ETC5512: Wild Caught Data

.info-box.w-50.bg-white[
These slides are viewed best by Chrome or Firefox and occasionally need to be refreshed if elements did not load properly. See <a href=lecture-06.pdf>here for the PDF </a>. 
]

---

# .monash-blue[ETC5512: Wild Caught Data]

<h2 style="font-weight:900!important;">Combining Australian census and election data</h2>

.bottom_abs.width100[

Lecturer: *Kate Saunders*

Department of Econometrics and Business Statistics

ETC5512.Clayton-x@monash.edu

Week 6

]

---

.aim-box.tl.w-70[
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
]

.aim-box.tl.w-70[
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

]

---

# Recall 2021 Federal Election Data

```r
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"
)

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

#UPDATED CODE - previous variable assignments from past weeks 
winners_fix = winners_with_map
auscolors = aus_colours
```

]

---

# Recall 2021 Federal Election Data

.flex[
.w-50[
<img src="images/lecture-06/aec-map-1.png" width="432" style="display: block; margin: auto;" />

]
.w-50[

There are two sources of data:

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

]
]

---

# Recall 2021 ABS Census Data

* DataPacks https://datapacks.censusdata.abs.gov.au/datapacks/
* GeoPackages https://datapacks.censusdata.abs.gov.au/geopackages/

<img src="images/lecture-07/datapack-download.png" width = "70%"/>
---

# ABS Census 2021

# GeoPackages

---

# GeoPackage

.blockquote[
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.

Recall: OGC also defines the WKT

---

# ABS GeoPackage (2021)

1. 2021
2. Victoria
3. Topic: Employment and Income or Table: G17 
4. GDA2020
]

* **Or use the [`strayr`](https://github.com/runapp-aus/strayr) package!** We'll use the one from the ABS website instead.

.f6.overflow-scroll.h-40[

```r
# 2022
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)

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

* **Or use the [`strayr`](https://github.com/runapp-aus/strayr) package!** We'll use the one from the ABS website instead.

.f6.overflow-scroll.h-40[

```r
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
```

```r
#UPDATED CODE - old variable assignments from years
# geopath = geopath_2016_eiuwa 
```
]

---

# The Australian Statistical Geography Standard (ASGS)

]

---

# The number of regions for each layer

```r
st_layers(geopath_2021_G02) %>%
  # make it into a data.frame first
  tibble(!!!.) %>%
  # then you can the dplyr operations
  dplyr::arrange(features)
```

```
## # 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>
```

]

---

# Data in the layer

.f5.overflow-scroll.h-90[

```r
vicmap_ste_G02 <- read_sf(geopath_2021_G02, layer = "G02_STE_2021_VIC")
vicmap_ste_G02$geom
```

