In HughParsonage/journey.to.work1116: Provide Journey To Work Data From Australian Censuses
knitr::opts_chunk$set(
collapse = TRUE,
fig.width = 12,
fig.height = 10,
cache = TRUE,
comment = "#>"
)
library(viridis)
library(leaflet)
library(scales)
library(data.table)
library(hutils)
if (!AND(requireNamespace("grattanCharts", quietly = TRUE),
requireNamespace("Census2016.DataPack", quietly = TRUE))) {
tempLib <- .libPaths()[1]
devtools::install_github("hughparsonage/grattanCharts", lib.loc = tempLib)
install.packages("Census2016.DataPack",
repos = "https://hughparsonage.github.io/drat",
type = "source",
lib = tempLib)
library(grattanCharts, lib.loc = tempLib)
library(Census2016.DataPack, lib.loc = tempLib)
} else {
library(grattanCharts)
library(Census2016.DataPack)
}
library(magrittr)
library(ASGS)
library(sp)
library(rgeos)
library(ggplot2)
library(journey.to.work1116)
SA2_DZN_2011 <- fread("~/ABS-Data/2011-Home_SA2-Work_DZN-Count.csv")
DZN_2011_persons <-
SA2_DZN_2011[, .(persons = sum(Count)), keyby = .(DZN_CODE11 = Work_DZN)]
DZN_2011_data <-
as.data.table(DZN_2011@data) %>%
.[, id := .I]
DZN_2011_centroids <- gCentroid(DZN_2011, byid = TRUE)
DZN_2011_centroids_tidy <-
as.data.frame(DZN_2011_centroids) %>%
as.data.table %>%
.[, id := .I] %>%
.[DZN_2011_data, on = "id"] %>%
setnames(c("x", "y"), c("lon", "lat")) %>%
.[]
DZN_2016_persons <-
fread("../data-raw/2016-DZN-persons.csv", skip = 10) %>%
setnames(1:2, c("DZN_CODE16", "persons")) %>%
drop_empty_cols %>%
.[DZN_CODE16 %enotin% c("Total",
"POW not applicable",
"POW No Fixed Address (OT)",
"Migratory - Offshore - Shipping (OT)")]
DZN_2016_data <-
as.data.table(DZN_2016@data) %>%
.[, id := .I]
DZN_2016_centroids <- gCentroid(DZN_2016, byid = TRUE)
DZN_2016_centroids_tidy <-
as.data.frame(DZN_2016_centroids) %>%
as.data.table %>%
.[, id := .I] %>%
.[DZN_2016_data, on = "id"] %>%
setnames(c("x", "y"), c("lon", "lat")) %>%
.[]
library(PSMA)
SYD <- geocode(number_first = 1, street_name = "Martin", street_type = "Place", postcode = 2000)
MEL <- geocode(number_first = 350, street_name = "Bourke", street_type = "Street", postcode = 3000)
BNE <- geocode(number_first = 261, street_name = "Queen", street_type = "Street", postcode = 4000)
ADL <- geocode(number_first = 141, street_name = "King William", street_type = "Street", postcode = 5000)
PER <- geocode(number_first = 378, street_name = "Wellington", street_type = "Street", postcode = 6000)
lat_lon_by_City <-
data.table(City = c("SYD", "MEL", "BNE", "ADL", "PER"),
GPO_lat = c(SYD$LATITUDE, MEL$LATITUDE, BNE$LATITUDE, ADL$LATITUDE, PER$LATITUDE),
GPO_lon = c(SYD$LONGITUDE, MEL$LONGITUDE, BNE$LONGITUDE, ADL$LONGITUDE, PER$LONGITUDE))
id_by_city <-
data.table::CJ(id = DZN_2011_centroids_tidy$id,
City = c("SYD", "MEL", "BNE", "ADL", "PER")) %>%
.[lat_lon_by_City, on = "City"]
distance2GPO_by_DZN_CODE11 <-
DZN_2011_centroids_tidy %>%
.[id_by_city, on = "id"] %>%
.[, distance2GPO := haversine_distance(lat, lon, GPO_lat, GPO_lon)] %>%
.[, .(distance2GPO = min(distance2GPO),
City = City[which.min(distance2GPO)]), keyby = c("DZN_CODE11")]
distance2GPO_by_DZN_CODE16 <-
DZN_2016_centroids_tidy %>%
.[id_by_city, on = "id"] %>%
.[, distance2GPO := haversine_distance(lat, lon, GPO_lat, GPO_lon)] %>%
.[, .(distance2GPO = min(distance2GPO),
City = City[which.min(distance2GPO)]), keyby = c("DZN_CODE16")]
persons_by_GPO_distance_2011 <-
distance2GPO_by_DZN_CODE11[DZN_2011_persons, on = "DZN_CODE11", nomatch=0L] %>%
.[, Year := "2011"]
persons_by_GPO_distance_2016 <-
distance2GPO_by_DZN_CODE16[DZN_2016_persons, on = "DZN_CODE16", nomatch=0L] %>%
.[, Year := "2016"]
workers_by_GPO <-
rbind(persons_by_GPO_distance_2011,
persons_by_GPO_distance_2016,
use.names = TRUE, fill = TRUE)
correspondence <-
readxl::read_excel("../data-raw/cg_dzn_2011_dzn_2016.xls",
sheet = "Table 3",
range = "A6:D9562") %>%
as.data.table %>%
.[complete.cases(.)]
