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library(pacman)

p_load(here, tidyverse, zoo, lubridate, ggplot2, plotly, gghighlight, sf, tigris, ggrepel, viridis)

options(scipen = 1000000)

specific_aor <- params$aor
# ENCOUNTERS DATA

enc <- read_delim(here('write', 'input', 'ice_encounters_fy12-23ytd.csv.gz'), delim='|',
                  col_types = cols(aor = col_factor(),
                                   event_date = col_character(),
                                   landmark = col_character(),
                                   operation = col_factor(),
                                   processing_disposition = col_factor(),
                                   citizenship_country = col_factor(),
                                   gender = col_factor(),
                                   hashid = col_character(),
                                   id = col_number()))

# glimpse(enc)

redacted <- c('encounter_threat_level', 'alien_file_number')

enc <- enc %>% 
  dplyr::select(-redacted)

enc <- enc %>% 
  mutate(aor = factor(aor, levels = sort(levels(enc$aor))),
         event_date = as_date(event_date, format="%m/%d/%Y"),
         year = year(event_date),
         month = month(event_date, label=TRUE, abbr=TRUE),
         year_mth = zoo::as.yearmon(event_date),
         fy_quarter = as.factor(quarter(event_date, fiscal_start=10, type="year.quarter")),
         fy = as.factor(substr(fy_quarter, 1,4)),
         gender = as.factor(toupper(gender)),
         operation = toupper(operation),
         processing_disposition = toupper(processing_disposition),
         citizenship_country = toupper(citizenship_country),
         landmark = toupper(str_squish(landmark))) %>% 
  filter(event_date >= "2011-10-01",
         event_date <= "2022-09-30")

# ARRESTS DATA

arr <- read_delim(here('write', 'input', 'ice_arrests_fy12-23ytd.csv.gz'), delim='|',
                  col_types = cols(aor = col_factor(),
                                  arrest_date = col_date(format="%m/%d/%Y"),
                                  departed_date = col_date(format="%m/%d/%Y"),
                                  apprehension_landmark = col_factor(),
                                  arrest_method = col_factor(),
                                  operation = col_factor(),
                                  processing_disposition = col_factor(),
                                  citizenship_country = col_factor(),
                                  gender = col_factor(),
                                  case_closed_date = col_date(format="%m/%d/%Y"),
                                  id = col_integer(),
                                  hashid = col_character()
                                  )) 

redacted <- c('removal_threat_level', 'apprehension_threat_level', 'alien_file_number')

arr <- arr %>% 
  dplyr::select(-all_of(redacted))

arr <- arr %>% 
  mutate(aor = factor(aor, levels = sort(levels(arr$aor))),
         arrest_date = as_date(arrest_date, format="%m/%d/%Y"),
         year = year(arrest_date),
         month = month(arrest_date, label=TRUE, abbr=TRUE),
         year_mth = zoo::as.yearmon(arrest_date),
         fy_quarter = as.factor(quarter(arrest_date, fiscal_start=10, type="year.quarter")),
         fy = as.factor(substr(fy_quarter, 1,4)),
         gender = as.factor(toupper(gender)),
         citizenship_country = as.factor(toupper(citizenship_country)),
         apprehension_landmark = toupper(str_squish(apprehension_landmark))) %>% 
  filter(arrest_date >= "2011-10-01",
         arrest_date <= "2022-09-30")

# REMOVALS DATA

pd_dict <- read_delim(here('share', 'hand', 'processing_disp.csv'), delim='|')

rem <- read_delim(here('write', 'input', 'ice_removals_fy12-23ytd.csv.gz'), delim='|',
                  col_types = cols(aor = col_factor(),
                                   arrest_date = col_date(format="%m/%d/%Y"),
                                  departed_date = col_date(format="%m/%d/%Y"),
                                  case_close_date = col_date(format="%m/%d/%Y"),
                                  removal_date = col_date(format="%m/%d/%Y"),
                                  apprehension_method_code = col_character(),
                                  processing_disposition_code = col_factor(),
                                  citizenship_country = col_factor(),
                                  gender = col_factor(),
                                  final_charge_section = col_factor(),
                                  id = col_integer(),
                                  hashid = col_character()
                                  )) 

redacted <- c('removal_threat_level', 'alien_file_number')

rem <- rem %>% 
  dplyr::select(-redacted, -case_closed_date)

rem <- rem %>% 
  mutate(aor = factor(aor, levels = sort(levels(rem$aor))),
         year = year(departed_date),
         month = month(departed_date, label=TRUE, abbr=TRUE),
         year_mth = zoo::as.yearmon(departed_date),
         processing_disp = toupper(coalesce(processing_disposition_code, processing_disposition)),
         fy_quarter = as.factor(quarter(departed_date, fiscal_start=10, type="year.quarter")),
         fy = as.factor(substr(fy_quarter, 1,4)),
         gender = as.factor(toupper(gender)),
         citizenship_country = toupper(citizenship_country)) %>% 
  filter(departed_date >= "2011-10-01",
         departed_date <= "2022-09-30")

rem <- left_join(rem, pd_dict, by=c('processing_disp' = 'processing_disposition_raw'))

