Today’s Lecture

What we’ll cover

Today you will:

  • look at the ABS geographical boundaries for the 2021 census
  • integrate data from different sources (census and election) to make exploratory inferences

From a coding perspective:

  • Further expand our understanding of how to read and use spatial data in R
  • Better understand how spatial data is organised
  • Learn how to intersect two spatial objections
  • Practice re-projecting maps

Recap

Recall Lecture 5: 2021 Federal Election Data

library(tidyverse)
library(sf)

aec_map <- read_sf(here::here("data/vic-july-2021-esri/E_VIC21_region.shp")) 

votes <- read_csv("https://results.aec.gov.au/27966/Website/Downloads/HouseDopByDivisionDownload-27966.csv", skip = 1) 

electoral_winners = votes |> 
  mutate(DivisionNm = toupper(DivisionNm)) |>
  filter(Elected == "Y") |> 
  select(PartyAb, DivisionID, DivisionNm, Elected) |>
  distinct()

aec_map = aec_map |> 
  mutate(Elect_div = toupper(Elect_div))

winners_with_map = electoral_winners |> 
  left_join(aec_map, by = c("DivisionNm" = "Elect_div"))

aus_colours <- c(
  "ALP" = "#DE3533", "LNP" = "#ADD8E6", "KAP" = "#8B0000", "GVIC" = "#10C25B", "XEN" = "#ff6300",
  "LP" = "#0047AB", "NP" = "#0a9cca", "IND" = "#000000"
)

winners_plot <- ggplot(winners_with_map) +
  geom_sf(aes(fill = PartyAb, geometry = geometry)) +
  scale_fill_manual(values = aus_colours)

Recall Lecture 5: 2021 Federal Election Data


There are two sources of data:

  1. Electoral boundary
  2. The votes for candidates in each electorate

Recall Lecture 6: 2021 ABS Census Data

There are two main types of data that you can download.

ABS Census 2021: GeoPackages

GeoPackage

A GeoPackage (GPKG) is an open, non-proprietary, platform-independent and standards-based data format for geographic information system implemented as a SQLite database container. Defined by the Open Geospatial Consortium (OGC) with the backing of the US military and published in 2014, GeoPackage has seen widespread support from various government, commercial, and open source organizations. https://en.wikipedia.org/wiki/GeoPackage


Recall: OGC also defines the WKT (basically how the maps is drawn.)

ABS GeoPackage (2021)

Download the data

https://datapacks.censusdata.abs.gov.au/geopackages/

  1. 2021
  2. Victoria
  3. Topic: Employment and Income or Table: G02
  4. GDA2020

You may also like to check out the strayr R package!

ABS GeoPackage (2021)

geopath_2021_G02 <- here::here("data/Geopackage_2021_G02_VIC_GDA2020/G02_VIC_GDA2020.gpkg")
st_layers(geopath_2021_G02)
Driver: GPKG 
Available layers:
           layer_name geometry_type features fields crs_name
1    G02_UCL_2021_VIC Multi Polygon      347     11  GDA2020
2    G02_SUA_2021_VIC Multi Polygon       22     11  GDA2020
3    G02_STE_2021_VIC Multi Polygon        1     11  GDA2020
4   G02_SOSR_2021_VIC Multi Polygon       12     11  GDA2020
5    G02_SOS_2021_VIC Multi Polygon        6     11  GDA2020
6    G02_SED_2021_VIC Multi Polygon       90     11  GDA2020
7    G02_SAL_2021_VIC Multi Polygon     2946     11  GDA2020
8    G02_SA4_2021_VIC Multi Polygon       19     11  GDA2020
9    G02_SA3_2021_VIC Multi Polygon       68     11  GDA2020
10   G02_SA2_2021_VIC Multi Polygon      524     11  GDA2020
11   G02_SA1_2021_VIC Multi Polygon    15482     11  GDA2020
12   G02_POA_2021_VIC Multi Polygon      694     11  GDA2020
13   G02_LGA_2021_VIC Multi Polygon       82     11  GDA2020
14 G02_GCCSA_2021_VIC Multi Polygon        4     11  GDA2020
15   G02_CED_2021_VIC Multi Polygon       41     11  GDA2020
16    G02_RA_2021_VIC Multi Polygon        6     11  GDA2020

