PGP - Candidate locations selection

Author

Eirini Zormpa

Published

May 2, 2024

The brief

The RSA is applying for funding to run a Playful Green Planet (PGP) pilot in one or two locations in the UK. Ideal locations for a pilot will be places that are experiencing high levels of deprivation and have low access to green spaces. PGP locations will be a collaboration between the RSA, our partners and the local community where the pilots will run. A PGP Stewart (most likely an RSA Fellow) will be pivotal for establishing a connection with place. As such, we are looking for 5-10 cities across at least 2 countries in the UK to open up a call for Stewards.

What is a city?

The first thing to establish is the type of area we will be targetting. A practical answer to the complicated question of how to define a city comes from the Centre for Cities. They have selected the UK’s 63 largest cities and towns, which they have defined as Primary Urban Areas (PUAs). As explained on their website:

This is a measure of the “built-up” area of a large city or town, rather than individual local authority districts. We use PUAs in our analysis because they provide a consistent measure to compare concentrations of economic activity across the UK. This makes them distinct from city region or combined authority geographies.

This report adopts the methodology of the Centre for Cities and limits itself to the 63 PUAs. Clearly, this excludes rural areas. This should not be a problem for the purposes of PGP, as rural areas are likely to have better access to green spaces. On the other hand, these green spaces may not be suitable for children to play in. Rural places could also suffer from high levels of deprivation. The question of whether rural areas should be excluded from consideration for PGP projects should be considered; however, for the purposes of this pilot restricting consideration to urban areas can be justified.

Another limitation of this approach is that the vast majority of the 63 PUAs is located in England:

  • 1 PUA in Northern Ireland (Belfast)
  • 3 PUAs in Wales (Cardiff, Newport, Swansea)
  • 4 PUAs in Scotland (Aberdeen, Dundee, Edinburgh, Glasgow)
  • 55 PUAs in England

Place selection

The primary considerations in selecting candidate PGP locations are: 1. high levels of deprivation, as measured by the Index of Multiple Deprivation and, where applicable, the Income Affecting Children Index 2. access to green spaces.

Index of Multiple Deprivation & Access to Nature

The data presented below, Access to gardens and public green space in Great Britain, comes from the ONS website and contains information on both deprivation and access to nature.

The data explored here relates to green space that is a public park, a public garden, or a playing field (public or private). There is also data available on private gardens, but there was not sufficient time to explore that data.

library(here)
library(readxl)
library(dplyr)
library(janitor)
library(tidyr)
library(ggplot2)

The spreadsheet on public parks, public gardens, and playing fields contains the following sheets:

excel_sheets(here("data-raw", "ospublicgreenspacereferencetables.xlsx"))
 [1] "Readme"                          "Country Parks only"             
 [3] "Country Parks and Playing Field" "Region Parks only"              
 [5] "Region Parks and Playing Fields" "LAD Parks only"                 
 [7] "LAD Parks and Playing Fields"    "LSOA Parks only"                
 [9] "LSOA Parks and Playing Fields"   "GB Access by Ethnicity"

To answer the question we’re interested in (“Which are the most deprived cities in the UK?”) we need to:

  1. combine Local Authority Districts (LADs) into Primary Urban Areas (PUAs)
  2. calculate the level of deprivation in each PUA, taking into account that deprivation is measured on the level of LSOAs (Lower layer Super Output Areas)
  3. order PUAs in order of deprivation

Let’s start by looking at the data in sheet number nine, LSOA Parks and Playing Fields. This is a sensible starting point because 1) it has the more expansive definition of green space and 2) the LSOA level is where the deprivation data is included.

Index of Multiple Deprivation

lsoa_parks_play <- read_excel(here("data-raw", "ospublicgreenspacereferencetables.xlsx"),
                              sheet = 9) %>% 
  clean_names() %>% 
  drop_na(country_code)

lsoa_parks_play_dim <- dim(lsoa_parks_play)
lsoa_parks_play_names <- names(lsoa_parks_play)

The dataset contains information on 41396 LSOAs in England, Scotland, and Wales. Note that no information on Northern Ireland is contained in this dataset.

The next step is to combine LADs (and their component LSOAs) into PUAs. This is done using the Centre for Cities 2022 Table. The code doing this is hidden because it is very long, but essentially what it does is create a new variable called PUA and assigns a value to it depending on the LAD name associated with each observation.

