Introduction

Overview

The WA State process for investigating law enforcement officers for misconduct and possible decertification was revised in 2021 by Senate Bill 5051 (see the “SB 5051” tab above for details). The process is tracked internally by CJTC with a “certification database”, and this report presents basic descriptive statistics from that database (see the “On the data” tab above for details).

For some of the tables and plots we split the data into 3 periods by the date the case was received:

pre-5051 (before July 2021),
“interim” (July 2021 - June 2022, a period when the new system was being set up), and
“current” (after June 2022, when the system was fully functional)

In other places, we combine the second two periods into “post-5051”.

A list of officers that have received multiple complaints or agency notices of misconduct can be found in the “Officers” section of this report. The list can be searched, sorted, filtered and downloaded in several different formats.

Navigating this report:

You can use the TOC on the left to view specific sections based on content.
Within sections, look for the tabs in the document to view all of the different tables and graphs. Tabs are typically just below the section headers, but some sections have additional nested tabs below.

This is a first draft, so there may be some errors in the precise numbers presented. Comments/suggestions/questions are welcomed and appreciated.

SB 5051

The text of SB5051 can be found here,
A simple description of the process can be found on the CJTC website here,
The current RCW that governs the process can be found online here.

On the data

The CJTC maintains a database to track publicly filed complaints, agency notifications of officer misconduct (“Form 1915”), and Division initiated investigations that require review and may lead to officer decertification. Note that decertification is, to date, a very rare outcome (you can verify this in the Outcomes section of this report).

Case processing involves 3 general steps: Intake (entered into the database, initial review), Assessment (may either be closed without investigation or forwarded to investigation) and Determination (may either decline to charge, or charge with a hearing and outcome). At any of these steps, an officer may voluntarily surrender their certification.

There are two versions of the database:

An internal version that includes all cases
A public version that excludes cases in the “Intake” process, and is available online at the CJTC website.

The format and case coverage of the two datasets is different, but they otherwise include almost the same information.

This report is based on the Internal Dataset

The internal dataset was obtained via PDR by Dom Campese of the WCPA, and contains all cases up through 9/24/2025.

One of the key differences between the internal and public dataset formats is how the individual records in the dataset are defined. In the public dataset, each record is a “case-person”, defined by a case number and a person’s name. In the internal dataset, one record is used per offense (a complaint or form 1915 can lead to multiple offenses being charged for the same case-person), with additional records used to track attorney- or union-represented officers, some complainants and witnesses, and a few other categories of persons. As a result there are many more records in the internal dataset than the public (5829 vs. 2200 as of 9/24/2025) .

For this report, we “de-duplicated” the internal dataset with the goal of obtaining a set of “case-person” records equivalent to the public dataset. This reduced the number of records in the internal dataset down from 5829 to 3931. The de-duplicated internal dataset still has more records than the public dataset because it includes cases that are in the Intake process.

Case Status

Summary

tab <- cjtc %>%
  group_by(Status = as.character(status3)) %>%
  summarize(Number = n()) %>%
  mutate(Percent = Number/sum(Number),
         index = if_else(is.na(Status), -1, rank(Number)),
         Status = replace_na(Status, "Missing")
         ) %>%
  arrange(desc(index)) %>%
  select(-index) %>%

  bind_rows(data.frame(Status ="Total",
                       Number = sum(.$Number),
                       Percent = sum(.$Percent))) %>%
  mutate(Percent = scales::percent(Percent, acc=.1))

tab %>%  
  kbl(caption = "Case Status:  all cases",
      align = "lrr") %>%
  kable_styling(bootstrap_options = "striped") %>%
  row_spec(which(tab$Status == "Missing"), italic=T) %>%
  row_spec(which(tab$Status == "Total"), bold=T)

Case Status: all cases
Status	Number	Percent
Closed	2003	51.0%
Open/Intake	1739	44.2%
Open/Active	183	4.7%
Missing	6	0.2%
Total	3931	100.0%

Detailed CJTC coding

tab <- cjtc %>%
  group_by(Status = as.character(status)) %>%
  summarize(Number = n()) %>%
  mutate(Percent = Number/sum(Number),
         index = if_else(is.na(Status), -1, rank(Number)),
         Status = replace_na(Status, "Missing")
         ) %>%
  arrange(desc(index)) %>%
  select(-index) %>%

  bind_rows(data.frame(Status ="Total",
                       Number = sum(.$Number),
                       Percent = sum(.$Percent))) %>%
  mutate(Percent = scales::percent(Percent, acc=.1)) 
  
tab %>% 
  kbl(caption = "Case Status (original detail):  all cases",
      align = "lrr") %>%
  kable_styling(bootstrap_options = "striped") %>%
  row_spec(which(tab$Status == "Missing"), italic=T) %>%
  row_spec(which(tab$Status == "Total"), bold=T)

Case Status (original detail): all cases
Status	Number	Percent
Closed	2003	51.0%
Intake Review	1333	33.9%
Intake	406	10.3%
Investigation	90	2.3%
Admin Review	54	1.4%
Hearing Coordinator	13	0.3%
AAG (Legal)	9	0.2%
Settlement	8	0.2%
Assistant Director Review	5	0.1%
Appeal - Post Discipline	2	0.1%
Discipline - Post Hearing	1	0.0%
Open - Pending Expiration	1	0.0%
Missing	6	0.2%
Total	3931	100.0%

Outcomes

Overall

Summary

tab <- cjtc %>%
  group_by(Outcome = as.character(outcat)) %>%
  summarize(Number = n()) %>%
  mutate(Percent = Number/sum(Number),
         index = if_else(is.na(Outcome), -1, rank(Number)),
         Outcome = replace_na(Outcome, "Missing")
         ) %>%
  arrange(desc(index)) %>%
  select(-index) %>%

  bind_rows(data.frame(Outcome ="Total",
                       Number = sum(.$Number),
                       Percent = sum(.$Percent))) %>%
  mutate(Percent = scales::percent(Percent, acc=.1)) 
  
tab %>%
  kbl(caption = "Case Outcome:  all cases",
      align = "lrr") %>%
  kable_styling(bootstrap_options = "striped") %>%
  row_spec(which(tab$Outcome == "Missing"), italic=T) %>%
  row_spec(which(tab$Outcome == "Total"), bold=T)

Case Outcome: all cases
Outcome	Number	Percent
Still Open	1914	48.7%
Admin Closure	740	18.8%
Duplicate	492	12.5%
Revoked/Denied Cert/Disciplined	449	11.4%
Declined/Dismissed/Settlement	322	8.2%
Other	14	0.4%
Total	3931	100.0%

