ETC5512: Wild Caught Data

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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-05.pdf>here for the PDF <i class="fas fa-file-pdf"></i></a>. 
]

<br>

---

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

<br>

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

.bottom_abs.width100[

Lecturer: *Kate Saunders*

Department of Econometrics and Business Statistics

<i class="fas fa-envelope"></i>  ETC5512.Clayton-x@monash.edu

<i class="fas fa-calendar-alt"></i> Week 5

<br>

]

---

# Australian election data

* Much like the census, .monash-blue[**election**] attempts to collect the data from the population.

]

* In Australia, it is compulsory by law to vote in elections if you are an Australian citizen <span class="f6">(or eligible British subject)</span> aged 18 years old or over and have lived in your address for at least one month.
{{content}}
--

* The Australian Electoral Commission (AEC) is an independent federal agency in charge of federal Australian elections and provides the geographical boundaries of the electoral divisions.
{{content}}

<div class="question-box">
<ol>
<li>When was the last federal election in Australia?</li>
<li>How often is the federal election conducted in Australia? </li>
<li>How many electoral divisions are there in the last federal election?</li>
<li>What is the population for the Australian federal election?</li>
</div>

---

.aim-box.w-70.tl[
Today you will:

- Learn about Australian election data 
- Look at the 2022 election results
- Learn to visualise the election results spatially in a few ways
- Learn about reprojecting geographic data into different coordinate reference systems

]

.aim-box.w-70.tl[
From a coding perspecting:

- This will require learning about **mapping in R**.  
- You will also need to learn about different **mapping projections**

]

---

# 2022 Australian Federal Election

.flex.h-100[
.w-55[
* Parliament of Australia comprises two houses: 
  * **Senate** (upper house) comprising 76 senators
  * **House of Representatives** (lower house) comprising 151 members
* Government is formed by the party or coalition with majority of the seats in the lower house
* The 2022 Australian Federal Election was held on Sat 21st May 2022
* The next federal election will be likely be held in 2025

]
.w-45.monash-bg-gray10[

* Major parties in Australia:
  * Coalition:
      * <img src="images/lecture-08/logo-liberal.png" height="60px"> Liberal
      * <img src="images/lecture-08/logo-national.png" height="30px"> National 
  * Labor <img src="images/lecture-08/logo-labor.png" height="40px">
* Some minor parties in Australia:
  * <img src="images/lecture-08/logo-green.png" height="50px"> The Greens
  * <img src="images/lecture-08/logo-one-nation.png" height="50px"> One Nation

]]

---

# Ballots

* **House of Representatives** uses the instant-runoff voting system
* Senate uses the single transferable voting system

<center>
<img class = "ba" src="images/lecture-08/hor-paper.jpg">.white[Senate]<img class = "ba" src="images/lecture-08/senate-atl-sample.png">
</center>

---

# 2022 Australian Federal Election Data

* Get the distribution of preferences by candidate by division for the 2022 Australian Federal Election

.f2.center[
<i class="fas fa-download"></i> https://results.aec.gov.au
] 
<br>

1. 2022 federal election
1. Downloads
1. Distribution of preferences by candidate by division

]]

* Or refer directly to the link:

]

---

# House of Representative Voting Data

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

```
## Rows: 35,096
## Columns: 14
## $ StateAb          <chr> "ACT", "ACT", "ACT", "ACT", "ACT", "ACT", "ACT", "ACT…
## $ DivisionID       <dbl> 318, 318, 318, 318, 318, 318, 318, 318, 318, 318, 318…
## $ DivisionNm       <chr> "Bean", "Bean", "Bean", "Bean", "Bean", "Bean", "Bean…
## $ CountNumber      <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ BallotPosition   <dbl> 1, 1, 1, 1, 2, 2, 2, 2, 3, 3, 3, 3, 4, 4, 4, 4, 5, 5,…
## $ CandidateID      <dbl> 36239, 36239, 36239, 36239, 37455, 37455, 37455, 3745…
## $ Surname          <chr> "CONWAY", "CONWAY", "CONWAY", "CONWAY", "AMBARD", "AM…
## $ GivenNm          <chr> "Sean", "Sean", "Sean", "Sean", "Benjamin", "Benjamin…
## $ PartyAb          <chr> "UAPP", "UAPP", "UAPP", "UAPP", "ON", "ON", "ON", "ON…
## $ PartyNm          <chr> "United Australia Party", "United Australia Party", "…
## $ Elected          <chr> "N", "N", "N", "N", "N", "N", "N", "N", "Y", "Y", "Y"…
## $ HistoricElected  <chr> "N", "N", "N", "N", "N", "N", "N", "N", "Y", "Y", "Y"…
## $ CalculationType  <chr> "Preference Count", "Preference Percent", "Transfer C…
## $ CalculationValue <dbl> 2831.00, 2.88, 0.00, 0.00, 2680.00, 2.72, 0.00, 0.00,…
```
]

