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OSMGlobalBars.R
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OSMGlobalBars.R
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rm(list=ls())
library(curl)
library(tidyverse)
library(sf)
library(ragg)
library(extrafont)
library(rnaturalearth)
library(paletteer)
library(forcats)
library(lwgeom)
library(readxl)
font <- "Lato"
theme_custom <- function() {
theme_classic() %+replace%
theme(plot.title.position="plot", plot.caption.position="plot",
strip.background=element_blank(), strip.text=element_text(face="bold", size=rel(1)),
plot.title=element_text(face="bold", size=rel(1.5), hjust=0,
margin=margin(0,0,5.5,0)),
text=element_text(family=font),
plot.subtitle=element_text(colour="Grey40", hjust=0, vjust=1),
plot.caption=element_text(colour="Grey40", hjust=1, vjust=1, size=rel(0.8)),
axis.text=element_text(colour="Grey40"),
axis.title=element_text(colour="Grey20"),
legend.text=element_text(colour="Grey40"),
legend.title=element_text(colour="Grey20"))
}
#Download osm data with all amenities IN THE WORLD (download is massive)
#THIS IS ABOUT TO BE DEPRECATED, SO GET YOUR DOWNLOAD IN NOW!
url <- "https://data.osmdata.xyz/amenity_EPSG4326.zip"
temp <- tempfile()
temp2 <- tempfile()
temp <- curl_download(url=url, destfile=temp, quiet=FALSE, mode="wb")
unzip(zipfile=temp, exdir=temp2)
name <- list.files(temp2, pattern=".gpkg")
#Extract all pubs
pubs <- st_read(file.path(temp2, name), layer="amenity_EPSG4326_point",
query="SELECT * FROM \"amenity_EPSG4326_point\" WHERE amenity IN ('pub')")
#Extract all bars
bars <- st_read(file.path(temp2, name), layer="amenity_EPSG4326_point",
query="SELECT * FROM \"amenity_EPSG4326_point\" WHERE amenity IN ('bar')")
#Extract all nightclubs
clubs <- st_read(file.path(temp2, name), layer="amenity_EPSG4326_point",
query="SELECT * FROM \"amenity_EPSG4326_point\" WHERE amenity IN ('nightclub')")
#Extract all biergartens (suggested by https://wiki.openstreetmap.org/wiki/Tag:amenity%3Dbar)
biergartens <- st_read(file.path(temp2, name), layer="amenity_EPSG4326_point",
query="SELECT * FROM \"amenity_EPSG4326_point\" WHERE amenity IN ('biergarten')")
#Stick it all together
fulldata <- pubs %>% select(osm_id, name, amenity, `_ogr_geometry_`) %>%
bind_rows(bars %>% select(osm_id, name, amenity, `_ogr_geometry_`)) %>%
bind_rows(clubs %>% select(osm_id, name, amenity, `_ogr_geometry_`)) %>%
bind_rows(biergartens %>% select(osm_id, name, amenity, `_ogr_geometry_`))
#Save it out
st_write(fulldata, "Outputs/OSMGlobalBars.shp")
#Bring in map
map <- ne_countries(scale = "large", returnclass = "sf") %>%
select(name, sovereignt, sov_a3, pop_est, iso_a3, continent, geometry)
#test <- st_read("X:/ScHARR/SARG_IARP/General/Data/Misc/OpenStreetMap/OSMGlobalBars.shp")
#Place all outlets within a country
countries <- st_join(test, map, join=st_within)
#Get outlets per capita
density <- countries %>%
filter(!is.na(name.y)) %>%
group_by(name.y) %>%
summarise(n=n(), pop_est=unique(pop_est), .groups="drop") %>%
mutate(pop_est=as.numeric(pop_est), density=n*100000/pop_est) %>%
st_drop_geometry() %>%
set_names("name", "outlets", "pop", "density")
densitymap <- left_join(map, density)
agg_png("Day28_2022_ComfortZone.png", units="in", width=9, height=5.5, res=600)
densitymap %>%
mutate(density=if_else(is.na(density), 0, density)) %>%
filter(density<100 & continent!="Antarctica") %>%
st_transform_proj(crs = "ESRI:54030") %>%
ggplot(aes(geometry=geometry, fill=density))+
geom_sf(colour=NA)+
scale_fill_paletteer_c("pals::ocean.haline", direction=-1, name="Pubs/bars per 100,000 people")+
theme_custom()+
theme(legend.position="top", axis.line=element_blank(),
axis.ticks=element_blank(), axis.text=element_blank(),
plot.title=element_text(size=rel(3)))+
guides(fill = guide_colorbar(title.position = 'top', title.hjust = .5,
barwidth = unit(20, 'lines'), barheight = unit(.5, 'lines')))+
labs(title="Is it far to the nearest bar?",
subtitle="Per capita density of locations tagged as pubs/bars/nightclubs/biergarten in OpenStreetMap by country",
caption="Data from OpenStreetMap | Map by @VictimOfMaths")
