city council – Page 3 – sixty-six wards

November 9, 2019November 9, 2019

A Surprising 2019 Council At Large Recap

We’re all still recovering from Tuesday night. Surprise wins for both Kendra Brooks and David Oh turned the Philadelphia political world on its head. I won’t waste your time narrating what others will write better. Let’s look at some numbers, with 99% of Divisions reporting.

Helen Gym paced the way with 15.3% of the vote. None of the Democrats lost. But most importantly, we’ll consider the two surprises: Kendra Brooks and David Oh.

View code

library(sf)
library(tidyverse)

load_data <- function(file){
  df <- read_download(file)
  df %<>% 
    filter_divs_and_offices() %>%
    format_columns()
  
  return(df)
}

read_download <- function(file){
  colnames <- read_csv(file, n_max = 1, col_names=F) %>% unlist()
  candidates <- read_csv(file, n_max = 1, skip=1, col_names=F) %>% unlist()
  
  df <- read_csv(file, skip=2, col_names=F) %>%
    rename(warddiv=X3) %>%
    select(-X1, -X2) %>%
    pivot_longer(values_to="votes", names_to="key", -warddiv) %>%
    filter(!is.na(votes)) %>% ##only removes X119
    mutate(key = asnum(gsub("^X", "", key))) %>%
    mutate(office = colnames[key], candidate=candidates[key]) %>%
    mutate(
      party = gsub("(.*) \\((.*)\\)$", "\\2", candidate),
      candidate = gsub("(.*) \\(.*\\)$", "\\1", candidate),
      office = ifelse(office == "COUNCIL AT-LARGE", "COUNCIL AT LARGE", office),
      office = ifelse(candidate %in% c('JUDY MOORE', "BRIAN O'NEILL"), "DISTRICT COUNCIL-10TH DISTRICT", office)
    ) %>% 
    select(warddiv, office, candidate, party, votes) %>%
    filter(warddiv != "COUNTY TOTALS")

  return(df)
}

filter_divs_and_offices <- function(df){
   returns <- df %>% 
     group_by(warddiv) %>%
     summarise(sum_votes = sum(votes)) %>%
     group_by() %>%
     filter(sum_votes > 0)
   
   df <- df %>% inner_join(returns %>% dplyr::select(warddiv))
   
   if(nrow(df) == 0) stop("No data yet!")
   
   df <- df %>% 
     filter(office %in% CONFIG$offices) %>%
     filter(!candidate %in% c("Write-In", "Write In"))
   
  return(df)     
} 

format_columns <- function(df) {
  df <- df %>% 
    mutate(candidate = format_candidate_name(candidate)) %>%
    group_by(warddiv, office) %>% 
    mutate(pvote = votes / sum(votes)) %>%
    group_by()
  return(df)
}

format_candidate_name <- function(x){
  x <- gsub("^[0-9]+\\-", "", x)
  x <- gsub("(.*)\\,.*", "\\1", x)
  x <- format_name(x)
  return(x)
}

CONFIG <- list(offices = c("MAYOR", "COUNCIL AT LARGE"))
ELECTION <- "20191105"
USE_LOG <- TRUE

df <- load_data(
  "../election_night_needle/raw_data/results_20191107_194343.csv"
)

divs <- st_read("../../data/gis/201911/Political_Divisions.shp") %>%
  mutate(warddiv=pretty_div(DIVISION_N))wards <- st_read("../../data/gis/2019/Political_Wards.shp") %>%
  mutate(ward=sprintf("%02d", asnum(WARD_NUM)))div_cats <- readRDS(
  most_recent_file("../../data/processed_data/div_cats_")
)

at_large_table <- df %>%
  filter(office == "COUNCIL AT LARGE") %>%
  group_by(candidate) %>%
  summarise(votes=sum(votes)) %>%
  mutate(
    pvote=100*votes/sum(votes)
  ) %>%
  arrange(desc(pvote))

knitr::kable(
  at_large_table, 
  digits=1, 
  col.names=c("Candidate", "Votes", "% Vote"), 
  format.args=list(big.mark=',')
)

Candidate	Votes	% Vote
Helen Gym	187,161	15.3
Isaiah Thomas	178,571	14.6
Katherine Gilmore Richardson	172,435	14.1
Derek S Green	172,264	14.1
Allan Domb	169,395	13.9
Kendra Brooks	55,599	4.6
David Oh	49,700	4.1
Al Taubenberger	44,084	3.6
Nicolas O’Rourke	43,012	3.5
Dan Tinney	42,962	3.5
Bill Heeney	40,187	3.3
Matt Wolfe	38,341	3.1
Sherrie Cohen	8,354	0.7
Joe Cox	8,229	0.7
Maj Toure	5,676	0.5
Steve Cherniavsky	3,225	0.3
Clarc King	2,731	0.2

First, the turnout. With 99% of divisions reporting, we’re at an unprecedented count of 295,955. (You all helped me predict that. We’ll talk about that in a future post. ::sunglassesemoji::)

The post-2016 surge in turnout continued, with South Philly, Fairmount, and University City pacing the city.