---

# Electoral district of .monash-blue[Monash]

* Let's have a look at the electoral district named "Monash"

```
## Rows: 224
## Columns: 14
## $ StateAb          <chr> "VIC", "VIC", "VIC", "VIC", "VIC", "VIC", "VIC", "VIC…
## $ DivisionID       <dbl> 323, 323, 323, 323, 323, 323, 323, 323, 323, 323, 323…
## $ DivisionNm       <chr> "Monash", "Monash", "Monash", "Monash", "Monash", "Mo…
## $ CountNumber      <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ BallotPosition   <dbl> 1, 1, 1, 1, 2, 2, 2, 2, 3, 3, 3, 3, 4, 4, 4, 4, 5, 5,…
## $ CandidateID      <dbl> 36561, 36561, 36561, 36561, 36737, 36737, 36737, 3673…
## $ Surname          <chr> "MORGAN", "MORGAN", "MORGAN", "MORGAN", "BROADBENT", …
## $ GivenNm          <chr> "Mat", "Mat", "Mat", "Mat", "Russell", "Russell", "Ru…
## $ PartyAb          <chr> "GVIC", "GVIC", "GVIC", "GVIC", "LP", "LP", "LP", "LP…
## $ PartyNm          <chr> "The Greens", "The Greens", "The Greens", "The Greens…
## $ Elected          <chr> "N", "N", "N", "N", "Y", "Y", "Y", "Y", "N", "N", "N"…
## $ HistoricElected  <chr> "N", "N", "N", "N", "Y", "Y", "Y", "Y", "N", "N", "N"…
## $ CalculationType  <chr> "Preference Count", "Preference Percent", "Transfer C…
## $ CalculationValue <dbl> 9533.00, 9.86, 0.00, 0.00, 36546.00, 37.79, 0.00, 0.0…
```

]

```r
# votes %>%
#   filter(DivisionNm == "Monash") %>%
#   DT::datatable(width = 1160, height = 400, options = list(pageLength = 400))
```

---

# District: Monash

```r
votes_monash <- votes %>%
  # get the preference count only
  filter(CalculationType == "Preference Count") %>%
  # get the Monash division
  filter(DivisionNm == "Monash")
glimpse(votes_monash)
```

```
## Rows: 56
## Columns: 14
## $ StateAb          <chr> "VIC", "VIC", "VIC", "VIC", "VIC", "VIC", "VIC", "VIC…
## $ DivisionID       <dbl> 323, 323, 323, 323, 323, 323, 323, 323, 323, 323, 323…
## $ DivisionNm       <chr> "Monash", "Monash", "Monash", "Monash", "Monash", "Mo…
## $ CountNumber      <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2,…
## $ BallotPosition   <dbl> 1, 2, 3, 4, 5, 6, 7, 8, 1, 2, 3, 4, 5, 6, 7, 8, 1, 2,…
## $ CandidateID      <dbl> 36561, 36737, 36065, 37629, 37637, 36455, 36914, 3603…
## $ Surname          <chr> "MORGAN", "BROADBENT", "LEONARD", "HICKEN", "WELSH", …
## $ GivenNm          <chr> "Mat", "Russell", "Deb", "Allan", "David Matthew", "J…
## $ PartyAb          <chr> "GVIC", "LP", "IND", "ON", "CYA", "ALP", "LDP", "UAPP…
## $ PartyNm          <chr> "The Greens", "Liberal", "Independent", "Pauline Hans…
## $ Elected          <chr> "N", "Y", "N", "N", "N", "N", "N", "N", "N", "Y", "N"…
## $ HistoricElected  <chr> "N", "Y", "N", "N", "N", "N", "N", "N", "N", "Y", "N"…
## $ CalculationType  <chr> "Preference Count", "Preference Count", "Preference C…
## $ CalculationValue <dbl> 9533, 36546, 10372, 7289, 674, 24759, 3548, 3991, 959…
```

]