Original CJTC detail

tab <- cjtc %>%
  mutate(case_determination = if_else(status3 != "Closed", "Still Open", case_determination)) %>%
  group_by(Outcome = as.character(case_determination)) %>%
  summarize(Number = n()) %>%
  mutate(Percent = Number/sum(Number),
         index = if_else(is.na(Outcome), -1, rank(Number)),
         Outcome = replace_na(Outcome, "Missing")
         ) %>%
  arrange(desc(index)) %>%
  select(-index) %>%

  bind_rows(data.frame(Outcome ="Total",
                       Number = sum(.$Number),
                       Percent = sum(.$Percent))) %>%
  mutate(Percent = scales::percent(Percent, acc=.1)) #%>%

tab %>%    
  kbl(caption = "Case Outcome (original detail):  all cases",
      align = "lrr") %>%
  kable_styling(bootstrap_options = "striped") %>%
  row_spec(which(tab$Outcome == "Missing"), italic=T) %>%
  row_spec(which(tab$Outcome == "Total"), bold=T)

Case Outcome (original detail): all cases
Outcome	Number	Percent
Still Open	1922	48.9%
Administrative Closure - Lack of Jurisdiction	623	15.8%
Administrative Closure - Duplicate Case	492	12.5%
Revocation - Default	295	7.5%
Decline	293	7.5%
Revocation - Surrendered Certification	82	2.1%
Revocation - Hearing	52	1.3%
Administrative Closure - Not Certified or Deceased	48	1.2%
Administrative Closure - Insufficient Information	37	0.9%
Charge(s) Dismissed	21	0.5%
Denied Certification	17	0.4%
Administrative Closure - Implausible	16	0.4%
Administrative Closure - Revoked	15	0.4%
Eligibility Reinstated	8	0.2%
Denied Reinstatement	2	0.1%
Administrative Closure - Lack of Jurisdiction; Administrative Closure - Not Certified or Deceased	1	0.0%
Revocation - Summary Judgment	1	0.0%
Missing	6	0.2%
Total	3931	100.0%

By period - closed cases

p <- ggplot(cjtc %>% filter(status3 == "Closed" & outcat != "Other"), 
          aes(x=period_r, group=desc(outcat), 
              fill=outcat)) + 
  
  geom_bar(alpha = 0.7, color="grey") +
  
  scale_fill_brewer(palette = "Spectral") +
  
  labs(title = "Closed case outcome by period received",
       x = "Period",
       y = "Number of closed cases",
       fill = "Outcome")

ggplotly(p) %>% reverse_legend_labels()

By year - recd post 5051, closed

xval = max(cjtc$yr_r)
yval = nrow(cjtc %>% filter(yr_r == max(cjtc$yr_r) & 
                              period_r != "pre.5051" & 
                              status3 == "Closed")) * 1.2

p <- ggplot(cjtc %>% filter(period_r != "pre.5051" & status3 == "Closed"), 
          aes(x=yr_r, group=desc(outcat), 
              fill=outcat)) + 
  
  geom_bar(alpha = 0.7, color="grey") +
  
  scale_fill_brewer(palette = "Spectral") +
  
  annotate("text", x=xval, y=yval, label= "Yr to date", size=4) +
  
  labs(title = "Closed case outcomes by year received (post-5051)",
       x = "Year received",
       y = "Number of closed cases",
       fill = "Outcome")

ggplotly(p) %>% reverse_legend_labels()

Processing time

By status and period

The “boxplots” below show the range of case processing times (in years) by period, separately for closed and still open cases. For closed cases, this is the difference between case date received and date closed. For open cases it is the difference between case date received and the current date.

The box shows the middle 50% of the processing times, with a line at the median.
The “whiskers” extend to roughly the lower 5% and upper 95% of processing times.
Any dots beyond the whiskers are cases with unusually low or high processing times.
The width of the box represents the number of cases in that group.

ggplot(cjtc %>% filter(!is.na(status3)) %>%
         mutate(status2 = if_else(status3=="Closed", "Closed", "Open")), 
       aes(x=period_r, y=case_length_yr, fill=period_r)) +
  geom_boxplot(alpha = 0.5, varwidth = T) +
  
  labs(title = "Case processing times by case status and period received",
       x = "Period received",
       y = "Time in years",
       fill = "Period received") +
  
  scale_y_continuous(breaks = seq(0,round(max(cjtc$case_length_yr)), 1)) +

  facet_wrap("status2")

By outcome (recd post-5051, closed)

ggplot(cjtc %>% filter(period_r != "pre.5051" & status3 == "Closed"), 
       aes(x=period_r, y=case_length_yr, fill=period_r)) +
  geom_boxplot(alpha = 0.5, varwidth = T) +
  
  labs(title = "Closed case processing times by outcome and period received",
       x = "Period received",
       y = "Time to close in years",
       fill = "Period received") +

  facet_wrap("outcat")

Caseload dynamics over time

rcd <- cjtc %>% 
  group_by(yr_mo = yr_mo_r) %>%
  summarize(received = n())

clo <- cjtc %>% 
  group_by(yr_mo = yr_mo_c) %>%
  summarize(closed = n())

index <- data.frame(
  seqnum = 1:(12*24),
  yr_mo = paste0(sort(rep(2002:2025, 12)), "-", rep(01:12, 24))) %>%
  filter(seqnum < (max(seqnum)-2))

monthly_df <- left_join(index, rcd) %>%
  left_join(clo) %>%
  arrange(seqnum) %>%
  mutate(across(received:closed, ~tidyr::replace_na(.x, 0)),
         net_number = closed - received,
         net_pct = 100*(closed/received -1),
         cumulative_backlog = -cumsum(net_number))

Received vs. Closed

p <- monthly_df %>% 
  select(seqnum, yr_mo, received, closed) %>%
  pivot_longer(cols = c(received:closed),
               names_to = "Measure",
               values_to = "num") %>%
  
  ggplot(aes(x=seqnum, y=num, group=Measure, color=Measure)) +
  
  geom_point(alpha = 0.7, size = 1) +
  geom_line(alpha = 0.5) +
  
  labs(title = "Number of cases received and closed by month",
       x = "Year",
       y = "Number of cases") +
  
  scale_x_continuous(
    breaks = seq(1, nrow(monthly_df), 12),
    label = as.character(2002:2025) # monthly_df$yr_mo[seq(1, nrow(monthly_df), 12)]
  )  +
  
  theme(axis.text.x = element_text(size = 5))
  
ggplotly(p)

