Month: November 2020

The data visualization with which I begin my 30-day challenge is a standard electoral map of the recently-completed British Columbia provincial election, the result of which is a solid (57 of 87 seats) majority government for the New Democratic Party, led by Premier John Horgan.

It’s a bit ironic that I begin with this type of map since, for a few reasons, I consider them to be poor representations of data. First, because electoral districts are mapped on the basis of territory (geography) they misrepresent and distort what they are purportedly meant to gauge–electoral support (by actual voters, not acreage) for political parties.

Though there are other pitfalls with basic electoral maps I’ll highlight what I believe to be the second major issue with them. They take what is a multinomial concept–voter support for each of a number of political parties in a specific electoral district–and summarize them into a single data point–which of the many parties in that electoral district has “won” that district. Most of these maps provide no information about either a) the relative size of the winning party’s victory in that district, or b) how many other parties competed in that district and how well each of these parties did in that district.

Although the standard electoral map provides some basic electoral information about the electoral outcome (and it is undeniable that in terms of determining who wins and runs government, it is the single most important piece of information), they are “information-poor” and in future posts I’ll show how researchers have tried to make their electoral maps more information-rich.

But, first, here are some standard electoral maps for the last two provincial elections in British Columbia (BC)–May 2017 and October 2020. Like many jurisdictions in North America, BC is comprised of relatively densely-populated urban areas–the Lower Mainland and southern Vancouver Island–combined with sparsely-populated hinterlands–forests, mountains, and deserts. Moreover, there is a strong partisan split between these areas–with the conservative BC Liberal Party (BCLP–the story of why the provincial Liberal Party in BC is actually the home of BC’s conservatives is too long for this post) dominating in the hinterlands while the left-centre New Democratic Party (NDP) generally runs more strongly in the urban southeast of the province. In Canada, electoral districts are often referred to as “ridings”, or “constituencies.”

If one were completely ignorant about BC’s provincial politics one would assume, simply from a quick perusal of the map above, that the “blue” party–the BC Liberal Party–was the dominant party in BC. In addition, it would seem that there was very little change in partisan support and electoral outcomes across the electoral districts over the course of the two elections. In fact, the BCLP lost 15 districts, all of which were won by the NDP. (The Green Party lost one of the districts it had won to the NDP as well, for a total NDP gain of 16 districts (seats on the provincial legislature) between 2017 and 2020. This factual story of a substantial increase in NDP seats in the legislature is poorly conveyed by the maps above because the maps match partisanship to area and not to voters.

To repeat, in future posts I will demonstrate some methods researchers have used to mitigate the problem of area-based electoral maps, but for now I’ll show that once we zoom into the southwest corner of the province (where most of the population resides) a simple electoral map does do a better job of conveying the change in electoral fortunes of the BCLP and NDP over the last two elections This is because there is a stronger link between area and population (voters) in these districts than in BC as a whole.

You can more easily see the orange NDP wave overtaking the population centres of the Lower Mainland (greater Vancouver area–upper left part of each map) and, to a lesser extent, southern Vancouver Island. Data visualization #2 will demonstrate how to create animated maps of the above, which more appropriately convey the nature of the change in each of the electoral districts over the two elections.

Here’s the R code that I used to create the two images in my post, using the ggplot2 package.

## Once you have created a sf_object in R (which I have named bc_final_sf, the following commands will create the image above.
 
library(ggplot2)
library(patchwork)

## First plot--2017
gg.ed.1 <- ggplot(bc_final_sf) +
  geom_sf(aes(fill = Winner_2017), col="black", lwd=0.025) + 
  scale_fill_manual(values=c("#295AB1","#26B44F","#ED8200")) +
  labs(title = "May 2017") +
  theme_void() + 
  theme(legend.title=element_blank(),
        plot.title = element_text(hjust = 0.5, size=12, face="bold"),
        legend.position = "none")

## Second plot--2020
gg.ed.2 <- ggplot(bc_final_final) +
  geom_sf(aes(fill = Winner_2020), col="black", lwd=0.025) + 
  scale_fill_manual(values=c("#295AB1","#26B44F","#ED8200")) +
  labs(title = "October 2020") +
  theme_void() +
  theme(legend.title=element_blank(),
        plot.title = element_text(hjust = 0.5, size=12, face="bold"),
        legend.position = "bottom")

## Combine the plots and do some annotation
gg.bc.comb.map <- gg.ed.1 + gg.ed.2 & theme(legend.position = "bottom") 
gg.bc.comb.map.final <- gg.bc.comb.map + plot_layout(guides = "collect") + 
  plot_annotation(
  title = "British Columbia Election Results \u2013 by Riding",
  theme = theme(plot.title = element_text(size = 16, hjust=0.5, face="bold"))
  )

gg.bc.comb.map.final    # to view the first image above

## For the maps of the Lower Mainland and southern Vancouver Island, the only difference is that we add the following line to each of the individual maps:

coord_sf(xlim = c(1140000,1300000), ylim = c(350000, 500000))  

## so, we get 

gg.ed.lmsvi.1 <- ggplot(bc_final_final) +
  geom_sf(aes(fill = Winner_2017), col="black", lwd=0.075) + 
  coord_sf(xlim = c(1140000,1300000), ylim = c(350000, 500000)) + 
  scale_fill_manual(values=c("#295AB1","#26B44F","#ED8200")) +
  labs(title = "May 2017") +
  theme_void() + 
  theme(legend.title=element_blank(),
        plot.title = element_text(hjust = 0.5, size=10, vjust=3),  
        legend.position = "none")