The hardware and bandwidth for this mirror is donated by dogado GmbH, the Webhosting and Full Service-Cloud Provider. Check out our Wordpress Tutorial.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]dogado.de.

TongFen for Canadian census data

Jens von Bergmann

2022-04-28

rent_variables <- c(rent_2001="v_CA01_1667",rent_2016="v_CA16_4901",  
                    rent_2011="v_CA11N_2292",rent_2006="v_CA06_2050")
meta <- meta_for_ca_census_vectors(rent_variables) 

regions=list(CMA="59933")
rent_data <- get_tongfen_ca_census(regions=regions, meta=meta, quiet=TRUE,
                                   method="estimate", level="CT", base_geo = "CA16")

periods <- c("2001-2006","2006-2011","2011-2016","2001-2016")
plot_data <- rent_data %>%
  mutate(`2001-2006`=rent_2006/rent_2001-1,
         `2006-2011`=rent_2011/rent_2006-1,
         `2011-2016`=rent_2016/rent_2011-1,
         `2001-2016`=rent_2016/rent_2001-1) %>%
  pivot_longer(cols = all_of(periods),
               names_to="Period",values_to="Change") %>%
  mutate(Period=factor(Period,level=periods)) %>%
  st_sf()
ggplot(plot_data,aes(fill=Change)) + 
  geom_sf(size=0.1) +
  scale_fill_gradient2(labels=scales::percent) +
  coord_sf(datum = NA,xlim=c(-123.3,-122.5),ylim=c(49,49.42)) +
  facet_wrap("Period",ncol=2) +
  labs(title="Change in average gross rent")

Aggregating up data across regions

Another application is simply aggregating up variables for a selection of regions from a single census. Suppose we want to understand the share of renters in the Vancouver School District, as well as the share of renter households spending more than 30% of income on housing.

vectors <- c("v_CA16_4836","v_CA16_4838","v_CA16_4899")
meta=meta_for_ca_census_vectors(vectors) %>%
  bind_rows(meta_for_additive_variables("CA16",c("Population","Dwellings","Households")))
vsb_regions <- list(CSD=c("5915022","5915803"),
                    CT=c("9330069.01","9330069.02","9330069.00"))
vsb <- get_census("CA16",regions=vsb_regions,vectors=meta$variable,labels="short")
vsb <- aggregate_data_with_meta(vsb, meta) %>%
  mutate(Total=v_CA16_4836,Renters=v_CA16_4838,rent_poor=v_CA16_4899/100) %>%
  mutate(rent_share=Renters/Total)

53% of Vancouver School District households rent, 45% of which are shelter cost burdened.

Change in Vancouver children

Data can also be obtained at dissemination area level. As an example we look at the change in children aged 0 to 14 in the Vancouver School District.

variables <- c("2016_0-14"="v_CA16_4",
               "2011_0-4"="v_CA11F_8","2011_5-9"="v_CA11F_11","2011_10-14"="v_CA11F_14")
meta <- meta_for_ca_census_vectors(variables) %>%
    bind_rows(meta_for_additive_variables(c("CA11","CA16"),"Population"))
children_data <- get_tongfen_ca_census(regions = vsb_regions, meta = meta, 
                                       level="DA", quiet = TRUE) %>%
  mutate(`2011_0-14`=purrr::reduce(select(sf::st_set_geometry(.,NULL), starts_with("2011_")), `+`)) %>%
  mutate(change=`2016_0-14`/Population_CA16-`2011_0-14`/Population_CA11)

ggplot(children_data,aes(fill=change)) +
  geom_sf(size=0.1) +
  scale_fill_gradient2(labels=scales::percent) +
  coord_sf(datum=NA) +
  labs(title="Percentage point change in share of children aged 0-14 between 2011 and 2016",fill=NULL)

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.