Monthly net throughput

p <- monthly_df %>% 
  mutate(posneg = if_else(net_number >= 0, "positive", "negative")) %>%
 
  ggplot(aes(x=seqnum, y=net_number, fill=posneg)) +
  
  geom_bar(stat = "identity",
           #fill = "blue",
           alpha = 0.7) +
  
  labs(title = "Net throughput: cases closed - cases received by month",
       x = "Year",
       y = "Net number of cases",
       fill = "Net throughput") +
  
  scale_x_continuous(
    breaks = seq(1, nrow(monthly_df), 12),
    label = as.character(2002:2025)
  )  +
  
  theme(axis.text.x = element_text(size = 5))
  
ggplotly(p)

Cumulative open caseload

This shows the growth in the monthly number of open cases (open intake + open active) over time.

All cases

p <- monthly_df %>% 

  ggplot(aes(x=seqnum, y=cumulative_backlog)) +
  
  geom_line(stat = "identity",
            color = "grey") +
  geom_point(stat = "identity",
             color = "blue",
             alpha = 0.7,
             size = 1) +
  
  labs(title = "Cumulative open caseload by month",
       x = "Year",
       y = "Number of open cases") +
  
  scale_x_continuous(
    breaks = seq(1, nrow(monthly_df), 12),
    label = as.character(2002:2025)
  )  +
  
  theme(axis.text.x = element_text(size = 5))
  
ggplotly(p)

Post 5051

    # date_received < as.Date("2021-06-20") ~ "pre.5051",
    # date_received > as.Date("2022-06-30") ~ "current",

p <- monthly_df %>% filter(yr_mo > "2021-6") %>%

  ggplot(aes(x=seqnum, y=cumulative_backlog)) +
  
  geom_line(stat = "identity",
            color = "grey") +
  geom_point(stat = "identity",
             color = "blue",
             alpha = 0.7,
             size = 1) +
  
  geom_vline(xintercept = monthly_df$seqnum[monthly_df$yr_mo == "2022-7"],
             color = "darkgrey") +
  
  annotate("text",
           x = monthly_df$seqnum[monthly_df$yr_mo == "2022-1"],
           y = max(monthly_df$cumulative_backlog),
           label = "Interim period", size = 3) +

    annotate("text",
           x = monthly_df$seqnum[monthly_df$yr_mo == "2024-1"],
           y = max(monthly_df$cumulative_backlog),
           label = "Current period", size = 3) +
  
  labs(title = "Cumulative open caseload by month - post 5051",
       x = "Year",
       y = "Number of open cases") +
  
  scale_x_continuous(
    breaks = seq(1, nrow(monthly_df), 12),
    label = as.character(2002:2025)
  )  +
  
  theme(axis.text.x = element_text(size = 5))
  
ggplotly(p)

Source of complaint

Overall

  tab <- cjtc %>%
  group_by(Source = opened_reason) %>%
  summarize(Number = n()) %>%
  mutate(Percent = Number/sum(Number),
         index = if_else(is.na(Source), -1, rank(Number)),
         Source = replace_na(Source, "Missing")
         ) %>%
  arrange(desc(index)) %>%
  select(-index) %>%

  bind_rows(data.frame(Source ="Total",
                       Number = sum(.$Number),
                       Percent = sum(.$Percent))) %>%
  mutate(Percent = scales::percent(Percent, acc=.1))

tab %>%  
  kbl(caption = "Case Source:  all cases",
      align = "lrr") %>%
  kable_styling(bootstrap_options = "striped") %>%
  row_spec(which(tab$Source == "Missing"), italic=T) %>%
  row_spec(which(tab$Source == "Total"), bold=T)

Case Source: all cases
Source	Number	Percent
Complaint	1488	37.9%
CJ Form 1915 Reporting	1427	36.3%
NOS Misconduct	957	24.3%
Division Initiated	49	1.2%
Request for Reinstatement	10	0.3%
Total	3931	100.0%

Post 5051

tab <- cjtc %>%
  filter(period_r != "pre.5051") %>%
  group_by(Source = opened_reason) %>%
  summarize(Number = n()) %>%
  mutate(Percent = Number/sum(Number),
         index = if_else(is.na(Source), -1, rank(Number)),
         Source = replace_na(Source, "Missing")
         ) %>%
  arrange(desc(index)) %>%
  select(-index) %>%

  bind_rows(data.frame(Source ="Total",
                       Number = sum(.$Number),
                       Percent = sum(.$Percent))) %>%
  mutate(Percent = scales::percent(Percent, acc=.1))

tab %>%  
  kbl(caption = "Case Source:  post 5051",
      align = "lrr") %>%
  kable_styling(bootstrap_options = "striped") %>%
  row_spec(which(tab$Source == "Missing"), italic=T) %>%
  row_spec(which(tab$Source == "Total"), bold=T)

Case Source: post 5051
Source	Number	Percent
Complaint	1488	44.0%
CJ Form 1915 Reporting	1427	42.2%
NOS Misconduct	421	12.4%
Division Initiated	49	1.4%
Total	3385	100.0%

Conduct investigated

By offense

Offenses are designated by the RCW that applies, and are coded by CJTC staff during the intake review process. Post-5051, offenses are classified into 2 categories: mandatory vs. discretionary decertification, if sustained. In addition, CJTC has proposed a priority ranking for each offense to help triage the existing caseload.

In the tables below each offense is counted separately. The number of records does not track the number of cases, because a single subject may have committed several alleged offenses, and because most cases in intake status have not yet been assigned an offense, so will also be missing in the Classification and Priority ranking tabs.