workers_by_SLA2006_Home_Dest <-
fread("~/ABS-data/inbox/SLA-v-JTW-SLA-2006-clean.csv", header = FALSE, na.strings = "") %>%
setnames(1:3, c("SLA_2006_Home", "SLA_2006_Work", "workers")) %>%
drop_empty_cols %>%
.[, SLA_2006_Home := zoo::na.locf(SLA_2006_Home, na.rm = FALSE)] %>%
.[SLA_2006_Home != "Total"] %>%
.[SLA_2006_Work != "Total"] %>%
.[workers > 0]
workers_by_SLA2006 <-
workers_by_SLA2006_Home_Dest[, .(workers = sum(workers)), keyby = "SLA_2006_Work"]
SydneyMelJobGrowth <-
read.table(text = " Sydney Sydney 2 Blank Melbourne Melbourne 2
Inner 01 -2500 54500 23500 68000
Middle 01 13000 20000 26000 24500
Outer 01 35500 31000 56500 50000",
sep = "\t", header = TRUE, as.is = TRUE) %>%
as.data.table %>%
drop_empty_cols %>%
setnames(c("Sydney", "Sydney.2"),
c("Sydney_0106", "Sydney_0611")) %>%
setnames(c("Melbourne", "Melbourne.2"),
c("Melbourne_0106", "Melbourne_0611")) %>%
setnames(1, "Ring") %>%
melt.data.table(id.vars = "Ring", value.name = "delta_workers") %>%
merge(data.table(Ring = c("Inner 01",
"Middle 01",
"Outer 01"),
distance_group = cut(c(1, 5, 25),
breaks = c(-Inf, 2, 15, Inf),
labels = c("0-2km", "2-15km", ">15km"),
ordered = TRUE)),
by = "Ring") %>%
.[, City := "SYD"] %>%
.[, Period := if_else(grepl("0611", variable), "2006-2011", "2001-2006")] %>%
.[variable %pin% "Melbourne", City := "MEL"] %>%
.[]
workers_by_km_group_distance <-
workers_by_GPO[distance2GPO < 35,
distance_group := cut(distance2GPO,
breaks = c(-Inf, 2, 15, Inf),
labels = c("0-2km", "2-15km", ">15km"),
ordered = TRUE)] %>%
.[, .(total_workers = sum(persons)), keyby = c("Year", "City", "distance_group")]
workers_by_km_group_distance %>%
.[, .(delta_workers = last(total_workers) - first(total_workers)), keyby = c("City", "distance_group")] %>%
.[, Period := "2011-2016"] %>%
.[] %>%
.[City %in% c("SYD", "MEL")] %>%
rbind(SydneyMelJobGrowth, fill = TRUE, use.names = TRUE) %>%
.[order(-Period)] %>%
.[City %in% c("SYD", "MEL")] %>%
.[order(City, distance_group, Period)] %>%
grplot(aes(x = Period, y = delta_workers)) +
geom_bar(stat = "identity") +
facet_grid(City ~ distance_group)
workers_by_GPO %>%
.[distance2GPO < 2] %>%
.[City == "SYD"] %>%
.[DZN_2011_centroids_tidy, on = "DZN_CODE11", nomatch=0L] %>%
ggplot(aes(lon, lat, size = persons)) +
geom_point() +
annotate_coastline("Sydney") +
coord_map(xlim = c(151.19, 151.23), ylim = c(-33.89, -33.85)) +
facet_wrap(~Year)
Where have jobs grown (2011-2016)?
Absolute change
n_employed_persons_2016_SYD_CBD <-
JTW_2016_SA2 %>%
.[D_SA2_NAME16 %ein% "Sydney - Haymarket - The Rocks"] %$%
sum(workers) %>%
round(-3)
n_employed_persons_2011_SYD_CBD <-
JTW_2011_SA2 %>%
.[D_SA2_NAME11 %ein% "Sydney - Haymarket - The Rocks"] %$%
sum(workers) %>%
round(-1)
n_employed_persons_2006_SYD_CBD <-
JTW_2006_SLA %>%
.[D_SLA_NAME06 %ein% "Sydney (C) - Inner"] %$%
sum(workers)
n_employed_persons_2001_SYD_CBD <- 186798
n_employed_persons_1996_SYD_CBD <- 162609
n_employed_persons_1991_SYD_CBD <-
JTW_1991_SYD[Work_SLA %ein% "Sydney (Inner)"] %$%
sum(workers) %>%
round(-3)
ERP_by_ASGS_1991_2016 <-
if (!file.exists("~/ABS-data/Estimated-Resident-Population-by-SA2-and-above-1991-2016.csv")) {
fread("https://github.com/HughParsonage/ABS-data/raw/82c5be16745077b8c5049ae2c098c695f1e5ee5c/Estimated-Resident-Population-by-SA2-and-above-1991-2016.csv")
} else {
fread("~/ABS-data/Estimated-Resident-Population-by-SA2-and-above-1991-2016.csv")
}
AREA_by_SA2_2011 <-
SA2_2011@data %>%
as.data.table %>%
.[, .(SA2_NAME11, ALBERS_SQKM = ALBERS_SQM / 1e6)]
percent0 <- function(x, d = 1) {
paste0(formatC(100*x, digits = d, flag = " ", format = "f"), "%")
}
delta_residents1116_density <-
ERP_by_ASGS_1991_2016 %>%
.[REGIONTYPE == "SA2"] %>%
.[Year %in% c(2011, 2016),
.(Year, Region = NAME_2011, ERP = EstimatedResidentPopulation)] %>%
dcast.data.table(Region ~ Year, value.var = "ERP") %>%
.[`2011` > 0, .(SA2_NAME11 = Region,
`2011`,
`2016`,
delta_residents = (`2016` / `2011`) ^ (1/4) - 1)] %>%
.[, breaks := .bincode(delta_residents,
breaks = quantile(delta_residents, probs = c(0:8)/8),
# breaks = c(-Inf, 200 * (0:6), Inf),
include.lowest = TRUE)] %>%
.[, fillColor := c("white", gpal(7, reverse = TRUE))[breaks]] %>%
.[, labelText := percent0(delta_residents)] %>%
.[, labelTitle := SA2_NAME11] %>%
.[]
delta_residents1116 <-
ERP_by_ASGS_1991_2016 %>%
.[REGIONTYPE == "SA2"] %>%
.[Year %in% c(2011, 2016),
.(Year, Region = NAME_2011, ERP = EstimatedResidentPopulation)] %>%
dcast.data.table(Region ~ Year, value.var = "ERP") %>%
.[`2011` > 0, .(SA2_NAME11 = Region,
`2011`,
`2016`,
delta_residents = (`2016` - `2011`))] %>%
.[, p90 := quantile(delta_residents, probs = 0.9, na.rm = TRUE)] %>%
.[delta_residents <= 0, fillColor := "white"] %>%
.[delta_residents >= p90[1], fillColor := "black"] %>%
.[is.na(fillColor),
fillColor := gpal(6, reverse = TRUE)[.bincode(delta_residents,
breaks = ((0:5)/5) * p90[1],
include.lowest = TRUE)]] %>%
# .[complete.cases(.)] %>%
.[, labelTitle := SA2_NAME11] %>%
.[, labelText := scales::comma(delta_residents)] %>%
.[]
delta_residents_density <-
ERP_by_ASGS_1991_2016 %>%
