library(ggplot2)
library(plotly)
library(kableExtra)
library(dplyr)
library(forcats)
library(here)
library(leaflet)
library(RColorBrewer)

# FE Data only for this range of years
load(file = here("data-outputs", "CleanData.rda"))
last.data.update = max(fe_clean$date)
curr_mo <- lubridate::month(Sys.Date())
curr_yr = lubridate::year(Sys.Date())

startmo = 1  # Jan 2000
startyr = 2000
endmo = 12
endyr = 2021

finaldata_2000 <- fe_clean %>% 
  filter(year >= startyr & year <= endyr & st == "WA")

# Lots of "Unknowns" in the FE homicide indicator

# Select homicides
# Remove suicides 
## note we don't remove the usual 3 cases:
## the deputy who ran into someone while having a stroke &
## 25804-5 the Tad Norman case in Lake City. 
## these are not victims of police violence,
## but we can't clean the entire series to be
## consistent so we leave them in

homicides <- finaldata_2000 %>% 
  filter(homicide==1) %>%
  arrange(date)

# for geocoded LDs
with(homicides, write.csv(cbind(feID,latitude,longitude),
                          here::here("data-outputs",
                                     "geocodes2000.csv")))

all.cases <- dim(finaldata_2000)[1]
last.case <- dim(homicides)[1]

last.date <- max(homicides$date)
last.name <- ifelse(homicides$lname[last.case] == "Unknown", 
                    "(Name not released)",        
                  paste(homicides$fname[last.case],
                        homicides$lname[last.case]))
last.age <- ifelse(is.na(homicides$age[last.case]), 
                         "not released",
                         homicides$age[last.case])
last.agency <- homicides$agency[last.case]
last.cod <- homicides$cod[last.case]
tot.by.yr <- table(homicides$year)
tot.this.yr <- tot.by.yr[[length(tot.by.yr)]]
num.suffix <- ifelse(tot.this.yr == 1, "st",
                     ifelse(tot.this.yr == 2, "nd",
                            ifelse(tot.this.yr == 3, "rd", "th")))

# Indices for plotting by time

numyrs = endyr - startyr + 1

month = month.abb[startmo:12]
year = rep(startyr, length(month))
for(i in 1:(numyrs-1)) {
  #print(i)
  thisyr = startyr+i
  #print(thisyr)
  year <- c(year, rep(thisyr, 12))
  month <- c(month, month.abb)
}


## Month index for date plotting

index <- data.frame(year = year,
                    month = month,
                    mo.yr = paste0(month, ".", year))

## Indicator for lag in data updates
data.update.flag = ifelse(Sys.Date() - last.data.update > 30, 
                          "(note: last data update is more than 1 month ago)", "")

Introduction

This report tracks the number of persons killed by police in WA state from 2000 until the last complete month of the current year.

Two sets of legislative reforms have happened during the later part of this period. On December 6, 2018, Initiative 940 was passed into WA state law after winning over 60% of the statewide vote. In 2021, a package of 13 additional bills focused on police reform were passed by the state Legislature.

Tracking the trends in persons killed by police, before and after the legal reforms, can help provide insight into the context and motivation for these reforms, and to assess whether they are having an impact.

MOST RECENT DATA UPDATE

  • Last FE project data update: 2021-12-31

  • Total homicides by police since 2000: 633

  • Last reported case in WA: Sorin Ardelean, age 43, on 2021-12-27 by Algona Police Department

    Sorin Ardelean is the 20th person killed by police in 2021. The cause of death is reported as Gunshot.

From 2010-20 roughly one out of every 5 persons killed in Washington state was killed by a law enforcement officer. (About 250 homicide deaths are recorded each year in WA State. You can find the state’s death data here).

Where the data come from

The data in this report are updated at least once each week, by pulling from the Fatal Encounters project (https://fatalencounters.org/). Fatal Encounters includes all deaths during encounters with police; it does not include deaths in custody after booking.

