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library(VancouvR)
library(dplyr)
#>
#> Attaching package: 'dplyr'
#> The following objects are masked from 'package:stats':
#>
#> filter, lag
#> The following objects are masked from 'package:base':
#>
#> intersect, setdiff, setequal, union
library(tidyr)
library(ggplot2)
get_cov_metadata("property-tax-report") %>%
tail(10)
#> # A tibble: 10 × 4
#> name type label description
#> <chr> <chr> <chr> <chr>
#> 1 current_land_value int CURRENT_LAND_VALUE The market valu…
#> 2 current_improvement_value int CURRENT_IMPROVEMENT_VALUE The market valu…
#> 3 tax_assessment_year text TAX_ASSESSMENT_YEAR Year in effect …
#> 4 previous_land_value int PREVIOUS_LAND_VALUE This value is f…
#> 5 previous_improvement_value int PREVIOUS_IMPROVEMENT_VALUE This value is f…
#> 6 year_built text YEAR_BUILT Year that the p…
#> 7 big_improvement_year text BIG_IMPROVEMENT_YEAR Year of major i…
#> 8 tax_levy double TAX_LEVY This is the tot…
#> 9 neighbourhood_code text NEIGHBOURHOOD_CODE This is a 3-dig…
#> 10 report_year text REPORT_YEAR Year when data …
search_cov_datasets("property-tax") %>%
pull(dataset_id) %>%
lapply(function(ds)
aggregate_cov_data(ds,
group_by="tax_assessment_year as Year",
where="zoning_district like 'RS-' or zoning_district like 'R1-1'",
select="sum(current_land_value) as Land, sum(current_improvement_value) as Building")) %>%
bind_rows() %>%
mutate(Date=as.Date(paste0(as.integer(Year)-1,"-07-01"))) %>%
pivot_longer(c("Land","Building")) %>%
ggplot(aes(x=Year,y=value,color=name,group=name)) +
geom_point(shape=21) +
geom_line() +
scale_y_continuous(labels=function(x)paste0("$",x/1000000000,"Bn")) +
labs(title="City of Vancouver RS/R1-1 zoned land and building values",
x="Tax year", color="", y="Aggregate value (nominal)")
#> Downloading data from CoV Open Data portal
#> Downloading data from CoV Open Data portal
#> Downloading data from CoV Open Data portal
#> Downloading data from CoV Open Data portal
When metadata indicates that the data has a spatial componenet the
package will automatically return the data in sf
format.
tax_data <- get_cov_data(dataset_id = "property-tax-report",
where="tax_assessment_year='2024'",
select = "current_land_value, land_coordinate as tax_coord")
#> Downloading data from CoV Open Data portal
property_polygons <- get_cov_data(dataset_id="property-parcel-polygons") %>%
sf::st_transform(26910)
#> Downloading data from CoV Open Data portal
plot_data <- property_polygons %>%
left_join(tax_data %>% group_by(tax_coord) %>% summarize(current_land_value=sum(current_land_value)),by="tax_coord") %>%
mutate(rlv=current_land_value/as.numeric(sf::st_area(geometry))) %>%
mutate(rlvd=cut(rlv,breaks=c(-Inf,1000,2000,3000,4000,5000,7500,10000,25000,50000,Inf),
labels=c("<$1k","$1k-$2k","$2k-$3k","$3k-$4k","$4k-$5k","$5k-$7.5k","$7.5k-$10k","$10k-$25k","$25k-$50k",">$50k"),
ordered_result = TRUE))
ggplot(plot_data) +
geom_sf(aes(fill=rlvd),color=NA) +
scale_fill_viridis_d(option="magma",na.value="darkgrey") +
labs(title="July 2023 relative land values",fill="Value per m^2",caption="CoV Open Data") +
coord_sf(datum=NA)
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They may not be fully stable and should be used with caution. We make no claims about them.
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