.[REGIONTYPE == "SA2"] %>%
.[Year %in% c(2011, 2016),
.(Year, Region = NAME_2011, ERP = EstimatedResidentPopulation)] %>%
.[AREA_by_SA2_2011, on = "Region==SA2_NAME11"] %>%
.[, density := ERP / ALBERS_SQKM] %>%
dcast.data.table(Region ~ Year, value.var = "density") %>%
.[`2011` > 0, .(SA2_NAME11 = Region,
`2011`,
`2016`,
delta_residents = (`2016` / `2011`)^(1/4) - 1)] %>%
.[, breaks := .bincode(delta_residents,
breaks = quantile(delta_residents, probs = c(0:8)/8),
# breaks = c(-Inf, 200 * (0:6), Inf),
include.lowest = TRUE)] %>%
.[, fillColor := c("white", gpal(7, reverse = TRUE))[breaks]] %>%
.[, labelText := paste0(if_else(delta_residents > 0, "+", ""),
percent(delta_residents), "<br>",
comma(floor(`2011`)), " --> ", comma(floor(`2016`)))] %>%
.[, labelTitle := paste(SA2_NAME11, " density")] %>%
.[]
workers_by_SA2_Year <-
# Use 2011 boundaries
rbindlist(list("2016" = JTW_2016_SA2[, .(workers = sum(workers)), keyby = .(SA2_NAME11 = D_SA2_NAME16)],
"2011" = JTW_2011_SA2[, .(workers = sum(workers)), keyby = .(SA2_NAME11 = D_SA2_NAME11)]),
idcol = "Year") %>%
dcast.data.table(SA2_NAME11 ~ Year, value.var = "workers")
workers_by_Sydney_SA3_Year <-
data.table(Year = c(1991, 2016),
workers = c(359795, 494315)) %>%
.[, AREA := 25.0641]
delta_workers1116 <- workers_by_SA2_Year[, .(SA2_NAME11, delta_workers = `2016` - `2011`)]
delta_residents_vs_workers <-
delta_workers1116[delta_residents1116, on = "SA2_NAME11", nomatch=0L]
delta_workers_0616_abs <-
JTW_2006_SLA
SA2__persons <-
SA2__Persons %>%
SA2016_decoder[., on = "SA2_MAIN16==SA2_MAINCODE_2016"] %>%
set_cols_first("SA2_NAME16") %>%
.[, fillColor := gpal(7, reverse = TRUE)[dplyr::ntile(persons, 7)]] %>%
.[, labelTitle := SA2_NAME16] %>%
.[, labelText := scales::comma(persons)]
n_workers_by_Year_SYD_CBD <-
data.table(Year = seq(from = 1991, to = 2016, by = 5),
workers = as.numeric(unlist(mget(x = paste0("n_employed_persons_", seq(1991, 2016, by = 5), "_SYD_CBD")))))
SA3_2011_centroids <-
cbind(SA3_2011@data, gCentroid(SA3_2011, byid = TRUE)@coords) %>%
as.data.table
SA3NAME_vs_dist2SYDGPO <-
SA3_2011_centroids %>%
.[, dist2SYDGPO := haversine_distance(y, x, SYD$LATITUDE, SYD$LONGITUDE)] %>%
.[]
SA2NAME_vs_dist2SYDGPO <-
SA2_2011_weighted_centroids %>%
.[, SA2_NAME11 := latlon2SA(lat, lon, to = "SA2", yr = "2011", return = "v")] %>%
.[, dist2SYDGPO := haversine_distance(lat, lon, SYD$LATITUDE, SYD$LONGITUDE)] %>%
.[]
ERP_by_ASGS_1991_2016 %>%
.[REGIONTYPE == "SA3"] %>%
.[NAME_2011 %in% SA3NAME_vs_dist2SYDGPO[dist2SYDGPO < 10][["SA3_NAME11"]]] %>%
ggplot(aes(x = Year, y = EstimatedResidentPopulation, group = NAME_2011, color = NAME_2011)) +
geom_line()
SydneyInnerCitySA3_minus_CBD_by_Year <-
ERP_by_ASGS_1991_2016 %>%
.[NAME_2011 %ein% c("Sydney Inner City", "Sydney - Haymarket - The Rocks")] %>%
.[, .(NAME_2011, Year, EstimatedResidentPopulation)] %>%
dcast.data.table(Year ~ NAME_2011, value.var = "EstimatedResidentPopulation") %>%
.[, `Sydney Inner City minus CBD` := `Sydney Inner City` - `Sydney - Haymarket - The Rocks`] %>%
.[, .(Year, `Sydney - Haymarket - The Rocks`, `Sydney Inner City minus CBD`)] %>%
melt.data.table(id.vars = "Year",
variable.name = "NAME_2011",
variable.factor = FALSE,
value.name = "EstimatedResidentPopulation")
ERP_by_ASGS_1991_2016 %>%
.[REGIONTYPE == "SA3"] %>%
.[NAME_2011 %in% SA3NAME_vs_dist2SYDGPO[dist2SYDGPO < 10][["SA3_NAME11"]]] %>%
# .[NAME_2011 %enotin% "Sydney Inner City"] %>%
# rbind(SydneyInnerCitySA3_minus_CBD_by_Year, use.names = TRUE, fill = TRUE) %>%
# .[NAME_2011 %enotin% c("Sydney - Haymarket - The Rocks")] %>%
.[, Index := EstimatedResidentPopulation / first(EstimatedResidentPopulation),
keyby = "NAME_2011"] %>%
.[, last_pop := last(Index), keyby = "NAME_2011"] %>%
.[, SA3 := reorder(NAME_2011, -last_pop)] %>%
ggplot(aes(x = Year, y = Index, group = SA3, color = SA3)) +
geom_line() +
scale_y_continuous(labels = function(x) 100 * x,
breaks = c(1, 1.5, 2, 2.5))
# ERP_by_ASGS_1991_2016
slope <- function(x, y) {
x <- as.double(x)
y <- as.double(y)
n <- length(x)
{sum(x * y) - sum(x) * sum(y) / n} /
{sum(x^2) - sum(x)^2 / n}
}
SA2011_decoder <- as.data.table(SA2_2011)
ERP_by_ASGS_1991_2016 %>%
.[NAME_2011 %ein% SA2011_decoder[GCC_NAME11 == "Greater Sydney"][["SA2_NAME11"]]] %>%
.[SA2_2011_weighted_centroids, on = "CODE_2011==SA2_MAIN11", nomatch=0L] %>%
.[, distance2GPO := haversine_distance(lat, lon, SYD$LATITUDE, SYD$LONGITUDE)] %>%
.[] %>%
.[, distance_group := cut(distance2GPO, breaks = c(-Inf, 2, 10, 20, Inf),
labels = c("<2km", "2-10km", "10-20km", "20km+"),
include.lowest = TRUE,
ordered_result = TRUE)] %>%
.[EstimatedResidentPopulation > 1000] %>%
.[, Index := EstimatedResidentPopulation / first(EstimatedResidentPopulation),
keyby = "NAME_2011"] %>%
# Some SA2s have zero population.
.[!is.nan(Index)] %>%
.[, .(Year, Index, NAME_2011, distance_group)] %>%
grplot(aes(x = Year, y = Index, group = NAME_2011, color = distance_group)) +
geom_line() +
geom_smooth(aes(group = distance_group)) +
facet_wrap(~distance_group)
#' @return Viridis palette of x by quantile (decile by default).
