+# 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"
+ ))
+
Note that there are LSOAs that don’t belong to a PUA. 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.
# A tibble: 9 × 5
- country_name PUA index_of_multiple_deprivation_decile_c…¹ n percentage
- <chr> <chr> <dbl> <int> <dbl>
-1 Wales Cardiff 1 39 18.2
-2 Wales Cardiff 2 20 9.35
-3 Wales Cardiff 3 15 7.01
-4 Wales Newport 1 26 16.8
-5 Wales Newport 2 26 16.8
-6 Wales Newport 3 11 7.10
-7 Wales Swansea 1 31 13.0
-8 Wales Swansea 2 34 14.2
-9 Wales Swansea 3 22 9.21
+
# A tibble: 6 × 4
+ country_name PUA index_of_multiple_deprivation_decile_country…¹ percentage
+ <chr> <chr> <dbl> <dbl>
+1 Wales Cardiff 1 18.2
+2 Wales Newport 1 16.8
+3 Wales Swansea 1 13.0
+4 Wales Newport 2 16.8
+5 Wales Swansea 2 14.2
+6 Wales Cardiff 2 9.35
# ℹ abbreviated name: ¹index_of_multiple_deprivation_decile_country_specific
+
The first thing to note is that Scotland and Wales have a much smaller number of PUAs. There are four in Scotland (Aberdeen, Dundee, Edinburgh and Glasgow) and three in Wales (Cardiff, Newport and Swansea).
+
+
Summary
+
In Scotland, Dundee faces by far the highest levels of deprivation, compared to Glasgow, Edinburgh and Aberdeen, and would be the best target in Scotland, if we were to pick only one.
+
In Wales, the three PUAs are not as dissimilar in terms of deprivation as they were in Scotland. That being said, Newport is experiencing the highest levels of deprivation, with Cardiff and Swansea following.
+
The vast majority of PUAs is located in England. In order of deprivation, again looking at the top two deciles,:
+
+
Liverpool
+
Blackburn
+
Hull
+
Birmingham
+
Bradford
+
Burnley
+
Peterborough
+
+
+
+
+
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.
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 %>%
+# 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 1400m. 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.
diff --git a/scripts/location-selection.qmd b/scripts/location-selection.qmd
index 4922301..51e6570 100644
--- a/scripts/location-selection.qmd
+++ b/scripts/location-selection.qmd
@@ -37,6 +37,8 @@ Additionally, rural places can suffer from high levels of deprivation.
## Place selection
### Index of Multiple Deprivation & Access to Nature
+
+#### Index of Multiple Deprivation
The data presented below comes from the ONS and relates to [Access to gardens and public green space in Great Britain](https://www.ons.gov.uk/economy/environmentalaccounts/datasets/accesstogardensandpublicgreenspaceingreatbritain).
@@ -50,6 +52,7 @@ library(readxl)
library(dplyr)
library(janitor)
library(tidyr)
+library(ggplot2)
```
This file contains the following sheets:
@@ -73,7 +76,8 @@ This is a sensible starting point because it is where the data that contains inf
lsoa_parks_play <- read_excel(here("data-raw", "ospublicgreenspacereferencetables.xlsx"),
sheet = 9) %>%
- clean_names()
+ clean_names() %>%
+ drop_na(country_code)
dim(lsoa_parks_play)
names(lsoa_parks_play)
@@ -88,7 +92,8 @@ The code doing this is very long, but essentially what it does is create a new v
puas <- lsoa_parks_play %>%
mutate(PUA = case_when(
- lad_name == "Aberdeen" ~ "Aberdeen",
+ # 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",
@@ -119,8 +124,10 @@ puas <- lsoa_parks_play %>%
lad_name == "Crawley" ~ "Crawley",
lad_name == "Derby" ~ "Derby",
lad_name == "Doncaster" ~ "Doncaster",
- lad_name == "Dundee" ~ "Dundee",
- lad_name == "Edinburgh" ~ "Edinburgh",
+ # 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",
@@ -128,7 +135,8 @@ puas <- lsoa_parks_play %>%
lad_name == "Renfrewshire" ~ "Glasgow",
lad_name == "Gloucester" ~ "Gloucester",
lad_name == "Kirklees" ~ "Huddersfield",
- lad_name == "Kingston upon Hull" ~ "Hull",
+ # 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",
@@ -202,7 +210,11 @@ puas <- lsoa_parks_play %>%
lad_name == "South Tyneside" ~ "Newcastle",
lad_name == "Newport" ~ "Newport",
lad_name == "Torfaen" ~ "Newport",
- lad_name == "West Northamptonshire" ~ "Northampton",
+ # 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",
@@ -244,6 +256,15 @@ puas <- lsoa_parks_play %>%
))
```
+Note that there are LSOAs that don't belong to a PUA.