Code 
puas <- lsoa_parks_play %>% 
  mutate(PUA = case_when(
    # the CfC table had this LAD down as Aberdeen. ideally check if Aberdeen City is what is meant, as there is also Aberdeenshire
           lad_name == "Aberdeen City" ~ "Aberdeen",
           lad_name == "Rusmoor" ~ "Aldershot",
           lad_name == "Surrey Heath" ~ "Aldershot",
           lad_name == "Barnsley" ~ "Barnsley",
           lad_name == "Basildon" ~ "Basildon",
           lad_name == "Wirral" ~ "Birkenhead",
           lad_name == "Birmingham" ~ "Birmingham",
           lad_name == "Dudley" ~ "Birmingham",
           lad_name == "Sandwell" ~ "Birmingham",
           lad_name == "Solihull" ~ "Birmingham",
           lad_name == "Walsall" ~ "Birmingham",
           lad_name == "Wolverhampton" ~ "Birmingham",
           lad_name == "Blackburn with Darwen" ~ "Blackburn",
           lad_name == "Blackpool" ~ "Blackpool",
           lad_name == "Fylde" ~ "Blackpool",
           lad_name == "Dorset" ~ "Bournemouth",
           lad_name == "Bournemouth, Christchurch and Poole" ~ "Bournemouth",
           lad_name == "Bradford" ~ "Bradford",
           lad_name == "Adur" ~ "Brighton",
           lad_name == "Brighton and Hove" ~ "Brighton",
           lad_name == "City of Bristol" ~ "Bristol",
           lad_name == "South Gloucestershire" ~ "Bristol",
           lad_name == "Burnley" ~ "Burnley",
           lad_name == "Pendle" ~ "Burnley",
           lad_name == "Cambridge" ~ "Cambridge",
           lad_name == "Cardiff" ~ "Cardiff",
           lad_name == "Medway" ~ "Chatham",
           lad_name == "Coventry" ~ "Coventry",
           lad_name == "Crawley" ~ "Crawley",
           lad_name == "Derby" ~ "Derby",
           lad_name == "Doncaster" ~ "Doncaster",
           # the CfC table had this LAD down as Dundee
           lad_name == "Dundee City" ~ "Dundee",
           # the CfC table had this LAD down as Edinburgh
           lad_name == "City of Edinburgh" ~ "Edinburgh",
           lad_name == "Exeter" ~ "Exeter",
           lad_name == "East Dunbartonshire" ~ "Glasgow",
           lad_name == "East Renfrewshire" ~ "Glasgow",
           lad_name == "Glasgow" ~ "Glasgow",
           lad_name == "Renfrewshire" ~ "Glasgow",
           lad_name == "Gloucester" ~ "Gloucester",
           lad_name == "Kirklees" ~ "Huddersfield",
           # the CfC table had this LAD down as Kingston upon Hull
           lad_name == "Kingston upon Hull, City of" ~ "Hull",
           lad_name == "Ipswich" ~ "Ipswich",
           lad_name == "Leeds" ~ "Leeds",
           lad_name == "Blaby" ~ "Leicester",
           lad_name == "Leicester" ~ "Leicester",
           lad_name == "Oadby and Wigston" ~ "Leicester",
           lad_name == "Knowsley" ~ "Liverpool",
           lad_name == "Barking and Dagenham" ~ "London",
           lad_name == "Barnet" ~ "London",
           lad_name == "Bexley" ~ "London",
           lad_name == "Brent" ~ "London",
           lad_name == "Bromley" ~ "London",
           lad_name == "Broxbourne" ~ "London",
           lad_name == "Camden" ~ "London",
           lad_name == "City of London" ~ "London",
           lad_name == "Croydon" ~ "London",
           lad_name == "Dartford" ~ "London",
           lad_name == "Ealing" ~ "London",
           lad_name == "Elmbridge" ~ "London",
           lad_name == "Enfield" ~ "London",
           lad_name == "Epping Forest" ~ "London",
           lad_name == "Epsom and Ewell" ~ "London",
           lad_name == "Gravesham" ~ "London",
           lad_name == "Greenwich" ~ "London",
           lad_name == "Hackney" ~ "London",
           lad_name == "Hammersmith and Fulham" ~ "London",
           lad_name == "Haringey" ~ "London",
           lad_name == "Harrow" ~ "London",
           lad_name == "Havering" ~ "London",
           lad_name == "Hertsmere" ~ "London",
           lad_name == "Hillingdon" ~ "London",
           lad_name == "Hounslow" ~ "London",
           lad_name == "Islington" ~ "London",
           lad_name == "Kensington and Chelsea" ~ "London",
           lad_name == "Kingston upon Thames" ~ "London",