There is typically a 1-2 week lag in the Fatal Encounters dataset, but it can sometimes be longer. The date shown in the “most recent data update” is an indication of the current lag.

This report is restricted to the cases that can be classified as homicides by police, it excludes cases identified in the Fatal Encounters dataset as suicides.

What is a homicide?

The deadly force incidents in this report are homicides. A homicide is simply defined as the killing of one person by another. In the context of this report it refers to any encounter with law enforcement officers that results in a fatality. Homicides normally result in a criminal investigation or inquest, but the word does not imply a crime has been committed.

  • The word homicide means only that the death was caused in some way by the officer.

  • It does not not mean the officer’s actions that led to the death were justified, or that they were unjustified.

The law then defines different types of homicides, in order to distinguish levels of intention and criminal culpability. In the U.S., these types and definitions are broadly similar across states, but do vary somewhat in the the how many different types are specified, and what names are used for each type.

The definitions below are taken from a useful online summary found here, based on California State laws.

Homicide
Homicide is the killing of one person by another. This is a broad term that includes both legal and illegal killings. For example, a soldier may kill another soldier in battle, but that is not a crime. The situation in which the killing happened determines whether it is a crime.

  • Murder is the illegal and intentional killing of another person. Under California Penal Code Section 187, for example, murder is defined as one person killing another person with malice aforethought. Malice is defined as the knowledge and intention or desire to do evil. Malice aforethought is found when one person kills another person with the intention to do so.

    In California, for example, a defendant may be charged with first-degree murder, second-degree murder, or capital murder.

    • First-degree murder is the most serious and includes capital murder – first-degree murder with “special circumstances” that make the crime even more egregious. These cases can be punishable by life in prison without the possibility of parole, or death.

    • Second-degree murder is murder without premeditation, but with intent that is typically rooted in pre-existing circumstances. The penalty for second-degree murder may be up to 15 years to life in prison in California.

    • Felony murder is a subset of first-degree murder and is charged when a person is killed during the commission of a felony, such as a robbery or rape.

  • Manslaughter is the illegal killing of another person without premeditation, and in some cases without the intent to kill. These cases are treated as less severe crimes than murder. Manslaughter can also be categorized as voluntary or involuntary.

    • Voluntary manslaughter occurs when a person kills another without premeditation, typically in the heat of passion. The provocation must be such that a reasonable person under the same circumstances would have acted the same way. Penalties for voluntary manslaughter include up to 11 years in prison in California.

    • Involuntary manslaughter is when a person is killed by actions that involve a wanton disregard for life by another. Involuntary manslaughter is committed without premeditation and without the true intent to kill, but the death of another person still occurs as a result. Penalties for involuntary manslaughter include up to four years in prison in California.

    • Vehicular manslaughter occurs when a person dies in a car accident due to another driver’s gross negligence or even simple negligence, in certain circumstances.

Interactive Map

You can click the numbered circles to reach the individual map pointers for each person killed by police.

  • Hovering over the pointer brings up the name of the person killed and agency of the officer who killed them;

  • Clicking the pointer will bring up a url to a news article on the case (if available).

map1 <- leaflet(data = homicides, width = "100%") %>% 
  addTiles() %>%
  addMarkers( ~ longitude,
              ~ latitude,
              popup = ~ url_click,
              label = ~ as.character(paste(name, "by", agency)),
              clusterOptions = markerClusterOptions())
map1
agencynum <- homicides %>%
  group_by(agency) %>%
  count() %>%
  rename(agency_num = n)

homicides <- right_join(homicides, agencynum)

map2 <- leaflet(data = homicides, width = "100%") %>% 
  addTiles() %>%
  addCircles(~longitude, ~latitude, 
             weight = 1, radius = ~sqrt(agency_num) * 5000, 
             label = ~ as.character(agency),
             popup = ~agency_num)

map2
map3 <- leaflet(data = homicides, width = "100%") %>% 
  addTiles() %>%
  addMarkers( ~ longitude,
              ~ latitude,
              popup = ~ url_click,
              label = ~ as.character(name))
map3

