ICE ERO-LESA enforcement actions data per AOR, FY12-22
UWCHR
2024-06-06
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: Newark Area of Responsibility
This notebook provides a basic descriptive overview of ICE ERO-LESA enforcement data for the Newark Area of Responsibility (NEW) 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
Newark 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 Newark 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, Newark 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
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 
# p2 <- arr_pc_per_aor %>%
# ggplot(aes(x = fy, y=pc_rank, color=aor, group=aor)) +
# geom_line() +
# gghighlight(aor == specific_aor, use_direct_label = FALSE) +
# scale_y_reverse() +
# labs(title = "ICE arrests per 100,000 residents, AOR rank",
# subtitle = paste0(specific_area_of_responsibility, " highlighted"))
#
# p2
p3 <- arr_pc_per_aor %>%
filter(aor == specific_aor) %>%
ggplot(aes(x = fy, y=n_per_cap, color=aor, group=aor)) +
geom_line() +
ylim(0, NA) +
labs(title = "ICE arrests per 100,000 residents",
subtitle = paste0(specific_area_of_responsibility))
p3 
Arrests by gender
p1 <- arr %>%
filter(aor == specific_aor,
arrest_date >= "2011-10-01",
arrest_date <= "2022-09-30") %>%
mutate(gender = as.factor(toupper(gender))) %>%
count(fy, gender) %>%
ggplot(aes(x=fy, y=n, fill=gender)) +
geom_col(position='fill') +
scale_y_continuous(labels = scales::percent) +
labs(title="Total ICE arrests, % by gender",
subtitle=specific_area_of_responsibility) +
theme_minimal()
p1
Arrests by citizenship_country
cit <- arr %>%
filter(aor == specific_aor,
arrest_date >= "2011-10-01",
arrest_date <= "2022-09-30") %>%
mutate(citizenship_country = toupper(citizenship_country)) %>%
group_by(citizenship_country) %>%
summarize(n = n()) %>%
arrange(desc(n))
cit_categories <- 10
p1 <- arr %>%
filter(aor == specific_aor,
arrest_date <= "2022-09-30") %>%
mutate(citizenship_country = case_when(
citizenship_country %in% head(cit$citizenship_country, cit_categories) ~ citizenship_country,
TRUE ~ "ALL OTHERS"
)) %>%
count(fy, citizenship_country) %>%
ggplot(aes(x=fy, y=n, fill=citizenship_country)) +
geom_col() +
labs(title = paste0("Total ICE arrests by country of citizenship (top ", cit_categories, ")"),
subtitle = specific_area_of_responsibility) +
theme_minimal()
ggplotly(p1)p2 <- arr %>%
filter(aor == specific_aor,
arrest_date <= "2022-09-30") %>%
mutate(citizenship_country = case_when(
citizenship_country %in% head(cit$citizenship_country, cit_categories) ~ citizenship_country,
TRUE ~ "ALL OTHERS"
)) %>%
count(fy, citizenship_country) %>%
ggplot(aes(x=fy, y=n, fill=citizenship_country)) +
geom_col(position = "fill") +
geom_hline(yintercept = c(.25, .5, .75), linetype="dashed",
linewidth=.25) +
scale_y_continuous(labels = scales::percent) +
labs(title = paste0("% ICE arrests by country of citizenship (top ", cit_categories, ")"),
subtitle = specific_area_of_responsibility) +
theme_minimal()
p2
cit_rank <- arr %>%
filter(aor == specific_aor,
arrest_date >= "2011-10-01",
arrest_date <= "2022-09-30") %>%
count(fy, citizenship_country) %>%
arrange(fy, desc(n), citizenship_country) %>%
group_by(fy) %>%
mutate(ranking = row_number())
p1 <- cit_rank %>%
filter(ranking <= 10) %>%