dev.off()
#Filter only UK data at Local Authority level
#Get shapefile
url2 <- "https://opendata.arcgis.com/api/v3/datasets/420e691a2e8e4db0a0e5acc8ea3d0ce4_0/downloads/data?format=shp&spatialRefId=27700&where=1%3D1"
temp <- tempfile()
temp2 <- tempfile()
temp <- curl_download(url=url2, destfile=temp, quiet=FALSE, mode="wb")
unzip(zipfile=temp, exdir=temp2)
name <- list.files(temp2, pattern=".shp")
UKmap <- st_read(file.path(temp2, name))
#Reproject global data into GB LTLAs
LACounts <- st_transform(fulldata, crs=27700) %>%
#Place all outlets within a country
st_join(UKmap, join=st_within) %>%
#Get outlets per capita
filter(!is.na(LAD21CD)) %>%
group_by(LAD21CD) %>%
summarise(Count=n(), .groups="drop") %>%
st_drop_geometry()
#Join back into the map
LTLAmap <- left_join(UKmap, LACounts)
ggplot(LTLAmap, aes(fill=Count, geometry=geometry))+
geom_sf(colour=NA)+
scale_fill_paletteer_c("viridis::rocket", direction=-1, limits=c(0,NA))+
theme_void()
#Download Carl Baker's lovely map
ltla <- tempfile()
source <- ("https://github.com/houseofcommonslibrary/uk-hex-cartograms-noncontiguous/raw/main/geopackages/LocalAuthorities-lowertier.gpkg")
ltla <- curl_download(url=source, destfile=ltla, quiet=FALSE, mode="wb")
Background <- st_read(ltla, layer="7 Background")
ltlapubs <- st_read(ltla, layer="6 LTLA-2021") %>%
rename("LAD21CD"="Lacode") %>%
left_join(LTLAs, by="LAD21CD")
Groups <- st_read(ltla, layer="2 Groups")
Group_labels <- st_read(ltla, layer="1 Group labels") %>%
mutate(just=if_else(LabelPosit=="Left", 0, 1))
ggplot()+
geom_sf(data=Background, aes(geometry=geom), fill="White")+
geom_sf(data=ltlapubs, aes(geometry=geom, fill=Count), colour="Black", size=0.1)+
geom_sf(data=Groups, aes(geometry=geom), fill=NA, colour="Black")+
geom_sf_text(data=Group_labels, aes(geometry=geom, label=Group.labe,
hjust=just), size=rel(2.4), colour="Black")+
scale_fill_paletteer_c("viridis::rocket", direction=-1, limits=c(0,NA))+
theme_void()+
theme(plot.title=element_text(face="bold", size=rel(1.2)),
text=element_text(family="Lato"))
#Bring in populations
url3 <- "https://www.ons.gov.uk/file?uri=/peoplepopulationandcommunity/populationandmigration/populationestimates/datasets/populationestimatesforukenglandandwalesscotlandandnorthernireland/mid2021/ukpopestimatesmid2021on2021geographyfinal.xls"
temp <- tempfile()
temp <- curl_download(url=url3, destfile=temp, quiet=FALSE, mode="wb")
pops <- read_excel(temp, sheet="MYE2 - Persons", range="A8:D428") %>%
select(c(1,4)) %>%
set_names("LAD21CD", "Pop")
PubPerCapita <- ltlapubs %>%
left_join(pops, by="LAD21CD") %>%
#Merge Scilly with Cornwall and City of London with Hackney, because otherwise their numbers are
#So extreme they dwarf everything else
mutate(LAD21CD=case_when(
LAD21CD=="E09000001" ~ "E09000012",
LAD21CD=="E06000053" ~ "E06000052",
TRUE ~ LAD21CD),
Laname=case_when(
Laname %in% c("Cornwall", "Isles of Scilly") ~ "Cornwall & Scilly",
Laname %in% c("Hackney", "City of London") ~ "Hackney & City",
TRUE ~ Laname)) %>%
group_by(LAD21CD, Laname) %>%
summarise(Count=sum(Count), Pop=sum(Pop), .groups="drop") %>%
mutate(ppc=Count*100000/Pop)
agg_tiff("Outputs/OSMPubsxLACartogram.tiff", units="in", width=6, height=8, res=600)
ggplot()+
geom_sf(data=Background %>% filter(Name!="Ireland"), aes(geometry=geom), fill="White")+
geom_sf(data=PubPerCapita %>% filter(substr(LAD21CD, 1, 1)!="N"),
aes(geometry=geom, fill=ppc), colour="Black", size=0.1)+
geom_sf(data=Groups %>% filter(Group!="Northern Ireland"), aes(geometry=geom), fill=NA, colour="Black")+
geom_sf_text(data=Group_labels %>% filter(Group.labe!="Northern Ireland"),
aes(geometry=geom, label=Group.labe, hjust=just), size=rel(2.4), colour="Black")+
scale_fill_paletteer_c("viridis::mako", direction=-1, limits=c(0,NA),
name="Pubs per\n100,000\npopulation")+
theme_void()+
theme(plot.title=element_text(face="bold", size=rel(1.2)),
text=element_text(family="Lato"))+
labs(title="How many locals for the locals?",
subtitle="Number of pubs and bars per capita in Great Britain",
caption="Data from OpenStreetMap\nCartogram by Carl Baker\nMap by @VictimOfMaths")
dev.off()