Applied GIS: Consumption spaces (Wk 2-4)

1 Introduction

The growth in online consumption and the shifting consumer culture towards convenience and value impact the health of retail centres in complex ways, potentially leading to long-term changes in their structure. The rise of online sales was particularly noticeable during the COVID-19 pandemic and the shift toward value for money in the current ‘cost of living crisis’. Adjusting to these trends often involved rationalising of their store portfolio and adoption of new business models. The traditional town centre ‘brick and mortar’ retailers have primarily considered supply-side effects such as competition, retail mix or vacant spaces. However, there has been less focus on demand effects, such as catchment demographics and socio-economic characteristics. Understanding the geography of consumer behaviour at a small area level is crucial for understanding the vitality and viability of both retail centres and the retailers themselves. Such consumer insights form the basis for site location analysis, in conjunction with an analysis of key competitors.

1.1 Learning Aims and Objectives

The Internet has revolutionised the way in which people consume products and services, however a variety of factors influence these use and engagement behaviours. Understanding the geography of these influences is complex, although, one way in which this has been made tractable is through various geodemographic classifications. However, these only tell you about the characteristics of the places in which people live and their environments, but not where they shop?

The objectives of this practical are:
a) to examine the geography of retail supply and demand related factors
b) to evaluate the extent to which retail centres are exposed to consumers with different income and online consumption behaviour;
c) to choose the most suitable location for a new discount supermarket in Liverpool

In this case study you take on the role of a property agent who is advising retail clients about those locations most suitable for their new premises.

In addition to reinforcing learning from earlier practicals, you will also be developing the following GIS skills and understanding:

1. Use of geodemographic classifications, the IMD (Index of Multiple Deprivation) and its sub-domains
2. Creating buffers in QGIS and their uses
   
3. Recoding variables in QGIS
   
4. Creating spatial joins
5. Basic understanding of retail catchment modelling techniques
6. Exploring suitable locations for a retail store based on various selection criteria (socio-economic, consumer behaviour and competition)

1.2 Assignment

Taking on the role of a location analyst, in no more than 500 words, write a short summary report that identifies the most suitable location in Liverpool for a discount grocery store that will have a click & collect facility, using the results of your GIS analysis as evidence. In your analysis you should consider: • store catchment characteristics (total population both day and nighttime, affluence derived from IMD, online shopping propensity derived from the Internet Use Classification (IUC) • competition between existing stores. Use population data from Practical 1 and IMD, IUC and store location data from Practical 2. Include 4 to 6 maps and a numeric table as your GIS analysis evidence. This assignment will be summatively assessed and counts for 50% of your overall module grade. For higher marks also provide a flowchart showing the steps that you have taken in your analysis (optional). More details on the Assignment will be provided in week 4 during the lecture.

1.3 Data

• In your “M:” drive, create a new folder for this practical - this is your “working directory”
• Go to Canvas and download the Practical 2 data files
• Unzip the files into your working directory
  
• Optional data for your assignment optional data can be downloaded from https://data.cdrc.ac.uk/geodata-packs. Search for the Index of Multiple Deprivation IMD: Liverpool (there are data available for 2010, 2015 and 2019) and Internet User Classification: Liverpool (data for 2018 available) data packs.

2 Geodemographic classifications (IMD & IUC) - Part 1

2.1 IMD income domain map

The Index of Multiple Deprivation (IMD) is a UK government measure used to assess levels of deprivation across small geographic areas (Lower Layer Super Output Areas, LSOAs). It provides a relative ranking of neighbourhoods based on multiple dimensions of deprivation. The IMD is widely used in policy-making, funding allocation, and research to identify disadvantaged areas.

The Income domain of the Index of Multiple Deprivation (IMD) measures the proportion of the population experiencing low income and financial hardship. It is one of the most heavily weighted components of the IMD, accounting for 22.5% of the overall deprivation score.

• Open the IMD 2015 folder in your working directory and load the shapefile for Liverpool

• Name the layer IMD2015

• Go to Properties open the Symbology tab and select Graduated from the drop down menu.

• In the Value field choose income, type 5 in the Classes field and choose Equal Count (Quantile) as your Mode

• Press Classify and then OK buttons; this will close the dialog window and show you the map.