ABS GeoPackage (2016)

Download the data (Notice it is stored differently)

https://datapacks.censusdata.abs.gov.au/geopackages/

  1. 2016
  2. Victoria
  3. Employment, Income and Unpaid Work (EIUW)
  4. EIUW GeoPackage A

ABS GeoPackage (2016)

geopath_2016_eiuwa <- here::here("data/Geopackage_2016_EIUWA_for_VIC/census2016_eiuwa_vic_short.gpkg")
st_layers(geopath_2016_eiuwa)
Driver: GPKG 
Available layers:
                         layer_name geometry_type features fields crs_name
1    census2016_eiuwa_vic_ced_short                     39    489    GDA94
2  census2016_eiuwa_vic_gccsa_short                      4    489    GDA94
3    census2016_eiuwa_vic_lga_short                     82    489    GDA94
4    census2016_eiuwa_vic_poa_short                    698    489    GDA94
5     census2016_eiuwa_vic_ra_short                      6    489    GDA94
6    census2016_eiuwa_vic_sa1_short                  14073    489    GDA94
7    census2016_eiuwa_vic_sa2_short                    464    489    GDA94
8    census2016_eiuwa_vic_sa3_short                     68    489    GDA94
9    census2016_eiuwa_vic_sa4_short                     19    489    GDA94
10   census2016_eiuwa_vic_sed_short                     90    489    GDA94
11   census2016_eiuwa_vic_sos_short                      6    489    GDA94
12  census2016_eiuwa_vic_sosr_short                     12    489    GDA94
13   census2016_eiuwa_vic_ssc_short                   2931    489    GDA94
14   census2016_eiuwa_vic_ste_short                      1    489    GDA94
15   census2016_eiuwa_vic_sua_short                     22    489    GDA94
16   census2016_eiuwa_vic_ucl_short                    353    489    GDA94

The Australian Statistical Geography Standard (ASGS)

The number of regions for each layer

# A tibble: 16 × 6
   name               geomtype  driver features fields crs   
   <chr>              <list>    <chr>     <dbl>  <dbl> <list>
 1 G02_STE_2021_VIC   <chr [1]> GPKG          1     11 <crs> 
 2 G02_GCCSA_2021_VIC <chr [1]> GPKG          4     11 <crs> 
 3 G02_SOS_2021_VIC   <chr [1]> GPKG          6     11 <crs> 
 4 G02_RA_2021_VIC    <chr [1]> GPKG          6     11 <crs> 
 5 G02_SOSR_2021_VIC  <chr [1]> GPKG         12     11 <crs> 
 6 G02_SA4_2021_VIC   <chr [1]> GPKG         19     11 <crs> 
 7 G02_SUA_2021_VIC   <chr [1]> GPKG         22     11 <crs> 
 8 G02_CED_2021_VIC   <chr [1]> GPKG         41     11 <crs> 
 9 G02_SA3_2021_VIC   <chr [1]> GPKG         68     11 <crs> 
10 G02_LGA_2021_VIC   <chr [1]> GPKG         82     11 <crs> 
11 G02_SED_2021_VIC   <chr [1]> GPKG         90     11 <crs> 
12 G02_UCL_2021_VIC   <chr [1]> GPKG        347     11 <crs> 
13 G02_SA2_2021_VIC   <chr [1]> GPKG        524     11 <crs> 
14 G02_POA_2021_VIC   <chr [1]> GPKG        694     11 <crs> 
15 G02_SAL_2021_VIC   <chr [1]> GPKG       2946     11 <crs> 
16 G02_SA1_2021_VIC   <chr [1]> GPKG      15482     11 <crs>

What is in a layer?