View code

TURNOUT_OFFICE <- "COUNCIL AT LARGE"

turnout_19 <- df %>% 
  filter(office==TURNOUT_OFFICE) %>%
  group_by(warddiv) %>%
  summarise(turnout=sum(votes))

ward_turnout_19 <- turnout_19 %>%
  mutate(ward=substr(warddiv,1,2)) %>%
  group_by(ward) %>%
  summarise(turnout=sum(turnout))

ggplot(
  divs %>% 
    left_join(turnout_19) %>%
    mutate(turnout_per_sf=turnout/as.numeric(st_area(geometry)))
) + 
  geom_sf(aes(fill=1609^2 * pmin(turnout_per_sf, 0.025)), color=NA) +
  scale_fill_viridis_c("Votes per mile", labels=scales::comma) +
  theme_map_sixtysix() +
  ggtitle("Votes Cast per Mile", "Council At Large")

Every single ward voted more than in 2015. And this race had an incumbent mayor.

View code

valid_divs <- unique(df$warddiv)

df_past <- readRDS("../../data/processed_data/df_major_2019-11-07.Rds") %>%
  mutate(warddiv=pretty_div(warddiv)) %>%
  filter(warddiv %in% valid_divs)

comp_year <- 2015

turnout_comp <- df_past %>% 
  filter(
    year==comp_year,
    election=="general",
    office==TURNOUT_OFFICE
  ) %>%
  group_by(warddiv) %>%
  summarise(turnout=sum(votes))

ward_turnout_comp <- turnout_comp %>%
  mutate(ward=substr(warddiv,1,2)) %>%
  group_by(ward) %>%
  summarise(turnout=sum(turnout))

ggplot(
  wards %>% 
    left_join(ward_turnout_comp) %>%
    left_join(ward_turnout_19, by="ward", suffix=c(".comp", ".19")) %>%
    mutate(turnout_ratio = turnout.19 / turnout.comp)
) + 
  geom_sf(aes(fill=turnout_ratio), color=NA) +
  scale_fill_viridis_c("Votes cast,\n 2019 / 2015") +
  theme_map_sixtysix() +
  ggtitle("Change in votes cast, 2015 - 2019", "Council At Large")

But the increase wasn’t evenly spread, and that helped Brooks. The biggest changes came from the ring around Center City, including Fishtown’s wards 18 and 31 (+91% and 84% more votes, respectively), and South Philly’s 1 and 48 (+66% and 65%). They’re not terrible bright on the map, though, because University City’s 27 (+109%) breaks the color scale.

Two candidates who won, and two who didn’t

Brooks and Oh won by different paths. Brooks dominated the Wealthy Progressive divisions and did well enough in Black divisions to cruise to victory. Oh did predictable well in the Northeast, and inched ahead of his competition by eking out votes in Democratic strongholds.

To illustrate just how narrow each of their paths to victory were, let’s consider the electoral doppleganger of each: Nicolas O’Rourke and Al Taubenberger.

View code

use_candidates <- c(
  "Kendra Brooks", "Nicolas O'Rourke", "David Oh", "Al Taubenberger"
)

at_large_df <- df %>% 
  filter(
    office=="COUNCIL AT LARGE",
    candidate %in% use_candidates
  ) %>% mutate(candidate=factor(candidate, levels=use_candidates))

total_votes <- df %>%
  filter(office == "COUNCIL AT LARGE") %>%
  group_by(warddiv) %>%
  summarise(total_votes=sum(votes))

ggplot(
  divs %>% 
    # add NA divs
    left_join(
      expand.grid(warddiv=divs$warddiv, candidate=use_candidates)
    ) %>%
    left_join(at_large_df)
) + 
  geom_sf(aes(fill=100*pmin(pvote, 0.2)), color=NA) +
  scale_fill_viridis_c("% of Vote") +
  theme_map_sixtysix() %+replace%
  theme(legend.position = "right") +
  facet_wrap(~candidate, ncol=2) +
  ggtitle("Two won and two didn't", "Council At Large")

It’s hard to tell the difference between the winners and losers from these maps. Kendra and Nicolas both dominated the Wealthy Progressive divisions of University City, South Philly, and Chestnut Hill; David and Al the Republican leaning Northeast, upper River Wards, and deep South Philly. You wouldn’t necessarily guess that Kendra would win but Nicolas wouldn’t, or that David would win without Al doing so, too.

But first, why was the race such a romp for Brooks? Notice the overlap between her map and the turnout map above. She enormously excited her base: the divisions where she won were also the ones that turned out at unprecedented rates.