---

# Visualising the counts

```r
ggplot(votes_monash) +
  geom_col(aes(x = CalculationValue, y = Surname)) +
  geom_text(aes(label = paste("Count", CountNumber)),
    x = 10000, y = 3, size = 16, color = "#ee64a4",
    alpha = 0.4, hjust = "left"
  ) +
  facet_wrap(~CountNumber)
```

---

# ... but better to order candidates by counts

```r
*mutate(votes_monash, Surname = fct_reorder(Surname, CalculationValue, sum)) %>%
  ggplot() +
  geom_col(aes(x = CalculationValue, y = Surname)) +
  geom_text(aes(label = paste("Count", CountNumber + 1)),
    x = 10000, y = 3, size = 16, color = "#ee64a4", alpha = 0.4, hjust = "left"
  ) +
  facet_wrap(~CountNumber)
```

<div style="position:absolute;right:20px;bottom:20px">
<b>Winner</b>: <br>Russel<br> Broadbent<br>
<img width = "180px" src="images/lecture-08/broadbent-russel.jpeg" />
</div>

---

# Where is the electoral district of .monash-blue[Monash]?

* ...*doesn't* include Monash Clayton campus

---

# Electoral district of .monash-blue[Hotham]

* *Does* include Monash Clayton campus

---

# Australian Electorates Divsions

There are 151 electorates in 2019.

.flex[
.w-60.center[
<img src="images/lecture-08/Australian_Electoral_Divisions_2019.png" width = "100%"/>
]
.w-40[

.info-box[
The **geographical boundaries of the electoral divisions** are determined by the [Redistribution Committee](https://www.aec.gov.au/footer/glossary.htm#redist-comm) and are [redrawn](https://www.aec.gov.au/Electorates/Redistributions/future.htm) every so often to ensure similar number of electors in each electoral division for a given state or territory.
]

{{content}}
]

]

<div class="warn-box">
This means that the geographical boundaries could be different across years.
</div>

---

# Federal electoral boundary GIS data

* GIS (Geographic Information System) is a framework that capture and inspect geographical data.
--

* This data is found at

.center.f2[
<i class="fas fa-download"></i> https://www.aec.gov.au/electorates/gis/licence.htm
]
--

* Agree to the license to get to the download page
.blockquote[
The Licensee must make End-users aware the data was sourced from the Australian Electoral Commission and is used under licence.
]
.center[
Note: the federal electoral boundary is provided by Australian Electoral Commission

* We download the ESRI zip file for Victoria.
--

* To work with spatial data, we use the `sf` R-package.

---

# Working with spatial data

.overflow-scroll.h-80[

```r
library(sf)
aec_map <- read_sf(here::here("data/vic-july-2021-esri/E_VIC21_region.shp"))
# aec_map
```
]