Breakdowns

Race

Table

The race of the victim is missing in 23% of the original data. Fatal Encounters employs an algorithm to try to impute these cases. Over half of the missing values are successfully imputed. These imputations are included in the analysis here.

tab <- homicides %>%
  mutate(raceImp = recode(raceImp,
    "API" = "Asian/Pacific Islander",
    "BAA" = "Black/African American",
    "HL" = "Hispanic/Latinx",
    "NAA" = "Native American/Indigenous",
    "WEA" = "White/European American")
  ) %>%
group_by(raceImp) %>%
  summarize(Number = n(),
            Percent = round(100*Number/nrow(homicides), 1)
  ) %>%
  bind_rows(data.frame(raceImp="Total", 
                       Number = sum(.$Number), 
                       Percent = sum(.$Percent))) %>%
  rename(Race = raceImp) 

tab %>%
  kable(caption = "Breakdown by Race") %>%
  kable_styling(bootstrap_options = c("striped","hover")) %>%
  row_spec(row=dim(tab)[1], bold = T) %>%
  add_footnote(label = "Percents may not sum to 100 due to rounding",
               notation = "symbol")
Breakdown by Race
Race Number Percent
Asian/Pacific Islander 31 4.9
Black/African American 75 11.8
Hispanic/Latinx 56 8.8
Native American/Indigenous 24 3.8
White/European American 418 66.0
Unknown 29 4.6
Total 633 99.9
* Percents may not sum to 100 due to rounding

Plots

homicides %>%
  mutate(raceImp = recode(raceImp,
    "API" = "Asian",
    "BAA" = "Black",
    "HL" = "Latinx",
    "NA" = "Native",
    "WEA" = "White")
  ) %>%
  count(raceImp) %>%
  mutate(perc = n / nrow(homicides)) %>%
  ggplot(aes(x=raceImp, 
             y = perc, 
             label = n)) +
  # label = round(100*perc, 1))) +
  geom_bar(stat="identity", fill="blue", alpha=.5) +
  geom_text(aes(y = perc), size = 3, nudge_y = .025) +
  labs(title = "Fatalities by Race",
       caption = "WA State since 2000; y-axis=pct, bar label=count") +
         xlab("Reported Race") +
         ylab("Percent of Total")

homicides %>%
  mutate(raceb = case_when(raceImp == "WEA" ~ "White",
                           raceImp == "Unknown" ~ "Unknown",
                           TRUE ~ "BIPOC"),
         raceb = fct_relevel(raceb, "Unknown", 
                             after = Inf)) %>%
  count(raceb) %>%
  mutate(perc = n / nrow(homicides)) %>%
  ggplot(aes(x=raceb, 
             y = perc, 
             label = n)) +
  geom_text(aes(y = perc), size = 3, nudge_y = .025) +
  geom_bar(stat="identity", fill="blue", alpha=.5) +
  labs(title = "Fatalities by Race",
       caption = "WA State since 2000; y-axis=pct, bar label=count") +
  xlab("Reported Race") +
  ylab("Percent of Total")

Discussion

Racial disparities in the risk of being killed by police are one of the most important factors driving the public demand for police accountability and reform. For that reason it is important to understand how these numbers can, and cannot be used.

TL;DR There are many uncertainties in the data that make it difficult/impossible to estimate exact values, but there are still some conclusions we can draw with confidence.

Many case reports are missing data on race

These cases are denoted “Unknown” in the tables and plots in this report.

For the Fatal Encounters dataset, about 23% of the cases for WA State do not have information that explicitly identifies the race of the person killed. It’s worth remembering that the Fatal Encounters project relies primarily on media reports (and some public records requests) to find these cases, so they are limited by the information provided in those sources. The Fatal Encounters team uses an “imputation” model to try to predict race for the missing cases. A brief description of the methodology is online here. They are able to impute just over half of the missing cases with reasonable confidence, and we include these imputations in the breakdowns we report. After imputation, about 5% of cases in WA are still missing race.