• Can you specify the threshold that defines the lowest quintile of the IMD Income domain for Liverpool?
  

• To find this out, go to the Histogram tab within Symbology. Click Load Values and check the thresholds and distribution of the Income domain

By now, you should have created a map of Income deprivation domain quintiles for Liverpool which looks something like the one below:

• What do terms quintile and quantiles mean?

• What is the mean value of the Income domain, and what are the standard deviation values?

• There are other classification methods (Modes) available in QGIS; try them and see what difference do they make to the outcome map

• Which method may be the most appropriate in this case

Your next task involves displaying the LSOAs within the lowest IMD quintile and then checking visibly whether there is an overlap between the IMD income domain and propensity for online shopping. Think about how you might proceed with this analysis… Give it a go; however, if you are stuck, follow the steps below:

• Go to Attribute table of the IMD2015 layer and click on Select features using an expression
  
• Click on Fields and Values from the Functions tab in the middle
  
• Specify all polygons that are within the lowest quintile by using a simple expression “income” <= 0.11
  
• Click Select Features and then Close buttons
  

- How many polygons have you got selected? - What does the selection actually displays and how would you interpret it?

2.2 Internet User Classification map of Liverpool

Next, we will explore the Internet User Classification (IUC) data for Liverpool created in 2014. The IUC was created from over seventy measures selected from survey and lifestyle data, alongside census and infrastructure performance statistics.

• Locate the IUC data within your working directory (Practical 2 > IUC 2014 Liverpool > E08000012 > E08000012_Liverpool.shp). Load in the E08000012_Liverpool.shp onto the map interface by dragging and dropping the shapefile. Alternatively, click on the Add Vector Layer button and navigate to your working directory.

In order to display the Internet Use Classification, we first need to familiarise ourselves with the IUC User Guide (attached in the folder) and then the structure of the associated database, so we can decide what field to use best in order to display our data. Follow the steps below:

• Right click the layer name and select Open Attribute Table
  
• Match the names of columns against the IUC_User_Guide and try to make sense of them
  
• Decide which column would be the most sensible to display for an IUC geodemographic map of Liverpool.

In fact, we are going to create two maps, the first showing a more aggregated classification based on the IUC supergroup and the second a disaggregated one, based on the IUC group.

• Right click the layer and select Properties
• Go to the Symbology tab and select Categorised from the dropdown menu
• Then from the Value dropdown menu select supgrp_nm and press Classify button
• Add numbers to the name of each supergroup by double clicking the name of each supergroup under the Legend column within the Symbology window
  
• Reorder the supergroups so that 1. E-unengaged appears first and 4. E-rural and Fringe at is the bottom
• Press OK

This will create your first IUC map for Liverpool, well done! However, those default colours do not look great?. In this instance, we are going to use ColorBrewer ColorBrewer pallets to display the different IUC groups and then we’ll remove the black LSOA boundaries.

• Go to Color ramp in the Symbology tab and select from the dropdown menu Create New Color Ramp
  
• Select Catalog: ColorBrewer from the drop down menu, set number of Colours to 4 and use Accent as your Scheme name
  
• Press OK twice
  
• Go back to Symbology in the Properties window and click on the Symbol box, select Simple fill and set the Stroke Style to No pen
• Lastly, change the layer’s name to IUC supergroup

Your map disaggregated by IUC supergroup should look similar to the one shown below:

• Now, create a copy of the IUC supergroup layer (right click IUC supergroup > Duplicate Layer) and create a map of IUC in Liverpool using the group variable (grp_nm) rather that a supergroup.
• Each of the supergroups contains a number of smaller and nested groups, so make sure that the colours are corresponding (e.g. if you use green for supergroup 1, then for groups 1a, 1b and 1c you also should use different shades/patterns of green)

(Note: you may need more colour schemes than there are available from ColorBrewer by default, so one way is to design your own symbol display such as line pattern fill/point pattern fill as your symbology.)