ggplot(aes(x = fy, y = ranking, color = citizenship_country, group = citizenship_country)) +
geom_line(alpha = .7, size = 1) +
geom_point(alpha = .7, size = 2) +
scale_y_reverse() +
labs(title = "Ranked country of citizenship for ICE arrests",
subtitle = specific_area_of_responsibility) +
theme_minimal()
ggplotly(p1)Arrests by arrest_method
methods <- arr %>%
filter(aor == specific_aor,
arrest_date >= "2011-10-01",
arrest_date <= "2022-09-30") %>%
count(arrest_method) %>%
arrange(desc(n))
top_methods <- methods %>%
filter(n > 100)
arr <- arr %>%
mutate(arrest_method_short = case_when(arrest_method %in% unlist(top_methods$arrest_method) ~ as.character(arrest_method),
TRUE ~ "All others"))
p1 <- arr %>%
filter(aor == specific_aor,
arrest_date >= "2011-10-01",
arrest_date <= "2022-09-30") %>%
group_by(fy, arrest_method_short) %>%
ggplot(aes(x = fy, fill=arrest_method_short)) +
geom_bar(stat='count', position='stack') +
theme_minimal()
ggplotly(p1)dat <- arr %>%
filter(aor == specific_aor,
arrest_method_short %in% c("CAP Local Incarceration",
"CAP State Incarceration",
"CAP Federal Incarceration"),
arrest_date >= "2011-10-01",
arrest_date <= "2022-09-30") %>%
count(arrest_method_short, fy)
p1 <- dat %>%
ggplot(aes(x = fy, y=n, fill=arrest_method_short)) +
geom_col(position='dodge') +
labs(title = "Total CAP arrests, FY12-22",
subtitle = specific_area_of_responsibility) +
theme_minimal()
p1
dat <- arr %>%
filter(arrest_method_short %in% c("CAP Local Incarceration",
"CAP State Incarceration",
"CAP Federal Incarceration"),
arrest_date >= "2011-10-01",
arrest_date <= "2022-09-30") %>%
count(arrest_method_short, fy)
p2 <- dat %>%
ggplot(aes(x = fy, y=n, fill=arrest_method_short)) +
geom_col(position='dodge') +
labs(title = "Total CAP arrests, FY12-22",
subtitle = "National") +
theme_minimal()
p2
dat1 <- arr %>%
filter(arrest_date >= "2011-10-01",
arrest_date <= "2022-09-30") %>%
count(fy, arrest_method_short) %>%
mutate(group = "National")
dat2 <- arr %>%
filter(aor == specific_aor,
arrest_date >= "2011-10-01",
arrest_date <= "2022-09-30") %>%
count(fy, arrest_method_short) %>%
mutate(group = specific_area_of_responsibility)
dat <- rbind(dat1, dat2)
p1 <- dat %>%
filter(group == "National") %>%
ggplot(aes(x = fy, y = n, color = arrest_method_short, group=arrest_method_short)) +
geom_line() +
facet_wrap(~group, scales = "free_y") +
theme_minimal()
p1
p2 <- dat %>%
filter(group == specific_area_of_responsibility) %>%
ggplot(aes(x = fy, y = n, color = arrest_method_short, group=arrest_method_short)) +
geom_line() +
facet_wrap(~group, scales = "free_y") +
theme_minimal()
p2
Arrests by processing_disposition
disps <- arr %>%
filter(aor == specific_aor) %>%
count(processing_disposition) %>%
arrange(desc(n))
arr <- arr %>%
mutate(disp_short = case_when(
processing_disposition %in%
head(disps$processing_disposition, 10) ~
as.character(processing_disposition),
TRUE ~ "ALL OTHERS"))
p1 <- arr %>%
filter(aor == specific_aor) %>%
group_by(fy, disp_short) %>%
summarize(n = n()) %>%
ggplot(aes(x = fy, y=n, fill=disp_short)) +
geom_col() +
labs(title = "Total ICE arrests per FY by processing disposition") +
theme_minimal()
ggplotly(p1)Arrests by apprehension_landmark
landmarks <- arr %>%
filter(aor == specific_aor) %>%
count(apprehension_landmark) %>%
arrange(desc(n))