           lad_name == "Lambeth" ~ "London",
           lad_name == "Lewisham" ~ "London",
           lad_name == "Merton" ~ "London",
           lad_name == "Newham" ~ "London",
           lad_name == "Redbridge" ~ "London",
           lad_name == "Richard upon Thames" ~ "London",
           lad_name == "Runnymede" ~ "London",
           lad_name == "Southwark" ~ "London",
           lad_name == "Spelthorne" ~ "London",
           lad_name == "Sutton" ~ "London",
           lad_name == "Three Rivers" ~ "London",
           lad_name == "Tower Hamlets" ~ "London",
           lad_name == "Waltham Forest" ~ "London",
           lad_name == "Wandsworth" ~ "London",
           lad_name == "Watford" ~ "London",
           lad_name == "Westminster" ~ "London",
           lad_name == "Woking" ~ "London",
           lad_name == "Luton" ~ "Luton",
           lad_name == "Bolton" ~ "Manchester",
           lad_name == "Bury" ~ "Manchester",
           lad_name == "Manchester" ~ "Manchester",
           lad_name == "Oldham" ~ "Manchester",
           lad_name == "Rochdale" ~ "Manchester",
           lad_name == "Salford" ~ "Manchester",
           lad_name == "Stockport" ~ "Manchester",
           lad_name == "Tameside" ~ "Manchester",
           lad_name == "Trafford" ~ "Manchester",
           lad_name == "Ashfield" ~ "Mansfield",
           lad_name == "Mansfield" ~ "Mansfield",
           lad_name == "Middlesbrough" ~ "Middlesbrough",
           lad_name == "Redcar and Cleveland" ~ "Middlesbrough",
           lad_name == "Stockton-on-Tees" ~ "Middlesbrough",
           lad_name == "Milton Keynes" ~ "Milton Keynes",
           lad_name == "Gateshead" ~ "Newcastle",
           lad_name == "Newcastle upon Tyne" ~ "Newcastle",
           lad_name == "North Tyneside" ~ "Newcastle",
           lad_name == "South Tyneside" ~ "Newcastle",
           lad_name == "Newport" ~ "Newport",
           lad_name == "Torfaen" ~ "Newport",
           # the CfC table only lists the LAD of West Northamptonshire
           # this does not exist in the data
           # only East Northamptonshire and Northampton exist
           # this warrants further investigation
           lad_name == "Northampton" ~ "Northampton",
           lad_name == "Broadland" ~ "Norwich",
           lad_name == "Norwich" ~ "Norwich",
           lad_name == "Broxtowe" ~ "Nottingham",
           lad_name == "Erewash" ~ "Nottingham",
           lad_name == "Gedling" ~ "Nottingham",
           lad_name == "Nottingham" ~ "Nottingham",
           lad_name == "Oxford" ~ "Oxford",
           lad_name == "Peterborough" ~ "Peterborough",
           lad_name == "Plymouth" ~ "Plymouth",
           lad_name == "Portsmouth" ~ "Portsmouth",
           lad_name == "Fareham" ~ "Portsmouth",
           lad_name == "Gosport" ~ "Portsmouth",
           lad_name == "Havant" ~ "Portsmouth",
           lad_name == "Chorley" ~ "Preston",
           lad_name == "Preston" ~ "Preston",
           lad_name == "South Ribble" ~ "Preston",
           lad_name == "Reading" ~ "Reading",
           lad_name == "Wokingham" ~ "Reading",
           lad_name == "Rotherham" ~ "Sheffield",
           lad_name == "Sheffield" ~ "Sheffield",
           lad_name == "Slough" ~ "Slough",
           lad_name == "Eastleigh" ~ "Southampton",
           lad_name == "Southampton" ~ "Southampton",
           lad_name == "Castlepoint" ~ "Southend",
           lad_name == "Southend-on-Sea" ~ "Southend",
           lad_name == "Rochford" ~ "Southend",
           lad_name == "Newcastle-under-Lyme" ~ "Stoke",
           lad_name == "Stoke-on-Trent" ~ "Stoke",
           lad_name == "Sunderland" ~ "Sunderland",
           lad_name == "Neath Port Talbot" ~ "Swansea",
           lad_name == "Swansea" ~ "Swansea",
           lad_name == "Swindon" ~ "Swindon",
           lad_name == "Telford and Wrekin" ~ "Telford",
           lad_name == "Wakefield" ~ "Wakefield",
           lad_name == "Warrington" ~ "Warrington",
           lad_name == "Wigan" ~ "Wigan",
           lad_name == "Worthing" ~ "Worthing",
           lad_name == "York" ~ "York"
          ))