Classification

tab <- offense_list_internal %>%
  group_by(Classification = offense_classification) %>%
  summarize(Number = n()) %>%
  mutate(Percent = Number/nrow(offense_list_internal)) %>%
  #arrange(desc(Number)) %>%

  bind_rows(data.frame(Classification ="Total",
                       Number = sum(.$Number),
                       Percent = sum(.$Percent))) %>%
  mutate(Percent = scales::percent(Percent, acc=.1))
  
tab %>%
  kbl(caption = "Classification of Offense",
      align = "lrr") %>%
  kable_styling(bootstrap_options = "striped") %>%
  row_spec(which(tab$Classification == "Not classified"), italic=T) %>%
  row_spec(which(tab$Classification == "Total"), bold=T)

Classification of Offense
Classification	Number	Percent
Mandatory	78	4.2%
Discretionary	637	34.1%
Not classified	1153	61.7%
Total	1868	100.0%

Priority ranking

The proposed priority ranking of an offense depends on the employment status of the offender. CJTC staff have proposed priority ranks for all of the RCW offense categories established by 5051, except for two: voluntary surrender of certification and separated for misconduct. These cases are defined by RCW and appear in the dataset, so we include them in the table:

43.101.105(k) “Has been suspended or discharged, has resigned or retired in lieu of discharge, or has separated from the agency after the alleged misconduct occurred, for any conduct listed in this section”
43.101.105(l) “Has voluntarily surrendered the person’s certification as a peace officer or corrections officer.”.

Employed

tab <- offense_list_internal %>%
  mutate(
    Offense_priority = factor(
      case_when(
        is.na(offense_priority_emp) ~ "Not ranked",
        offense_priority_emp < 4 ~ as.character(offense_priority_emp),
        offense_priority_emp == 4 ~ "Separated for misconduct",
        offense_priority_emp == 5 ~ "Surrendered certification")),
    Offense_priority = fct_relevel(Offense_priority, "Not ranked", after = Inf )
  ) %>%
  group_by(Priority = Offense_priority) %>%
  summarize(Number = n()) %>%
  mutate(Percent = Number/nrow(offense_list_internal)) %>%
  #arrange(desc(Number)) %>%

  bind_rows(data.frame(Priority ="Total",
                       Number = sum(.$Number),
                       Percent = sum(.$Percent))) %>%
  mutate(Percent = scales::percent(Percent, acc=.1))
  
tab %>%
  kbl(caption = "Offense Priority: Employed",
      align = "lrr") %>%
  kable_styling(bootstrap_options = "striped") %>%
  row_spec(which(tab$Priority == "Total"), bold=T)

Offense Priority: Employed
Priority	Number	Percent
1	156	8.4%
2	365	19.5%
Separated for misconduct	109	5.8%
Surrendered certification	80	4.3%
Not ranked	1158	62.0%
Total	1868	100.0%

Separated

tab <- offense_list_internal %>%
  mutate(
    Offense_priority = factor(
      case_when(
        is.na(offense_priority_sep) ~ "Not ranked",
        offense_priority_sep < 4 ~ as.character(offense_priority_sep),
        offense_priority_sep == 4 ~ "Separated for misconduct",
        offense_priority_sep == 5 ~ "Surrendered certification")),
    Offense_priority = fct_relevel(Offense_priority, "Not ranked", after = Inf )
  ) %>%
  group_by(Priority = Offense_priority) %>%
  summarize(Number = n()) %>%
  mutate(Percent = Number/nrow(offense_list_internal)) %>%
  #arrange(desc(Number)) %>%

  bind_rows(data.frame(Priority ="Total",
                       Number = sum(.$Number),
                       Percent = sum(.$Percent))) %>%
  mutate(Percent = scales::percent(Percent, acc=.1))
  
tab %>%
  kbl(caption = "Offense Priority: Separated",
      align = "lrr") %>%
  kable_styling(bootstrap_options = "striped") %>%
  row_spec(which(tab$Priority == "Total"), bold=T)

Offense Priority: Separated
Priority	Number	Percent
2	72	3.9%
3	449	24.0%
Separated for misconduct	109	5.8%
Surrendered certification	80	4.3%
Not ranked	1158	62.0%
Total	1868	100.0%

Original CJTC RCW code

Only cases that have been assigned an RCW offense code are included in the table.
Offenses defined by 5051 will begin with RCW 43.101.105
When a description of the RCW has been included in the data, we include it in the table.

tab <- offense_list_internal %>%

  group_by(RCW=offense_rcw, Description=offense_desc) %>%
  summarize(Number = n()) %>%
  mutate(Percent = Number/nrow(offense_list_internal)) %>%
  arrange(desc(Number)) %>%

  bind_rows(data.frame(RCW ="Total",
                       Number = sum(.$Number),
                       Percent = sum(.$Percent))) %>%
  mutate(Percent = scales::percent(Percent, acc=.1))

tab %>%  
  kbl(caption = "Offense investigated",
      align = "llrr") %>%
  kable_styling(bootstrap_options = "striped") %>%
  row_spec(which(tab$RCW == "Total"), bold=T)