---

# Geometry object and visualisation in as `ggplot`

```r
aec_map$geometry[[1]]
```

```
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145.2013 -37.86091 0, 145.2013 -37.86088 0, 145.2013 -37.86086 0, 145.2012 -37.86077 0, 145.2012 -37.86071 0, 145.2011 -37.86065 0, 145.2011 -37.86063 0, 145.2011 -37.86062 0, 145.2011 -37.86061 0, 145.2011 -37.8606 0, 145.2011 -37.8606 0, 145.2011 -37.8606 0, 145.201 -37.8606 0, 145.201 -37.8606 0, 145.201 -37.8606 0, 145.2009 -37.86064 0, 145.2008 -37.86068 0, 145.2008 -37.8607 0, 145.2008 -37.8607 0, 145.2007 -37.86071 0, 145.2007 -37.86071 0, 145.2007 -37.8607 0, 145.2007 -37.8607 0, 145.2006 -37.86069 0, 145.2006 -37.86068 0, 145.2006 -37.86065 0, 145.2005 -37.86058 0, 145.2005 -37.86057 0, 145.2005 -37.86055 0, 145.2005 -37.86055 0, 145.2005 -37.86054 0, 145.2005 -37.86053 0, 145.2005 -37.86053 0, 145.2004 -37.86049 0, 145.2004 -37.86048 0, 145.2004 -37.86045 0, 145.2004 -37.86043 0, 145.2005 -37.8604 0, 145.2005 -37.86037 0, 145.2005 -37.86035 0, 145.2005 -37.86033 0, 145.2005 -37.86031 0, 145.2005 -37.86029 0, 145.2005 -37.86026 0, 145.2004 -37.86024 0, 145.2004 -37.86023 0, 145.2004 -37.86022 0, 145.2004 -37.86019 0, 145.2004 -37.86018 0, 145.2004 -37.86017 0, 145.2004 -37.86016 0, 145.2003 -37.86015 0, 145.2003 -37.86014 0, 145.2003 -37.86013 0, 145.2003 -37.86013 0, 145.2003 -37.86012 0, 145.2002 -37.86011 0, 145.2002 -37.8601 0, 145.2002 -37.8601 0, 145.2002 -37.86008 0, 145.2002 -37.86007 0, 145.2002 -37.86006 0, 145.2002 -37.86006 0, 145.2002 -37.86004 0, 145.2002 -37.86003 0, 145.2002 -37.86002 0, 145.2001 -37.86002 0, 145.2001 -37.86 0, 145.2001 -37.85999 0, 145.2001 -37.85997 0, 145.2001 -37.85995 0, 145.2 -37.85994 0, 145.2 -37.85992 0, 145.2 -37.85991 0, 145.2 -37.85991 0, 145.2 -37.8599 0, 145.2 -37.85988 0, 145.1999 -37.85987 0, 145.1999 -37.85985 0, 145.1999 -37.85983 0, 145.1999 -37.85981 0, 145.1999 -37.8598 0, 145.1999 -37.85979 0, 145.1999 -37.85979 0, 145.1999 -37.85979 0, 145.1998 -37.85979 0, 145.1998 -37.85979 0, 145.1997 -37.85981 0, 145.1997 -37.85982 0, 145.1997 -37.85981 0, 145.1997 -37.85981 0, 145.1997 -37.85981 0, 145.1997 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0, 145.2668 -37.83702 0, 145.2668 -37.83703 0, 145.2669 -37.83703 0, 145.2669 -37.83703 0, 145.267 -37.83704 0, 145.2671 -37.83704 0, 145.2671 -37.83704 0, 145.2671 -37.83704 0, 145.2671 -37.83704 0, 145.2672 -37.83703 0, 145.2673 -37.83703 0, 145.2673 -37.83702 0, 145.2674 -37.837 0, 145.2674 -37.83699 0, 145.2675 -37.83698 0, 145.2675 -37.83695 0, 145.2676 -37.83694 0, 145.2676 -37.83693 0, 145.2676 -37.83691 0, 145.2677 -37.83689 0, 145.2678 -37.83684 0, 145.2679 -37.8368 0, 145.2679 -37.83678 0, 145.2679 -37.83676 0, 145.2679 -37.83674 0, 145.268 -37.83671 0, 145.268 -37.83669 0, 145.2681 -37.83668 0, 145.2681 -37.83666 0, 145.2682 -37.83665 0, 145.2682 -37.83663 0, 145.2682 -37.83662 0, 145.2683 -37.8366 0, 145.2683 -37.8366 0, 145.2684 -37.83653 0, 145.2684 -37.83651 0, 145.2685 -37.83643 0, 145.2686 -37.83636 0, 145.2689 -37.8362 0, 145.2689 -37.83618 0, 145.2689 -37.83616 0, 145.2689 -37.83614 0, 145.269 -37.83612 0, 145.269 -37.83611 0, 145.269 -37.8361 0, 145.269 -37.8361 0, 145.2691 -37.83607 0, 145.2692 -37.83606 0, 145.2693 -37.83603 0, 145.2693 -37.83602 0, 145.2693 -37.83602 0, 145.2694 -37.836 0, 145.2695 -37.83597 0, 145.2695 -37.83596 0, 145.2696 -37.83595 0, 145.2697 -37.83593 0, 145.2698 -37.8359 0, 145.2698 -37.8359 0, 145.2699 -37.83589 0, 145.2699 -37.83589 0, 145.2699 -37.83589 0, 145.27 -37.83589 0, 145.27 -37.8359 0, 145.2702 -37.83592 0, 145.2703 -37.83593 0, 145.2703 -37.83595 0, 145.2704 -37.83596 0, 145.2704 -37.83597 0, 145.2705 -37.83599 0, 145.2705 -37.83602 0, 145.2708 -37.83611 0, 145.271 -37.83617 0, 145.2711 -37.83624 0, 145.2713 -37.8363 0, 145.2714 -37.83633 0, 145.2715 -37.83635 0, 145.2716 -37.83636 0, 145.2717 -37.83635 0, 145.2718 -37.83634 0, 145.2718 -37.83633 0, 145.2718 -37.83632 0, 145.2719 -37.8363 0, 145.272 -37.83627 0, 145.2721 -37.83625 0, 145.2722 -37.83623 0, 145.2723 -37.8362 0, 145.2724 -37.83619 0, 