# Abbreviating values in data can collapse some categories inadvertently.
# Need to figure out how to shorten labels only, preferably in a way that works with plotly
p1 <- arr %>%
filter(aor == specific_aor) %>%
mutate(apprehension_landmark = case_when(
apprehension_landmark %in%
head(landmarks$apprehension_landmark, 15) ~
as.character(apprehension_landmark),
TRUE ~ "ALL OTHERS")) %>%
group_by(fy, apprehension_landmark) %>%
summarize(n = n()) %>%
ggplot(aes(x = fy, y=n,
fill = apprehension_landmark,
color = apprehension_landmark,
text = apprehension_landmark)) +
geom_col() +
scale_fill_discrete(label = function(x) stringr::str_trunc(x, 20)) +
scale_color_discrete(label = function(x) stringr::str_trunc(x, 20)) +
labs(title = "Total ICE arrests per FY by `apprehension_landmark` (top 15)",
subtitle = specific_area_of_responsibility) +
theme_minimal()
# ggplotly(p1, tooltip=c('x', 'y', 'text'))
p1
Removals
For national overview, see the Removals notebook.
Removals per capita
rem_per_aor <- rem %>%
filter(!is.na(aor),
aor != "HQ",
departed_date >= "2011-10-01",
departed_date <= "2022-09-30") %>%
group_by(fy, aor) %>%
summarize(n = n())
rem_pc_per_aor <- left_join(rem_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_removals_rank_fy12 <- as.numeric(rem_pc_per_aor[rem_pc_per_aor$aor == specific_aor & rem_pc_per_aor$fy == 2012, 'pc_rank'])
pc_removals_rank_fy22 <- as.numeric(rem_pc_per_aor[rem_pc_per_aor$aor == specific_aor & rem_pc_per_aor$fy == 2022, 'pc_rank'])
p1 <- rem_pc_per_aor %>%
ggplot(aes(x = fy, y=n_per_cap, color=aor, group=aor)) +
geom_line() +
gghighlight(aor == specific_aor) +
labs(title = "ICE removals per 100,000 residents",
subtitle = paste0(specific_area_of_responsibility, " highlighted")) +
theme_minimal()
p1 
Removals by gender
# rem %>%
# mutate(gender = tolower(gender)) %>%
# group_by(gender) %>%
# summarize(n = n())
p1 <- rem %>%
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) +
labs(title="Total ICE removals, % by gender") +
theme_minimal()
p1
Removals by citizenship_country
cit <- rem %>%
filter(aor == specific_aor) %>%
mutate(citizenship_country = toupper(citizenship_country)) %>%
group_by(citizenship_country) %>%
summarize(n = n()) %>%
arrange(desc(n))
p1 <- rem %>%
filter(aor == specific_aor) %>%
mutate(citizenship_country = case_when(
citizenship_country %in% head(cit$citizenship_country, 15) ~ citizenship_country,
TRUE ~ "ALL OTHERS"
)) %>%
count(fy, citizenship_country) %>%
ggplot(aes(x=fy, y=n, fill=citizenship_country, color=citizenship_country)) +
geom_col() +
labs(title = "Total ICE removals by country of citizenship (top 15)") +
theme_minimal()
ggplotly(p1)p2 <- rem %>%
filter(aor == specific_aor) %>%
mutate(citizenship_country = case_when(
citizenship_country %in% head(cit$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(position="fill") +
scale_y_continuous(labels = scales::percent) +
labs(title = "% ICE removals by country of citizenship (top 15)") +
theme_minimal()
ggplotly(p2)# % change in removal by group?Removals by processing_disposition
disps <- rem %>%
filter(aor == specific_aor) %>%
count(processing_disposition_clean) %>%
arrange(desc(n))
p1 <- rem %>%
filter(aor == specific_aor) %>%
mutate(disp_short = case_when(processing_disposition_clean %in% head(disps$processing_disposition_clean, 10) ~ as.character(processing_disposition_clean),
TRUE ~ "ALL OTHERS")) %>%
group_by(fy, disp_short) %>%
summarize(n = n()) %>%
ggplot(aes(x = fy, y=n, fill=disp_short)) +
geom_col() +
labs(title = "Total removals per FY by processing disposition",
subtitle = specific_area_of_responsibility,
x = "Fiscal year",
y = "Count") +
theme_minimal()
ggplotly(p1)