+These are dropped here.
+
+```{r}
+puas <- puas %>%
+ drop_na(PUA)
+```
+
+
Following procedure used in the RSA's [UK Urban Futures Commission report](https://www.thersa.org/design-for-life-our-mission/hubs/uk-urban-futures-commission), relative deprivation for each PUA is determined by the percentage of LSOAs that are in the most deprived deciles.
```{r}
@@ -255,7 +276,7 @@ pua_imd <- puas %>%
group_by(country_name, PUA) %>%
mutate(percentage = (n/sum(n)) * 100) %>%
ungroup() %>%
- filter(index_of_multiple_deprivation_decile_country_specific %in% 1:3) %>%
+ filter(index_of_multiple_deprivation_decile_country_specific %in% 1:2) %>%
drop_na(PUA)
```
@@ -268,7 +289,8 @@ England:
pua_imd %>%
filter(country_name == "England") %>%
- arrange(index_of_multiple_deprivation_decile_country_specific, desc(percentage))
+ arrange(index_of_multiple_deprivation_decile_country_specific, desc(percentage)) %>%
+ select(-n)
```
Scotland:
@@ -277,7 +299,9 @@ Scotland:
#| echo: true
pua_imd %>%
- filter(country_name == "Scotland")
+ filter(country_name == "Scotland") %>%
+ arrange(index_of_multiple_deprivation_decile_country_specific, desc(percentage)) %>%
+ select(-n)
```
Wales:
@@ -286,8 +310,80 @@ Wales:
#| echo: true
pua_imd %>%
- filter(country_name == "Wales")
+ filter(country_name == "Wales") %>%
+ arrange(index_of_multiple_deprivation_decile_country_specific, desc(percentage)) %>%
+ select(-n)
```
+The first thing to note is that Scotland and Wales have a much smaller number of PUAs.
+There are four in Scotland (Aberdeen, Dundee, Edinburgh and Glasgow) and three in Wales (Cardiff, Newport and Swansea).
+
+##### Summary
+
+In Scotland, **Dundee** faces by far the highest levels of deprivation, compared to Glasgow, Edinburgh and Aberdeen, and would be the best target in Scotland, if we were to pick only one.
+
+In Wales, the three PUAs are not as dissimilar in terms of deprivation as they were in Scotland.
+That being said, **Newport** is experiencing the highest levels of deprivation, with Cardiff and Swansea following.
+
+The vast majority of PUAs is located in England. In order of deprivation, again looking at the top two deciles,:
+
+1. **Liverpool**
+2. **Blackburn**
+3. **Hull**
+4. Birmingham
+5. Bradford
+6. Burnley
+7. Peterborough
+
+#### 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.
+
+```{r}
+#| echo: true
+
+names(puas)
+```
+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.
+
+```{r}
+#| echo: true
+#| warning: false
+
+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 1400m.
+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.
+
+```{r}
+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)
+```
+
+
+```{r}
+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))
+```
+
### Cross-checking with harmonised IMD data
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