```
## 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
```

```r
str(vicmap_ste_G02)
```

```
## 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
##   .. ..$ : num [1:859, 1:2] 150 150 150 150 150 ...
##   ..$ :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
##   .. ..$ : num [1:1149, 1:2] 145 145 145 145 145 ...
##   ..$ :List of 1
##   .. ..$ : num [1:76, 1:2] 145 145 145 145 145 ...
##   ..$ :List of 1
##   .. ..$ : num [1:114, 1:2] 145 145 145 145 145 ...
##   ..$ :List of 1
##   .. ..$ : num [1:34, 1:2] 145 145 145 145 145 ...
##   ..$ :List of 1
##   .. ..$ : num [1:12, 1:2] 145 145 145 145 145 ...
##   ..$ :List of 1
##   .. ..$ : num [1:10, 1:2] 145 145 145 145 145 ...
##   ..$ :List of 1
##   .. ..$ : num [1:7, 1:2] 145 145 145 145 145 ...
##   ..$ :List of 1
##   .. ..$ : num [1:1501, 1:2] 145 145 145 145 145 ...
##   ..$ :List of 1
##   .. ..$ : num [1:281, 1:2] 142 142 142 142 142 ...
##   ..$ :List of 1
##   .. ..$ : num [1:29, 1:2] 145 145 145 145 145 ...
##   ..$ :List of 1
##   .. ..$ : num [1:16, 1:2] 145 145 145 145 145 ...
##   ..$ :List of 1
##   .. ..$ : num [1:8, 1:2] 145 145 145 145 145 ...
##   ..$ :List of 1
##   .. ..$ : num [1:18, 1:2] 145 145 145 145 145 ...
##   ..$ :List of 1
##   .. ..$ : num [1:12, 1:2] 145 145 145 145 145 ...
##   ..$ :List of 1
##   .. ..$ : num [1:12, 1:2] 145 145 145 145 145 ...
##   ..$ :List of 1
##   .. ..$ : num [1:10, 1:2] 145 145 145 145 145 ...
##   ..$ :List of 1
##   .. ..$ : num [1:7, 1:2] 145 145 145 145 145 ...
##   ..$ :List of 1
##   .. ..$ : num [1:152979, 1:2] 145 145 145 145 145 ...
##   ..$ :List of 1
##   .. ..$ : num [1:4281, 1:2] 145 145 145 145 145 ...
##   ..$ :List of 1
##   .. ..$ : num [1:7592, 1:2] 147 147 147 147 147 ...
##   ..$ :List of 1
##   .. ..$ : num [1:2378, 1:2] 147 147 147 147 147 ...
##   ..$ :List of 1
##   .. ..$ : num [1:3789, 1:2] 147 147 147 147 147 ...
##   ..$ :List of 1
##   .. ..$ : num [1:1718, 1:2] 146 146 146 146 146 ...
##   ..$ :List of 1
##   .. ..$ : num [1:2093, 1:2] 147 147 147 147 147 ...
##   ..$ :List of 1
##   .. ..$ : num [1:2021, 1:2] 147 147 147 147 147 ...
##   ..$ :List of 1
##   .. ..$ : num [1:1138, 1:2] 147 147 147 147 147 ...
##   ..$ :List of 1
##   .. ..$ : num [1:796, 1:2] 147 147 147 147 147 ...
##   ..$ :List of 1
##   .. ..$ : num [1:348, 1:2] 146 146 146 146 146 ...
##   ..$ :List of 1
##   .. ..$ : num [1:896, 1:2] 147 147 147 147 147 ...
##   ..$ :List of 1
##   .. ..$ : num [1:1095, 1:2] 147 147 147 147 147 ...
##   ..$ :List of 1
##   .. ..$ : num [1:699, 1:2] 147 147 147 147 147 ...
##   ..$ :List of 1
##   .. ..$ : num [1:1431, 1:2] 147 147 147 147 147 ...
##   ..$ :List of 1
##   .. ..$ : num [1:549, 1:2] 147 147 147 147 147 ...
##   ..$ :List of 1
##   .. ..$ : num [1:252, 1:2] 146 146 146 146 146 ...
##   ..$ :List of 1
##   .. ..$ : num [1:334, 1:2] 145 145 145 145 145 ...
##   ..$ :List of 1
##   .. ..$ : num [1:219, 1:2] 146 146 146 146 146 ...
##   ..$ :List of 1
##   .. ..$ : num [1:469, 1:2] 147 147 147 147 147 ...
##   ..$ :List of 1
##   .. ..$ : num [1:94, 1:2] 146 146 146 146 146 ...
##   ..$ :List of 1
##   .. ..$ : num [1:329, 1:2] 147 147 147 147 147 ...
##   ..$ :List of 1
##   .. ..$ : num [1:375, 1:2] 147 147 147 147 147 ...
##   ..$ :List of 1
##   .. ..$ : num [1:91, 1:2] 147 147 147 147 147 ...
##   ..$ :List of 1
##   .. ..$ : num [1:12, 1:2] 147 147 147 147 147 ...
##   ..$ :List of 1
##   .. ..$ : num [1:225, 1:2] 147 147 147 147 147 ...
##   ..$ :List of 1
##   .. ..