Geometry set for 1 feature 
Geometry type: MULTIPOLYGON
Dimension:     XY
Bounding box:  xmin: 140.9619 ymin: -39.15918 xmax: 149.9762 ymax: -33.98064
Geodetic CRS:  GDA2020
sf [1 × 12] (S3: sf/tbl_df/tbl/data.frame)
 $ STE_CODE_2021                : chr "2"
 $ STE_NAME_2021                : chr "Victoria"
 $ Median_age_persons           : num 38
 $ Median_mortgage_repay_monthly: num 1859
 $ Median_tot_prsnl_inc_weekly  : num 803
 $ Median_rent_weekly           : num 370
 $ Median_tot_fam_inc_weekly    : num 2136
 $ Average_num_psns_per_bedroom : num 0.8
 $ Median_tot_hhd_inc_weekly    : num 1759
 $ Average_household_size       : num 2.5
 $ AREA_ALBERS_SQKM             : num 227496
 $ geom                         :sfc_MULTIPOLYGON of length 1; first list element: List of 157
  ..$ :List of 1
  .. ..$ : num [1:39, 1:2] 146 146 146 146 146 ...
  ..$ :List of 1
  .. ..$ : num [1:25, 1:2] 146 146 146 146 146 ...
  ..$ :List of 1
  .. ..$ : num [1:17, 1:2] 146 146 146 146 146 ...
  ..$ :List of 1
  .. ..$ : num [1:112, 1:2] 146 146 146 146 146 ...
  ..$ :List of 1
  .. ..$ : num [1:151, 1:2] 146 146 146 146 146 ...
  ..$ :List of 1
  .. ..$ : num [1:18, 1:2] 146 146 146 146 146 ...
  ..$ :List of 1
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  ..$ :List of 1
  .. ..$ : num [1:64, 1:2] 150 150 150 150 150 ...
  ..$ :List of 1
  .. ..$ : num [1:18, 1:2] 145 145 145 145 145 ...
  ..$ :List of 1
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  .. [list output truncated]
  ..- attr(*, "class")= chr [1:3] "XY" "MULTIPOLYGON" "sfg"
 - attr(*, "sf_column")= chr "geom"
 - attr(*, "agr")= Factor w/ 3 levels "constant","aggregate",..: NA NA NA NA NA NA NA NA NA NA ...
  ..- attr(*, "names")= chr [1:11] "STE_CODE_2021" "STE_NAME_2021" "Median_age_persons" "Median_mortgage_repay_monthly" ...

State or Territory (STE)

vicmap_ste_G02 <- read_sf(geopath_2021_G02, layer = "G02_STE_2021_VIC")

ggplot(vicmap_ste_G02) +
  geom_sf(aes(geometry = geom, fill = Median_age_persons)) 
nrow(vicmap_ste)
[1] 1

Breakout Session

Try it yourself time

  • Download the geopackage for the 2021 census and variable G02

  • Look at how that spatial data is organised

  • Look at the different layers

What are the differences between the regionalisations?

How does the median age change across the different regionalisations?

A closer look at each region

Greater Capital City Statistical Areas (GCCSA)

  • Each region has a different population
vicmap_gccsa_G02 <- read_sf(geopath_2021_G02, layer = "G02_GCCSA_2021_VIC")
ggplot(vicmap_gccsa_G02) +
  geom_sf(aes(geometry = geom, fill = Median_age_persons))
nrow(vicmap_gccsa_G02)
[1] 4

Section of State (SOS)

  • Major urban, other urban, bounded locally & rural balance
vicmap_sos_G02 <- read_sf(geopath_2021_G02, layer = "G02_SOS_2021_VIC")
ggplot(vicmap_sos_G02) +
  geom_sf(aes(geometry = geom, fill = Median_age_persons))
nrow(vicmap_sos_G02)
[1] 6

Remoteness Areas (RA)

vicmap_ra_G02 <- read_sf(geopath_2021_G02, layer = "G02_RA_2021_VIC")
ggplot(vicmap_ra_G02) +
  geom_sf(aes(geometry = geom, fill = Median_age_persons))
nrow(vicmap_ra_G02)
[1] 6