palette_v <- function(x, n = 10) {
input <- data.table(x = x)
input[, order := 1:.N]
input[, decile := ntile(x, n)]
colortbl <- data.table(decile = 1:10,
colour = viridis(10))
colortbl[input, on = "decile"] %>%
setorder(order) %>%
.[["colour"]]
}
pal_v <- colorNumeric(palette = viridis(10),
domain = NULL)
gpal_v <- colorNumeric(palette = c("white", gpal(7, reverse = TRUE)),
domain = NULL)
slope_by_SA2 <-
ERP_by_ASGS_1991_2016 %>%
.[Year > 2005] %>%
.[NAME_2011 %ein% SA2011_decoder[GCC_NAME11 == "Greater Sydney"][["SA2_NAME11"]]] %>%
.[SA2_2011_weighted_centroids, on = "CODE_2011==SA2_MAIN11", nomatch=0L] %>%
.[, distance2GPO := haversine_distance(lat, lon, SYD$LATITUDE, SYD$LONGITUDE)] %>%
.[, .(Slope = slope(Year, EstimatedResidentPopulation)), keyby = "NAME_2011"] %>%
.[, fillColor := gpal_v(rank(Slope))] %>%
.[, labelTitle := NAME_2011] %>%
.[, labelText := scales::comma(floor(Slope))] %>%
.[, SA2_NAME11 := NAME_2011] %>%
set_cols_first("SA2_NAME11") %>%
.[]
grattan_leaflet(slope_by_SA2, "2011", simple = TRUE)
density_by_SA2_by_Year <-
as.data.table(SA2_2011) %>%
.[ERP_by_ASGS_1991_2016, on = "SA2_NAME11==NAME_2011", nomatch=0L] %>%
.[SA2_NAME11 %ein% SA2011_decoder[GCC_NAME11 == "Greater Sydney"][["SA2_NAME11"]]] %>%
.[, pop_density := EstimatedResidentPopulation / (ALBERS_SQM / 1e6)] %>%
.[]
density_by_SA3_by_Year <-
as.data.table(SA3_2011) %>%
.[ERP_by_ASGS_1991_2016, on = "SA3_NAME11==NAME_2011", nomatch=0L] %>%
.[SA3_NAME11 %ein% SA2011_decoder[GCC_NAME11 == "Greater Sydney"][["SA3_NAME11"]]] %>%
.[, pop_density := EstimatedResidentPopulation / (ALBERS_SQM / 1e6)] %>%
.[]
slopeDensity_by_SA2_1991_2016 <-
density_by_SA2_by_Year %>%
.[, .(Slope_density = slope(Year, pop_density)), keyby = "SA2_NAME11"] %>%
.[, Slope_density := round(Slope_density)] %>%
.[, fillColor := gpal_v(rank(Slope_density))] %>%
.[Slope_density <= 0, fillColor := "white"] %>%
.[, labelTitle := SA2_NAME11] %>%
.[, labelText := paste(scales::comma(floor(Slope_density)),
"persons per sqkm per year")] %>%
.[]
grattan_leaflet(slopeDensity_by_SA2_1991_2016, Year = "2016", simple = TRUE)
slopeDensity_by_SA3_1991_2016 <-
density_by_SA3_by_Year %>%
.[, .(Slope_density = slope(Year, pop_density)), keyby = "SA3_NAME11"] %>%
.[, Slope_density := round(Slope_density)] %>%
.[, fillColor := gpal_v(Slope_density)] %>%
.[Slope_density <= 0, fillColor := "white"] %>%
.[, labelTitle := SA3_NAME11] %>%
.[, labelText := paste(scales::comma(floor(Slope_density)),
"persons per sqkm per year")] %>%
.[]
grattan_leaflet(slopeDensity_by_SA3_1991_2016, Year = "2011", simple = TRUE)
SALM <-
if (file.exists("~/data-employment-gov-au/salm_smoothed_sa2_datafile_june_quarter_2017.csv")) {
fread("~/data-employment-gov-au/salm_smoothed_sa2_datafile_june_quarter_2017.csv")
} else {
tryCatch({
fread("https://docs.employment.gov.au/system/files/doc/other/salm_smoothed_sa2_datafiles_-_june_quarter_2017.csv") %>%
melt.data.table(id.vars = c("Data item",
"Statistical Area Level 2 (SA2)",
"SA2 Code"),
variable.factor = FALSE,
variable.name = "Month") %>%
.[, Date := as.Date(paste0("01-", Month), format = "%d-%b-%y")] %>%
setnames(c("Statistical Area Level 2 (SA2)", "SA2 Code"),
c("SA2_NAME16", "SA2_MAIN16")) %>%
.[, .(SA2_NAME16, SA2_MAIN16, Date, Item = `Data item`, value)] %T>%
fwrite("~/data-employment-gov-au/salm_smoothed_sa2_datafile_june_quarter_2017.csv") %>%
.[]
}, error = function(e) {
fread("https://github.com/HughParsonage/data-employment-gov-au/raw/101c7dc2f137e31b8f4de5669f8653340784c0f9/salm_smoothed_sa2_datafile_june_quarter_2017.csv")
})
}
slopeDensity_by_SA2_2006_2016 <-
density_by_SA2_by_Year %>%
.[Year >= 2006] %>%
.[, .(Slope_density = slope(Year, pop_density)), keyby = "SA2_NAME11"] %>%
.[, Slope_density := round(Slope_density)] %>%
.[, fillColor := gpal_v(rank(Slope_density))] %>%
.[Slope_density <= 0, fillColor := "white"] %>%
.[, labelTitle := SA2_NAME11] %>%
.[, labelText := paste(scales::comma(floor(Slope_density)), "persons per sqkm per year")] %>%
.[]
Australia's economic future lies in its cities.
But city planning is moving jobs and residential areas in different directions.
The city sprawl is a well-understood phenomenon but what is less well-known is that jobs are becoming more concentrated.
The modern economy more than ever thrives with a high concentration of people working in the same square kilometre,
but the modern planning system limits how close they can live to these hot spots.
In 1991, r comma(n_employed_persons_1991_SYD_CBD) worked in the Sydney CBD.
Twenty years later, r comma(n_employed_persons_2011_SYD_CBD) did and in the most recent census, r comma(n_employed_persons_2016_SYD_CBD) of the population's main job was located in the
r SA2_2016@data %>% as.data.table %>% .[SA2_NAME16 %pin% "The Rocks$"] %>% .[["AREASQKM16"]] %>% round(1) km^2.
at the heart of the city.
SCG_area <- 0.0186 # sqkm
WatsonsBaySlope <-
slopeDensity_by_SA2_1991_2016 %>%
.[SA2_NAME11 %ein% "Rose Bay - Vaucluse - Watsons Bay"] %>%
.[["Slope_density"]]
Over this period, with the exception of the Sydney CBD proper, the population of the inner ring grew modestly.
The density of the Eastern Suburbs from Rose Bay to Watsons Bay increased by a mere 13 persons per square kilometre per year, the same rate of density increase as one person walking on to the SCG every r floor(1/(WatsonsBaySlope * SCG_area)) years.