Bottom line: This makes it impossible to say exactly how many people killed by police are in each racial group.

We are reporting the raw counts in this report, not per capita rates

Breaking the total count down by race, the largest single group of persons killed by police are identified as White/European-American: 66%

So, does this mean that we can say: “There aren’t any racial disparities in persons killed by police?”.

No. This raw count can not be used to assess racial disparities, because the word disparity implies the risk is “disproportionate” – that is, higher (or lower) than proportional. Proportional is a comparative term: it compares the proportion of fatalities by race, to the proportion of the population by race. If the two proportions are the same, then we can say there are no disparities. In this case, if 66% of the WA population is White, then we can say that their risk of being killed by police is proportional to their share of the population.

The population of Washington State overwhelmingly identifies as White – almost 80% as of 2020 source: WA State OFM. If we exclude Hispanics (which the Census treats as ethnicity, not race), non-Hispanic Whites comprise 70% of the WA state population source: Statistical Atlas of the US.

So there is a disparity in the risk of being killed by police – for non-Hispanic Whites, their share of fatalities is lower than their population share.

The “risk ratio” is a common way to combine this information into a single number that is easy to understand: the ratio of the fatality share, to the population share. When the two shares are the same, the risk ratio equals 1. When it is less than one, this means the share of fatalities is lower than the population share; they are disproportionately low. When the risk ratio is greater than one it means the share of fatalities is larger than the population share; they are disproportionately high. Then, taking 100*(risk ratio-1) tells you the percent lower (or higher) the ratio is than expected, if the risk was proportional to their share of population.

  • The risk ratio for non-Hispanic whites is 66% / 70% = 0.94 – their risk of being killed by police is 6% lower than expected.

  • By contrast, only 4% of the WA population identifies as Black/African American, but 12% of the persons killed by police in WA are identified as being in this racial group. So their risk ratio is 12% / 4% = 2.96 – their risk of being killed by police is 196% higher than expected.

Note that the number of “unknown race” cases is not enough to change the direction of these disparities. Even if we assume that all of the “unknown race” cases are White, their share of incidents would still be below their share in the population.

So, even though we can’t be certain of the exact value of the risk ratio, we can say with some confidence that there are racial disparities in the risk of being killed by police, and that these disparities indicate that non-Hispanic Whites are less likely than other racial groups to be killed.

Bottom line: while the majority of persons killed by police in WA are identified as White, the risk ratio for this group, which adjusts for their share of the population, shows they are disproportionately less likely to be killed than other groups.

So why don’t we calculate standardized per capita rates?

Because the classification of cases by race, in both the Fatal Encounters and WaPo datasets, is not consistent with the classification of race in population data provided by the US Census.

The calculation of per capita rates takes the ratio of the fatality count (in the numerator) to the population count (in the denominator). To break this down by race we need a consistent measure of race to use for the numerator and denominator for all groups. And we don’t have that.

  • About 5% of people in WA state report two or more races in the US Census. This multiple-race classification does not exist in the datasets on persons killed by police.

  • Hispanic/Latinx is an ethnicity that crosses several racial groups, primarily White, Black and Native American. In the US Census data, race is measured separately from ethnicity, so you can see these overlaps. But in the Fatal Encounters and WaPo datasets, “Hispanic” is coded as a racial group, rather than as a separate ethnicity classification.

Perceived vs. self-identified race

The race classifications in all of our data (including the US Census) do not represent what the officer perceived the person’s race to be. It is likely that there is a strong correlation between these two, but we can’t use these data to answer the question of the officer’s intention, or implicit bias, with certainty.