View code

ggplot(
  df %>% 
    filter(
      office=="COUNCIL AT LARGE",
      candidate=="Kendra Brooks"
    ) %>%
    mutate(ward=substr(warddiv,1,2)) %>%
    group_by(ward) %>% 
    summarise(
      pvote=sum(votes, na.rm=TRUE) / sum(votes/pvote, na.rm=TRUE),
      total_votes=sum(votes/pvote, na.rm=TRUE)
    ) %>%
    left_join(ward_turnout_19) %>%
    left_join(ward_turnout_comp, by="ward", suffix=c(".19", ".comp")) %>%
    mutate(turnout_ratio = turnout.19/turnout.comp),
  aes(y=turnout_ratio, x=100*pvote)
) + 
  geom_text(aes(label=ward, size=total_votes)) +
  scale_size_area("Total Council At Large Votes in 2019", label=scales::comma) +
  expand_limits(x=0) +
  theme_sixtysix() +
  labs(
    title="Kendra Brooks' base surged in turnout",
    x="% of Vote for Brooks",
    y=sprintf("Votes cast for Council At Large, 2019 / %s", comp_year)
  )

But that would seem to help O’Rourke too. Why did Brooks win and O’Rourke lose? He ran a steady 2 points behind her across their supportive divisions.

View code

turnout_df <- df_past %>%
  filter(is_primary_office) %>%
  group_by(year, warddiv, election) %>%
  summarise(turnout=sum(votes)) %>%
  bind_rows(
    df %>% 
      filter(office=='MAYOR') %>% 
      group_by(warddiv) %>% 
      summarise(turnout=sum(votes)) %>% 
      mutate(year = '2019', election="general")
  )

ggplot(
  at_large_df %>%
    filter(candidate %in% c("Kendra Brooks", "Nicolas O'Rourke")) %>%
    select(warddiv, candidate, pvote) %>%
    spread(key=candidate, value=pvote) %>%
    left_join(turnout_df %>% filter(year == 2019, election == "general")),
  aes(x=100*`Kendra Brooks`, y=100*`Nicolas O'Rourke`)
) +
  geom_point(
    aes(size = turnout),
    color=strong_purple,
    alpha=0.1
    # pch=16
  ) +
  scale_size_area("Division Turnout") +
  geom_abline(slope=1) +
  coord_fixed() +
  theme_sixtysix() +
  labs(
    title="O'Rourke ran two points behind\n Brooks in supportive divisions",
    x="Percent for Brooks",
    y="Percent for O'Rourke"
  )

How about on the Republican side? Why did Oh win and Taubenberger lose? Taubenberger nudged out Oh in the Republican bases, but Oh outpaced him in the rest of the city. And the rest of the city has more votes.

View code

winsorize <- function(x, t=0.99){
  cutoff <- quantile(abs(x), probs=t, na.rm=TRUE)
  return(sign(x) * pmin(abs(x), cutoff))
}

ggplot(
  wards %>% left_join(
    at_large_df %>%
      filter(candidate %in% c("David Oh", "Al Taubenberger")) %>%
      mutate(ward = substr(warddiv,1,2)) %>%
      group_by(ward, candidate) %>%
      summarise(votes=sum(votes)) %>%
      left_join(ward_turnout_19) %>%
      mutate(pvote=votes/turnout) %>%
      select(ward, candidate, pvote) %>%
      spread(key=candidate, value=pvote)
  )
) +
  geom_sf(
    aes(fill=100*winsorize(`David Oh`-`Al Taubenberger`)),
    color=NA
  ) +
  scale_fill_gradient2(
    "Oh's % -\n Taubenberger's %",
    low=strong_purple,
    high=strong_green
  ) +
  theme_map_sixtysix() +
  labs(
    title="Taubenberger won the Northeast,\n Oh won everywhere else"
  )

Philadelphia’s Voting Blocs

My new favorite way to cleanly describe a race is with Philadelphia’s Voting Blocs. I use historic correlations to identify sets of divisions that vote for the same candidates. The patterns are striking, and tell a clear story: Philadelphia’s voting is a story of race and class.

I’ve updated my data and tweaked the methodology since the first post, so here’s a new map of the voting blocs.

View code

cat_colors <- c(light_blue, light_red, light_orange, light_green)
names(cat_colors) <- levels(div_cats$cat)             

ggplot(
  divs %>% 
    left_join(div_cats) 
) + 
  geom_sf(aes(fill=cat), color=NA) +
  scale_fill_manual(NULL, values=cat_colors) +
  theme_map_sixtysix() +
  ggtitle("Philadelphia's Neighborhood Voting Blocs")

The Trump era has changed the city’s dynamics. After hovering below 23% of General Elections’ turnout up to November 2016, Wealthy Progressives had 29% of the turnout for Krasner’s 2017 General, 24% in 2018 and 25% on Tuesday.