• Name the layer: IUC group
• In Symbology, double click the symbol of each individual classified group name and explore different Fill style options (within Simple fill) such as line pattern fill or point pattern fill
  
• Your second map should look something like that:

Comment on the spatial patterns of Internet use in Liverpool

2.3 Online shopping prevalence

Nationally, rates of online shopping equated to 53% in 2014, however there were differences between IUC Groups (clusters) as some customers are more likely to shop online than others. For example, groups 4c (low density but high connectivity), 4b (constrained by infrastructure), 4a (e-fringe) and to an extent 2a (next generation users) are most likely to engage in online shopping; whereas: 3a (uncommitted and casual users), 1b (e-marginals: not a necessity) and 3b (young and mobile) have lower than average propensities as shown on the plot below.

2.3.1 Recoding variables

So, in this part of the practical we will look at the issue of online shopping prevalence and will display those areas in Liverpool that exhibit the highest prevalence of online shopping. In order to do so, we’ll need to do some variable recoding; in other words create a new variable where 1 is associated with higher prevalence of online shopping and 0 with lower. Do the following tasks:

• Refer to the pen portraits (descriptions) of different IUC groups, available from the IUC User Guide

• Open the Attribute Table of the IUC_group layer

• Click on Open field calculator and make sure that the Create a new field box is ticked
  

• In the Output field type: OnlineShop

• Choose from Functions window the Conditionals and then the CASE conditional

• Create a statement in the following format:
  “WHEN condition = x THEN the assigned value is 1, otherwise (ELSE) the assigned value is 0”. In our case we assign the value of 1 to groups 4a, 4b and 4c (high online shopping prevalence), and all other IUC groups (lower prevalence) have the value of 0. Give it a go and then check your conditional statement against the picture below:
  

• Click OK to create the new variable
  

• Check the Attribute table - a new column with our binary variable should be added

Now, we will display the new variable and then create a map that can be printed or published; more specifically a map showing the LSOAs with the highest propensity for online shopping. Follow the steps below:

• Next, right click the IUC_group layer and click on Duplicate
  
• A copy of the layer has been created; name it Online shopping prevalence
  
• Then following those steps used earlier to create the IUC maps classify the newly created binary variable, so both ‘low prevalence’ and ‘high prevalence’ areas are clearly visible
  
• Save your QGIS map

In order for your map to be publishable, you need to add a legend, a scale bar and a north arrow, and possibly a title. Utilising the skills from Practical 1 (Part 3), you should be able to do it yourself. Nevertheless, the key steps are listed below:

• Open New Print layout from the Project menu and name it: Online shopping prevalence, click OK
• Adjust the scale to 1:75,000 (or any other that is appropriate) in the Item properties tab
  
• Then go to Add Items and choose Add Legend, Scale Bar and North arrow respectively
  
• Export your map using the Export as Image button from the Layout menu; hopefully it looks something like the one below. (You can add labels using the RetailCentres_Liverpool layer from section 3.1)
  

However, creating only a binary variable masks some variance present in our data, so in this step, we will create an ordinal variable for which the values are ordered (the online shopping prevalence is captured from high to low).

• Open the Attribute Table of the IUC_group layer

• Click on Open field calculator and make sure that the Create a new field box is ticked
  

• Name the Output field: IUC_ordinal (there is a 10 character limit with column names so it is okay if the name is cut off)

• Choose from Functions window the Conditionals and then CASE

• Assign the highest value (e.g.’4’ - high prevalence) to the following IUC groups: ‘4a, 4b and 4c’

• Assign ‘3’ (above the average prevalence) to groups ‘2a and 1c’

• Assign ‘2’ (below the average prevalence) to groups ‘1b and 3a’

• Assign ‘1’ (low prevalence) to the remaining IUC groups

• Your Conditional should look something like this:
  

• You may wish to create another (text) column that will record the names (high prevalence, above the average etc.) rather than just the pure numbers
  

• Map the output;duplicate the layer and name the layer Online Shopping Ordinal

• Comment on the spatial variation; is there a likelihood of a spatial autocorrelation?