Offense investigated
RCW	Description	Number	Percent
RCW 43.101.105(3)(j)(iv)	Unethical or Unprofessional Conduct	271	14.5%
RCW 9A.76.175		155	8.3%
RCW 43.101.105(3)(k)	Separated for Misconduct	109	5.8%
RCW 43.101.010(8)(b)		101	5.4%
RCW 43.101.105(3)(l)	Voluntary Surrender of Certification	80	4.3%
RCW 43.101.010(8)		74	4.0%
RCW 43.101.105(1)(d)		71	3.8%
RCW 43.101.010(9)		63	3.4%
RCW 43.101.010(7)(b)		58	3.1%
RCW 43.101.105(2)(d)	False or Misleading Statements	56	3.0%
RCW 9A.80.010	Official Misconduct	50	2.7%
RCW 43.101.105(d)		46	2.5%
RCW 43.101.105(3)(e)	Excessive Force	43	2.3%
RCW 43.101.105(4)		38	2.0%
RCW 42.20.100	Failure of Duty	27	1.4%
RCW 43.101.105(3)(h)	Discrimination	27	1.4%
FormerRCW 43.101.105(1)(d) (2011)		24	1.3%
RCW 43.101.105(3)(j)(i)	Committed a Felony	24	1.3%
RCW 43.101.010(7)		22	1.2%
RCW 9A.72.040		21	1.1%
RCW 43.101.010(7)(a)		18	1.0%
RCW 9A.36.041		18	1.0%
RCW 43.101.105(1)(c)		17	0.9%
RCW 43.101.105(3)(j)(iii)	Unsafe Practices Involving Firearms, Weapons, or Vehicles	16	0.9%
RCW 9.41.040(2)(a)(i)		16	0.9%
RCW 42.20.040	False Report	15	0.8%
RCW 43.101.105(3)(f)	Sexual Harassment	15	0.8%
RCW 43.101.105(c)		15	0.8%
RCW 43.101.105(3)(j)(ii)	Violation of Rights	14	0.7%
RCW 43.101.105(1)(a)		13	0.7%
RCW 43.101.010(8)(a)(i)(B)		11	0.6%
RCW 43.101.010(8)(a)(iv)		11	0.6%
RCW 43.101.105(2)(a)(i)(B)	Convicted of a Gross Misdemeanor or Domestic Violence Offense	11	0.6%
RCW 43.101.010(8)(b) (2008)		9	0.5%
RCW 43.101.105(3)(g)	Use of Position for Personal Gain	9	0.5%
RCW 43.101.010(7)(c)		8	0.4%
RCW 43.101.010(8)(a)(i)		8	0.4%
RCW 43.101.010(b)		8	0.4%
RCW 43.101.105(3)(b)	Administrative Error	7	0.4%
RCW 43.101.010(7)(a)(i)		6	0.3%
RCW 9A.56.030(1)(a)		6	0.3%
RCW 9A.56.050		6	0.3%
RCW 10.99.020(5)		5	0.3%
RCW 43.101.010(7)(a)(ii)		5	0.3%
RCW 43.101.010(8)(a)(ii)		5	0.3%
RCW 43.101.105(2)(e)	Prohibited from Possessing Weapons	5	0.3%
RCW 43.101.105(3)		5	0.3%
RCW 43.101.105(3)(c)	Falsified Information on Application	5	0.3%
RCW 43.101.105(3)(i)	Extremism	5	0.3%
RCW 43.101.115(4)		5	0.3%
RCW 69.50.4013		5	0.3%
RCW 43.101.010(8)(a) and (b) (2008)		4	0.2%
RCW 43.101.105(1)(b)		4	0.2%
RCW 43.101.105(2)(a)(i)(A)	Convicted of a Felony Offense	4	0.2%
RCW 10.99.020		3	0.2%
RCW 43.101.010 (8)(a)(i)(B)		3	0.2%
RCW 43.101.010(8)(a)(i)(A)(II)		3	0.2%
RCW 43.101.010(8)(a)(ii) and (b) (2008)		3	0.2%
RCW 43.101.010(8)(b) (2020)		3	0.2%
RCW 43.101.010(8)(iii)		3	0.2%
RCW 43.101.105(3)(d)(i)	Interfered with a Certification Investigation	3	0.2%
RCW 43.101.105(d) (2011)		3	0.2%
RCW 69.50.403(1)(c)		3	0.2%
RCW 9A.44.160		3	0.2%
RCW 9A.44.160(1)(b)		3	0.2%
RCW 9A.46.110(1)		3	0.2%
RCW 9A.52.110		3	0.2%
RCW 9A.60.020		3	0.2%
RCW 9A.72.020		3	0.2%
RCW 9A.76.020		3	0.2%
RCW 10.99.020(3)		2	0.1%
RCW 43.101.010(4)		2	0.1%
RCW 43.101.010(7)(iv)		2	0.1%
RCW 43.101.010(8)(a)		2	0.1%
RCW 43.101.010(8)(a) and (b)		2	0.1%
RCW 43.101.095		2	0.1%
RCW 43.101.105(3)(a)	Failed to MeetRequirements for Certification	2	0.1%
RCW 43.101.155(1)		2	0.1%
RCW 43.101.200		2	0.1%
RCW 69.41.030		2	0.1%
RCW 69.50.4013(1)		2	0.1%
RCW 69.50.4014		2	0.1%
RCW 9.41.040		2	0.1%
RCW 9.73.030		2	0.1%
RCW 9A.28.020		2	0.1%
RCW 9A.36.031(1)(g)		2	0.1%
RCW 9A.40.040		2	0.1%
RCW 9A.44.050		2	0.1%
RCW 9A.46.020(1)(a)(i)		2	0.1%
RCW 9A.46.020(2)(b)		2	0.1%
RCW 9A.52.030		2	0.1%
RCW 9A.52.100		2	0.1%
RCW 9A.52.120		2	0.1%
RCW 9A.56.020(1)(a)		2	0.1%
RCW 9A.56.040		2	0.1%
RCW 9A.76.175	False or Misleading Statements	2	0.1%
RCW 10.99.040		1	0.1%
RCW 26.50.010(7)		1	0.1%
RCW 26.50.110(1)		1	0.1%
RCW 42.20.050		1	0.1%
RCW 43.101.010 (8)(b)		1	0.1%
RCW 43.101.010(6)		1	0.1%
RCW 43.101.010(7)(a)(iii)		1	0.1%
RCW 43.101.010(7)(a)(iv)		1	0.1%
RCW 43.101.010(7)(b) and (8)		1	0.1%
RCW 43.101.010(8)(a)(i)(A)		1	0.1%
RCW 43.101.010(8)(a)(i)(A)(IV)		1	0.1%
RCW 43.101.010(8)(a)(i)(B) (2008)		1	0.1%
RCW 43.101.010(8)(a)(i)(B)(b)		1	0.1%
RCW 43.101.010(8)(a)(i)(ii)(iv)		1	0.1%
RCW 43.101.010(8)(a)(iii)(iv)		1	0.1%
RCW 43.101.010(8)(b) and (9)		1	0.1%
RCW 43.101.010(9) (2008)		1	0.1%
RCW 43.101.105(1)		1	0.1%
RCW 43.101.105(2)(a)(ii)(B)		1	0.1%
RCW 43.101.105(2)(c)(i)	Terminated for Failure to Intervene in Use of Force	1	0.1%
RCW 43.101.105(3)(d)(ii)	Interfered with a Certification Investigation	1	0.1%
RCW 43.101.105(3)(d)(iii)	Interfered with a Certification Investigation	1	0.1%
RCW 43.101.157		1	0.1%
RCW 43.89.010		1	0.1%
RCW 46.52.010(1		1	0.1%
RCW 46.61.465		1	0.1%
RCW 46.61.500		1	0.1%
RCW 46.61.502		1	0.1%
RCW 46.61.520		1	0.1%
RCW 49.50.4013(1)		1	0.1%
RCW 50.36.010		1	0.1%
RCW 69.50.101(g)		1	0.1%
RCW 69.50.204(c)(22)		1	0.1%
RCW 69.50.206(b)(4)		1	0.1%
RCW 69.50.401		1	0.1%
RCW 69.50.401(1)(2)(c)		1	0.1%
RCW 69.50.401(1)(a)		1	0.1%
RCW 69.50.403(a)		1	0.1%
RCW 9.35.020		1	0.1%
RCW 9.35.020(1)		1	0.1%
RCW 9.35.020(3)		1	0.1%
RCW 9.41.020(2)(a)(i)		1	0.1%
RCW 9.68A.070(1)		1	0.1%
RCW 9.68A.070(2)(a)		1	0.1%
RCW 9.68A.090(1)		1	0.1%
RCW 9.94A.535(2)(f)		1	0.1%
RCW 9A.020(1)(b)		1	0.1%
RCW 9A.04.040		1	0.1%
RCW 9A.04.110(23)		1	0.1%
RCW 9A.08.020		1	0.1%
RCW 9A.28.040		1	0.1%
RCW 9A.28.040(3)(c)		1	0.1%
RCW 9A.36.021		1	0.1%
RCW 9A.36.021(1)		1	0.1%
RCW 9A.36.021(1)(a)		1	0.1%
RCW 9A.36.021(1)(c)		1	0.1%
RCW 9A.36.021(2)(a)		1	0.1%
RCW 9A.36.021(c)		1	0.1%
RCW 9A.36.031(f)		1	0.1%
RCW 9A.36.041(1)		1	0.1%
RCW 9A.36.050		1	0.1%
RCW 9A.40.010(1)		1	0.1%
RCW 9A.44.010(1)(c)		1	0.1%
RCW 9A.44.050(1)(B)		1	0.1%
RCW 9A.44.050(1)(F)		1	0.1%
RCW 9A.44.060		1	0.1%
RCW 9A.44.076		1	0.1%
RCW 9A.44.079		1	0.1%
RCW 9A.44.083		1	0.1%
RCW 9A.44.115		1	0.1%
RCW 9A.44.170(1)(b)		1	0.1%
RCW 9A.46.110		1	0.1%
RCW 9A.46.110(5)(b)(ii)		1	0.1%
RCW 9A.52.025(1)		1	0.1%
RCW 9A.52.080		1	0.1%
RCW 9A.56.020		1	0.1%
RCW 9A.56.020(1)		1	0.1%
RCW 9A.56.020(1)(b)		1	0.1%
RCW 9A.56.130		1	0.1%
RCW 9A.56.160		1	0.1%
RCW 9A.56.170		1	0.1%
RCW 9A.56.300		1	0.1%
RCW 9A.64.020(1)		1	0.1%
RCW 9A.72.030		1	0.1%
RCW 9A.72.110		1	0.1%
RCW 9A.72.150		1	0.1%
RCW 9A.76.070		1	0.1%
RCW 9A.82.050		1	0.1%
RCW 9A.83.020		1	0.1%
RCW 9A.83.020(1)		1	0.1%
RCW 9A.83.020(4)		1	0.1%
RCW 9A.88.080(1)(b)		1	0.1%
RCW 9A.88.110		1	0.1%
RCW 9A.90.040		1	0.1%
Total		1868	100.0%