View code

turnout_divcat <- turnout_df %>%
  left_join(div_cats) %>%
  group_by(cat, year, election) %>%
  summarise(turnout=sum(turnout)) %>%
  group_by(year, election) %>%
  mutate(pct_of_turnout=turnout/sum(turnout))

ggplot(
  turnout_divcat, 
  aes(x=year, y=100*pct_of_turnout, color=cat, alpha=election)
) +
  geom_line(
    aes(
      group=interaction(election, cat), 
      linetype=election
    ), 
    size=1
  ) +
  geom_point(pch=16, size=2) +
  scale_color_manual(NULL, values=cat_colors, guide=FALSE) +
  scale_alpha_manual(NULL, values=c(general=1, primary=0.3), guide=FALSE) +
  annotate(
    geom="text",
    x=12.1,
    y=c(42, 17, 33, 8),
    hjust=0,
    label=names(cat_colors),
    color=cat_colors,
    fontface="bold"
  ) +
  scale_y_continuous(breaks=seq(0,100,10))+
  labs(
    x=NULL,
    y="Percent of Turnout",
    title="Voting blocs' share of the vote",
    linetype=NULL
  ) +
  theme_sixtysix() %+replace%
  theme(axis.text.x = element_text(angle=90), panel.grid.major.x = element_blank())

Brooks would have won even if the turnout shares had looked like pre-2016, though. She received 8.6% of the vote in the Wealthy Progressive Divisions, and 3.2% in the rest of the city. The difference between Wealthy Progressives comprising 25% of the vote versus 20% gave her an extra 0.3 percentage points. That difference was the difference between a nail-biter and the romp that we saw, but didn’t itself cause the win.

Instead, for both Oh and Brooks, it was the performance outside of their bases that carried them to victory. Performance by bloc makes it clear why Brooks and Oh won, and their party-mates came up just short.

View code

bar_df <- at_large_df %>%
  left_join(total_votes) %>%
  left_join(div_cats) %>%
  group_by(candidate, cat) %>%
  summarise(
    total_votes = sum(total_votes),
    votes = sum(votes)
  ) %>%
  mutate(pvote = votes/total_votes)

overall <- bar_df %>% group_by(candidate) %>%
  summarise(pvote = weighted.mean(pvote, w=total_votes))

ggplot(
  bar_df,
  aes(x=cat, y=100*pvote)
) + 
  geom_bar(aes(fill=cat), stat = "identity") +
  geom_text(aes(label=sprintf("%0.1f", 100*pvote)), color="white", y=0, vjust=-0.4) +
  geom_text(
    data = overall,
    aes(label=sprintf("%0.1f%%\noverall", 100*pvote)),
    hjust=0.5,
    lineheight=0.8,
    x=4, y=7.5,
    size=6, fontface="bold", color="grey50"
  ) +
  scale_fill_manual(NULL, values=cat_colors) +
  facet_wrap(~candidate) +
  theme_sixtysix() %+replace%
  theme(axis.text.x = element_blank()) +
  labs(
    x=NULL,
    y="% of Vote",
    title="Performance by Neighborhood Bloc"
  )

Brooks won because she ran ahead of O’Rourke everywhere, including by a whopping two points in the supportive Wealthy Progressive divisions, and by one point in the voting-dominant Black wards.

Oh won because of his support outside of his base: he tortoised his way slowly but surely through the rest of the city, collecting votes in the Wealthy Progressive divisions (0.9 percentage points more than Taubenberger), Black Voter divisions (0.7 more), and Hispanic North Philly (0.5 more). My hunch is that’s a function of his greater name recognition and activity.

Why was I wrong?

When I looked at this race in August, I thought third parties were a long shot.

(In my defense, Helen Gym endorsed Brooks literally the day after I hit publish. I would have written the piece very differently had she pulled that unprecedented move 24 hours earlier.)

I wrote that Brooks would need 10.6% of the vote from Wealthy Progressives, and even then it would be toss up. Instead, she got 8.6% and cruised to victory.

What happened? The Black Voter divisions voted for her, and did so hard. Those neighborhoods hadn’t voted for non-Democrats before, and I proposed maybe she could get 1.7% in my high projection. Those wards are strongly organized by the Democratic Party, and hadn’t strayed from their Party loyalty before. But they voted for Brooks at 4%, more than double what I thought plausible. That’s the story of this election, and the reason the impossible happened.

How did the Working Families Party pull that off? I’ll let the real reporters work that out, but probably through some combination of running candidates that Black voters found compelling and a massive outreach program teaching voters about the intricacies of Philadelphia’s Charter and encouraging strategic voters.

So I’m gonna claim I wasn’t that wrong. 🙂

September 8, 2019September 11, 2019

Could Brian O’Neill lose District 10?

Brian O’Neill has held the City Council seat for the Northeast’s District 10 since 1980. He’s the lone Republican in a Council District seat, in addition to the two Republicans who hold the reserved At Large seats.