As mentioned previously, PUAs are defined as the largest cities and towns in the UK. As such, there are LSOAs in rural areas that do not belong to any of the PUAs. These are dropped here.

puas <- puas %>% 
  drop_na(PUA)

Following procedure used in the RSA’s UK Urban Futures Commission report, relative deprivation for each PUA is determined by the percentage of LSOAs that are in the most deprived deciles.

pua_imd <- puas %>%
  count(country_name, PUA, index_of_multiple_deprivation_decile_country_specific) %>% 
  group_by(country_name, PUA) %>% 
  mutate(percentage = (n/sum(n)) * 100) %>% 
  ungroup() %>%
  filter(index_of_multiple_deprivation_decile_country_specific %in% 1:2) %>% 
  drop_na(PUA)

These PUAs are then displayed for each country in Great Britain.

England:

pua_imd %>% 
  filter(country_name == "England") %>% 
  rename(imd_decile = index_of_multiple_deprivation_decile_country_specific) %>% 
  arrange(imd_decile, desc(percentage)) %>% 
  select(-n) %>% 
  knitr::kable()
country_name PUA imd_decile percentage
England Liverpool 1 46.9387755
England Hull 1 45.1807229
England Blackburn 1 36.6666667
England Burnley 1 35.0427350
England Bradford 1 33.5483871
England Middlesbrough 1 30.2721088
England Blackpool 1 28.2758621
England Birmingham 1 28.0134680
England Birkenhead 1 25.2427184
England Manchester 1 24.2362525
England Doncaster 1 23.7113402
England Leeds 1 23.6514523
England Sheffield 1 23.0468750
England Sunderland 1 22.7027027
England Stoke 1 22.1757322
England Barnsley 1 21.7687075
England Newcastle 1 19.2883895
England Plymouth 1 17.3913043
England Telford 1 16.8224299
England Wigan 1 16.5000000
England Derby 1 15.8940397
England Wakefield 1 15.7894737
England Mansfield 1 15.6028369
England Nottingham 1 15.1364764
England Coventry 1 14.3589744
England Peterborough 1 14.2857143
England Ipswich 1 14.1176471
England Leicester 1 13.5416667
England Huddersfield 1 12.0155039
England Gloucester 1 11.5384615
England Basildon 1 10.9090909
England Norwich 1 10.1796407
England Preston 1 9.9099099
England Swindon 1 9.0909091
England Northampton 1 9.0225564
England Chatham 1 8.5889571
England Southampton 1 8.4444444
England Warrington 1 7.8740157
England Brighton 1 7.2463768
England Portsmouth 1 6.6869301
England Southend 1 5.6250000
England Milton Keynes 1 5.2631579
England Bournemouth 1 3.3333333
England Luton 1 3.3057851
England Reading 1 2.5510204
England London 1 2.0396913
England Crawley 1 1.5151515
England Oxford 1 1.2048193
England York 1 0.8333333
England Peterborough 2 26.7857143
England Luton 2 23.9669421
England Blackburn 2 20.0000000
England Birmingham 2 19.5959596
England Ipswich 2 18.8235294
England Mansfield 2 18.4397163
England Huddersfield 2 18.2170543
England Wakefield 2 18.1818182
England Derby 2 17.8807947
England Sunderland 2 17.8378378
England Barnsley 2 17.6870748
England Doncaster 2 16.4948454
England Liverpool 2 16.3265306
England Wigan 2 16.0000000
England Stoke 2 15.8995816
England Gloucester 2 15.3846154
England Nottingham 2 15.1364764
England Newcastle 2 14.7940075
England Manchester 2 14.3923965
England Northampton 2 14.2857143
England Chatham 2 14.1104294
England Bradford 2 13.5483871
England London 2 13.0466740
England Basildon 2 12.7272727
England Blackpool 2 12.4137931
England Portsmouth 2 11.8541033
England Plymouth 2 11.8012422
England Preston 2 11.7117117
England Sheffield 2 11.5234375
England Coventry 2 11.2820513
England Southampton 2 11.1111111
England Warrington 2 11.0236220
England Oxford 2 10.8433735
England Burnley 2 10.2564103
England Leicester 2 10.0694444
England Leeds 2 9.9585062
England Birkenhead 2 9.7087379
England Norwich 2 9.5808383
England Southend 2 9.3750000
England Telford 2 9.3457944
England Hull 2 9.0361446
England Middlesbrough 2 8.8435374
England Slough 2 8.7500000
England Exeter 2 8.1081081
England Worthing 2 7.6923077
England Brighton 2 7.2463768
England Milton Keynes 2 6.5789474
England Swindon 2 6.0606061
England Bournemouth 2 4.8888889
England Cambridge 2 4.3478261
England York 2 4.1666667
England Reading 2 2.5510204
England Crawley 2 1.5151515
England Bristol 2 1.2121212

Scotland:

pua_imd %>% 
  filter(country_name == "Scotland") %>% 
  rename(imd_decile = index_of_multiple_deprivation_decile_country_specific) %>% 
  arrange(imd_decile, desc(percentage)) %>% 
  select(-n) %>% 
  knitr::kable()
country_name PUA imd_decile percentage
Scotland Dundee 1 21.808511
Scotland Glasgow 1 8.154506
Scotland Edinburgh 1 6.197655
Scotland Aberdeen 1 1.063830
Scotland Dundee 2 14.893617
Scotland Edinburgh 2 7.537688
Scotland Glasgow 2 7.296137
Scotland Aberdeen 2 6.737589

Wales:

pua_imd %>% 
  filter(country_name == "Wales") %>% 
  rename(imd_decile = index_of_multiple_deprivation_decile_country_specific) %>% 
  arrange(imd_decile, desc(percentage)) %>% 
  select(-n) %>% 
  knitr::kable()
country_name PUA imd_decile percentage
Wales Cardiff 1 18.224299
Wales Newport 1 16.774193
Wales Swansea 1 12.970711
Wales Newport 2 16.774193
Wales Swansea 2 14.225941
Wales Cardiff 2 9.345794
Summary

In England the PUAs experiencing the highest levels of deprivation (percentage of LSOAs in the two most deprived deciles) are:

  1. Liverpool
  2. Blackburn
  3. Hull
  4. Birmingham
  5. Bradford
  6. Burnley
  7. Peterborough

In Scotland, Dundee faces by far the highest levels of deprivation, followed (distantly) by Glasgow, Edinburgh and Aberdeen. If we were to pick only one location in Scotland, Dundee would be the best one.

In Wales, the three PUAs are not as dissimilar in terms of deprivation as they are in Scotland. That being said, Newport is experiencing the highest levels of deprivation, with Cardiff and Swansea following.

Access to Nature

Let’s now focus on what access people have to green spaces. It should be noted that parks and public gardens will be public, but playing fields may be private.

lsoa_parks_play_names

Looking at the variables included in this dataset again, the variables average_distance_to_nearest_park_public_garden_or_playing_field_m and average_size_of_nearest_park_public_garden_or_playing_field_m2 seem like the most promising for our purposes.

puas %>% 
  ggplot(aes(x = average_distance_to_nearest_park_public_garden_or_playing_field_m)) +
  geom_histogram(binwidth = 20)

puas %>% 
  # converting to km2 to avoid the plot having scientific notation
  mutate(avg_green_space_size_km2 = average_size_of_nearest_park_public_garden_or_playing_field_m2/(1000^2)) %>% 
  ggplot(aes(x = avg_green_space_size_km2)) +
  geom_histogram(binwidth = .5)

It looks like the vast majority of green spaces are at a distance of no more than 1500m. Most are also reasonably small.