145.2724 -37.83618 0, 145.2725 -37.83616 0, 145.2727 -37.83611 0, 145.273 -37.83604 0, 145.273 -37.83603 0, 145.2731 -37.83602 0, 145.2732 -37.836 0, 145.2733 -37.83597 0, 145.2735 -37.83592 0, 145.2735 -37.83591 0, 145.2736 -37.83589 0, 145.2737 -37.83588 0, 145.2738 -37.83587 0, 145.2738 -37.83586 0, 145.2739 -37.83585 0, 145.274 -37.83584 0, 145.2741 -37.8358 0, 145.2743 -37.83576 0, 145.2743 -37.83575 0, 145.2744 -37.83574 0, 145.2744 -37.83573 0, 145.2745 -37.83574 0, 145.2747 -37.83576 0, 145.2748 -37.83577 0, 145.275 -37.83577 0, 145.2751 -37.83578 0, 145.2752 -37.83578 0, 145.2753 -37.83579 0, 145.2754 -37.8358 0, 145.2754 -37.83581 0, 145.2755 -37.83581 0, 145.2757 -37.8358 0, 145.2758 -37.83579 0, 145.276 -37.83578 0, 145.2761 -37.83575 0, 145.2764 -37.83574 0, 145.2764 -37.83574 0, 145.2765 -37.83574 0, 145.2766 -37.83574 0, 145.2768 -37.83574 0, 145.2769 -37.83575 0, 145.277 -37.83576 0, 145.2771 -37.83577 0, 145.2772 -37.83578 0, 145.2773 -37.8358 0, 145.2774 -37.83582 0, 145.2775 -37.83584 0, 145.2778 -37.83588 0, 145.2778 -37.83589 0, 145.2778 -37.83589 0, 145.2779 -37.8359 0, 145.278 -37.83589 0, 145.2781 -37.83587 0, 145.2782 -37.83586 0, 145.2788 -37.83579 0, 145.2788 -37.83578 0, 145.2789 -37.83576 0, 145.2789 -37.83575 0, 145.2791 -37.83571 0, 145.2792 -37.83565 0, 145.2793 -37.8356 0, 145.2793 -37.83556 0, 145.2794 -37.83544 0, 145.2795 -37.83532 0, 145.2796 -37.83522 0, 145.2797 -37.83506 0, 145.2798 -37.83492 0, 145.28 -37.83472 0, 145.2801 -37.83461 0, 145.2802 -37.83438 0, 145.2802 -37.83423 0, 145.2802 -37.83418 0, 145.2803 -37.83371 0, 145.2803 -37.83365 0, 145.2804 -37.8335 0, 145.2805 -37.83332 0, 145.2805 -37.8333 0, 145.2807 -37.83322 0, 145.2807 -37.83318 0, 145.281 -37.83314 0, 145.2815 -37.83304 0, 145.2816 -37.83303 0, 145.2819 -37.83305 0, 145.2827 -37.83314 0, 145.2828 -37.83317 0, 145.2831 -37.83322 0, 145.2832 -37.83322 0, 145.2834 -37.83325 0, 145.2834 -37.83328 0, 145.2835 -37.83329 0, 145.2836 -37.8333 0, 145.2836 -37.83331 0, 145.2837 -37.83332 0, 145.2837 -37.83334 0, 145.2838 -37.83334 0, 145.2838 -37.83334 0, 145.2839 -37.83336 0, 145.2841 -37.83339 0, 145.2842 -37.8334 0, 145.2844 -37.83343 0, 145.2846 -37.83344 0, 145.2846 -37.83344 0, 145.2847 -37.83346 0, 145.2847 -37.83348 0, 145.285 -37.83353 0, 145.2851 -37.83355 0, 145.2852 -37.83357 0, 145.2855 -37.8336 0, 145.2855 -37.8336 0, 145.2857 -37.83362 0, 145.2857 -37.83363 0, 145.2858 -37.83366 0, 145.2859 -37.83368 0, 145.2859 -37.83368 0, 145.286 -37.83368 0, 145.2861 -37.83367 0, 145.2862 -37.83366 0, 145.2862 -37.83365 0, 145.2863 -37.83364 0, 145.2864 -37.83364 0, 145.2865 -37.83365 0, 145.2865 -37.83365 0, 145.2866 -37.83363 0, 145.2867 -37.83362 0, 145.2868 -37.83362 0, 145.2868 -37.83361 0, 145.2869 -37.83359 0, 145.287 -37.83358 0, 145.2871 -37.83358 0, 145.2871 -37.83358 0, 145.2872 -37.83357 0, 145.2873 -37.83356 0, 145.2873 -37.83333 0, 145.2889 -37.83352 0, 145.289 -37.83353 0, 145.2891 -37.83355 0, 145.29 -37.83365 0, 145.291 -37.83377 0, 145.2915 -37.83383 0, 145.2935 -37.83406 0, 145.2941 -37.83433 0, 145.2958 -37.83519 0, 145.2958 -37.8352 0, 145.2963 -37.83544 0, 145.2973 -37.83595 0, 145.3011 -37.83784 0, 145.3012 -37.83792 0, 145.3014 -37.838 0, 145.3033 -37.83894 0, 145.3063 -37.84047 0, 145.3089 -37.84175 0, 145.3087 -37.84268 0, 145.3135 -37.84328 0, 145.3178 -37.84389 0, 145.318 -37.84392 0, 145.3178 -37.84486 0, 145.3178 -37.84496 0, 145.3202 -37.84489 0, 145.323 -37.8462 0, 145.3238 -37.8463 0, 145.3239 -37.84632 0, 145.3241 -37.84635 0, 145.3253 -37.84653 0, 145.326 -37.84664 0, 145.3328 -37.84743 0, 145.3321 -37.85102 0, 145.3324 -37.85106 0, 145.3352 -37.85139 0, 145.3362 -37.85105 0, 145.3363 -37.85111 0, 145.3385 -37.85236 0, 145.3378 -37.85319 0, 145.3412 -37.85378 0, 145.3425 -37.85491 0, 145.3435 -37.85578 0, 145.3431 -37.85631 0, 145.3431 -37.85655 0, 145.3437 -37.85679 0, 145.3444 -37.85689 0, 145.3449 -37.8572 0, 145.3452 -37.85722 0, 145.3457 -37.8576 0, 145.3459 -37.85764 0, 145.3476 -37.85862 0, 145.3476 -37.85941 0)))
```
--

```r
ggplot(aec_map) +
  geom_sf()
```