After running unopposed in 2015, O’Neill is being challenged this November by Democratic candidate Judy Moore.

Could he lose?

In the the surprising Primary in West Philly’s District 3, the cohorts that would decide the race were clear. In District 10, it’s harder to say. Is O’Neill a beloved incumbent whose ability to cross party lines will ensure his victory? Or have all elections become nationalized, dooming even a city-level official in Philadelphia’s most Republican (but still Democratic) neighborhoods?

District 10

District 10 covers the farthest sections of the Northeast, stretching down to Fox Chase and Rhawnhurt.

View code

library(tidyverse)
library(sf)
library(ggmap)
source("../../admin_scripts/util.R")

sf_council <- st_read("../../data/gis/city_council/Council_Districts_2016.shp")sf_divs <- st_read("../../data/gis/2019/Political_Divisions.shp")df <- readRDS("../../data/processed_data/df_all_2019_09_05.Rds")
df <- df %>%
  group_by(year, election, office, district, candidate, party, warddiv19) %>%
  summarise(votes=sum(votes)) %>%
  ungroup() %>%
  mutate(party = ifelse(party == "DEMOCRAT", "DEMOCRATIC", party))

bb <- st_bbox(sf_council %>% filter(DISTRICT == "10"))

expand_bb <- function(x, factor){
  mean_x <- mean(x)
  return(mean_x + factor * (x - mean_x))
}

bb[c(1,3)] <- expand_bb(bb[c(1,3)], 1.2)
bb[c(2,4)] <- expand_bb(bb[c(2,4)], 1.2)

names(bb) <- c("left", "bottom", "right", "top")
basemap <- get_map(location=bb, maptype="toner-lite")


get_labpt_df <- function(sf){
  df <- st_centroid(sf) %>%
    mutate(
      x=sapply(geometry, "[", 1),
      y=sapply(geometry, "[", 2)
    ) %>%
    as.data.frame() %>%
    select(-geometry)
  return(df)
}

phila_whole <- st_union(sf_council)

ggplot(sf_council) +
  geom_sf(data=phila_whole, fill="white", color=NA) +
  geom_sf(
    fill=strong_green, color = "white", size = 1, alpha=0.5
  ) +
  geom_sf(
    data=sf_council %>% filter(DISTRICT == "10"),
    fill=strong_green, color = "white", size = 1
  ) +
  geom_text(
    data = get_labpt_df(sf_council),
    aes(x=x,y=y,label=DISTRICT)
  ) +
  ggtitle("Philadelphia Council Districts") +
  theme_map_sixtysix()

View code

district_map <- ggmap(
  basemap, 
  extent="device",
  base_layer = ggplot(sf_council),
  maprange = FALSE
) +
  theme_map_sixtysix()

sf_divs$council_district <- sf_divs %>%
  st_centroid() %>%
  st_covered_by(sf_council) %>%
  (function(x){sf_council$DISTRICT[unlist(x)]})

district_map +
  geom_sf(
    aes(alpha = (DISTRICT == "10")),
    fill="black",
    color = "grey50",
    size=2
  ) +
  scale_alpha_manual(values = c(`TRUE` = 0.2, `FALSE` = 0), guide = FALSE) +
  ggtitle(sprintf("Council District %s", 10))

The district is consistently Philadelphia’s most Republican. It voted the most strongly for Trump (though Clinton still won overall), and is home to PA State Legislative District 170, represented by Republican Martina White.

(You can find all of these election maps, in interactive form, at the Ward Portal!)

View code

races <- tribble(
  ~election, ~office, ~district, ~short_office, 
  "general", "PRESIDENT OF THE UNITED STATES", NA, "President",
  "general", "GOVERNOR", NA, "Governor",
  "general", "REPRESENTATIVE IN CONGRESS", "2", "US Congress District 2",
  "general", "MAYOR", NA, "Mayor"
  # "2018", "general", "SENATOR IN THE GENERAL ASSEMBLY-2ND DISTRICT", "PA Senate"
)

candidate_votes <- df %>% 
  inner_join(races) %>%
  group_by(warddiv19, short_office, year, election) %>%
  mutate(
    total_votes = sum(votes),
    pvote = votes / sum(votes)
  ) %>% 
  group_by()

candidates_overall <- candidate_votes %>%
  group_by(short_office, year, election, candidate, party) %>%
  summarise(votes = sum(votes)) %>%
  group_by(short_office, year, election) %>%
  mutate(
    pvote = votes/sum(votes),
    use = pvote > 0.05
  ) %>%
  filter(use)

candidate_votes <- candidate_votes %>% 
  inner_join(candidates_overall %>% select(year, election, short_office, candidate))

div_winners <- candidate_votes %>%
  group_by(year, election, short_office, warddiv19) %>%
  summarise(
    winner = candidate[which.max(votes)],
    winner_party = party[which.max(votes)],
    winner_pvote = pvote[which.max(votes)],
    twoparty_pvote = pvote[which.max(votes)] / 
      sum(pvote[party %in% c("DEMOCRATIC", "REPUBLICAN")], na.rm=TRUE),
    total_votes = total_votes[1]
  )
  