The following plot shows the distribution of distances to nearest green spaces for each PUA. To make it easier to parse the plot, distances over 1500m have been excluded. Its purpose is to understand how evenly distributed distances in to green spaces in each LSOA are. London, Manchester and Birmingham look different to the rest because they are comprised of a vastly larger number of LSOAs.

puas %>% 
  filter(average_distance_to_nearest_park_public_garden_or_playing_field_m < 1500) %>% 
  ggplot(aes(x = average_distance_to_nearest_park_public_garden_or_playing_field_m)) +
  geom_histogram(binwidth = 10) +
  facet_wrap(~PUA)

puas %>% 
  group_by(PUA) %>% 
  summarise(median_distance_to_gs = median(average_distance_to_nearest_park_public_garden_or_playing_field_m),
            median_size_gs = median(average_size_of_nearest_park_public_garden_or_playing_field_m2),
            mean_distance_to_gs = mean(average_distance_to_nearest_park_public_garden_or_playing_field_m),
            mean_size_gs = mean(average_size_of_nearest_park_public_garden_or_playing_field_m2)) %>%
  arrange(desc(median_distance_to_gs)) %>% 
  knitr::kable()
PUA median_distance_to_gs median_size_gs mean_distance_to_gs mean_size_gs
Southend 435.7975 44180.48 480.7923 98015.42
Norwich 380.6522 33955.80 426.9153 54824.99
Bournemouth 376.8446 29026.00 492.9136 94828.62
Hull 369.0063 54560.64 397.3585 100272.57
Aberdeen 356.1986 45445.71 436.8731 115832.54
Portsmouth 351.0838 31623.72 410.6370 95448.57
Glasgow 349.9164 35245.64 431.0001 194403.87
Preston 342.5431 46568.68 389.7703 187819.65
Swindon 327.3520 43757.19 409.1535 117255.48
Blackpool 324.2464 25994.17 369.9097 67704.70
Newport 324.0550 25802.28 365.3463 61686.27
Swansea 320.8128 29069.38 384.2717 102805.47
Bristol 320.7222 33084.61 373.7540 59980.44
Luton 319.7229 55819.23 357.4806 91794.14
Aldershot 319.3042 38751.76 373.4855 61327.84
Reading 319.0948 46446.87 361.8391 86616.23
Worthing 317.7796 25318.40 346.9190 36576.55
Birmingham 317.0683 60941.85 360.0615 202255.23
Blackburn 314.8325 34917.37 345.9169 97256.03
Southampton 308.5161 45211.58 355.1405 144663.38
York 307.3236 25644.62 363.9983 44356.31
Sunderland 307.1010 40483.47 352.8014 78117.91
Coventry 306.5977 50026.75 344.5680 95523.53
Ipswich 305.9810 50559.54 336.3464 132663.26
Northampton 304.4572 70091.43 354.2851 135931.80
Middlesbrough 302.7591 48590.11 400.9074 92454.52
Crawley 302.0082 52333.78 348.3327 84680.01
Birkenhead 300.4390 47960.06 316.5288 90371.48
Doncaster 296.6762 29810.79 389.7785 59804.65
Cambridge 295.3498 44241.79 334.0155 72077.20
Dundee 293.0332 39846.23 326.5368 77006.72
Basildon 289.4019 51682.81 377.7235 109757.53
Milton Keynes 284.2410 82438.33 315.2818 160540.78
Plymouth 283.3780 24185.25 356.1489 77676.46
Leeds 282.5147 35048.66 344.2917 80080.00
Brighton 280.6280 27116.75 313.6029 55018.21
Sheffield 280.0162 51126.42 318.3833 96380.70
Wigan 279.3162 30540.51 349.4226 108833.93
Derby 279.0213 47889.95 312.8657 102906.52
Leicester 277.8655 50931.59 309.2846 88607.76
Nottingham 276.3806 28610.90 328.2084 126998.75
Chatham 273.0040 26932.48 302.9952 71754.71
Peterborough 269.2324 35984.50 340.6852 108514.40
Manchester 268.6999 37003.01 314.1329 95336.34
Wakefield 268.1722 35257.61 305.8791 61202.44
Mansfield 268.0402 26038.61 298.7354 67872.90
Bradford 266.9105 24445.81 312.4879 41404.47
London 265.2033 47824.39 300.8989 166385.74
Slough 262.0483 39769.10 301.8238 64745.06
Burnley 260.9662 30305.36 303.7838 37238.11
Cardiff 258.7643 41520.86 295.4089 86871.55
Edinburgh 258.1939 53851.07 307.5713 139277.90
Gloucester 255.6391 43626.32 277.9223 81944.83
Exeter 248.9105 34102.18 299.4274 66662.03
Barnsley 243.0617 34986.22 274.4908 76807.89
Huddersfield 241.5561 20089.18 275.9462 41013.53
Newcastle 236.9647 37867.82 275.3651 86985.39
Oxford 236.8857 34346.81 268.2949 76436.94
Liverpool 217.9780 54851.73 265.7847 96999.55
Warrington 210.7551 43085.68 242.8104 66868.64
Telford 209.1717 31881.45 278.2674 55407.03
Stoke 196.6337 49093.15 266.5114 120014.92
Summary

Of the candidate places from the deprivation data, Hull is the only one that scores pretty high in distance to green spaces. Birmingham and Blackburn are in second and third place, though quite far behind Hull. The rest of the places in England are actually pretty close to green spaces. All in all, however, even in the places with the largest distances from green space, the median distances are pretty small and less than 500m in all cases.