3 Retail catchments - Part 2

A retail catchment can be defined as the areal extent from which the main patrons of a store or retail centre will typically be found. There are numerous ways in which catchments can be delineated, depending on the requirements for a particular study, available data, software used or the analytical capability of a practitioner. The simplest technique might be to draw buffer rings around a store or retail centre; however, such a technique is naive as it doesn’t consider geographical barriers or competition. More advanced methods referred to as ‘Gravity’ and ‘Spatial Interaction Models’ delineate catchment areas by considering the spatial distribution of competing locations and evaluating their relative attractiveness to different groups of the population.

In the remainder of this practical, we will create retail catchment areas using both simple and more sophisticated methods. First, we will create buffer rings around retail centres in Liverpool and then we will use drive distance polygons too (you can also create drive distances, but this is optional). The latter method will incorporate a ‘Retail Attractiveness Index’ to depict the possible impact of retail hierarchy on the catchment extents.

3.1 Buffer rings (primary and secondary catchments)

• First, load retail centres data for the entire country GB_RetailCentres (located in Practical 2 data > retail)

• From the Vector menu choose Geoprocessing tools and then Clip

• Fill in the window so that you have the GB_RetailCentres layer as your input layer, IMD2015 (or any other layer with Liverpool boundaries) as your Overlay layer and save the new shapefile to your output folder. Name it RetailCentres_Liverpool
  

• Click OK; the new layer should be automatically added to the Layers Panel
  

• Remove the GB_RetailCentres layer
  

  Now, let’s create the primary catchments (typically depicted by a small distance from the retail centre or more than 50% of the patronage) and secondary ones (typically larger distances from the retail centre or patronage levels of between 20%-50%) using a simple technique such as buffer rings. Follow the steps below:

• Create Primary catchments by using a buffer distance of 2000m (go to Vector/Geoprocessing Tools/Buffer)

• Fill in the dialog box as follows: select RetailCentres_Liverpool as your Input layer and type 2000 in the Distance field

• Save the Output shapefile to your working directory; name it Buffer2000 (see the picture below)
  

• Create Secondary catchments by using Buffer distance of 4000m (repeat the above steps and name the new layer: Buffer4000)
  

• Render the image by adjusting colours, transparency etc.

• Add labels by going to Properties of the RetailCentres_Liverpool layer

  • Choose the Single Labels tab and use NAME from the drop down menu
    
  • Explore the labels tab (e.g. change the font to Arial, size to 10, add buffer and shadow)

Note: if the name is too long you can adjust it in the Toggle editing mode - Go to the Attribute Table turn on the Toggle editing mode and delete part of the name e.g. Wavertree - You should now have created a map that looks similar to the one below:

Comment on the retail catchments computed by the buffer rings method:
- Can the primary catchments be distinguished easily from the - secondary ones?
- Is the hierarchy within the retail centres accounted for in any way?
- How do you think these representations could be improved?

3.1.1 Catchment estimation and retail hierarchy

Although the distinction between the primary and secondary catchment areas is reasonably clear, their extents are far from being realistic. One of the major reasons for it is that the so-called hierarchy of retail centres has not been accounted for. Typically, such hierarchy relate to their size, attractiveness and the geographical extent of a retail centre influence, with those centres towards the upper end of a hierarchy typically offering a ‘multi-purpose shopping’ experience, and as such, drawing consumers from a wider area. Conversely, smaller town or district centres will typically serve a different function, and therefore be patronised more prevalently by local communities. Based on the Index of Retail Centres Attractiveness developed by Dolega et al., (2016) available at https://www.sciencedirect.com/science/article/pii/S0969698915300412, in Liverpool at least 3 types of town/retail centres can be distinguished: City Centre (Liverpool), District Centres (Allerton Rd, Old Swan, Kirkdale) and Local Centres (the remaining centres). Note: this classification doesn’t include Retail and Leisure Parks.

We can add information on retail hierarchy in Liverpool to the attribute table of the relevant Shapefile. As such, we will have to create a new variable and again code some new values for each polygon (retail catchment). We will do this by recoding our variables using the “CASE” conditional function introduced earlier on.