By contrast the density of jobs in the Sydney Inner City SA3 increased by r round(workers_by_Sydney_SA3_Year %$% {(last(workers) - first(workers)) / AREA[1] / 25}) workers per square kilometre per year and the Sydney CBD proper by 1600 per square kilometre per year, or 181,000 in 25 years.
n_jobs_SYD_2016 <-
JTW_2016_SA2 %>%
.[SA2016_decoder, on = "D_SA2_NAME16==SA2_NAME16"] %>%
.[, .(workers = sum(workers, na.rm = TRUE)), keyby = "GCC_NAME16"] %>%
.[GCC_NAME16 == "Greater Sydney"] %>%
.[["workers"]]
The Greater Metropolitan region of Sydney had 1.38~million jobs.
This increased to r comma(n_jobs_SYD_2016).
HughParsonage/journey.to.work1116 documentation built on May 28, 2019, 12:41 p.m.
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knitr::opts_chunk$set( collapse = TRUE, fig.width = 12, fig.height = 10, cache = TRUE, comment = "#>" )
library(viridis) library(leaflet) library(scales) library(data.table) library(hutils) if (!AND(requireNamespace("grattanCharts", quietly = TRUE), requireNamespace("Census2016.DataPack", quietly = TRUE))) { tempLib <- .libPaths()[1] devtools::install_github("hughparsonage/grattanCharts", lib.loc = tempLib) install.packages("Census2016.DataPack", repos = "https://hughparsonage.github.io/drat", type = "source", lib = tempLib) library(grattanCharts, lib.loc = tempLib) library(Census2016.DataPack, lib.loc = tempLib) } else { library(grattanCharts) library(Census2016.DataPack) } library(magrittr) library(ASGS) library(sp) library(rgeos) library(ggplot2) library(journey.to.work1116)
SA2_DZN_2011 <- fread("~/ABS-Data/2011-Home_SA2-Work_DZN-Count.csv") DZN_2011_persons <- SA2_DZN_2011[, .(persons = sum(Count)), keyby = .(DZN_CODE11 = Work_DZN)] DZN_2011_data <- as.data.table(DZN_2011@data) %>% .[, id := .I] DZN_2011_centroids <- gCentroid(DZN_2011, byid = TRUE) DZN_2011_centroids_tidy <- as.data.frame(DZN_2011_centroids) %>% as.data.table %>% .[, id := .I] %>% .[DZN_2011_data, on = "id"] %>% setnames(c("x", "y"), c("lon", "lat")) %>% .[] DZN_2016_persons <- fread("../data-raw/2016-DZN-persons.csv", skip = 10) %>% setnames(1:2, c("DZN_CODE16", "persons")) %>% drop_empty_cols %>% .[DZN_CODE16 %enotin% c("Total", "POW not applicable", "POW No Fixed Address (OT)", "Migratory - Offshore - Shipping (OT)")] DZN_2016_data <- as.data.table(DZN_2016@data) %>% .[, id := .I] DZN_2016_centroids <- gCentroid(DZN_2016, byid = TRUE) DZN_2016_centroids_tidy <- as.data.frame(DZN_2016_centroids) %>% as.data.table %>% .[, id := .I] %>% .[DZN_2016_data, on = "id"] %>% setnames(c("x", "y"), c("lon", "lat")) %>% .[] library(PSMA) SYD <- geocode(number_first = 1, street_name = "Martin", street_type = "Place", postcode = 2000) MEL <- geocode(number_first = 350, street_name = "Bourke", street_type = "Street", postcode = 3000) BNE <- geocode(number_first = 261, street_name = "Queen", street_type = "Street", postcode = 4000) ADL <- geocode(number_first = 141, street_name = "King William", street_type = "Street", postcode = 5000) PER <- geocode(number_first = 378, street_name = "Wellington", street_type = "Street", postcode = 6000) lat_lon_by_City <- data.table(City = c("SYD", "MEL", "BNE", "ADL", "PER"), GPO_lat = c(SYD$LATITUDE, MEL$LATITUDE, BNE$LATITUDE, ADL$LATITUDE, PER$LATITUDE), GPO_lon = c(SYD$LONGITUDE, MEL$LONGITUDE, BNE$LONGITUDE, ADL$LONGITUDE, PER$LONGITUDE)) id_by_city <- data.table::CJ(id = DZN_2011_centroids_tidy$id, City = c("SYD", "MEL", "BNE", "ADL", "PER")) %>% .[lat_lon_by_City, on = "City"] distance2GPO_by_DZN_CODE11 <- DZN_2011_centroids_tidy %>% .[id_by_city, on = "id"] %>% .[, distance2GPO := haversine_distance(lat, lon, GPO_lat, GPO_lon)] %>% .[, .(distance2GPO = min(distance2GPO), City = City[which.min(distance2GPO)]), keyby = c("DZN_CODE11")] distance2GPO_by_DZN_CODE16 <- DZN_2016_centroids_tidy %>% .[id_by_city, on = "id"] %>% .[, distance2GPO := haversine_distance(lat, lon, GPO_lat, GPO_lon)] %>% .[, .(distance2GPO = min(distance2GPO), City = City[which.min(distance2GPO)]), keyby = c("DZN_CODE16")] persons_by_GPO_distance_2011 <- distance2GPO_by_DZN_CODE11[DZN_2011_persons, on = "DZN_CODE11", nomatch=0L] %>% .[, Year := "2011"] persons_by_GPO_distance_2016 <- distance2GPO_by_DZN_CODE16[DZN_2016_persons, on = "DZN_CODE16", nomatch=0L] %>% .[, Year := "2016"] workers_by_GPO <- rbind(persons_by_GPO_distance_2011, persons_by_GPO_distance_2016, use.names = TRUE, fill = TRUE) correspondence <- readxl::read_excel("../data-raw/cg_dzn_2011_dzn_2016.xls", sheet = "Table 3", range = "A6:D9562") %>% as.data.table %>% .[complete.cases(.)]