$ : num [1:142, 1:2] 146 146 146 146 146 ...
##   ..$ :List of 1
##   .. ..$ : num [1:236, 1:2] 147 147 147 147 147 ...
##   ..$ :List of 1
##   .. ..$ : num [1:104, 1:2] 146 146 146 146 146 ...
##   ..$ :List of 1
##   .. ..$ : num [1:279, 1:2] 147 147 147 147 147 ...
##   ..$ :List of 1
##   .. ..$ : num [1:274, 1:2] 147 147 147 147 147 ...
##   ..$ :List of 1
##   .. ..$ : num [1:10, 1:2] 147 147 147 147 147 ...
##   ..$ :List of 1
##   .. ..$ : num [1:255, 1:2] 147 147 147 147 147 ...
##   ..$ :List of 1
##   .. ..$ : num [1:206, 1:2] 147 147 147 147 147 ...
##   ..$ :List of 1
##   .. ..$ : num [1:224, 1:2] 147 147 147 147 147 ...
##   ..$ :List of 1
##   .. ..$ : num [1:248, 1:2] 147 147 147 147 147 ...
##   ..$ :List of 1
##   .. ..$ : num [1:469, 1:2] 147 147 147 147 147 ...
##   ..$ :List of 1
##   .. ..$ : num [1:159, 1:2] 147 147 147 147 147 ...
##   ..$ :List of 1
##   .. ..$ : num [1:311, 1:2] 147 147 147 147 147 ...
##   ..$ :List of 1
##   .. ..$ : num [1:169, 1:2] 147 147 147 147 147 ...
##   ..$ :List of 1
##   .. ..$ : num [1:196, 1:2] 147 147 147 147 147 ...
##   ..$ :List of 1
##   .. ..$ : num [1:43, 1:2] 146 146 146 146 146 ...
##   ..$ :List of 1
##   .. ..$ : num [1:15, 1:2] 147 147 147 147 147 ...
##   ..$ :List of 1
##   .. ..$ : num [1:178, 1:2] 147 147 147 147 147 ...
##   ..$ :List of 1
##   .. ..$ : num [1:117, 1:2] 146 146 146 146 146 ...
##   ..$ :List of 1
##   .. ..$ : num [1:16, 1:2] 147 147 147 147 147 ...
##   ..$ :List of 1
##   .. ..$ : num [1:192, 1:2] 147 147 147 147 147 ...
##   ..$ :List of 1
##   .. ..$ : num [1:50, 1:2] 145 145 145 145 145 ...
##   ..$ :List of 1
##   .. ..$ : num [1:162, 1:2] 147 147 147 147 147 ...
##   ..$ :List of 1
##   .. ..$ : num [1:144, 1:2] 147 147 147 147 147 ...
##   ..$ :List of 1
##   .. ..$ : num [1:128, 1:2] 147 147 147 147 147 ...
##   ..$ :List of 1
##   .. ..$ : num [1:189, 1:2] 147 147 147 147 147 ...
##   ..$ :List of 1
##   .. ..$ : num [1:309, 1:2] 147 147 147 147 147 ...
##   ..$ :List of 1
##   .. ..$ : num [1:139, 1:2] 147 147 147 147 147 ...
##   ..$ :List of 1
##   .. ..$ : num [1:139, 1:2] 147 147 147 147 147 ...
##   ..$ :List of 1
##   .. ..$ : num [1:146, 1:2] 147 147 147 147 147 ...
##   ..$ :List of 1
##   .. ..$ : num [1:85, 1:2] 146 146 146 146 146 ...
##   ..$ :List of 1
##   .. ..$ : num [1:194, 1:2] 147 147 147 147 147 ...
##   ..$ :List of 1
##   .. ..$ : num [1:99, 1:2] 146 146 146 146 146 ...
##   ..$ :List of 1
##   .. ..$ : num [1:140, 1:2] 147 147 147 147 147 ...
##   ..$ :List of 1
##   .. ..$ : num [1:101, 1:2] 147 147 147 147 147 ...
##   ..$ :List of 1
##   .. ..$ : num [1:83, 1:2] 147 147 147 147 147 ...
##   ..$ :List of 1
##   .. ..$ : num [1:100, 1:2] 147 147 147 147 147 ...
##   ..$ :List of 1
##   .. ..$ : num [1:103, 1:2] 147 147 147 147 147 ...
##   ..$ :List of 1
##   .. ..$ : num [1:74, 1:2] 147 147 147 147 147 ...
##   ..$ :List of 1
##   .. ..$ : num [1:37, 1:2] 145 145 145 145 145 ...
##   ..$ :List of 1
##   .. ..$ : num [1:91, 1:2] 147 147 147 147 147 ...
##   ..$ :List of 1
##   .. ..$ : num [1:77, 1:2] 147 147 147 147 147 ...
##   ..$ :List of 1
##   .. ..$ : num [1:47, 1:2] 143 143 143 143 143 ...
##   ..$ :List of 1
##   .. ..$ : num [1:80, 1:2] 147 147 147 147 147 ...
##   ..$ :List of 1
##   .. ..$ : num [1:127, 1:2] 147 147 147 147 147 ...
##   ..$ :List of 1
##   .. ..$ : num [1:61, 1:2] 147 147 147 147 147 ...
##   ..$ :List of 1
##   .. ..$ : num [1:85, 1:2] 147 147 147 147 147 ...
##   .. [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" ...
```