By officer

At the officer level, multiple offenses may be coded for each case (officers may also have multiple cases in the dataset, that is a different issue and is covered in the last section of the report)

Number of offenses

tab <- cjtc %>%
  group_by(Number = num_offenses) %>%
  summarize(Persons = n()) %>%
  mutate(Number = if_else(is.na(Number), "Not coded", as.character(Number)),
         Percent = Persons/sum(Persons)) %>%
  #arrange(desc(Persons)) %>%

  bind_rows(data.frame(Number ="Total",
                       Persons = sum(.$Persons),
                       Percent = sum(.$Percent))) %>%
  mutate(Percent = scales::percent(Percent, acc=.1))
  
tab %>%
  kbl(caption = "Number of Offenses",
      align = "lrr") %>%
  kable_styling(bootstrap_options = "striped") %>%
  row_spec(which(tab$Number == "Not coded"), italic=T) %>%
  row_spec(which(tab$Number == "Total"), bold=T)

Number of Offenses
Number	Persons	Percent
1	245	6.2%
2	137	3.5%
3	186	4.7%
4	102	2.6%
5	34	0.9%
6	18	0.5%
7	10	0.3%
8	2	0.1%
9	1	0.0%
10	1	0.0%
Not coded	3195	81.3%
Total	3931	100.0%

Classification

Since subjects can commit multiple offenses, with multiple classifications, here we identify those with at least one offense that would require mandatory decertification if sustained

tab <- cjtc %>%

  mutate(Classification = case_when(
    num_mandatory_offenses > 0 ~ "At least one mandatory",
    num_discretionary_offenses > 1 ~ "Multiple discretionary",
    num_discretionary_offenses == 1 ~ "Single discretionary")
    ) %>%

  group_by(Classification) %>%
  summarize(Number = n()) %>%
  mutate(Percent = Number/sum(Number),
         index = if_else(is.na(Classification), -1, rank(Number)),
         Classification = replace_na(Classification, "Not classified")
         ) %>%
  arrange(desc(index)) %>%
  select(-index) %>%

  bind_rows(data.frame(Classification ="Total",
                       Number = sum(.$Number),
                       Percent = sum(.$Percent))) %>%
  mutate(Percent = scales::percent(Percent, acc=.1))

tab %>%  
  kbl(caption = "Case classification at the officer level:  all cases",
      align = "lrr") %>%
  kable_styling(bootstrap_options = "striped") %>%
  row_spec(which(tab$Classification == "Not classified"), italic=T) %>%
  row_spec(which(tab$Classification == "Total"), bold=T)

Case classification at the officer level: all cases
Classification	Number	Percent
Single discretionary	143	3.6%
Multiple discretionary	141	3.6%
At least one mandatory	76	1.9%
Not classified	3571	90.8%
Total	3931	100.0%

Documents

The documents for each case provide critical information. They are, however, often missing, even for closed cases.