workers_by_SLA2006_Home_Dest <- fread("~/ABS-data/inbox/SLA-v-JTW-SLA-2006-clean.csv", header = FALSE, na.strings = "") %>% setnames(1:3, c("SLA_2006_Home", "SLA_2006_Work", "workers")) %>% drop_empty_cols %>% .[, SLA_2006_Home := zoo::na.locf(SLA_2006_Home, na.rm = FALSE)] %>% .[SLA_2006_Home != "Total"] %>% .[SLA_2006_Work != "Total"] %>% .[workers > 0]
workers_by_SLA2006 <- workers_by_SLA2006_Home_Dest[, .(workers = sum(workers)), keyby = "SLA_2006_Work"]
SydneyMelJobGrowth <- read.table(text = " Sydney Sydney 2 Blank Melbourne Melbourne 2 Inner 01 -2500 54500 23500 68000 Middle 01 13000 20000 26000 24500 Outer 01 35500 31000 56500 50000", sep = "\t", header = TRUE, as.is = TRUE) %>% as.data.table %>% drop_empty_cols %>% setnames(c("Sydney", "Sydney.2"), c("Sydney_0106", "Sydney_0611")) %>% setnames(c("Melbourne", "Melbourne.2"), c("Melbourne_0106", "Melbourne_0611")) %>% setnames(1, "Ring") %>% melt.data.table(id.vars = "Ring", value.name = "delta_workers") %>% merge(data.table(Ring = c("Inner 01", "Middle 01", "Outer 01"), distance_group = cut(c(1, 5, 25), breaks = c(-Inf, 2, 15, Inf), labels = c("0-2km", "2-15km", ">15km"), ordered = TRUE)), by = "Ring") %>% .[, City := "SYD"] %>% .[, Period := if_else(grepl("0611", variable), "2006-2011", "2001-2006")] %>% .[variable %pin% "Melbourne", City := "MEL"] %>% .[]
workers_by_km_group_distance <- workers_by_GPO[distance2GPO < 35, distance_group := cut(distance2GPO, breaks = c(-Inf, 2, 15, Inf), labels = c("0-2km", "2-15km", ">15km"), ordered = TRUE)] %>% .[, .(total_workers = sum(persons)), keyby = c("Year", "City", "distance_group")] workers_by_km_group_distance %>% .[, .(delta_workers = last(total_workers) - first(total_workers)), keyby = c("City", "distance_group")] %>% .[, Period := "2011-2016"] %>% .[] %>% .[City %in% c("SYD", "MEL")] %>% rbind(SydneyMelJobGrowth, fill = TRUE, use.names = TRUE) %>% .[order(-Period)] %>% .[City %in% c("SYD", "MEL")] %>% .[order(City, distance_group, Period)] %>% grplot(aes(x = Period, y = delta_workers)) + geom_bar(stat = "identity") + facet_grid(City ~ distance_group)
workers_by_GPO %>% .[distance2GPO < 2] %>% .[City == "SYD"] %>% .[DZN_2011_centroids_tidy, on = "DZN_CODE11", nomatch=0L] %>% ggplot(aes(lon, lat, size = persons)) + geom_point() + annotate_coastline("Sydney") + coord_map(xlim = c(151.19, 151.23), ylim = c(-33.89, -33.85)) + facet_wrap(~Year)
Where have jobs grown (2011-2016)?
Absolute change
n_employed_persons_2016_SYD_CBD <- JTW_2016_SA2 %>% .[D_SA2_NAME16 %ein% "Sydney - Haymarket - The Rocks"] %$% sum(workers) %>% round(-3)
n_employed_persons_2011_SYD_CBD <- JTW_2011_SA2 %>% .[D_SA2_NAME11 %ein% "Sydney - Haymarket - The Rocks"] %$% sum(workers) %>% round(-1)
n_employed_persons_2006_SYD_CBD <- JTW_2006_SLA %>% .[D_SLA_NAME06 %ein% "Sydney (C) - Inner"] %$% sum(workers)
n_employed_persons_2001_SYD_CBD <- 186798
n_employed_persons_1996_SYD_CBD <- 162609
n_employed_persons_1991_SYD_CBD <- JTW_1991_SYD[Work_SLA %ein% "Sydney (Inner)"] %$% sum(workers) %>% round(-3)
ERP_by_ASGS_1991_2016 <- if (!file.exists("~/ABS-data/Estimated-Resident-Population-by-SA2-and-above-1991-2016.csv")) { fread("https://github.com/HughParsonage/ABS-data/raw/82c5be16745077b8c5049ae2c098c695f1e5ee5c/Estimated-Resident-Population-by-SA2-and-above-1991-2016.csv") } else { fread("~/ABS-data/Estimated-Resident-Population-by-SA2-and-above-1991-2016.csv") }
AREA_by_SA2_2011 <- SA2_2011@data %>% as.data.table %>% .[, .(SA2_NAME11, ALBERS_SQKM = ALBERS_SQM / 1e6)]
percent0 <- function(x, d = 1) { paste0(formatC(100*x, digits = d, flag = " ", format = "f"), "%") }
delta_residents1116_density <- ERP_by_ASGS_1991_2016 %>% .[REGIONTYPE == "SA2"] %>% .[Year %in% c(2011, 2016), .(Year, Region = NAME_2011, ERP = EstimatedResidentPopulation)] %>% dcast.data.table(Region ~ Year, value.var = "ERP") %>% .[`2011` > 0, .(SA2_NAME11 = Region, `2011`, `2016`, delta_residents = (`2016` / `2011`) ^ (1/4) - 1)] %>% .[, breaks := .bincode(delta_residents, breaks = quantile(delta_residents, probs = c(0:8)/8), # breaks = c(-Inf, 200 * (0:6), Inf), include.lowest = TRUE)] %>% .[, fillColor := c("white", gpal(7, reverse = TRUE))[breaks]] %>% .[, labelText := percent0(delta_residents)] %>% .[, labelTitle := SA2_NAME11] %>% .[]
delta_residents1116 <- ERP_by_ASGS_1991_2016 %>% .[REGIONTYPE == "SA2"] %>% .[Year %in% c(2011, 2016), .(Year, Region = NAME_2011, ERP = EstimatedResidentPopulation)] %>% dcast.data.table(Region ~ Year, value.var = "ERP") %>% .[`2011` > 0, .(SA2_NAME11 = Region, `2011`, `2016`, delta_residents = (`2016` - `2011`))] %>% .[, p90 := quantile(delta_residents, probs = 0.9, na.rm = TRUE)] %>% .[delta_residents <= 0, fillColor := "white"] %>% .[delta_residents >= p90[1], fillColor := "black"] %>% .[is.na(fillColor), fillColor := gpal(6, reverse = TRUE)[.bincode(delta_residents, breaks = ((0:5)/5) * p90[1], include.lowest = TRUE)]] %>% # .[complete.cases(.)] %>% .[, labelTitle := SA2_NAME11] %>% .[, labelText := scales::comma(delta_residents)] %>% .[]