• Go to the Attribute Table of the RetailCentres_Liverpool layer, open Field Calculator and put Hierarchy as the Output field name

• In the Expression window create a conditional statement: CASE WHEN condition THEN result, ELSE result. In our case, we want to assign a value of 1 to Liverpool; value of 2 to Allerton Rd, Old Swan and Kirkdale and value of 3 to the remaining centres. Give it a go, however if you get stuck try the following statement:
  CASE
  WHEN “NAME” = ‘Liverpool’ THEN ‘1’
  WHEN “NAME” = ‘Allerton Road’ THEN ‘2’
  WHEN “NAME” = ‘Old Swan’ THEN ‘2’
  WHEN “NAME” = ‘Kirkdale’ THEN ‘2’
  ELSE ‘3’
  END

• Go to Vector > Geoprocessing Tools > Buffer

• In the buffer window select RetailCentres Liverpool layer as your Input vector layer and this time click on Data defined override > Edit (next to the Distance window)
  

• Create another CASE conditional statement (similar to the one above although the ‘condition’ will be different this time)

• Create a buffer distance based on the hierarchy: 5,000 meters for Hierarchy 1; then 2,000 meters for Hierarchy 2; and 1,500 meters for Hierarchy 3

• Save the new Buffer rings to your working directory as BufferHierarchy

• The new buffers should be added automatically to the Layers Panel

• Since you need to create a map of Buffer rings taking into account the hierarchy of retail centres you should make your map very clear. Try the following:
  

• Adjust transparency of the newly created layer and try different colours and transparency so the overlaps are visible

• Label the centres and adjust the size of your points to reflect the hierarchy - as shown on the map below (Use the CASE, WHEN condition THEN result, ELSE, END) statement in the layer’s Property/Size field to display different sizes of points e.g. for Hierarchy 1 - point size 5, Hierarchy 2 - point size 3 and Hierarchy 3 - point size 2. Analogically, you could also use the CASE conditional to adjust the size of labels.
  

3.2 Drive/walking distances

Despite considerating hierarchy, there are still serious limitations to the buffer approach as this does not account for the real world/geographical barriers such as rivers, lakes, or railway tracks etc. A more accurate approach could be to consider road distances using a drive time/distance technique. Both techniques are still popular amongst the major retailers, so we will make some use of it too. This method is more complicated that the buffers and typically involves either using pgRouting extension or Network Analyst in ArcMap Pro. Delineating drive/walking distances is beyond the scope of this tutorial so these catchments are available from the Practical 2 data > Drive distances folder.

• Locate the drive_distance2000 shapefile, drag and drop it to your QGIS Layers Panel and overlay it onto (place above) the Buffer2000 layer
• Compare spatial extents of both layers
  
• Once ready, export your map as a picture image and answer the questions below:

- What are the main differences in the catchment extents drive distances vs. buffer rings? - How do they differ and why? - What are the limitations of these approaches?

However, if you wish to give it a go at creating your own drive distances catchments (this exercise comprises of 2 steps and is optional), the steps are below:

3.2.1 Setting up ArcGIS Pro (Optional)

First we need to set up ArcGIS and then we’ll do network analysis
- Open ArcGIS by going to Start menu > ArcGIS Pro - Click on the Map icon - a new map/project will open

• Go to Project > Licensing > Configure you licensing options and then check the box next to Network Analyst
• Then go back to your ARCGIS Project/Map and in the Catalog window, located on the right, right click on Folders and then Add Folder Connection
• Browse to your Practical 4 data > Retail folder, click OK

The Retail folder should appear now in your Catalog window.

3.2.2 Creating service areas (Optional)

The ArcGIS setup is done now, so you’re ready to start your analysis

• Add the RetailCentres_Liverpool layer by dragging and dropping the .shp file to the Table of Contents
• Then add the RoadLiv layer to the Table of Contents. (If you use a different version of ArcGIS Pro than 3.3.0, you’ll need to add the RoadLiv_ND.nd layer, which is your Liverpool road network).

