Anteil an sozialem Wohnbau in österreichischen Städten (https://twitter.com/matschnetzer/status/1601228388659654657)

socialhousing.r

library(tidyverse)
library(pdftools)
library(msthemes)
library(sf)
library(ggforce)
library(ggrepel)
sf::sf_use_s2(FALSE)

# Download PDF from https://www.staedtebund.gv.at/services/publikationen/staedte-in-zahlen-oestiz/ and extract page 55
rawpdf <- pdf_text("https://www.staedtebund.gv.at/ePaper-oestiz/ausgaben-pdf/OESTIZ_2020.pdf")
rawpdf <- rawpdf[55]

lines <- strsplit(rawpdf, '\n') %>% unlist  %>% str_trim(.,side = "left")
lines <- lines[c(13:194)]

df <- as.data.frame(lines) %>% 
  separate(1,c("Gemeinde","Gesamt","HWS","NWS","Privat","Öffentlich","Gemeinnützig","Sonstige","Wohnungslos"), sep="\\s+\\s+") %>% 
  filter(!is.na(Gesamt)) %>% 
  mutate_at(.vars = -1, list(~as.numeric(gsub("\\.", "", .)))) %>%
  replace(is.na(.),0) %>%
  mutate_at(.vars = 1, list(~str_replace_all(.,c(
    "Bgl.Eisenstadt" = "Eisenstadt",
    "Saalfelden/Steinernen Meer" = "Saalfelden am Steinernen Meer",
    "TirolSchwaz" = "Schwaz")
    )))

# Manually deleting Bundesländer
df <- df[-c(76:85),]

# Get map from https://github.com/ginseng666/GeoJSON-TopoJSON-Austria
map <- st_read("https://raw.githubusercontent.com/ginseng666/GeoJSON-TopoJSON-Austria/master/2017/simplified-99.9/gemeinden_999_geo.json", quiet=TRUE, stringsAsFactors=FALSE) |> 
  mutate(
    center = map(geometry, st_centroid),
    centercoord = map(center, st_coordinates),
    ccordx = map_dbl(centercoord, 1),
    ccordy = map_dbl(centercoord, 2)
  )

# Breaks for shares of social housing
breaks <- c(0,20,35,50,100)
df$social <- (df$Gemeinnützig + df$Öffentlich)/df$Gesamt*100
df$socialcat <- cut(df$social, breaks)

mapdat <- left_join(map,df,by= c("name"="Gemeinde")) |> filter(!is.na(social))

set.seed(1238)

mapdat |>  ggplot() +
  geom_sf(data=st_union(map), fill="gray99", linewidth = 0.5) +
  coord_sf(datum = NA) +
  geom_point(aes(x=ccordx, y=ccordy, color=socialcat, size=Gesamt),
             data=mapdat |>  filter(!is.na(social))) +
  scale_size(name = "Wohnungen",
             breaks = c(50000,250000,500000),
             labels = c("50.000","250.000","500.000"),
             range = c(1, 7)) +
  scale_colour_manual(name="Anteil",
                    labels = c("<20%","20-35%","35-50%",">50%"),
                    values = c("#189ad3","#ffc300","#1e8449","#C70039")) +
  theme_ms(alt=T) +
  guides(colour = guide_legend(override.aes = list(size=4))) +
  labs(x="",y="",
       title = "Sozialer Wohnbau in Österreich",
       subtitle = "Anteil der öffentlichen und gemeinnützigen Wohnungen in Gemeinden über 11.000 Einwohnern",
       caption = "Quelle: Städte in Zahlen 2020, Städtebund. Grafik: @matschnetzer") +
  geom_mark_circle(aes(x=ccordx,y=ccordy,
                       filter = name == "Feldkirch", label = "Feldkirch",
                       description = "In Feldkirch sind nur 15% der Wohnungen in öffentlichem oder gemeinnützigem Eigentum"), 
                   label.buffer = unit(10,"mm"), 
                   label.fontsize = c(9, 7),
                   label.minwidth = unit(55,"mm"),
                   expand = unit(3,"mm"),
                   con.cap = 2) +
  geom_mark_circle(aes(x=ccordx,y=ccordy,
                       filter = name == "Linz", label = "Linz",
                       description = "Linz hat mit mehr als 55% den höchsten Anteil an sozialem Wohnbau"), 
                   label.buffer = unit(14,"mm"), 
                   label.fontsize = c(9, 7),
                   expand = unit(3,"mm"),
                   con.cap = 2) +
  geom_label_repel(aes(x=ccordx, y=ccordy, 
                       label = glue::glue("{name} ({round(social,0)}%)")),
    data = subset(mapdat |>  
                    filter(name %in% c("Knittelfeld", "Kapfenberg", "Wien"))),
    size = 2.3,
    box.padding = 0.4,
    label.size = 0.15,
    segment.size = 0.1,
    nudge_x = c(1,0,0))

ggsave("socialhousing.png", dpi=320, width = 9, height = 5)