Section of State Ranges (SOSR)

vicmap_sosr_G02 <- read_sf(geopath_2021_G02, layer = "G02_SOSR_2021_VIC")
ggplot(vicmap_sosr_G02) +
  geom_sf(aes(geometry = geom, fill = Median_age_persons))
nrow(vicmap_sosr_G02)
[1] 12

Statistical Area Level 4 (SA4)

  • Each region with population of 100,000 - 500,000
vicmap_sa4_G02 <- read_sf(geopath_2021_G02, layer = "G02_SA4_2021_VIC")
ggplot(vicmap_sa4_G02) +
  geom_sf(aes(geometry = geom, fill = Median_age_persons))
nrow(vicmap_sa4_G02)
[1] 19

Significant Urban Areas (SUA)

vicmap_sua_G02 <- read_sf(geopath_2021_G02, layer = "G02_SUA_2021_VIC")
ggplot(vicmap_sua_G02) +
  geom_sf(aes(geometry = geom, fill = Median_age_persons))
nrow(vicmap_sua_G02)
[1] 22

Commonwealth Electoral Division (CED)

vicmap_ced_G02 <- read_sf(geopath_2021_G02, layer = "G02_CED_2021_VIC")
ggplot(vicmap_ced_G02) +
  geom_sf(aes(geometry = geom, fill = Median_age_persons))
nrow(vicmap_ced_G02)
[1] 41

Statistical Area Level 3 (SA3)

  • Each region with population of 30,000 - 130,000
vicmap_sa3_G02 <- read_sf(geopath_2021_G02, layer = "G02_SA3_2021_VIC")
ggplot(vicmap_sa3_G02) +
  geom_sf(aes(geometry = geom, fill = Median_age_persons))
nrow(vicmap_sa3_G02)
[1] 68

Local Government Area (LGA)

vicmap_lga_G02 <- read_sf(geopath_2021_G02, layer = "G02_SA3_2021_VIC")
ggplot(vicmap_lga_G02) +
  geom_sf(aes(geometry = geom, fill = Median_age_persons))
nrow(vicmap_lga_G02)
[1] 68

State Electoral Division (SED)

vicmap_sed_G02 <- read_sf(geopath_2021_G02, layer = "G02_SED_2021_VIC")
ggplot(vicmap_sed_G02) +
  geom_sf(aes(geometry = geom, fill = Median_age_persons))
nrow(vicmap_sed_G02)
[1] 90

Urban Centres and Localities (UCL)

vicmap_ucl_G02 <- read_sf(geopath_2021_G02, layer = "G02_UCL_2021_VIC")
ggplot(vicmap_ucl_G02) +
  geom_sf(aes(geometry = geom, fill = Median_age_persons))
nrow(vicmap_ucl_G02)
[1] 347

Statistical Area Level 2 (SA2)

  • Each region with populations in the range of 3,000-25,000
vicmap_sa2_G02 <- read_sf(geopath_2021_G02, layer = "G02_SA3_2021_VIC")
ggplot(vicmap_sa2_G02) +
  geom_sf(aes(geometry = geom, fill = Median_age_persons))
nrow(vicmap_sa2_G02)
[1] 68

Postal Areas (POA)

vicmap_poa_G02 <- read_sf(geopath_2021_G02, layer = "G02_POA_2021_VIC")
ggplot(vicmap_poa_G02) +
  geom_sf(aes(geometry = geom, fill = Median_age_persons))
nrow(vicmap_poa_G02)
[1] 694

State Area Localitites (SAL) (Formerly SSC)

vicmap_sal_G02 <- read_sf(geopath_2021_G02, layer = "G02_SAL_2021_VIC")
ggplot(vicmap_sal_G02) +
  geom_sf(aes(geometry = geom, fill = Median_age_persons))
nrow(vicmap_sal_G02)
[1] 2946

Statistical Area Level 1 (SA1)

  • Each region with a population of range 200-800
vicmap_sa1_G02 <- read_sf(geopath_2021_G02, layer = "G02_SA1_2021_VIC")
ggplot(vicmap_sa1_G02) +
  geom_sf(aes(geometry = geom, fill = Median_age_persons))
nrow(vicmap_sa1_G02)
[1] 15482