Note: If the RetailCentres_Liverpool has been saved as a geopackage, resave the layer in QGIS as a shapefile first, and then add it to your Practical 4 data > Retail folder

• Click on the Analysis tab and then Network Analysis > Service Area
  

• A new layer called Service Area will be added to the Table of Contents

• Click on the Facilities and then select the Service Area Layer tab located at the top of ArcGIS toolbar

• Add the retail centre locations by clicking on the Import Facilities icon and choose RetailCentres_Liverpool as your Input Locations, click Apply and then OK
  

• Once the retail centres locations have been added to the Facilities, set your Cutoffs to 1 (the distance units are preset to km) and set your Mode as Driving Distance

• By now, the NetworkAnalyst > Service Area Layer window should look something like the picture below:
  

• Play with other settings e.g. use Polygons or Polygons and Lines under the Polygons tab drop down menu

• Click the Run button

• This will generate 10 service areas (retail catchments), each delineated for 1km drive distance
  

• You can also generate multiple drive times/distances by specifying two or more different distances in the Cutoffs window (e.g. type 1, 2 and hit the Run button again)

• The output below shows the map of service areas for Liverpool Retail Centres delineated for 1km and 2km
  

So if you want to export for example the 1km service areas, do the following:

• Right click on the Polygons layer > Data > Export Features and save it to your Retail or any other folder you save your work to.
  
• Name it Liv_DriveDist.shp.
• Now, you should be able to use these drive polygons in QGIS, just drag and drop the newly created file to your QGIS Practical 4 project

4 Siting a discount supermarket - Part 3

Since the global economic crisis of 2008-09, market share of the hard discounters in the UK has been growing, often at the expense of the major grocery retailers - the Big 4 (Tesco, Sainsbury’s, Asda and Morrisons). This trend has been exacerbated by the recent cost of living crisis. The aim of this exercise is to find the most suitable location(s) (could be more than one) for a discount supermarket in Liverpool using GIS analysis. Typically, an analyst would use some sophisticated location analysis tools such as traffic pattern information, demographics, lifestyle data and footfall, and would carry out an analyses of its competitors. A location analysis often requires looking at footfall traffic generators (in particular when siting convenience stores), as other retailers in the neighbourhood may draw customers from various employment sites that are nearby. These could include industrial or office parks, schools, colleges and hospital complexes.
In the case of a discount supermarket, it is important to choose the right neighbourhood as typically, the most affluent catchments tend to shop more in the upmarket stores (not heavy discounters). First, we will explore the potential competition and different catchment estimation techniques.

4.1 Store catchments

• Add the Liverpool_Foodstores shapefile (located in the retail folder) to the Layers Panel
• Check the Attribute table > Store Type column

As we want to find the most suitable location for a discount supermarket, we can make an assumption that there will be more competition from the supermarkets than convenience stores. As such, for our analysis we can remove the convenience stores from our database.

• Display only the supermarkets by creating a spatial query for the Liverpool_Foodstoreslayer by using the Select features using an expression tool in the Attribute Table
• Create a query: “Store Type” = ‘supermarket’). Click on the Select features button
• Save the selected features as a shapefile to your working directory, name it Liv_Supermarkets.

However, there are 73 supermarkets in Liverpool and therefore creating that many buffers is not very useful as the map would be very cluttered. Therefore, in our analysis we will focus only on the direct competitors - the heavy discounters - Aldi and Lidl stores. - Create a new layer for Aldi and Lidl supermarkets by using the Select features using an expression tool in the Attribute Table (“retailer” = ‘Aldi’ OR “retailer” = ‘Lidl’), name it Discount supermarkets. How many discounters there are in Liverpool? - Then create 1500m buffers for the Discount supermarketslayer and name it 1500buffer_Discounters - You could also check how similar the distance catchments are when compared to the buffers. Go to your data folder > Retail > Disc_DriveTimes1500.shp and add it to your map.
- Make the drive time catchments transparent, but keep the catchment boundaries. Make them red and set the stroke width to 0.3

It is clear from the map that some areas fall outside the delineated catchments, indicating that these areas are not effectively served by the analysed discount supermarkets. From a competition perspective, these areas could be potential locations for the new discount supermarkets. However, this would depend on demand-related factors like total population, affluence etc.

Locating a new discounter in a vicinity of a larger supermarket doesn’t necessary have to be viewed as a disadvantage, providing of course, that the level of market saturation is not very high and you know the catchment’s demographics. One way of obtaining new insights to decide where to locate a store is to examine the analogue stores. As such, we will examine the catchments of other discount supermarkets in Liverpool.

