In wfmackey/absmapsdata: A catalogue of ready-to-use ASGS (and other) sf objects

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.path = "man/figures/README-",
  out.width = "100%"
)

devtools::load_all()
library(sf)
library(tidyverse)

options(width = 1000)

:warning: This package is not owned, run, or endorsed by the ABS. Data contained in this package are compressed, re-projected, renamed and stored as sf objects to be useful for making maps in R. If conducting spatial analysis, or any analysis that requires precise area boundaries, please use the original shapefiles provided by the ABS and others.

absmapsdata

absmapsdata is a user-generated package to make it easier for R users to access ABS (and other) spatial structure names/codes and produce maps using this data. The package contains compressed (lossy), tidied, and lazily-loadable sf objects that hold geometric information about data structures in Australia. It also contains a correspondences files provided by the ABS.

:white_check_mark: It is now recommended that you use strayr::read_absmap to access data stored in absmapsdata. To download and read these data files without installing the whole absmapsdata package, please use strayr::read_absmap, for example:

# remotes::install_github("runapp-aus/strayr")
strayr::read_absmap("sa42021")

Installation

You probably don't need to install the full absmapsdata package (see above).

But if you want to, you can install absmapsdata from Github. The package contains a lot of data, so installing using remotes::install_github may fail if the download times out. If this happens, set the timeout option to a large value and try again, i.e. run:

options(timeout = 1000)
remotes::install_github("wfmackey/absmapsdata")

The sf package is required to handle the sf objects:

library(sf)

Data loaded with this package

Available maps are listed below. These will be added to over time. If you would like to request a map to be added, let me know via an issue on this Github repo.

ASGS Main Structures

• Statistical Area 1 2011: sa12011; 2016: sa12016; and 2021: sa12021.
• Statistical Area 2 2011: sa22011; 2016: sa22016; and 2021: sa22021.
• Statistical Area 3 2011: sa32011; 2016: sa32016; and 2021: sa32021.
• Statistical Area 4 2011: sa42011; 2016: sa42016; and 2021: sa42021.
• Greater Capital Cities 2011: gcc2011; 2016: gcc2016; and 2021: gcc2021.
• Remoteness Areas 2011: ra2011; and 2016: ra2016
• State 2011: state2011; 2016: state2016; and state2021.

ASGS Indigenous Structures

• Indigenous Locations 2021: iloc2021
• Indigenous Areas 2021: iare2021
• Indigenous Regions 2021: ireg2021

Significant Urban Areas and Urban Centres and Localities

• Significant Urban Areas 2016: sua2016; and 2021: sua2021
• Urban Centre and Locality 2016: ucl2016; and 2021: ucl2021
• Section of State Range 2016: sosr2016
• Section of State 2016: sos2016

ASGS Non-ABS Structures

• Commonwealth Electoral Divisions 2018: ced2018; and 2021: ced2021
• State Electoral Divisions 2018:sed2018; 2021: sed2021; and 2022: sed2022
• Local Government Areas 2016: lga2016; 2018: lga2018; 2021: lga2021; and 2022: lga2022
• Regions for the Internet Vacancy Index 2008: regional_ivi2008
• Postcodes 2016: postcode2016; and 2021: postcode2021
• Suburbs (SSC) 2016: suburb2016; and (SAL) 2021: suburb2021
• Census of Population and Housing Destination Zones 2011: dz2011; 2016: dz2016; and 2021: dz2021.

Non-ABS Australian Government Structures

• Employment Regions 2015-2020: employment_regions2015
• BITRE Working Zones 2016: bitre_work_zones2016
• NSW Local Health District 2023: nsw_lhd2023
• Regional Development Australia areas 2015-16: rda2016

Correspondences

This package also contains a number of 2016 population-weighted ABS correspondences (the most recent) that can be found on the data.gov.au website.

:white_check_mark: Use strayr::read_correspondence_tbl to access correspondence this data, rather than loading the whole absmapsdata package, e.g.:

# remotes::install_github("runapp-aus/strayr")
strayr::read_correspondence_tbl(from_area = "sa2", from_year = 2011,
                                to_area = "sa2", to_year = 2016) 

Within absmapsdata, you can retrieve correspondences with the get_correspondence_absmaps function.

Just show me how to make a map with this package

Using the package’s pre-loaded data

The absmapsdata package comes with pre-downloaded and pre-processed data. To load a particular geospatial object: load the package, then call the object (see list above for object names).

library(tidyverse)
library(sf)
library(absmapsdata)

mapdata1 <- sa32021

glimpse(mapdata1)

Or

mapdata2 <- sa22016

glimpse(mapdata2)

The resulting sf object contains one observation per area (in the following examples, one observation per sa3). It stores the geometry information in the geometry variable, which is a nested list describing the area’s polygon. The object can be joined to a standard data.frame or tibble and can be used with dplyr functions.