---

# Integrating data of election winners

```r
winners <- votes %>%
  # get the winner
  filter(Elected == "Y" & CountNumber == 0 & CalculationType == "Preference Count") %>%
  # join the data
  right_join(aec_map, by = c("DivisionNm" = "Elect_div")) %>%
  select(DivisionNm, PartyAb, PartyNm, geometry)

ggplot(winners) +
  geom_sf(aes(fill = PartyAb, geometry = geometry))
```

<img src="images/lecture-05/winner-map-1.png" width="432" style="display: block; margin: auto;" />
]

---

# Investigating missing observation

```r
winners %>%
  filter(is.na(PartyAb))
```

```
## # A tibble: 1 × 4
##   DivisionNm PartyAb PartyNm                                            geometry
##   <chr>      <chr>   <chr>                                    <MULTIPOLYGON [°]>
## 1 Mcewen     <NA>    <NA>    Z (((145.3664 -37.54513 0, 145.3663 -37.5459 0, 14…
```
--

```r
votes %>%
  # approximate string matching (or fuzzy matching)
  filter(agrepl("Mcewen", DivisionNm))
```

```
## # A tibble: 168 × 14
##    StateAb DivisionID DivisionNm CountNumber BallotPosition CandidateID Surname
##    <chr>        <dbl> <chr>            <dbl>          <dbl>       <dbl> <chr>  
##  1 VIC            226 McEwen               0              1       36547 BARKER 
##  2 VIC            226 McEwen               0              1       36547 BARKER 
##  3 VIC            226 McEwen               0              1       36547 BARKER 
##  4 VIC            226 McEwen               0              1       36547 BARKER 
##  5 VIC            226 McEwen               0              2       36054 McRAE  
##  6 VIC            226 McEwen               0              2       36054 McRAE  
##  7 VIC            226 McEwen               0              2       36054 McRAE  
##  8 VIC            226 McEwen               0              2       36054 McRAE  
##  9 VIC            226 McEwen               0              3       37619 NEIL   
## 10 VIC            226 McEwen               0              3       37619 NEIL   
## # ℹ 158 more rows
## # ℹ 7 more variables: GivenNm <chr>, PartyAb <chr>, PartyNm <chr>,
## #   Elected <chr>, HistoricElected <chr>, CalculationType <chr>,
## #   CalculationValue <dbl>
```
]

.absolute.bottom-3.right-2.bg-white.ba.pa3[
So what went wrong here?
]