delta_residents_density <- ERP_by_ASGS_1991_2016 %>% .[REGIONTYPE == "SA2"] %>% .[Year %in% c(2011, 2016), .(Year, Region = NAME_2011, ERP = EstimatedResidentPopulation)] %>% .[AREA_by_SA2_2011, on = "Region==SA2_NAME11"] %>% .[, density := ERP / ALBERS_SQKM] %>% dcast.data.table(Region ~ Year, value.var = "density") %>% .[`2011` > 0, .(SA2_NAME11 = Region, `2011`, `2016`, delta_residents = (`2016` / `2011`)^(1/4) - 1)] %>% .[, breaks := .bincode(delta_residents, breaks = quantile(delta_residents, probs = c(0:8)/8), # breaks = c(-Inf, 200 * (0:6), Inf), include.lowest = TRUE)] %>% .[, fillColor := c("white", gpal(7, reverse = TRUE))[breaks]] %>% .[, labelText := paste0(if_else(delta_residents > 0, "+", ""), percent(delta_residents), "<br>", comma(floor(`2011`)), " --> ", comma(floor(`2016`)))] %>% .[, labelTitle := paste(SA2_NAME11, " density")] %>% .[]
workers_by_SA2_Year <- # Use 2011 boundaries rbindlist(list("2016" = JTW_2016_SA2[, .(workers = sum(workers)), keyby = .(SA2_NAME11 = D_SA2_NAME16)], "2011" = JTW_2011_SA2[, .(workers = sum(workers)), keyby = .(SA2_NAME11 = D_SA2_NAME11)]), idcol = "Year") %>% dcast.data.table(SA2_NAME11 ~ Year, value.var = "workers")
workers_by_Sydney_SA3_Year <- data.table(Year = c(1991, 2016), workers = c(359795, 494315)) %>% .[, AREA := 25.0641]
delta_workers1116 <- workers_by_SA2_Year[, .(SA2_NAME11, delta_workers = `2016` - `2011`)]
delta_residents_vs_workers <- delta_workers1116[delta_residents1116, on = "SA2_NAME11", nomatch=0L]
delta_workers_0616_abs <-
JTW_2006_SLA
SA2__persons <- SA2__Persons %>% SA2016_decoder[., on = "SA2_MAIN16==SA2_MAINCODE_2016"] %>% set_cols_first("SA2_NAME16") %>% .[, fillColor := gpal(7, reverse = TRUE)[dplyr::ntile(persons, 7)]] %>% .[, labelTitle := SA2_NAME16] %>% .[, labelText := scales::comma(persons)]
n_workers_by_Year_SYD_CBD <- data.table(Year = seq(from = 1991, to = 2016, by = 5), workers = as.numeric(unlist(mget(x = paste0("n_employed_persons_", seq(1991, 2016, by = 5), "_SYD_CBD")))))
SA3_2011_centroids <- cbind(SA3_2011@data, gCentroid(SA3_2011, byid = TRUE)@coords) %>% as.data.table
SA3NAME_vs_dist2SYDGPO <- SA3_2011_centroids %>% .[, dist2SYDGPO := haversine_distance(y, x, SYD$LATITUDE, SYD$LONGITUDE)] %>% .[]
SA2NAME_vs_dist2SYDGPO <- SA2_2011_weighted_centroids %>% .[, SA2_NAME11 := latlon2SA(lat, lon, to = "SA2", yr = "2011", return = "v")] %>% .[, dist2SYDGPO := haversine_distance(lat, lon, SYD$LATITUDE, SYD$LONGITUDE)] %>% .[]
ERP_by_ASGS_1991_2016 %>% .[REGIONTYPE == "SA3"] %>% .[NAME_2011 %in% SA3NAME_vs_dist2SYDGPO[dist2SYDGPO < 10][["SA3_NAME11"]]] %>% ggplot(aes(x = Year, y = EstimatedResidentPopulation, group = NAME_2011, color = NAME_2011)) + geom_line()
SydneyInnerCitySA3_minus_CBD_by_Year <- ERP_by_ASGS_1991_2016 %>% .[NAME_2011 %ein% c("Sydney Inner City", "Sydney - Haymarket - The Rocks")] %>% .[, .(NAME_2011, Year, EstimatedResidentPopulation)] %>% dcast.data.table(Year ~ NAME_2011, value.var = "EstimatedResidentPopulation") %>% .[, `Sydney Inner City minus CBD` := `Sydney Inner City` - `Sydney - Haymarket - The Rocks`] %>% .[, .(Year, `Sydney - Haymarket - The Rocks`, `Sydney Inner City minus CBD`)] %>% melt.data.table(id.vars = "Year", variable.name = "NAME_2011", variable.factor = FALSE, value.name = "EstimatedResidentPopulation")
ERP_by_ASGS_1991_2016 %>% .[REGIONTYPE == "SA3"] %>% .[NAME_2011 %in% SA3NAME_vs_dist2SYDGPO[dist2SYDGPO < 10][["SA3_NAME11"]]] %>% # .[NAME_2011 %enotin% "Sydney Inner City"] %>% # rbind(SydneyInnerCitySA3_minus_CBD_by_Year, use.names = TRUE, fill = TRUE) %>% # .[NAME_2011 %enotin% c("Sydney - Haymarket - The Rocks")] %>% .[, Index := EstimatedResidentPopulation / first(EstimatedResidentPopulation), keyby = "NAME_2011"] %>% .[, last_pop := last(Index), keyby = "NAME_2011"] %>% .[, SA3 := reorder(NAME_2011, -last_pop)] %>% ggplot(aes(x = Year, y = Index, group = SA3, color = SA3)) + geom_line() + scale_y_continuous(labels = function(x) 100 * x, breaks = c(1, 1.5, 2, 2.5))
# ERP_by_ASGS_1991_2016
slope <- function(x, y) { x <- as.double(x) y <- as.double(y) n <- length(x) {sum(x * y) - sum(x) * sum(y) / n} / {sum(x^2) - sum(x)^2 / n} }
SA2011_decoder <- as.data.table(SA2_2011)
ERP_by_ASGS_1991_2016 %>% .[NAME_2011 %ein% SA2011_decoder[GCC_NAME11 == "Greater Sydney"][["SA2_NAME11"]]] %>% .[SA2_2011_weighted_centroids, on = "CODE_2011==SA2_MAIN11", nomatch=0L] %>% .[, distance2GPO := haversine_distance(lat, lon, SYD$LATITUDE, SYD$LONGITUDE)] %>% .[] %>% .[, distance_group := cut(distance2GPO, breaks = c(-Inf, 2, 10, 20, Inf), labels = c("<2km", "2-10km", "10-20km", "20km+"), include.lowest = TRUE, ordered_result = TRUE)] %>% .[EstimatedResidentPopulation > 1000] %>% .[, Index := EstimatedResidentPopulation / first(EstimatedResidentPopulation), keyby = "NAME_2011"] %>% # Some SA2s have zero population. .[!is.nan(Index)] %>% .[, .(Year, Index, NAME_2011, distance_group)] %>% grplot(aes(x = Year, y = Index, group = NAME_2011, color = distance_group)) + geom_line() + geom_smooth(aes(group = distance_group)) + facet_wrap(~distance_group)
#' @return Viridis palette of x by quantile (decile by default). palette_v <- function(x, n = 10) { input <- data.table(x = x) input[, order := 1:.N] input[, decile := ntile(x, n)] colortbl <- data.table(decile = 1:10, colour = viridis(10)) colortbl[input, on = "decile"] %>% setorder(order) %>% .[["colour"]] } pal_v <- colorNumeric(palette = viridis(10), domain = NULL)
gpal_v <- colorNumeric(palette = c("white", gpal(7, reverse = TRUE)), domain = NULL)