Cause of death

Plot

homicides %>%
  mutate(codb = case_when(cod == "Gunshot" ~ "Gunshot",
                          TRUE ~ "Other")
         ) %>%
  count(codb) %>%
  mutate(perc = n / nrow(homicides)) %>%
  ggplot(aes(x=codb, 
             y = perc, 
             label = n)) +
  geom_text(aes(y = perc), size = 3, nudge_y = .025) +
  geom_bar(stat="identity", fill="blue", alpha=.5) +
  labs(title = "Fatalities by Cause of Death",
       caption = "WA State since 2000; y-axis=pct, bar label=count") +
  xlab("Reported Weapon Used by Police") +
  ylab("Percent of Total")

Table

tab <- homicides %>%
  group_by(cod) %>%
  summarize(Number = n(),
            Percent = round(100*Number/nrow(homicides), 1)
  ) %>%
  arrange(desc(Number)) %>%
  bind_rows(data.frame(cod ="Total", 
                       Number = sum(.$Number), 
                       Percent = sum(.$Percent))) 

tab %>%
  kable(caption = "Breakdown by Cause of Death",
        col.names = c("Cause of Death", "Number", "Percent")) %>%
  kable_styling(bootstrap_options = c("striped","hover")) %>%
  row_spec(row=dim(tab)[1], bold = T)  %>%
  add_footnote(label = "Percents may not sum to 100 due to rounding",
               notation = "symbol")
Breakdown by Cause of Death
Cause of Death Number Percent
Gunshot 488 77.1
Vehicle 82 13.0
Tasered 28 4.4
Asphyxiated/Restrained 14 2.2
Medical emergency 6 0.9
Beaten/Bludgeoned with instrument 5 0.8
Drowned 4 0.6
Drug overdose 2 0.3
Burned/Smoke inhalation 2 0.3
Other 2 0.3
Total 633 99.9
* Percents may not sum to 100 due to rounding

Victim armed

This information is in the process of being coded by the Fatal Encounters project. It is not available yet for 2021, or prior to 2013, so all cases in those years are coded “Unknown”.

Note that the media often rely on the law enforcement narrative when reporting this information, so the validity of these data are unknown.

Plot

homicides %>%
  count(weapon) %>%
  mutate(perc = n / nrow(homicides)) %>%
  ggplot(aes(reorder(weapon, perc),
             y = perc, 
             label = n)) +
  geom_text(aes(y = perc), size = 3, nudge_y = .025) +
  geom_bar(stat="identity", fill="blue", alpha=.5) +
  labs(title = "Fatalities by Report of Victim Weapon",
       caption = "WA State since 2000; y-axis=pct, bar label=count") +
  xlab("Reported Weapon Used by Victim") +
  ylab("Percent of Total")

Table

tab <- homicides %>%
  group_by(weapon) %>%
  summarize(Number = n(),
            Percent = round(100*Number/nrow(homicides), 1)
  ) %>%
  arrange(desc(Number)) %>%
  bind_rows(data.frame(weapon ="Total", 
                       Number = sum(.$Number), 
                       Percent = sum(.$Percent)))

tab %>%
  kable(caption = "Breakdown by Report of Victim Weapon (incomplete data)",
        col.names = c("Weapon", "Number", "Percent")) %>%
  kable_styling(bootstrap_options = c("striped","hover")) %>%
  row_spec(row=dim(tab)[1], bold = T)  %>%
  add_footnote(label = "Percents may not sum to 100 due to rounding",
               notation = "symbol")
Breakdown by Report of Victim Weapon (incomplete data)
Weapon Number Percent
Unknown 318 50.2
Alleged firearm 135 21.3
No weapon 97 15.3
Alleged edged weapon 58 9.2
Other 25 3.9
Total 633 99.9
* Percents may not sum to 100 due to rounding

County

counties.obs <- unique(homicides$county)

Of the 39 counties in Washington state, Spokane, King, Clark, Yakima, Pierce, Cowlitz, Jefferson, Whatcom, Kitsap, Thurston, Snohomish, Island, Clallam, Benton, Grant, Skamania, Ferry, Mason, Skagit, Stevens, Pend Oreille, Okanogan, Adams, Lewis, Grays Harbor, Chelan, Franklin, Klickitat, Douglas, Kittitas, Walla Walla have had at least one person killed by police since 2000 that we know of. It is worth reiterating that our data source may not be incomplete. If the data include no fatalities from police use of force, the county is not included in the plot or table below.