# SUPPLEMENTAL DATA

demog <- read_delim(here('share', 'input', 'aor_demog_indicators.csv'), delim='|') %>%
  arrange(aor, year) %>% 
  mutate(year = as.factor(year))

pc_scale = 100000

# Should get this from an external constants file along with other AOR characteristics
specific_area_of_responsibility <- arr %>%
  filter(aor == specific_aor) %>%
  distinct(area_of_responsibility) %>% 
  as.character()

ICE ERO enforcement data: Denver Area of Responsibility

This notebook provides a basic descriptive overview of ICE ERO-LESA enforcement data for the Denver Area of Responsibility (DEN) for the time period from October 1, 2011, through January 29, 2023, (full U.S. Government Fiscal Years 2012 through 2022), obtained by the University of Washington Center for Human Rights (UWCHR) pursuant to FOIA request 2022-ICFO-09023.

For data and code used to generate this notebook, see: https://github.com/UWCHR/ice-enforce

Total enforcement actions by FY

enc_fy <- enc %>% 
  filter(aor == specific_aor) %>% 
  group_by(fy) %>% 
  summarize(n_encounters = n())

arr_fy <- arr %>% 
  filter(aor == specific_aor) %>% 
  group_by(fy) %>% 
  summarize(n_arrests = n())

rem_fy <- rem %>% 
  filter(aor == specific_aor) %>% 
  group_by(fy) %>% 
  summarize(n_removals = n())

dat_aor <- left_join(enc_fy, arr_fy, by='fy') %>% 
  left_join(rem_fy, by='fy')

p1 <- dat_aor %>%
  pivot_longer(cols=-c('fy')) %>% 
  ggplot(aes(x = fy, y=value, color=name, group=name)) +
  geom_line() +
  ylim(0, NA) +
  labs(title = paste0("Total ICE enforcement events per FY",
                      '<br>',
                      '<sup>',
                      specific_area_of_responsibility,
                      '</sup>'),
       caption = "Figure: Univ. of Wash. Center for Human Rights, Data: ICE ERO-LESA",
       x = "Fiscal Year",
       y = "") +
  guides(color = guide_legend("Enforcement type")) +
  theme_minimal()

ggplotly(p1)

The following chart displays basic comparative measures for the Denver Area of Responsibility: difference between total encounters and total arrests (diff_enc_arr); and difference between total removals and total arrests (diff_rem_arr). Values greater than zero denote periods in which there were either more encounters than arrests, or more removals than arrests, respectively.

dat_diff <- dat_aor %>% 
  mutate(diff_enc_arr = n_encounters - n_arrests,
         diff_rem_arr = n_removals - n_arrests)

p2 <- dat_diff %>% 
  pivot_longer(cols=-c('fy')) %>% 
  filter(name %in% c("diff_enc_arr", "diff_rem_arr")) %>% 
  ggplot(aes(x = fy, y=value, color=name, group=name)) +
  geom_line() +
  geom_hline(aes(yintercept=0),
             linetype="dashed",
             linewidth=.25) +
  # annotate("text", x = 2, y = 0, label = "Fewer arrests\nMore arrests") +
  labs(title = paste0("Difference of enforcement event totals per FY",
                      '<br>',
                      '<sup>',
                      specific_area_of_responsibility,
                      '</sup>'),
       x = "Fiscal Year",
       y = "") +
  theme_minimal()

ggplotly(p2)

Here we compare trends for the Denver Area of Responsibility with national trends:

enc_fy_natl <- enc %>% 
  filter(
    event_date <= "2022-09-30") %>% 
  group_by(fy) %>% 
  summarize(n_encounters = n())

arr_fy_natl <- arr %>% 
  filter(
    arrest_date <= "2022-09-30") %>% 
  group_by(fy) %>% 
  summarize(n_arrests = n())

rem_fy_natl <- rem %>% 
  filter(
    departed_date <= "2022-09-30") %>% 
  group_by(fy) %>% 
  summarize(n_removals = n())

dat_natl <- left_join(enc_fy_natl, arr_fy_natl, by='fy') %>% 
  left_join(rem_fy_natl, by='fy')