4.1.1 Catchment affluence

Understanding the potential consumers and their spatial distribution that discount supermarkets are likely to target is vital here. Research shows that the predominant locations are either city/town centres or other urban areas, especially those with less affluent catchments. So let’s explore that - we will examine the relationship between Income deprivation in Liverpool and the discount supermarket locations.This exercise will use 1500m buffer catchments to obtain the statistics.

• Create a Chloropleth Map of Income deprivation variable from the IMD 2015 layer (or use it the one from Section 2.1 if you have it open)
• Use the Select by Location tool (Vector > Research Tools), select all the LSOAs that intersect with the 1500buffer_Discounters (Select features from: IMD2015, Where the features: intersect, By comparing to the features from: 1500buffer_Discounters)
• Go to the Basic statistics for Fields tool and check what is the mean/median Income deprivation value for the selected LSOAs (is it 0.259 mean/0.27 median with StdDev 0.135?)
• Now check what is the mean/median value for the Income deprivation within the boundary of Liverpool City. It should be 0.256/0.255 respectively, and to put that into a broader context the mean value for Great Britain is 0.15. Also the StdDev of 0.135 suggest that there is a relatively large degree of variation within those catchments.

So based only on this very simple analysis we can make an assumption that the new discount supermarket should be located in a neighbourhood that is slightly less affluent than the city/regional average and has mixed demographics (population characteristics).

4.1.2 Catchment total population

• Next, we will examine total population for our catchments (this could be extended to checking other population-related factors such as density or difference in total population between various catchments, as well as the demographic/employment composition of each catchment)

• Similarly to the above section (4.1.1), select any catchment area and check what is the total population of it (this can be referred to as potential patronage of that store).

• Again, use the Select by Location tool to select all the LSOAs in your residential/night time population layer that intersect with a discount store catchment area of your choice

• Repeat this for other catchment areas
  

• The total population should vary between 8,000 and 60,000 people per catchment

Nevertheless, it is important to bear in mind that many of these catchments are overlapping so in some cases we count the same residential population twice - a phenomenon also called cannibalisation. So for this reason it would be useful to calculate the average population count per catchments.

• Select all LSOAs that intersect with the catchment, generate basic statistics for all selected LSOAs and divide the total population (SUM 406581) by 14 (no of discount stores)
• Is the average population per catchment around 29,000? (if your average varies dramatically please ask for help)

On the other hand, the day time population within the city centre is much higher that the residential population (ONS estimates) so combining the above results with the workplace zone population may be useful. In particular, this is important for the location of corporate convenience stores, as the larger employment sites play important role in creating demand for convenience goods retail.

4.1.3 Location of the new discount store

Having done some analyses, we can now decide on the most suitable location for our new discount store. It is pretty obvious from the map that there are no discount stores in the north-east and especially in south Liverpool. The map of income deprivation suggests that both areas could be considered. The population distribution and the relatively low affluence levels suggest that Garston or Speke area (South Liverpool) can be most suitable.

• Taking into consideration the existing road network, create a new point indicating the potential location of a new discounter,

• Go to Layer > Create Layer > New Shapefile Layer

• In the window that pops up give it a name: New Discounter

• Choose Point from the drop down menu as your Geometry Type

• Set Your CRS to British National Grid and Click OK

• The newly created (empty) layer will be added to your Layer Panel

• Right click on it > Toggle Editing and then from your Toolbar menu use the Add Point feature tool to create a point in Speke Boulevard

• Open the Attribute Table of the new layer

• Toggle Editing > New Field

• Type Name as your field name, choose Text (String), length 50

• You can now enter a name for your newly created store in the Attribute Table

• Call it New Discounter

• Toggle Editing Mode off > Save Edits

• Now you can create a 1500m buffer

• You can also add a service area/drive distance catchment from your data folder (N_discounter_drive_time.shp) for the newly allocated store.
  

• Now you can check the total population and income deprivation levels for the New Discounter catchment. Is it in line with other similar stores?

Please note that the LSOA level is too coarse for the real world location analysis, OAs would produce much more accurate estimates; however we use free data only and most of our variables are available at LSOA level only.

• You have completed now this practical, so well done!!!