Regarding the other GB countries, Newport is the Welsh PUA with the largest distances from green spaces and Aberdeen the Scottish one. Note, however, that Aberdeen had the lowest levels of deprivation in the Scottish PUAs.

There is a longer discussion to be had here about which the most appropriate metric is when thinking about access to nature, especially in the context of PGP. Critically, because PGP will not create new green spaces, we perhaps should be looking at the places with the best access to green spaces. This could be beneficial for the success of the project, as PGPs will have to be created in existing areas; the easier to access these are the better.

Additionally, this dataset has many limitations, one of which being that the play areas included in the dataset could be private.

Cross-checking with Income Deprivation Affecting Children Index (IDACI; England)

This data relates to the English indices of deprivation 2019.

The data was downloaded from the GOV.UK website and was made available by the Ministry of Housing, Communities & Local Government.

excel_sheets(here("data-raw", "File_3_-_IoD2019_Supplementary_Indices_-_IDACI_and_IDAOPI.xlsx"))
idaci_idaopi <- read_excel(here("data-raw", "File_3_-_IoD2019_Supplementary_Indices_-_IDACI_and_IDAOPI.xlsx"),
                              sheet = 2) %>% 
  clean_names()

names(idaci_idaopi)
Code 
idaci <- idaci_idaopi %>% 
  select(lsoa_code = lsoa_code_2011,
         lsoa_name = lsoa_name_2011,
         lad_name = local_authority_district_name_2019,
         lad_code = local_authority_district_code_2019,
         imd_rank = index_of_multiple_deprivation_imd_rank_where_1_is_most_deprived,
         imd_decile = index_of_multiple_deprivation_imd_decile_where_1_is_most_deprived_10_percent_of_lso_as,
         idaci_rank = income_deprivation_affecting_children_index_idaci_rank_where_1_is_most_deprived,
         idaci_decile = income_deprivation_affecting_children_index_idaci_decile_where_1_is_most_deprived_10_percent_of_lso_as) %>% 
  mutate(pua = case_when(
    # the CfC table had this LAD down as Aberdeen. ideally check if Aberdeen City is what is meant, as there is also Aberdeenshire
           lad_name == "Aberdeen City" ~ "Aberdeen",
           lad_name == "Rusmoor" ~ "Aldershot",
           lad_name == "Surrey Heath" ~ "Aldershot",
           lad_name == "Barnsley" ~ "Barnsley",
           lad_name == "Basildon" ~ "Basildon",
           lad_name == "Wirral" ~ "Birkenhead",
           lad_name == "Birmingham" ~ "Birmingham",
           lad_name == "Dudley" ~ "Birmingham",
           lad_name == "Sandwell" ~ "Birmingham",
           lad_name == "Solihull" ~ "Birmingham",
           lad_name == "Walsall" ~ "Birmingham",
           lad_name == "Wolverhampton" ~ "Birmingham",
           lad_name == "Blackburn with Darwen" ~ "Blackburn",
           lad_name == "Blackpool" ~ "Blackpool",
           lad_name == "Fylde" ~ "Blackpool",
           lad_name == "Dorset" ~ "Bournemouth",
           lad_name == "Bournemouth, Christchurch and Poole" ~ "Bournemouth",
           lad_name == "Bradford" ~ "Bradford",
           lad_name == "Adur" ~ "Brighton",
           lad_name == "Brighton and Hove" ~ "Brighton",
           lad_name == "City of Bristol" ~ "Bristol",
           lad_name == "South Gloucestershire" ~ "Bristol",
           lad_name == "Burnley" ~ "Burnley",
           lad_name == "Pendle" ~ "Burnley",
           lad_name == "Cambridge" ~ "Cambridge",
           lad_name == "Cardiff" ~ "Cardiff",
           lad_name == "Medway" ~ "Chatham",
           lad_name == "Coventry" ~ "Coventry",
           lad_name == "Crawley" ~ "Crawley",
           lad_name == "Derby" ~ "Derby",
           lad_name == "Doncaster" ~ "Doncaster",
           # the CfC table had this LAD down as Dundee
           lad_name == "Dundee City" ~ "Dundee",
           # the CfC table had this LAD down as Edinburgh
           lad_name == "City of Edinburgh" ~ "Edinburgh",
           lad_name == "Exeter" ~ "Exeter",
           lad_name == "East Dunbartonshire" ~ "Glasgow",
           lad_name == "East Renfrewshire" ~ "Glasgow",
           lad_name == "Glasgow" ~ "Glasgow",
           lad_name == "Renfrewshire" ~ "Glasgow",
           lad_name == "Gloucester" ~ "Gloucester",
           lad_name == "Kirklees" ~ "Huddersfield",