All cases

tab <- cjtc %>%

  mutate(Available = if_else(is.na(url), "No", "Yes")
         ) %>%
  
  group_by(Available) %>%
  summarize(Number = n()) %>%
  mutate(Percent = Number/sum(Number)) %>%
  arrange(desc(Number)) %>%

  bind_rows(data.frame(Available ="Total",
                       Number = sum(.$Number),
                       Percent = sum(.$Percent))) %>%
  mutate(Percent = scales::percent(Percent, acc=.1)) 
  
tab %>% kbl(caption = "Documents available?",
            align = "lrr") %>%
  kable_styling(bootstrap_options = "striped") %>%
  row_spec(which(tab$Available == "Missing"), italic=T) %>%
  row_spec(which(tab$Available == "Total"), bold=T)

Documents available?
Available	Number	Percent
No	2507	63.8%
Yes	1424	36.2%
Total	3931	100.0%

Post-5051

tab <- cjtc %>%
  filter(period_r != "pre.5051") %>%
  
  mutate(Available = if_else(is.na(url), "No", "Yes")
         ) %>%
  
  group_by(Available) %>%
  summarize(Number = n()) %>%
  mutate(Percent = Number/sum(Number)) %>%
  arrange(desc(Number)) %>%

  bind_rows(data.frame(Available ="Total",
                       Number = sum(.$Number),
                       Percent = sum(.$Percent))) %>%
  mutate(Percent = scales::percent(Percent, acc=.1)) 
  
tab %>% kbl(caption = "Documents available for post 5051 cases",
            align = "lrr") %>%
  kable_styling(bootstrap_options = "striped") %>%
  row_spec(which(tab$Available == "Missing"), italic=T) %>%
  row_spec(which(tab$Available == "Total"), bold=T)

Documents available for post 5051 cases
Available	Number	Percent
No	2501	73.9%
Yes	884	26.1%
Total	3385	100.0%

Post 5051, closed, not duplicate

These cases really should have documents available.

tab <- cjtc %>%
  filter(period_r != "pre.5051" & status3 == "Closed" & outcat != "Duplicate") %>%
  
  mutate(Available = if_else(is.na(url), "No", "Yes")
         ) %>%
  
  group_by(Available) %>%
  summarize(Number = n()) %>%
  mutate(Percent = Number/sum(Number)) %>%
  arrange(desc(Number)) %>%

  bind_rows(data.frame(Available ="Total",
                       Number = sum(.$Number),
                       Percent = sum(.$Percent))) %>%
  mutate(Percent = scales::percent(Percent, acc=.1)) 
  
tab %>% kbl(caption = "Documents available for post 5051 cases",
            align = "lrr") %>%
  kable_styling(bootstrap_options = "striped") %>%
  row_spec(which(tab$Available == "Missing"), italic=T) %>%
  row_spec(which(tab$Available == "Total"), bold=T)

Documents available for post 5051 cases
Available	Number	Percent
No	487	50.1%
Yes	485	49.9%
Total	972	100.0%

Employers

Agency type

Overall

tab <- cjtc %>%

  group_by(Type = organization_type) %>%
  summarize(Number = n()) %>%
  mutate(Percent = Number/sum(Number),
         index = if_else(is.na(Type), -1, rank(Number)),
         Type = replace_na(Type, "Missing")
         ) %>%
  arrange(desc(index)) %>%
  select(-index) %>%

  bind_rows(data.frame(Type ="Total",
                       Number = sum(.$Number),
                       Percent = sum(.$Percent))) %>%
  mutate(Percent = scales::percent(Percent, acc=.1))
  
tab %>%
  kbl(caption = "Employer agency type",
      align = "lrr") %>%
  kable_styling(bootstrap_options = "striped") %>%
  row_spec(which(tab$Type == "Missing"), italic=T) %>%
  row_spec(which(tab$Type == "Total"), bold=T)

Employer agency type
Type	Number	Percent
Law Enforcement Agency	3462	88.1%
WA State Agency	234	6.0%
Correctional Facility	177	4.5%
WA State Agency; Law Enforcement Agency	9	0.2%
Law Enforcement Agency; Correctional Facility	5	0.1%
Law Enforcement Agency; NA	4	0.1%
Correctional Facility; Law Enforcement Agency	1	0.0%
Law Enforcement Agency; WA State Agency	1	0.0%
Police Department	1	0.0%
Missing	37	0.9%
Total	3931	100.0%

Post 5051

tab <- cjtc %>%
  filter(period_r != "pre.5051") %>%
  
  group_by(Type = organization_type) %>%
  summarize(Number = n()) %>%
  mutate(Percent = Number/sum(Number),
         index = if_else(is.na(Type), -1, rank(Number)),
         Type = replace_na(Type, "Missing")
         ) %>%
  arrange(desc(index)) %>%
  select(-index) %>%

  bind_rows(data.frame(Type ="Total",
                       Number = sum(.$Number),
                       Percent = sum(.$Percent))) %>%
  mutate(Percent = scales::percent(Percent, acc=.1))
  
tab %>%
  kbl(caption = "Employer agency type",
      align = "lrr") %>%
  kable_styling(bootstrap_options = "striped") %>%
  row_spec(which(tab$Type == "Missing"), italic=T) %>%
  row_spec(which(tab$Type == "Total"), bold=T)

Employer agency type
Type	Number	Percent
Law Enforcement Agency	2945	87.0%
WA State Agency	209	6.2%
Correctional Facility	174	5.1%
WA State Agency; Law Enforcement Agency	9	0.3%
Law Enforcement Agency; Correctional Facility	5	0.1%
Law Enforcement Agency; NA	4	0.1%
Correctional Facility; Law Enforcement Agency	1	0.0%
Law Enforcement Agency; WA State Agency	1	0.0%
Police Department	1	0.0%
Missing	36	1.1%
Total	3385	100.0%

Agency Name

Overall

tbl <- cjtc  %>%
  filter(!is.na(agency)) %>%
  
  mutate(employer = gsub("County Sheriff's Office", "Cnty SO", agency),
         employer = gsub("Police Department", "PD", employer)
         ) %>%
  
  group_by(employer) %>%
  summarise(Number = n()) %>%
  mutate(Percent = Number/sum(Number)) %>%
  arrange(Number) %>%
  filter(Number > 19)

ggplot(tbl,
       aes(x = reorder(employer, Percent), y = Percent, 
           label = Number)) +
  geom_text(aes(y = Percent), size = 3, nudge_y = .002) +
  geom_bar(stat="identity", fill="blue", alpha=.5) +
  labs(title = "Cases by Employer: Agencies with 20+ cases",
       caption = "CJTC certification database",
       x = "",
       y = "Percent of Total") +
  
  theme(axis.text.y = element_text(size=7)) +
  
  scale_y_continuous(labels = scales::percent_format(acc=1)) +
  coord_flip()