```r
vicmap_ste = vicmap_ste_G02
```

]

---

# State or Territory (STE)

```r
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)) 
```

```r
nrow(vicmap_ste)
```

```
## [1] 1
```
]

---

# Breakout Session

<center>
.aim-box.tl.w-70[
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?  
What do you notice?  
Want a challenge visualise the 2016 data!

]
</center>

---

# Greater Capital City Statistical Areas (GCCSA)

* Each region with variable population

```r
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))
```

```r
nrow(vicmap_gccsa_G02)
```

```
## [1] 4
```

---

# Section of State (SOS)

* Major urban, other urban, bounded locally & rural balance

```r
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))
```

```r
nrow(vicmap_sos_G02)
```

```
## [1] 6
```

---

# Remoteness Areas (RA)

```r
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))
```

```r
nrow(vicmap_ra_G02)
```

```
## [1] 6
```

---

# Section of State Ranges (SOSR)

```r
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))
```

```r
nrow(vicmap_sosr_G02)
```

```
## [1] 12
```

---

# Statistical Area Level 4 (SA4)

* Each region with population of 100,000 - 500,000

```r
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))
```

```r
nrow(vicmap_sa4_G02)
```

```
## [1] 19
```

---

# Significant Urban Areas (SUA)

```r
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))
```

```r
nrow(vicmap_sua_G02)
```

```
## [1] 22
```

---

# Commonwealth Electoral Division (CED)

```r
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))
```

```r
nrow(vicmap_ced_G02)
```

```
## [1] 41
```
---

# Statistical Area Level 3 (SA3)

* Each region with population of 30,000 - 130,000

```r
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))
```

```r
nrow(vicmap_sa3_G02)
```

```
## [1] 68
```
---

# Local Government Area (LGA)

```r
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))
```

```r
nrow(vicmap_lga_G02)
```

```
## [1] 68
```

---

# State Electoral Division (SED)

```r
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))
```

```r
nrow(vicmap_sed_G02)
```

```
## [1] 90
```
---

# Urban Centres and Localities (UCL)

```r
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))
```

```r
nrow(vicmap_ucl_G02)
```

```
## [1] 347
```

---

# Statistical Area Level 2 (SA2)

* Each region with populations in the range of 3,000-25,000

```r
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))
```

```r
nrow(vicmap_sa2_G02)
```

```
## [1] 68
```

---

# Postal Areas (POA)

```r
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))
```

```r
nrow(vicmap_poa_G02)
```

```
## [1] 694
```

---

# State Area Localitites (SAL) (Formerly SSC)

```r
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))
```

```r
nrow(vicmap_sal_G02)
```

```
## [1] 2946
```

---

# Statistical Area Level 1 (SA1)

* Each region with a population of range 200-800

```r
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))
```

```r
nrow(vicmap_sa1_G02)
```

```
## [1] 15482
```

---

# Electorate boundary vs Census boundary

🎯 Estimate a median age for an electorate

---

# Comparing SED 2021 and electorates divisions 2022

.f4[See [here](https://wcd.numbat.space/lectures/lecture-05.html#35) for  `electoral_winners` data was.]

```r
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()
```

<img src="images/lecture-06/zoomed-map-1.png" width="576" style="display: block; margin: auto;" />
]

---

# Closer look 🔬 Hotham electorate .circle.monash-bg-black.white[1]

```r
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))
```

]

```
## Coordinate Reference System:
##   User input: GDA94 
##   wkt:
## GEOGCRS["GDA94",
##     DATUM["Geocentric Datum of Australia 1994",
##         ELLIPSOID["GRS 1980",6378137,298.257222101,
##             LENGTHUNIT["metre",1]]],
##     PRIMEM["Greenwich",0,
##         ANGLEUNIT["degree",0.0174532925199433]],
##     CS[ellipsoidal,2],
##         AXIS["geodetic latitude (Lat)",north,
##             ORDER[1],
##             ANGLEUNIT["degree",0.0174532925199433]],
##         AXIS["geodetic longitude (Lon)",east,
##             ORDER[2],
##             ANGLEUNIT["degree",0.0174532925199433]],
##     USAGE[
##         SCOPE["Horizontal component of 3D system."],
##         AREA["Australia including Lord Howe Island, Macquarie Island, Ashmore and Cartier Islands, Christmas Island, Cocos (Keeling) Islands, Norfolk Island. All onshore and offshore."],
##         BBOX[-60.55,93.41,-8.47,173.34]],
##     ID["EPSG",4283]]
```

---

# Closer look 🔬 Hotham electorate .circle.monash-bg-black.white[1]

There are 7 SED regions that intersect with Hotham electorate.

---

# Closer look 🔬 Hotham electorate .circle.monash-bg-black.white[2]

```r
*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 .circle.monash-bg-black.white[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 .circle.monash-bg-black.white[3]

.flex[
.w-40[
<img src="images/lecture-06/district-map2-1.png" width="432" style="display: block; margin: auto;" />
]
.w-60.f5[
**Strategy 1**

```r
sort(sed_intersect2$Median_age_persons)
```

```
## [1] 35 37 37 40 41
```

]]