Plot

homicides %>%
  group_by(county) %>%
  summarize(n = n(),
            perc = n / nrow(homicides)) %>%
  ggplot(aes(reorder(county, perc),
             y = perc, 
             label = n)) +
  geom_text(aes(y = perc), size = 3, nudge_y = .025) +
  geom_bar(stat="identity", fill="blue", alpha=.5) +
  labs(title = "County",
       caption = "WA State since 2000; y-axis=pct, bar label=count") +
  xlab("County where fatal encounter happened") +
  ylab("Percent of Total") +
  theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))

Table

homicides %>%
  group_by(county) %>%
  summarize(Number = n(),
            Percent = round(100*Number/nrow(homicides), 1)
  ) %>%
  DT::datatable(rownames = F,
                caption = "Breakdown by County")

Agency/PD involved

WA State has about 260 Law Enforcement agencies, at the State, County, Local, Tribal and College/University levels (see this Wikipedia page for a list). Only those with a known officer-involved homicide are listed in the plot and table here.

homicides %>%
  group_by(agency) %>%
  summarize(Number = n(),
            Percent = round(100*Number/nrow(homicides), 1)
  ) %>%
  DT::datatable(rownames = F,
                caption = "Breakdown by Agency/PD of Involved Officer")

Online information availability

This information comes from Fatal Encounters. It takes the form of a single url to a news article that is available online. There are often multiple news articles available online, and they may report the conflicting details of the event, as well as conflicting perspectives on the justifiability of the use of lethal force.

The clickable urls are available in this report in the Interactive Map and Say their names sections. They should be treated as a place to start research, not as the definitive description of the event.

tab <- homicides %>%
  mutate(url_info = case_when(url_info == "" ~ "No",
                               is.na(url_info) ~ "No",
                               TRUE ~ "Yes")) %>%
  group_by(url_info) %>%
  summarize(Number = n(),
            Percent = round(100*Number/nrow(homicides), 1)
  ) %>%
  bind_rows(data.frame(url_info ="Total", 
                       Number = sum(.$Number), 
                       Percent = sum(.$Percent))) 

tab %>%
  kable(caption = "URL for news article in Fatal Encounters",
        col.names = c("Availability", "Number", "Percent")) %>%
  kable_styling(bootstrap_options = c("striped","hover")) %>%
  row_spec(row=dim(tab)[1], bold = T) %>%
  add_footnote(label = "Percents may not sum to 100 due to rounding",
               notation = "symbol")
URL for news article in Fatal Encounters
Availability Number Percent
Yes 633 100
Total 633 100
* Percents may not sum to 100 due to rounding

Say their names

Name known

Of the 633 persons killed by police, 620 of the victim’s names are known at this time.

homicides %>%
  filter(name != "Unknown") %>%
  select(name, date, age=age, county, agency, url = url_click) %>%
  arrange(desc(date)) %>%
  DT::datatable(rownames = F,
                caption = paste("The Names We Know:  as of", scrape_date),
                filter = 'top',
                escape = FALSE)

Name Unknown

The remaining 13 of the victim’s names are not known at this time.

homicides %>%
  filter(name == "Unknown") %>%
  select(name, date, age=age, county, agency, url = url_click) %>%
  arrange(desc(date)) %>%
  DT::datatable(rownames = F,
                caption = paste("The Names We Don't Know:  as of", scrape_date),
                filter = 'top',
                escape = FALSE)