Electorate boundary
vs
Census boundary

Comparing SED 2021 and electorates divisions 2022

ggplot() +
  geom_sf(data = vicmap_sed_G02, 
          aes(geometry = geom, fill = Median_age_persons),
    alpha = 1, color = "white", size = 2) +
  geom_sf(data = winners_with_map, aes(geometry = geometry),
    fill = "transparent", color = "red", size = 2) +
  coord_sf(xlim = c(144.95, 145.24), ylim = c(-38.05, -37.85)) + 
  theme_bw()

Closer look: Hotham electorate 1

electorate <- winners_with_map |>
  filter(DivisionNm == "HOTHAM") 

# Set projection to GDA1994 using EPSG:4283
st_crs(electorate$geometry,4283)
  
# Transform projection from GDA1994 to GDA2020 using EPSG:7844
electorate$geometry = st_transform(electorate$geometry, 7844) 

sed_intersect <- vicmap_sed_G02 |>
  filter(st_intersects(geom,
    electorate$geometry,
    sparse = FALSE
  )[, 1])

ggplot() +
  geom_sf(data = sed_intersect,
    aes(geometry = geom), color = "red", fill = "transparent") +
  geom_sf_text(data = sed_intersect,
    aes(label = SED_CODE_2021, geometry = geom), color = "red") +
  geom_sf(data = electorate, aes(geometry = geometry), fill = "transparent") +
  geom_sf_text(data = electorate, aes(geometry = geometry, label = DivisionNm))

Closer look: Hotham electorate 1

Closer look: Hotham electorate 2

sed_intersect2 <- sed_intersect |> #<<
  mutate(
    geometry = st_intersection(geom, electorate$geometry), #<<
    perc_area = 100 * st_area(geometry) / st_area(geom), #<<
    perc_area = as.numeric(perc_area)
  ) |> #<<
  filter(perc_area > 5) 
  
ggplot(sed_intersect2, aes(geometry = geometry)) +
  geom_sf(data = electorate) +
  geom_sf_text(
    data = electorate,
    aes(label = DivisionNm)
  ) +
  geom_sf(color = "red", aes(fill = Median_age_persons)) +
  geom_sf_text(
    aes(
      label = glue::glue("{SED_CODE_2021}
                        ({scales::comma(perc_area, 1)}%, {Median_age_persons})")
    ),
    color = "red"
  ) +
  theme(legend.position = "bottom")

Closer look: Hotham electorate 2

  • There are 5 SED areas with at least 5% intersection with the electoral area.

  • How would you characterise the median age for Hotham?

Closer look: Hotham electorate 3

Strategy 1

sort(sed_intersect2$Median_age_persons)
[1] 35 37 37 40 41

Strategy 2

mean(sed_intersect2$Median_age_persons)
[1] 38

Strategy 3

weighted.mean(
  sed_intersect2$Median_age_persons,
  sed_intersect2$perc_area
)
[1] 38.06118

Closer look: Hotham electorate 4

sa1_intersect <- vicmap_sa1_G02 %>% #<<
  filter(st_intersects(geom,
    electorate$geometry,
    sparse = FALSE
  )[, 1])

sa1_intersect2 <- sa1_intersect %>%
  mutate(
    geometry = st_intersection(geom, electorate$geometry),
    perc_area = 100 * st_area(geometry) / st_area(geom),
    perc_area = as.numeric(perc_area)
  ) %>% #<<
  filter(perc_area > 5)

ggplot(sa1_intersect) +
  geom_sf(color = "red", 
          aes( fill = Median_age_persons, geometry = geom)) +
  geom_sf(data = electorate, 
          color = "white", size = 2, fill = "transparent", 
          aes(geometry = geometry)) +
  theme(legend.position = "bottom")