           # the CfC table had this LAD down as Kingston upon Hull
           lad_name == "Kingston upon Hull, City of" ~ "Hull",
           lad_name == "Ipswich" ~ "Ipswich",
           lad_name == "Leeds" ~ "Leeds",
           lad_name == "Blaby" ~ "Leicester",
           lad_name == "Leicester" ~ "Leicester",
           lad_name == "Oadby and Wigston" ~ "Leicester",
           lad_name == "Knowsley" ~ "Liverpool",
           lad_name == "Barking and Dagenham" ~ "London",
           lad_name == "Barnet" ~ "London",
           lad_name == "Bexley" ~ "London",
           lad_name == "Brent" ~ "London",
           lad_name == "Bromley" ~ "London",
           lad_name == "Broxbourne" ~ "London",
           lad_name == "Camden" ~ "London",
           lad_name == "City of London" ~ "London",
           lad_name == "Croydon" ~ "London",
           lad_name == "Dartford" ~ "London",
           lad_name == "Ealing" ~ "London",
           lad_name == "Elmbridge" ~ "London",
           lad_name == "Enfield" ~ "London",
           lad_name == "Epping Forest" ~ "London",
           lad_name == "Epsom and Ewell" ~ "London",
           lad_name == "Gravesham" ~ "London",
           lad_name == "Greenwich" ~ "London",
           lad_name == "Hackney" ~ "London",
           lad_name == "Hammersmith and Fulham" ~ "London",
           lad_name == "Haringey" ~ "London",
           lad_name == "Harrow" ~ "London",
           lad_name == "Havering" ~ "London",
           lad_name == "Hertsmere" ~ "London",
           lad_name == "Hillingdon" ~ "London",
           lad_name == "Hounslow" ~ "London",
           lad_name == "Islington" ~ "London",
           lad_name == "Kensington and Chelsea" ~ "London",
           lad_name == "Kingston upon Thames" ~ "London",
           lad_name == "Lambeth" ~ "London",
           lad_name == "Lewisham" ~ "London",
           lad_name == "Merton" ~ "London",
           lad_name == "Newham" ~ "London",
           lad_name == "Redbridge" ~ "London",
           lad_name == "Richard upon Thames" ~ "London",
           lad_name == "Runnymede" ~ "London",
           lad_name == "Southwark" ~ "London",
           lad_name == "Spelthorne" ~ "London",
           lad_name == "Sutton" ~ "London",
           lad_name == "Three Rivers" ~ "London",
           lad_name == "Tower Hamlets" ~ "London",
           lad_name == "Waltham Forest" ~ "London",
           lad_name == "Wandsworth" ~ "London",
           lad_name == "Watford" ~ "London",
           lad_name == "Westminster" ~ "London",
           lad_name == "Woking" ~ "London",
           lad_name == "Luton" ~ "Luton",
           lad_name == "Bolton" ~ "Manchester",
           lad_name == "Bury" ~ "Manchester",
           lad_name == "Manchester" ~ "Manchester",
           lad_name == "Oldham" ~ "Manchester",
           lad_name == "Rochdale" ~ "Manchester",
           lad_name == "Salford" ~ "Manchester",
           lad_name == "Stockport" ~ "Manchester",
           lad_name == "Tameside" ~ "Manchester",
           lad_name == "Trafford" ~ "Manchester",
           lad_name == "Ashfield" ~ "Mansfield",
           lad_name == "Mansfield" ~ "Mansfield",
           lad_name == "Middlesbrough" ~ "Middlesbrough",
           lad_name == "Redcar and Cleveland" ~ "Middlesbrough",
           lad_name == "Stockton-on-Tees" ~ "Middlesbrough",
           lad_name == "Milton Keynes" ~ "Milton Keynes",
           lad_name == "Gateshead" ~ "Newcastle",
           lad_name == "Newcastle upon Tyne" ~ "Newcastle",
           lad_name == "North Tyneside" ~ "Newcastle",
           lad_name == "South Tyneside" ~ "Newcastle",
           lad_name == "Newport" ~ "Newport",
           lad_name == "Torfaen" ~ "Newport",
           # the CfC table only lists the LAD of West Northamptonshire
           # this does not exist in the data
           # only East Northamptonshire and Northampton exist
           # this warrants further investigation
           lad_name == "Northampton" ~ "Northampton",
           lad_name == "Broadland" ~ "Norwich",
           lad_name == "Norwich" ~ "Norwich",
           lad_name == "Broxtowe" ~ "Nottingham",
           lad_name == "Erewash" ~ "Nottingham",
           lad_name == "Gedling" ~ "Nottingham",
           lad_name == "Nottingham" ~ "Nottingham",
           lad_name == "Oxford" ~ "Oxford",
           lad_name == "Peterborough" ~ "Peterborough",