sf_divs <- sf_divs %>% rename(warddiv19 = DIVISION_N)

sf_divs$in_bbox <- sapply(
  sf_divs$geometry,
  function(g) {
    coords <- as.matrix(g)
    any(
      coords[,1] > bb["left"] &
      coords[,1] < bb["right"] &
      coords[,2] > bb["bottom"] &
      coords[,2] < bb["top"] 
    )
  }
)

ggraces <- sf_divs %>% 
      filter(in_bbox) %>%
      left_join(div_winners) %>% 
      mutate(
        winner_party = format_name(substr(winner_party,1,3)),
        race=sprintf("%s (%s)", short_office, year)
      ) %>%
      mutate(race=factor(race, levels=unique(race[order(year)])))

district_map + 
  geom_sf(data=phila_whole, fill="white", color=NA) +
  geom_sf(
    data=ggraces %>% filter(year %in% c(2015, 2016, 2018)),
    aes(fill = winner_party, alpha=100 * 2 * (twoparty_pvote - 0.5)),
    color=NA
  ) +
  geom_sf(
    data=sf_council %>% filter(DISTRICT == "10"),
    fill=NA,
    color="white",
    size=1
  )+
  facet_wrap(~race) +
  scale_fill_manual(NULL, values=c("Dem"=strong_blue, "Rep"=strong_red)) +
  theme_map_sixtysix() %+replace% 
  theme(legend.position = "bottom", legend.direction = "horizontal") +
  scale_alpha_continuous("Win Margin (%)") +
  guides(alpha = guide_legend(override.aes = list(fill = strong_blue)))+
  ggtitle("District 10's recent elections")

View code

sth_re <- "REPRESENTATIVE IN THE GENERAL ASSEMBLY"
sth_candidates <- df %>% 
  filter(
    year == "2018", election=="general", 
    office == sth_re
  ) %>%
  mutate(
    party=format_name(substr(party,1,3))
  ) %>%
  group_by(warddiv19, district, year, election) %>%
  mutate(
    total_votes = sum(votes),
    pvote = votes / sum(votes)
  ) %>% 
  group_by()

sth_div_winners <- sth_candidates %>%
  filter(party %in% c("Dem", "Rep")) %>%
  group_by(year, election, district, warddiv19) %>%
  summarise(
    winner = candidate[which.max(votes)],
    winner_party = party[which.max(votes)],
    winner_pvote = pvote[which.max(votes)],
    twoparty_pvote = pvote[which.max(votes)] / sum(pvote)
  )

sf_sth <- st_read("../../data/gis/state_house/PaHouse2017_01.shp")sth_labpts <- sf_divs %>%
  filter(in_bbox) %>%
  left_join(sth_div_winners) %>%
  group_by(district) %>% 
  dplyr::do(data.frame(geometry = st_union(.$geometry))) %>%
  ungroup() %>%
  mutate(geometry = st_sfc(geometry)) %>%
  st_as_sf() %>%
  get_labpt_df()

sth_labpts[sth_labpts$district=="170", c('x','y')] <- c(-75.02, 40.111)
sth_labpts[sth_labpts$district=="172", c('x','y')] <- c(-75.07, 40.079)
sth_labpts[sth_labpts$district=="173", c('x','y')] <- c(-75.002, 40.096)

district_map + 
  geom_sf(data=phila_whole, fill="white", color=NA) +
  geom_sf(
    data=sf_divs %>% 
      filter(in_bbox) %>%
      left_join(sth_div_winners),
    aes(fill = winner_party, alpha=100 * 2 * (twoparty_pvote - 0.5)),
    color=NA
  ) +
  geom_sf(
    data=sf_sth, fill=NA, color="black"
  ) +
  geom_sf(
    data=sf_council %>% filter(DISTRICT == "10"),
    fill=NA,
    color=rgb(1,1,1,0.8),
    size=2
  )+
  geom_text(
    data=sth_labpts,
    aes(x=x,y=y, label=district)
  )+
  scale_fill_manual(NULL, values=c("Dem"=strong_blue, "Rep"=strong_red)) +
  theme_map_sixtysix() %+replace% 
  theme(legend.position = "bottom", legend.direction = "horizontal") +
  scale_alpha_continuous("Win Margin (%)") +
  guides(alpha = guide_legend(override.aes = list(fill = strong_blue)))+
  ggtitle("District 10's 2018 PA House elections")

Many of the State House races were won by unopposed Democrats, with the two exceptions being Martina White’s 170, and the nub of Montgomery County’s 152. This makes it hard to project these races onto the District 10 race: how would those 100% districts have voted if there were a contest? There’s a big difference between if they would have gone 70% Democratic or 90%.