dat_aor$group <- specific_area_of_responsibility
dat_natl$group <- "National total"

dat <- rbind(dat_aor, dat_natl)

p1 <- dat %>%
  rename(encounters = n_encounters,
         arrests = n_arrests,
         removals = n_removals) %>% 
  pivot_longer(cols=-c('fy', 'group')) %>% 
  ggplot(aes(x = fy, y=value, color=name, group=name, text=group)) +
  geom_line() +
  labs(title = "Total ICE enforcement events, FY2012-22") +
  xlab('Fiscal Year') +
  scale_x_discrete(breaks=as.character(seq(2013,2024,4))) +
  ylab("") +
  ylim(0, NA) +
  scale_color_discrete(name = "Enforcement type") +
  facet_wrap(~group, scales = "free_y") +
  theme_minimal()

ggplotly(p1, tooltip = c("x", "y", "color", "text"))

Percent change

Annual percent change in enforcement actions per FY, Denver Area of Responsibility compared to national.

dat_pct <- dat %>% 
  group_by(group) %>% 
  mutate(across(starts_with('n_'), ~ (.x/lag(.x) - 1))) %>% 
  pivot_longer(cols = c(-fy, -group))

p3 <- dat_pct %>% 
  filter(!is.na(value)) %>% 
  ggplot(aes(x = fy, y = value, fill=name, group=name)) +
  geom_col(position='dodge') +
  scale_y_continuous(labels = scales::percent) +
  scale_x_discrete(breaks=seq(2013,2024,4)) +
  facet_wrap(~group) +
  labs(title = "Annual % change total ICE enforcement actions, FY12-22") +
  theme_minimal()

ggplotly(p3)

Encounters

For national overview, see the Encounters notebook.

Encounters per capita

enc_per_aor <- enc %>% 
  filter(!is.na(aor),
         aor != "HQ",
        event_date <= "2022-09-30") %>% 
  group_by(fy, aor) %>% 
  summarize(n = n())

enc_pc_per_aor <- left_join(enc_per_aor, demog, by=c('fy' = 'year', 'aor' = 'aor')) %>% 
  group_by(aor) %>% 
  fill(contains('pop')) %>% 
  mutate(n_per_cap = (n / total_pop) * pc_scale,
         n_per_undocu = (n / undocu_pop) * pc_scale) %>% 
  arrange(fy, desc(n_per_cap)) %>% 
  group_by(fy) %>% 
  mutate(pc_rank = row_number())

pc_encounter_rank_fy12 <- as.numeric(enc_pc_per_aor[enc_pc_per_aor$aor == specific_aor & enc_pc_per_aor$fy == 2012, 'pc_rank'])
pc_encounter_rank_fy22 <- as.numeric(enc_pc_per_aor[enc_pc_per_aor$aor == specific_aor & enc_pc_per_aor$fy == 2022, 'pc_rank'])

p1 <- enc_pc_per_aor %>% 
  ggplot(aes(x = fy, y=n_per_cap, color=aor, group=aor)) +
  geom_line() +
  gghighlight(aor == specific_aor, use_direct_label = FALSE) +
  xlab("Fiscal Year") +
  ylab("Encounters per capita") +
  labs(title = "ICE encounters per 100,000 residents",
       subtitle = paste0(specific_area_of_responsibility, " highlighted")) +
  theme_minimal()

p1

Encounters by gender

p1 <- enc %>%
  filter(aor == specific_aor) %>% 
  count(fy, gender) %>% 
  ggplot(aes(x=fy, y=n, fill=gender)) +
  geom_col(position='fill') +
  scale_y_continuous(labels = scales::percent) +
  ylab("") +
  xlab('Fiscal Year') +
  labs(title="ICE encounters, % by gender",
       subtitle=specific_area_of_responsibility) +
  theme_minimal()

p1

Encounters by citizenship_country

cit <- enc %>%
  filter(aor == specific_aor) %>%
  mutate(citizenship_country = toupper(citizenship_country)) %>% 
  group_by(citizenship_country) %>% 
  summarize(n = n()) %>% 
  arrange(desc(n))

p1 <- enc %>%
  filter(aor == specific_aor) %>%
  mutate(citizenship_country = case_when(
    citizenship_country %in% head(cit$citizenship_country, 15) ~ citizenship_country,
    TRUE ~ "ALL OTHERS"
  )) %>% 
  count(fy, citizenship_country) %>% 
  ggplot(aes(x=fy, y=n, fill=citizenship_country, color=citizenship_country)) +
  geom_col() +
  labs(title = paste0("ICE encounters by country of citizenship (top 15)",
                      '<br>',
                      '<sup>',
                      specific_area_of_responsibility,
                      '</sup>')) +
  theme_minimal()