---

# Victoria map of election winners

```r
winners_fix <- votes %>%
  mutate(DivisionNm = ifelse(DivisionNm == "McEwen", "Mcewen", DivisionNm)) %>%
  # get the winner
  filter(Elected == "Y" & CountNumber == 0 & CalculationType == "Preference Count") %>%
  # join the data
  right_join(aec_map, by = c("DivisionNm" = "Elect_div")) %>%
  select(DivisionNm, PartyAb, PartyNm, geometry)

ggplot(winners_fix) +
  geom_sf(aes(fill = PartyAb, geometry = geometry))
```

<img src="images/lecture-05/winner-map-fix-1.png" width="432" style="display: block; margin: auto;" />
]

---

# Maps visualisation

---

# National map of election winners

```r
ausmap <- read_sf(here::here("data/2021-Cwlth_electoral_boundaries_ESRI/2021_ELB_region.shp"))

all_winners <- votes %>%
  mutate(DivisionNm = case_when(
    DivisionNm == "McEwen" ~ "Mcewen",
    DivisionNm == "McPherson" ~ "Mcpherson",
    DivisionNm == "Eden-Monaro " ~ "Eden-monaro",
    DivisionNm == "McMahon" ~ "Mcmahon",
    DivisionNm == "O'Connor" ~ "O'connor",
    TRUE ~ DivisionNm
  )) %>%
  # Another way to select the winner
  filter(Elected == "Y") %>%
  group_by(DivisionID) %>%
  slice(1) %>%
  ungroup() %>%
  # then join the map data
  right_join(ausmap, by = c("DivisionNm" = "Elect_div"))
```
]

---

# Code fix for previous slide to remove NAs on map

```r
ausmap <- read_sf(here::here("data/2021-Cwlth_electoral_boundaries_ESRI/2021_ELB_region.shp"))

# Wrangle vote data: Want the winner for each electoral division 
electoral_winners = votes |> 
  # Make everything capitals to fix mismatches 
  mutate(DivisionNm = toupper(DivisionNm)) |>
  # Filter to winners
  filter(Elected == "Y") |> 
  # Select only the key variables we need for plotting
  select(PartyAb, DivisionID, DivisionNm, Elected) |>
  # We get a copy of the winner for each count, only need one
  distinct()

# Wrangle map data
ausmap = ausmap |> 
  # Make everything capitals to fix mismatches 
  mutate(Elect_div = toupper(Elect_div))

#Combine map data and electoral winners
all_winners = electoral_winners |> 
  left_join(ausmap, by = c("DivisionNm" = "Elect_div"))
```
]

---

# Using colors wisely

```r
auscolours <- c(
  "ALP" = "#DE3533", "LNP" = "#ADD8E6", "KAP" = "#8B0000",
  "GVIC" = "#10C25B", "XEN" = "#ff6300", "LP" = "#0047AB",
  "NP" = "#0a9cca", "IND" = "#000000", "GRN" = "#006400"
)
# update this from lecture to include Greens

aus_winners_chloropleth_map <- ggplot(all_winners) +
  geom_sf(aes(fill = PartyAb, geometry = st_zm(geometry))) +
  scale_fill_manual(name = "Party", values = auscolours) +
  theme_void() +
  theme(legend.position = "bottom")

ggsave(aus_winners_chloropleth_map, 
       file = "images/aus_winners_chloropleth_map.png")

# aus_winners_chloropleth_map
```
]

---

# .monash-blue[Choropleth Map]

Which party won from looking at this map and by how much?

<div class="border-box bg-white" style="position:absolute;top:20%;right:10px;width:320px;padding:5px;font-size:18pt;">
<span class="blue">Liberal/National Coalition: 
<b>
56
</b></span><br>
<span class="red">Labor: 
<b>75
</b></span><br>
<span class="green">Greens: 
<b>4
</b></span><br>
Katter's Australian: <b>1</b><br>
Centre Alliance: <b>1</b><br>
Independents: <b>10</b>
</div>

---

# Mapping the centroids

```r
all_winners_centroid <- all_winners %>%
  # some issues with a duplicate edge
  # slice(-79,-80) |> # for original code
  slice(-93, -94) |>  # for update post lecture
  mutate(centroid = st_centroid(geometry))

aus_winners_centroid_map <- ggplot(all_winners_centroid) +
  geom_sf(aes(geometry = st_zm(geometry))) +
  geom_sf(aes(geometry = centroid, color = PartyAb)) +
  theme_void() +
  theme(legend.position = "bottom")

ggsave(aus_winners_centroid_map, 
       file = "images/aus_winners_centroid_map.png")

# aus_winners_centroid_map
```
]

