracy/resolution of the extent of the new raster layer, can this be improved anyhow? How about the size of a grid? Importantly, you can apply this method to any vector layer (not necessary a local authority).

3.2.1 Clip to the extent of Liverpool City Region (optional)

  • Try also to clip the Grid 2011-1991 change layer to the extent of Liverpool City Region, which consists of six local authorities.
  • Download the boundary of the following local authorities: Liverpool, Wirral, Sefton, Knowsley, St Helens and Halton
  • Load them into QGIS
  • Join the newly downloaded boundary layers for all 6 local authorities by going to Vector > Data Management Tools > Merge Vector Layers > Select the 6 local authorities as the layers to be merged > Save the output Alternatively, download spatial extent of Liverpool City Region from the CDRC website: https://data.cdrc.ac.uk/geodata-packs (any available dataset will be suitable; choose combined authority as your Geography-Type and select the Liverpool City Region)
  • Clip the raster layer of Grid 2011-1991 change to the extent of Liverpool City Region
    LCR Pop Change

4 Practical - part III (week 4)