Post-5051

tbl2 <- cjtc  %>%
  filter(period_r != "pre.5051") %>%
  filter(!is.na(agency)) %>%
  
  mutate(employer = gsub("County Sheriff's Office", "Cnty SO", agency),
         employer = gsub("Police Department", "PD", employer)
         ) %>%
  
  group_by(employer) %>%
  summarise(Number = n()) %>%
  mutate(Percent = Number/sum(Number)) %>%
  arrange(Number) %>%
  filter(Number > 19)

ggplot(tbl2,
       aes(x = reorder(employer, Percent), y = Percent, 
           label = Number)) +
  geom_text(aes(y = Percent), size = 3, nudge_y = .002) +
  geom_bar(stat="identity", fill="blue", alpha=.5) +
  labs(title = "Cases by Employer post 5051: Agencies with 20+ cases",
       caption = "CJTC certification database",
       x = "",
       y = "Percent of Total") +
  
  theme(axis.text.y = element_text(size=7)) +
  
  scale_y_continuous(labels = scales::percent_format(acc=1)) +
  coord_flip()

Officers

Certification status

tab <- cjtc %>%

  group_by(Certification.status = certification_status) %>%
  summarize(Number = n()) %>%
  mutate(Percent = Number/sum(Number),
         index = if_else(is.na(Certification.status), -1, rank(Number)),
         Certification.status = replace_na(Certification.status, "Missing")
         ) %>%
  arrange(desc(index)) %>%
  select(-index) %>%

  bind_rows(data.frame(Certification.status ="Total",
                       Number = sum(.$Number),
                       Percent = sum(.$Percent))) %>%
  mutate(Percent = scales::percent(Percent, acc=.1))

tab %>%  
  kbl(caption = "Certification status of subject:  all cases",
      align = "lrr") %>%
  kable_styling(bootstrap_options = "striped") %>%
  row_spec(which(tab$Certification.status == "Missing"), italic=T) %>%
  row_spec(which(tab$Certification.status == "Total"), bold=T)

Certification status of subject: all cases
Certification.status	Number	Percent
Certified	2782	70.8%
Not Certified	182	4.6%
Lapsed	79	2.0%
Expired	29	0.7%
Certified; NA	4	0.1%
Missing	855	21.8%
Total	3931	100.0%

Occupation type

tab <- cjtc %>%

  group_by(Occupation = occupation_type) %>%
  summarize(Number = n()) %>%
  mutate(Percent = Number/sum(Number),
         index = if_else(is.na(Occupation), -1, rank(Number)),
         Occupation = replace_na(Occupation, "Missing")
         ) %>%
  arrange(desc(index)) %>%
  select(-index) %>%

  bind_rows(data.frame(Occupation ="Total",
                       Number = sum(.$Number),
                       Percent = sum(.$Percent))) %>%
  mutate(Percent = scales::percent(Percent, acc=.1))

tab %>%  
  kbl(caption = "Occupation type of subject:  all cases",
      align = "lrr") %>%
  kable_styling(bootstrap_options = "striped") %>%
  row_spec(which(tab$Occupation == "Missing"), italic=T) %>%
  row_spec(which(tab$Occupation == "Total"), bold=T)

Occupation type of subject: all cases
Occupation	Number	Percent
Peace Officer	3101	78.9%
Corrections Officer	221	5.6%
Recruit	91	2.3%
Non-Certified	88	2.2%
Peace Officer; NA	4	0.1%
Missing	426	10.8%
Total	3931	100.0%

Officers with multiple cases

Here we pull out and list by name officers with more than one case in the database – i.e., they have been the subject of multiple complaints and/or agency notifications of misconduct. Note that when the name is missing in the database, we are unable to identify these officers.

The information shown in the columns of the table for each officer is aggregated from all of their cases in the dataset; the list of distinct entries for each type of information is given, separated by “;”. The exception is the “outcomes” column – here if their certification was revoked for any of their cases, we list just the revocation.

The table is interactive: data can be searched, sorted, filtered and downloaded using one of the buttons or boxes shown at the top of the table.

cjtc_name <- cjtc %>%
  filter(!is.na(first_name) & first_name != "Missing" &
           !grepl("REDACT", first_name) & !grepl("Unknown", first_name) &
           !is.na(last_name) & last_name != "Missing" & 
           !grepl("REDACT", last_name) & !grepl("Complainant", first_name)) %>%
  mutate(employer = gsub("County Sheriff's Office", "CSO", agency),
         employer = gsub("Police Department", "PD", employer),
         employer = gsub("Department of Corrections", "DOC", employer),
         source = case_when(
           grepl("1915", opened_reason) ~ "1915 report",
           grepl("Request", opened_reason) ~ "Reinstate rqst",
           grepl("Division", opened_reason) ~ "Division",
           TRUE ~ opened_reason),
         name = paste(last_name, first_name, sep=", ")
           ) %>%
  arrange(date_received) %>%
  select(name, employer, source, date_received, status, case_determination, outcat) %>%
  group_by(name) %>%
  summarise(nrec = n(),
            across(employer:outcat, ~paste(unique(.x), collapse="; ")), .groups = "drop") %>%
  mutate(across(where(is.character), ~if_else(. == "NA", NA, .))) %>% # recover NAs 
  mutate(outcat = gsub("Revoked\\/", "", outcat),
         final_outcome = if_else(grepl("Revo", case_determination), "Cert Revoked", outcat))


cjtc_name %>%
  filter(nrec > 1) %>%
  arrange(desc(nrec)) %>%
  select(name, cases=nrec, sources=source, dates=date_received, employers=employer, 
         statuses=status, outcomes=final_outcome) %>%
  
  DT::datatable(rownames = F,
                caption = paste("Officers with more than one record in the database as of", 
                                max(cjtc$date_received)),
                filter = 'top',
                escape = FALSE, 
                extensions = 'Buttons', 
                options = 
                  list(dom = 'Bfrtip',
                       buttons = c('copy', 'csv', 'excel', 'pdf', 'print'))
  )

CJTC certification database

Next Steps Washington

10 October, 2025

Introduction

Overview

SB 5051

On the data

Case Status

Summary

Detailed CJTC coding

Outcomes

Overall

Summary

Original CJTC detail

By period - closed cases

By year - recd post 5051, closed

Processing time

By status and period

By outcome (recd post-5051, closed)

Caseload dynamics over time

Received vs. Closed

Monthly net throughput

Cumulative open caseload

All cases

Post 5051

Source of complaint

Overall

Post 5051

Conduct investigated

By offense

Classification

Priority ranking

Employed

Separated

Original CJTC RCW code

By officer

Number of offenses

Classification

Documents

All cases

Post-5051

Post 5051, closed, not duplicate

Employers

Agency type

Overall

Post 5051

Agency Name

Overall

Post-5051

Officers

Certification status

Occupation type

Officers with multiple cases