Closer look: Hotham electorate 5

Strategy 1

fivenum(sa1_intersect2$Median_age_persons)
[1]  0 35 38 41 81

Strategy 2

mean(sa1_intersect2$Median_age_persons)
[1] 38.07674

Strategy 3

weighted.mean(sa1_intersect2$Median_age_persons, sa1_intersect2$perc_area)
[1] 38.02792

Closer look: Hotham electorate 5

Strategy 4

ggplot(sa1_intersect2, aes(x = Median_age_persons)) +
  geom_histogram(binwidth = 1)

Closer look️: Zero median age

Hotham 2022

BEFORE (No Filter)

Strategy 1

fivenum(sa1_intersect2$Median_age_persons)
[1]  0 35 38 41 81

Strategy 2

mean(sa1_intersect2$Median_age_persons)
[1] 38.07674

Strategy 3

weighted.mean(sa1_intersect2$Median_age_persons, sa1_intersect2$perc_area)
[1] 38.02792

AFTER (With Filter)

sa1_intersect3 <- sa1_intersect2 %>%
  filter(Median_age_persons != 0)

Strategy 1

fivenum(sa1_intersect3$Median_age_persons)
[1] 19 35 38 41 81

Strategy 2

mean(sa1_intersect3$Median_age_persons)
[1] 38.25467

Strategy 3

weighted.mean(sa1_intersect3$Median_age_persons, sa1_intersect3$perc_area)
[1] 38.20661

Dorling Cartogram

sa1_intersect4 <- sa1_intersect %>%
  mutate(centroid = st_centroid(geom))

dorling_plot <- ggplot(sa1_intersect4) +
  geom_sf(
    data = electorate,
    aes(geometry = geometry), size = 4, fill = "grey60"
  ) +
  geom_sf(aes(geometry = centroid, color = Median_age_persons),
    size = 0.5, shape = 3
  ) +
  scale_color_viridis_c(name = "Median age", option = "magma")

Closer look: Hotham electorate 7

Filter out zero age electorates

sa1_intersect5 <- sa1_intersect4 %>%
  filter(st_intersects(centroid, electorate$geometry, sparse = FALSE)[, 1],
    Median_age_persons != 0)

Strategy 1

fivenum(sa1_intersect5$Median_age_persons)
[1] 19 35 38 41 81

Strategy 2

mean(sa1_intersect5$Median_age_persons)
[1] 38.20706

Strategy 4

ggplot(sa1_intersect5, aes(x = Median_age_persons)) +
  geom_histogram(binwidth = 1)

Wrap Up

Wrap Up

What did we learn

  • Learnt about geopackages and different census boundaries

  • We mapped the 2021 census boundaries and projected a summary of the census variable (i.e. median age) onto a 2022 electoral district

  • Found there are many approaches one can use to characterise an electorates demographics.

  • Estimates of median age of an electorate are more reliable using SA1 map data than SED map data.

  • Discovered some of the challenges with matching two different types of data! (Like zero age!)

For your assignment

Takeaways

  • Be very clear about which boundaries you choose for your assignment and why

  • Be sure to explain how you matched those boundaries - did you use area overlap, centroids, minimal area overlap criterion?

  • Was there anything odd you encountered when joining your data?

  • Did you need to handle any locations with no demographic data? Tell us.

Every year

CED regions are not the same as electoral boundaries!

They should be a close approximation, but they won’t necesasrily be exact. Better check on your assignment! Remember I want you to justify any decisions.

Comparing CED 2021 and electorates divisions 2022

ggplot() +
  geom_sf(data = vicmap_ced_G02, 
          aes(geometry = geom, fill = Median_age_persons),
    alpha = 1, color = "white", size = 2) +
  geom_sf(data = winners_with_map, aes(geometry = geometry),
    fill = "transparent", color = "red", size = 2) +
  coord_sf(xlim = c(144.95, 145.24), ylim = c(-38.05, -37.85)) + 
  theme_bw()

Further reading

Want to know more

Read Forbes, Cook & Hyndman (2020) Spatial modelling of the two-party preferred vote in Australian federal elections: 2001–2016. Australian & New Zealand Journal of Statisitcs. for a more sophisticated approach to studying the census variables and election results together.

ETC5512