slope_by_SA2 <- ERP_by_ASGS_1991_2016 %>% .[Year > 2005] %>% .[NAME_2011 %ein% SA2011_decoder[GCC_NAME11 == "Greater Sydney"][["SA2_NAME11"]]] %>% .[SA2_2011_weighted_centroids, on = "CODE_2011==SA2_MAIN11", nomatch=0L] %>% .[, distance2GPO := haversine_distance(lat, lon, SYD$LATITUDE, SYD$LONGITUDE)] %>% .[, .(Slope = slope(Year, EstimatedResidentPopulation)), keyby = "NAME_2011"] %>% .[, fillColor := gpal_v(rank(Slope))] %>% .[, labelTitle := NAME_2011] %>% .[, labelText := scales::comma(floor(Slope))] %>% .[, SA2_NAME11 := NAME_2011] %>% set_cols_first("SA2_NAME11") %>% .[]
grattan_leaflet(slope_by_SA2, "2011", simple = TRUE)
density_by_SA2_by_Year <- as.data.table(SA2_2011) %>% .[ERP_by_ASGS_1991_2016, on = "SA2_NAME11==NAME_2011", nomatch=0L] %>% .[SA2_NAME11 %ein% SA2011_decoder[GCC_NAME11 == "Greater Sydney"][["SA2_NAME11"]]] %>% .[, pop_density := EstimatedResidentPopulation / (ALBERS_SQM / 1e6)] %>% .[]
density_by_SA3_by_Year <- as.data.table(SA3_2011) %>% .[ERP_by_ASGS_1991_2016, on = "SA3_NAME11==NAME_2011", nomatch=0L] %>% .[SA3_NAME11 %ein% SA2011_decoder[GCC_NAME11 == "Greater Sydney"][["SA3_NAME11"]]] %>% .[, pop_density := EstimatedResidentPopulation / (ALBERS_SQM / 1e6)] %>% .[]
slopeDensity_by_SA2_1991_2016 <- density_by_SA2_by_Year %>% .[, .(Slope_density = slope(Year, pop_density)), keyby = "SA2_NAME11"] %>% .[, Slope_density := round(Slope_density)] %>% .[, fillColor := gpal_v(rank(Slope_density))] %>% .[Slope_density <= 0, fillColor := "white"] %>% .[, labelTitle := SA2_NAME11] %>% .[, labelText := paste(scales::comma(floor(Slope_density)), "persons per sqkm per year")] %>% .[]
grattan_leaflet(slopeDensity_by_SA2_1991_2016, Year = "2016", simple = TRUE)
slopeDensity_by_SA3_1991_2016 <- density_by_SA3_by_Year %>% .[, .(Slope_density = slope(Year, pop_density)), keyby = "SA3_NAME11"] %>% .[, Slope_density := round(Slope_density)] %>% .[, fillColor := gpal_v(Slope_density)] %>% .[Slope_density <= 0, fillColor := "white"] %>% .[, labelTitle := SA3_NAME11] %>% .[, labelText := paste(scales::comma(floor(Slope_density)), "persons per sqkm per year")] %>% .[]
grattan_leaflet(slopeDensity_by_SA3_1991_2016, Year = "2011", simple = TRUE)
SALM <- if (file.exists("~/data-employment-gov-au/salm_smoothed_sa2_datafile_june_quarter_2017.csv")) { fread("~/data-employment-gov-au/salm_smoothed_sa2_datafile_june_quarter_2017.csv") } else { tryCatch({ fread("https://docs.employment.gov.au/system/files/doc/other/salm_smoothed_sa2_datafiles_-_june_quarter_2017.csv") %>% melt.data.table(id.vars = c("Data item", "Statistical Area Level 2 (SA2)", "SA2 Code"), variable.factor = FALSE, variable.name = "Month") %>% .[, Date := as.Date(paste0("01-", Month), format = "%d-%b-%y")] %>% setnames(c("Statistical Area Level 2 (SA2)", "SA2 Code"), c("SA2_NAME16", "SA2_MAIN16")) %>% .[, .(SA2_NAME16, SA2_MAIN16, Date, Item = `Data item`, value)] %T>% fwrite("~/data-employment-gov-au/salm_smoothed_sa2_datafile_june_quarter_2017.csv") %>% .[] }, error = function(e) { fread("https://github.com/HughParsonage/data-employment-gov-au/raw/101c7dc2f137e31b8f4de5669f8653340784c0f9/salm_smoothed_sa2_datafile_june_quarter_2017.csv") }) }
slopeDensity_by_SA2_2006_2016 <- density_by_SA2_by_Year %>% .[Year >= 2006] %>% .[, .(Slope_density = slope(Year, pop_density)), keyby = "SA2_NAME11"] %>% .[, Slope_density := round(Slope_density)] %>% .[, fillColor := gpal_v(rank(Slope_density))] %>% .[Slope_density <= 0, fillColor := "white"] %>% .[, labelTitle := SA2_NAME11] %>% .[, labelText := paste(scales::comma(floor(Slope_density)), "persons per sqkm per year")] %>% .[]
Australia's economic future lies in its cities. But city planning is moving jobs and residential areas in different directions. The city sprawl is a well-understood phenomenon but what is less well-known is that jobs are becoming more concentrated. The modern economy more than ever thrives with a high concentration of people working in the same square kilometre, but the modern planning system limits how close they can live to these hot spots.
In 1991, r comma(n_employed_persons_1991_SYD_CBD) worked in the Sydney CBD.
Twenty years later, r comma(n_employed_persons_2011_SYD_CBD) did and in the most recent census, r comma(n_employed_persons_2016_SYD_CBD) of the population's main job was located in the
r SA2_2016@data %>% as.data.table %>% .[SA2_NAME16 %pin% "The Rocks$"] %>% .[["AREASQKM16"]] %>% round(1) km^2.
at the heart of the city.
SCG_area <- 0.0186 # sqkm WatsonsBaySlope <- slopeDensity_by_SA2_1991_2016 %>% .[SA2_NAME11 %ein% "Rose Bay - Vaucluse - Watsons Bay"] %>% .[["Slope_density"]]
Over this period, with the exception of the Sydney CBD proper, the population of the inner ring grew modestly.
The density of the Eastern Suburbs from Rose Bay to Watsons Bay increased by a mere 13 persons per square kilometre per year, the same rate of density increase as one person walking on to the SCG every r floor(1/(WatsonsBaySlope * SCG_area)) years.
By contrast the density of jobs in the Sydney Inner City SA3 increased by r round(workers_by_Sydney_SA3_Year %$% {(last(workers) - first(workers)) / AREA[1] / 25}) workers per square kilometre per year and the Sydney CBD proper by 1600 per square kilometre per year, or 181,000 in 25 years.
n_jobs_SYD_2016 <- JTW_2016_SA2 %>% .[SA2016_decoder, on = "D_SA2_NAME16==SA2_NAME16"] %>% .[, .(workers = sum(workers, na.rm = TRUE)), keyby = "GCC_NAME16"] %>% .[GCC_NAME16 == "Greater Sydney"] %>% .[["workers"]]
The Greater Metropolitan region of Sydney had 1.38~million jobs.
This increased to r comma(n_jobs_SYD_2016).
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