Recent races have varied in how divisive and how nationalized they were. In 2015, Kenney won broadly. The correlation between his performance and Clinton’s performance one year later was surprisingly weak. He won the district by 34 points, versus Clinton’s 5 point win, and won Divisions that Clinton lost handily.

View code

d10_scatter <- div_winners %>% 
  inner_join(sf_divs %>% filter(council_district == "10")) %>%
  unite("office", short_office, year) %>%
  mutate(
    pdem_twoparty = ifelse(winner_party == "DEMOCRATIC", twoparty_pvote, 1-twoparty_pvote),
    win_gap = 2 * 100 * (pdem_twoparty-0.5)
  ) %>%
  select(office, warddiv19, win_gap, total_votes) %>%
  gather("key", "value", win_gap, total_votes) %>%
  unite(key, office, key) %>%
  spread(key, value) 

gap_labeller <- function(x){
  sprintf(
    "%s%s%%%s",
    ifelse(x==0, "", "+"),
    abs(x),
    ifelse(x>0, " D", ifelse(x<0, " R", ""))
  )
}

plot_scatter <- function(col, office, title){
  col <- enquo(col)
  ggplot(
    d10_scatter,
    aes(x=President_2016_win_gap, y=!!col)
  ) +
    geom_abline(slope=1, intercept=0, linetype="dashed") +
    geom_hline(yintercept=0, color="black") +
    geom_vline(xintercept=0, color="black") +
    geom_point(aes(size = President_2016_total_votes), alpha = 0.3, pch = 16) +
    coord_fixed() +
    theme_sixtysix() %+replace%
    theme(legend.position = "right", legend.direction="vertical") +
    scale_size_area("Votes in 2016") +
    scale_x_continuous(
      "Win Gap for President (2016)", 
      labels=gap_labeller  
    )+
    scale_y_continuous(
      sprintf("Win Gap for %s", office), 
      labels=gap_labeller  
    ) +
    ggtitle(title, "Division-level results") 
}

plot_scatter(Mayor_2015_win_gap, "Mayor (2015)", "Kenney was popular even in Trump's Divisions")

In comparison, Brendan Boyle’s 24-point win was much more correlated with Clinton. I interpret this as the race being nationalized, with party affiliation becoming salient. The main question for November is if O’Neill is a Kenney-esque candidate, who transcends party and Northeasters will continue to support, or if all elections are nationalized in a post-2016 world.

View code

plot_scatter(`US Congress District 2_2018_win_gap`, "US Congress District 2 (2018)", "Boyle won a nationalized race")

The challenge for Moore is that O’Neill consistenty runs ahead of every other Republican in the district.

Since 2003, there have been 21 uncontested District Council races. Only one of those was a Republican: O’Neill himself in 2015. So there’s not any history of uncontested Republicans to gain insights from.

View code

council <- df %>%
  filter(election == "general") %>%
  filter(office == "DISTRICT COUNCIL") 

council_totals <- council %>% 
  filter(candidate != "Write In") %>%
  filter(party %in% c("DEMOCRATIC", "REPUBLICAN")) %>%
  group_by(year, candidate, party, district) %>% 
  summarise(votes=sum(votes)) %>%
  mutate(is_competitive = votes > 100) %>%
  group_by(year, district) %>%
  summarise(
    n_contested = sum(is_competitive),
    winner = candidate[which.max(votes)],
    winner_pct = votes[which.max(votes)] / sum(votes),
    winner_party = party[which.max(votes)]
  )

## council_totals %>% filter(winner_pct == 1)

oneill_df <- council_totals %>%
  filter(district == 10) %>%
  mutate(office = "Council", short_office="Council District 10") %>%
  rename(prep = winner_pct) %>%
  select(year, prep, short_office) %>%
  bind_rows(
    df %>% 
      filter(
        office %in% c("MAYOR", "PRESIDENT OF THE UNITED STATES", "GOVERNOR"),
        election == "general",
        party %in% c("DEMOCRATIC", "REPUBLICAN")
      ) %>%
      mutate(short_office = format_name(office)) %>%
      inner_join(sf_divs %>% as.data.frame() %>% filter(council_district == 10) %>% select(warddiv19)) %>%
      group_by(year, party, short_office) %>%
      summarise(votes = sum(votes)) %>%
      group_by(year, short_office) %>%
      summarise(
        prep = votes[party == "REPUBLICAN"] / sum(votes)
      )
  )