**Strategy 2**

```r
mean(sed_intersect2$Median_age_persons)
```

```
## [1] 38
```

**Strategy 3**

```r
weighted.mean(
  sed_intersect2$Median_age_persons,
  sed_intersect2$perc_area
)
```

```
## [1] 38.06118
```

---

# Closer look 🔬 Hotham electorate .circle.monash-bg-black.white[4]

```r
*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")
```

<img src="images/lecture-06/sa1-intersect-1.png" width="432" style="display: block; margin: auto;" />
]]

.w-50[
<img src="images/lecture-06/sa1-intersect-1.png" width="432" style="display: block; margin: auto;" />
]]

---

# Closer look 🔬 Hotham electorate .circle.monash-bg-black.white[5]

.flex[
.w-40[
<img src="images/lecture-06/sa1-intersect-1.png" width="432" style="display: block; margin: auto;" />
]
.w-60.f5[
**Strategy 1**

```r
fivenum(sa1_intersect2$Median_age_persons)
```

```
## [1]  0 35 38 41 81
```

]]

**Strategy 2**

```r
mean(sa1_intersect2$Median_age_persons)
```

```
## [1] 38.07674
```

**Strategy 3**

```r
weighted.mean(sa1_intersect2$Median_age_persons, sa1_intersect2$perc_area)
```

```
## [1] 38.02792
```

**Strategy 4**

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

---

# Closer look 🕵️ Zero median age

(Hotham 2022)[https://www.aec.gov.au/profiles/vic/hotham.htm]

```r
sa1_intersect2 %>%
  filter(Median_age_persons == 0) %>%
  ggplot() +
  geom_sf() +
  geom_sf(
    data = electorate, color = "red",
    fill = "transparent",
    aes(geometry = geometry)
  )
```

]
]

---

# Closer look 🔬 Hotham electorate .circle.monash-bg-black.white[6]

## Before

**Strategy 1**

```r
fivenum(sa1_intersect2$Median_age_persons)
```

```
## [1]  0 35 38 41 81
```

**Strategy 2**

```r
mean(sa1_intersect2$Median_age_persons)
```

```
## [1] 38.07674
```

**Strategy 3**

```r
weighted.mean(sa1_intersect2$Median_age_persons, sa1_intersect2$perc_area)
```

```
## [1] 38.02792
```
]
.w-50.f5.pl3[

## After

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

]]

**Strategy 1**

```r
fivenum(sa1_intersect3$Median_age_persons)
```

```
## [1] 19 35 38 41 81
```

**Strategy 2**

```r
mean(sa1_intersect3$Median_age_persons)
```

```
## [1] 38.25467
```

**Strategy 3**

```r
weighted.mean(sa1_intersect3$Median_age_persons, sa1_intersect3$perc_area)
```

```
## [1] 38.20661
```

---

# Dorling Cartogram

.f6.w-50[

```r
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 .circle.monash-bg-black.white[7]

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

**Strategy 1**

```r
fivenum(sa1_intersect5$Median_age_persons)
```

```
## [1] 19 35 38 41 81
```

**Strategy 2**

```r
mean(sa1_intersect5$Median_age_persons)
```

```
## [1] 38.20706
```

**Strategy 4**

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

]

---

## Summary

<div class="idea-box">
<ul>
<li>There are many ways to characterise an electorate.</li>
<li>Estimates of median age of an electorate is more consistent using SA1 map data than SED map data.</li>
</ul>
</div>

]

.idea-box.tl.w-90[

* We looked at mapping the 2021 census boundaries and projected a summary of the census variable (i.e. median age) onto a 2022 electoral district

* Discovered some of the challenges with matching two different types of data

]

.info-box.tl.w-90[
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*. ](https://onlinelibrary.wiley.com/doi/abs/10.1111/anzs.12292) for a more sophisticated approach to studying the census variables and election results together.
]

</center>

---

#### Slides updated and maintained by Dr. Kate Saunders. Slides originally developed by Dr. Emi Tanaka

---

background-size: cover
class: title-slide
background-image: url("images/bg-01.png")

<a rel="license" href="http://creativecommons.org/licenses/by-sa/4.0/"><img alt="Creative Commons License" style="border-width:0" src="https://i.creativecommons.org/l/by-sa/4.0/88x31.png" /></a> This work is licensed under a <a rel="license" href="http://creativecommons.org/licenses/by-sa/4.0/">Creative Commons Attribution-ShareAlike 4.0 International License</a>.

.bottom_abs.width100[

Lecturer: *Kate Saunders*

Department of Econometrics and Business Statistics

ETC5512.Clayton-x@monash.edu

Week 6

]