Creating maps with your sf object

We do all this so we can create gorgeous maps. And with the sf object in hand, plotting a map via ggplot and geom_sf is simple.

map <-
sa32016 %>%
  filter(gcc_name_2016 == "Greater Melbourne") %>%   # let's just look Melbourne
  ggplot() +
  geom_sf(aes(geometry = geometry))  # use the geometry variable

map

The data also include centroids of each area, and we can add these points to the map with the cent_lat and cent_long variables using geom_point.

map <- sa32016 %>%
  filter(gcc_name_2016 == "Greater Melbourne") %>%   # let's just look Melbourne
  ggplot() +
  geom_sf(aes(geometry = geometry)) +   # use the geometry variable
  geom_point(aes(cent_long, cent_lat))  # use the centroid long (x) and lats (y)

map

Cool. But this all looks a bit ugly. We can pretty it up using ggplot tweaks. See the comments on each line for its objective. Also note that we’re filling the areas by their areasqkm size, another variable included in the sf object (we’ll replace this with more interesting data in the next section).

map <- sa32016 %>%
  filter(gcc_name_2016 == "Greater Melbourne") %>%   # let's just look Melbourne
  ggplot() +
  geom_sf(aes(geometry = geometry,  # use the geometry variable
              fill = areasqkm_2016),     # fill by area size
          lwd = 0,                  # remove borders
          show.legend = FALSE) +    # remove legend
  geom_point(aes(cent_long,
                 cent_lat),        # use the centroid long (x) and lats (y)
             colour = "white") +    # make the points white
  theme_void() +                    # clears other plot elements
  coord_sf()

map

Joining with other datasets

At some point, we’ll want to join our spatial data with data-of-interest. The variables in our mapping data—stating the numeric code and name of each area and parent area—will make this relatively easy.

For example: suppose we had a simple dataset of median income by SA3 over time.

# Read data in some data
income <- read_csv("https://raw.githubusercontent.com/wfmackey/absmapsdata/master/img/data/median_income_sa3.csv")
head(income)

This income data contains a variable sa3_name_2016, and we can use dplyr::left_join() to combine with our mapping data.

combined_data <- left_join(income, 
                           sa32016, 
                           by = "sa3_name_2016")

Now that we have a tidy dataset with 1) the income data we want to plot, and 2) the geometry of the areas, we can plot income by area:

map <- combined_data %>%
  filter(gcc_name_2016 == "Greater Melbourne") %>%   # let's just look Melbourne
  ggplot() +
  geom_sf(aes(geometry = geometry,   # use the geometry variable
              fill = median_income), # fill by unemployment rate
          lwd = 0) +                 # remove borders
  theme_void() +                     # clears other plot elements
  labs(fill = "Median income")

map

Get correspondence files

:white_check_mark: Use strayr::read_correspondence_tbl to access correspondence this data, rather than loading the whole absmapsdata package, e.g.:

# remotes::install_github("runapp-aus/strayr")
strayr::read_correspondence_tbl(from_area = "sa2", from_year = 2011,
                                to_area = "sa2", to_year = 2016) 

You can use the absmapsdata::get_correspondence_absmaps function to get population-weighted correspondence tables provided by the ABS. Note that while there are lots of correspondence tables, not every combination is available.

For example:

get_correspondence_absmaps("cd", 2006,
                           "sa1", 2016)

Why does this package exist?

The motivation for this package is that maps are cool and fun and are, sometimes, the best way to communicate data. And making maps is R with ggplot is relatively easy when you have the right object.

Getting the right object is not technically difficult, but requires research into the best-thing-to-do at each of the following steps:

• Find the ASGS ABS spatial-data page and determine the right file to download.
• Read the shapefile into R using one-of-many import tools.
• Convert the object into something usable.
• Clean up any inconsistencies and apply consistent variable naming/values across areas and years.
• Find an appropriate compression function and level to optimise output.

For me at least, finding the correct information and developing the best set of steps was a little bit interesting but mostly tedious and annoying. The absmapsdata package holds this data for you, so you can spend more time making maps, and less time on Stack Overflow, the ABS website, and lovely-people’s wonderful blogs.

Comments/complaints/requests

The best avenue is via a Github issue at wfmackey/absmapsdata/issues. This is also the best place to request data that isn't yet available in the package.

wfmackey/absmapsdata documentation built on July 28, 2023, 9:15 p.m.