ggplotly(p1)
p2 <- enc %>%
  filter(aor == specific_aor) %>%
  mutate(citizenship_country = case_when(
    citizenship_country %in% head(cit$citizenship_country, 15) ~ citizenship_country,
    TRUE ~ "ALL OTHERS"
  )) %>% 
  count(fy, citizenship_country) %>% 
  ggplot(aes(x=fy, y=n, fill=citizenship_country, color=citizenship_country)) +
  scale_y_continuous(labels = scales::percent) +
  geom_col(position = "fill") +
  labs(title = paste0("ICE encounters by country of citizenship (top 15)",
                      '<br>',
                      '<sup>',
                      specific_area_of_responsibility,
                      '</sup>')) +
  theme_minimal()

ggplotly(p2)

Encounter landmark

For more on landmark values, see the Landmarks notebook.

landmarks <- enc %>% 
  filter(aor == specific_aor) %>% 
  count(landmark) %>% 
  arrange(desc(n))

p1 <- enc %>% 
  filter(aor == specific_aor) %>% 
  mutate(landmark = case_when(landmark %in% head(landmarks$landmark, 15) ~ as.character(landmark), 
                                         TRUE ~ "ALL OTHERS"),
         landmark_abbrv = str_trunc(landmark, 20)) %>% 
  group_by(fy, landmark_abbrv, landmark) %>% 
  summarize(n = n()) %>% 
  ggplot(aes(x = fy, y=n,
             fill=landmark_abbrv,
             color=landmark_abbrv,
             text = landmark)) +
  geom_col() +
  labs(title = paste0("Total ICE encounters per FY by `landmark` (top 15)",
                      '<br>',
                      '<sup>',
                      specific_area_of_responsibility,
                      '</sup>')) +
  theme_minimal()

ggplotly(p1, tooltip=c('x', 'y', 'text'))

Encounters by operation

ops <- enc %>% 
  filter(aor == specific_aor) %>% 
  count(operation) %>% 
  arrange(desc(n))

p1 <- enc %>% 
  filter(aor == specific_aor) %>% 
  mutate(operation_short = case_when(operation %in% head(ops$operation, 10) ~ as.character(operation), 
                                         TRUE ~ "ALL OTHERS")) %>% 
  group_by(fy, operation_short) %>% 
  summarize(n = n()) %>% 
  ggplot(aes(x = fy, y=n, fill=operation_short)) +
  geom_col() +
  labs(title = "Total ICE encounters per FY by operation") +
  theme_minimal()

ggplotly(p1)

Encounters by processing_disposition

aor_disps <- enc %>%
  filter(aor == specific_aor) %>% 
  count(processing_disposition) %>% 
  arrange(desc(n))

p1 <- enc %>% 
  filter(aor == specific_aor) %>%
  mutate(disp_short = case_when(processing_disposition %in% head(aor_disps$processing_disposition, 10) ~ as.character(processing_disposition), 
                                         TRUE ~ "ALL OTHERS")) %>% 
  group_by(fy, disp_short) %>% 
  summarize(n = n()) %>% 
  ggplot(aes(x = fy, y=n, fill=disp_short, color=disp_short)) +
  geom_col() +
  labs(title = paste0("Total ICE encounters per FY by `processing disposition` (top 15)",
                      '<br>',
                      '<sup>',
                      specific_area_of_responsibility,
                      '</sup>')) +
  theme_minimal()

ggplotly(p1, dynamicTicks = TRUE)

Arrests

For national overview, see the Arrests notebook.

Arrests per capita

arr_per_aor <- arr %>% 
  filter(!is.na(aor),
         aor != "HQ",
        arrest_date <= "2022-09-30") %>% 
  group_by(fy, aor) %>% 
  summarize(n = n())

arr_pc_per_aor <- left_join(arr_per_aor, demog, by=c('fy' = 'year', 'aor' = 'aor')) %>% 
  group_by(aor) %>% 
  fill(contains('pop')) %>% 
  mutate(n_per_cap = (n / total_pop) * pc_scale,
         n_per_undocu = (n / undocu_pop) * pc_scale) %>% 
  arrange(fy, desc(n_per_cap)) %>% 
  group_by(fy) %>% 
  mutate(pc_rank = row_number())

p1 <- arr_pc_per_aor %>% 
  ggplot(aes(x = fy, y=n_per_cap, color=aor, group=aor)) +
  geom_line() +
  gghighlight(aor == specific_aor, use_direct_label = FALSE) +
  labs(title = "ICE arrests per 100,000 residents",
       subtitle = paste0(specific_area_of_responsibility, " highlighted")) +
  theme_minimal()

p1