ggplot(
  oneill_df ,
  aes(x=asnum(year), y=2*100*(prep-0.5), color=short_office)
) +
  geom_hline(yintercept = 00, color="grey20") +
  geom_point(
    size=3
  ) +
  geom_line(
    aes(group=short_office),
    size=1
  ) +
  theme_sixtysix() +
  ylab("Percent for Republican") +
  geom_text(
    data=data.frame(
      year=2015.2, prep=c(0.30, 0.95, 0.55, 0.4), label=c("Mayor", "O'Neill", "President", "Governor"), 
      short_office=c("Mayor", "Council District 10", "President of the United States", "Governor")
    ),
    hjust=0, fontface="bold",
    aes(label=label)
  ) +
  scale_color_manual(
    guide=FALSE,
    values=c(
      "Mayor"=light_blue, 
      "Council District 10"=light_red, 
      "President of the United States"=light_purple, 
      "Governor"=light_green
    )
  )+
  xlab(NULL) +
  scale_x_continuous(breaks = seq(2003, 2015, 4)) +
  scale_y_continuous(labels=function(x) gap_labeller(-x)) +
  ggtitle("O'Neill runs far ahead of other Republicans")

O’Neill ran 17 points ahead of the Republican candidate for Mayor in 2007 and 2011 (meaning a gap of 34 points). Then in 2015 he was unupposed. In thatDemocrats have won every single Presidential, Gubernatorial, and Mayoral race in the district since, with the only exception being voting for Katz over Street in 2003.

Comparative turnout in Municipal Elections

If the race becomes close, relative turnout of Republican and Democratic divisions might be decisive. The Republican divisions typically turn out more strongly in non-Presidential elections. Using turnout in 2016 as the baseline (my preferred method), very-Republican divisions had nearly 1.5 the relative turnout of the very-Democratic ones in 2015.

View code

turnout <- df %>% filter(
  election=="general",
  year %in% 2015:2018,
  office %in% c("MAYOR", "GOVERNOR", "PRESIDENT OF THE UNITED STATES", "DISTRICT ATTORNEY")
) %>%
  group_by(year, warddiv19) %>%
  summarise(turnout=sum(votes))

turnout <- sf_divs %>% 
  as.data.frame() %>%
  select(warddiv19, Shape__Are) %>%
  left_join(turnout) %>%
  group_by(year) %>%
  mutate(
    turnout_per_area = turnout / Shape__Are,
    turnout_per_area_std = (
      turnout_per_area - mean(turnout_per_area)
    ) / sd(turnout_per_area)
  )

turnout_wide <- turnout %>% 
  select(-turnout_per_area_std) %>%
  gather("key", "value", turnout, turnout_per_area) %>%
  unite("key", key, year) %>%
  spread(key, value) %>%
  inner_join(sf_divs %>% as.data.frame() %>% filter(council_district == "10") %>% select(warddiv19))

turnout_scatter <- function(year, title){
  turnout_col <- sym(paste0("turnout_", year))
  ggplot(
    turnout_wide %>% left_join(d10_scatter),
    aes(
      x=President_2016_win_gap,
      y=100*!!turnout_col / turnout_2016
    )
  ) + 
    geom_point(
      aes(size=President_2016_total_votes),
      alpha = 0.5, pch=16
    ) +
    theme_sixtysix() +
    geom_smooth(
      aes(weight=President_2016_total_votes),
      color = "black", method=lm
    ) +
    scale_y_continuous(
      sprintf("Votes in %s / Votes in 2016 (%%)", year),
      breaks=seq(0,100,10)
    )+
    scale_x_continuous(
      "Win Gap for President (2016)",
      labels=gap_labeller
    ) +
    scale_size_area("Votes in 2016") +
    expand_limits(y=0) +
    ggtitle(title)
}

turnout_scatter(2015, "Republican Divisions voted disproportionately\n in 2015")

That changed in 2018. The gap was still about ten percentage points, but at a much higher overall level. Republicans’ proportional advantage was significantly weakened.

View code

turnout_scatter(2018, "Republican Divisions had less turnout advantage\n in 2018")

How many votes is the turnout difference between 2015 and 2018 worth? Using the division results from 2016, but turnout from 2015 and 2018, the difference between 2015 and 2018 would have been a gap of 1.2 for the Democrat. Not enough to change the results in all but the closest of races.

View code

turnout_wide %>% 
  left_join(d10_scatter) %>%
  summarise(
    gap_2015=weighted.mean(President_2016_win_gap, w=turnout_2015),
    gap_2016=weighted.mean(President_2016_win_gap, w=turnout_2016),
    gap_2018=weighted.mean(President_2016_win_gap, w=turnout_2018)
  )

## # A tibble: 1 x 3
##   gap_2015 gap_2016 gap_2018
##      <dbl>    <dbl>    <dbl>
## 1     3.44     5.35     4.60

Moore needs to hope that even City Council has become nationalized.

Really the only way Moore can win is if the divisions that regularly vote for Democrats, the divisions that carried Kenney and Clinton and Boyle to victory, turn against O’Neill. This is increasingly happening across the country, but O’Neill has a big cushion.

Maybe, just maybe, Boyle’s decisive 2018 win was a sign of a changing district. The only way to know for sure would be polling. Without that, we’ll see in November.