           lad_name == "Plymouth" ~ "Plymouth",
           lad_name == "Portsmouth" ~ "Portsmouth",
           lad_name == "Fareham" ~ "Portsmouth",
           lad_name == "Gosport" ~ "Portsmouth",
           lad_name == "Havant" ~ "Portsmouth",
           lad_name == "Chorley" ~ "Preston",
           lad_name == "Preston" ~ "Preston",
           lad_name == "South Ribble" ~ "Preston",
           lad_name == "Reading" ~ "Reading",
           lad_name == "Wokingham" ~ "Reading",
           lad_name == "Rotherham" ~ "Sheffield",
           lad_name == "Sheffield" ~ "Sheffield",
           lad_name == "Slough" ~ "Slough",
           lad_name == "Eastleigh" ~ "Southampton",
           lad_name == "Southampton" ~ "Southampton",
           lad_name == "Castlepoint" ~ "Southend",
           lad_name == "Southend-on-Sea" ~ "Southend",
           lad_name == "Rochford" ~ "Southend",
           lad_name == "Newcastle-under-Lyme" ~ "Stoke",
           lad_name == "Stoke-on-Trent" ~ "Stoke",
           lad_name == "Sunderland" ~ "Sunderland",
           lad_name == "Neath Port Talbot" ~ "Swansea",
           lad_name == "Swansea" ~ "Swansea",
           lad_name == "Swindon" ~ "Swindon",
           lad_name == "Telford and Wrekin" ~ "Telford",
           lad_name == "Wakefield" ~ "Wakefield",
           lad_name == "Warrington" ~ "Warrington",
           lad_name == "Wigan" ~ "Wigan",
           lad_name == "Worthing" ~ "Worthing",
           lad_name == "York" ~ "York"
          ))
idaci %>% 
  count(pua, idaci_decile) %>%
  group_by(pua) %>% 
  mutate(percentage = (n/sum(n)) * 100) %>% 
  ungroup() %>% 
  filter(idaci_decile %in% 1:2) %>% 
  arrange(idaci_decile, desc(percentage)) %>% 
  select(-n) %>% 
  knitr::kable()
pua idaci_decile percentage
Liverpool 1 44.897959
Hull 1 37.951807
Middlesbrough 1 30.612245
Sunderland 1 25.945946
Birmingham 1 25.521885
Birkenhead 1 23.300971
Newcastle 1 22.097378
Blackpool 1 22.068966
Doncaster 1 20.618557
Sheffield 1 20.117188
Leeds 1 19.917012
Nottingham 1 19.602978
Blackburn 1 18.681319
Manchester 1 18.194162
Stoke 1 17.991632
Barnsley 1 17.687075
Bradford 1 16.451613
Coventry 1 16.410256
Telford 1 14.814815
Derby 1 14.569536
Mansfield 1 14.184397
Plymouth 1 13.043478
Leicester 1 12.847222
Basildon 1 11.818182
Ipswich 1 11.764706
Peterborough 1 11.607143
Burnley 1 11.111111
Chatham 1 10.429448
Gloucester 1 10.256410
Norwich 1 10.179641
Wakefield 1 10.047847
Wigan 1 10.000000
Southend 1 9.375000
Luton 1 9.090909
Southampton 1 8.444444
Swindon 1 8.333333
Brighton 1 8.212560
Huddersfield 1 7.722008
Northampton 1 7.518797
Portsmouth 1 6.990882
Preston 1 6.756757
London 1 6.413083
NA 1 6.343360
Warrington 1 4.724409
Milton Keynes 1 4.605263
Exeter 1 4.054054
Oxford 1 3.614458
Bournemouth 1 2.876106
Reading 1 2.040816
York 1 1.666667
Crawley 1 1.515151
Burnley 2 22.222222
Mansfield 2 20.567376
Blackburn 2 19.780220
Birmingham 2 18.787879
Peterborough 2 17.857143
Bradford 2 17.741936
Barnsley 2 17.006803
Ipswich 2 16.470588
Manchester 2 15.818058
Plymouth 2 15.527950
Coventry 2 15.384615
Chatham 2 15.337423
Sunderland 2 15.135135
Stoke 2 15.062762
Nottingham 2 14.888338
Hull 2 14.457831
Newcastle 2 14.419476
Wigan 2 14.000000
London 2 13.947078
Doncaster 2 13.917526
Derby 2 13.907285
Wakefield 2 13.875598
Basildon 2 13.636364
Preston 2 13.513514
Sheffield 2 12.304688
Leicester 2 12.152778
Gloucester 2 11.538462
Huddersfield 2 10.424710
Liverpool 2 10.204082
Norwich 2 10.179641
Leeds 2 9.751037
Portsmouth 2 9.726444
Oxford 2 9.638554
Middlesbrough 2 9.523810
Telford 2 9.259259
Southampton 2 8.888889
Blackpool 2 8.275862
Luton 2 8.264463
Warrington 2 7.874016
Brighton 2 7.729469
Worthing 2 7.692308
York 2 7.500000
Milton Keynes 2 7.236842
Southend 2 6.875000
Swindon 2 6.818182
Birkenhead 2 6.796117
Bournemouth 2 6.415929
NA 2 6.118273
Northampton 2 6.015038
Reading 2 5.102041
Crawley 2 4.545454
Exeter 2 4.054054
Cambridge 2 2.898551
Slough 2 2.500000
Bristol 2 2.424242

The children-specific index would suggest that the most relevant cities for our purposes are:

  1. Liverpool
  2. Hull
  3. Birmingham
  4. Blackburn
  5. Bradford

Candidate places

Country PUA
England Hull
England Liverpool
England Birmingham
England Blackburn
England Bradford
Scotland Dundee
Scotland Glasgow
Wales Newport
Wales Cardiff
N. Ireland Belfast