---

# .monash-blue[Non-Contiguous, Dorling Cartogram]

---

# Improving the national map of election winners

.f4.oveflow.overflow-scroll.h-90[

```r
library(sugarbag)

centroids <- all_winners_centroid %>%
  bind_cols(sf::st_coordinates(all_winners_centroid$centroid)) %>%
  select(DivisionNm, longitude = X, latitude = Y)

grid <- create_grid(centroids = centroids, hex_size = 0.9, buffer_dist = 5)

hex_allocated <- allocate(
  centroids = centroids,
  sf_id = "DivisionNm",
  hex_grid = grid,
  hex_size = 0.9, # same size used in create_grid
  hex_filter = 10,
  focal_points = capital_cities,
  width = 30, verbose = TRUE
)

hex_map <- hex_allocated %>%
  fortify_hexagon(hex_size = 0.9, sf_id = "DivisionNm") |>
  left_join(all_winners_centroid, by = "DivisionNm")

aus_winners_hex_map <- ggplot(hex_map) +
  geom_sf(data = all_winners, aes(geometry = geometry)) +
  geom_polygon(aes(long, lat, fill = PartyAb, group = DivisionNm), color = "black") +
  scale_fill_manual(name = "Party", values = auscolours) +
  theme_void() +
  theme(legend.position = "bottom")

ggsave(aus_winners_hex_map, 
       file = "images/aus_winners_hex_map.png")

# aus_winners_hex_map
```
]

---

# .monash-blue[Tessellated Hexagon Map]

.bottom_abs.font_small.width100[
Kobakian, Stephanie (2020) New Algorithms For Effectively Visualising Australian Spatio-Temporal Disease Data. <br>Supervised by Dianne Cook. *Master of Philosophy (Statistics), Queensland University of Technology*.
]

---

# Coordinate reference system (CRS)

---

# Geographic coordinate reference systems

* **Geographic CRSs** identify a location on the Earth's surface by *longitude* and *latitude*.
* **Longitude** is the East-West direction in angular distance from the Prime Meridian plane.
* **Latitude** is the angular distance North or South of the equatorial plane.

---

# Projected coordinate reference systems

.flex[
.w-50[
* All projected CRSs are based on a geographic CRS.
* Map projections convert the three-dimensional surface of the Earth into Easting and Northing (x and y) values (typically meters) in a projected CRS. 
* These projected CRSs are based on Cartesian coordinates on a implicitly flat surface.
* Some deformations are introduced in the process, e.g. area, direction, distance or shape, while preserving one or two of these properties.

]
.w-50[

]
]

.footnote.f4[
[Lovelace, Robin, Jakub Nowosad and Jannes Muenchow (2019). Geocomputation with R. The R Series. CRC Press.](https://geocompr.robinlovelace.net/spatial-class.html#crs-intro)
]

---

# Well Known Text (WKT)

* Open Geospatial Consortium (OGC) developed an open standard format for describing CRSs called [**WKT**](https://portal.opengeospatial.org/files/18-010r7)

```r
st_crs(ausmap)
```

```
## 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]]
```
]

```r
ggplot(ausmap) +
  geom_sf(aes(fill = Area_SqKm))
```

<img src="images/lecture-05/ausmap-1.png" width="432" style="display: block; margin: auto;" />
]

---

# Changing map projections

* Map projections may be modified in multiple methods (it's beyond this unit to delve deep into this).
* Below uses the Lambert azimuthal equal-area projection centered on the longitude and latitude of (rough) Melbourne coordinates via [`proj4string`](https://proj.org/index.html):

```r
ausmap %>%
  st_transform(crs = "+proj=laea +x_0=0 +y_0=0 +lon_0=145 +lat_0=-38") %>%
  ggplot() +
  geom_sf(aes(fill = Area_SqKm))
```

---

.idea-box.tl.w-70[

## Summary

* We had a look a the 2022 federal election data
* We looked at visualising this data spatially in various ways 
* We learnt more about mapping in R
* We looked at reprojecting geographic data into different coordinate reference systems

]

---

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

---

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

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Lecturer: *Kate Saunders*

Department of Econometrics and Business Statistics

<i class="fas fa-envelope"></i>  ETC5512.Clayton-x@monash.edu

<i class="fas fa-calendar-alt"></i> Week 5

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