Data overview: Encounters

This notebook presents a national overview of U.S. Immigration and Customs Enforcement (ICE) Enforcement and Removal Operations (ERO) Law Enforcement Systems and Analysis Division (LESA) data from ICE’s Integrated Decision Support (IIDS) database regarding nationwide ICE encounters 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

options(scipen = 1000000)

library(pacman)

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

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')
redacted_text <- paste0('`', paste(unlist(redacted), collapse = '`, `'), '`')

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

cy_months <- c("Jan","Feb","Mar", "Apr", "May", "Jun", "Jul", "Aug", "Sep", "Oct", "Nov", "Dec")
fy_months <- c("Oct", "Nov", "Dec", "Jan", "Feb", "Mar", "Apr", "May", "Jun", "Jul", "Aug", "Sep")

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 = factor(month(event_date, label=TRUE, abbr=TRUE), levels = fy_months),
         year_mth = zoo::as.yearmon(event_date),
         fy = substr(quarter(event_date, fiscal_start=10, type="year.quarter"), 1,4),
         gender = toupper(gender),
         operation = toupper(operation),
         processing_disposition = toupper(processing_disposition),
         citizenship_country = toupper(citizenship_country))

An encounter occurs when an individual is subjected to revision of admissibility/removability by ICE, and may or may not lead to an arrest.¹ The encounters dataset (enc) includes 5525050 observations of 14 variables; 2 fully redacted fields (encounter_threat_level, alien_file_number) are dropped from analysis.

The following provides an summary of dataset characteristics via skimr::skim(enc):

# This is slow

skimr::skim(enc)

Data summary
Name	enc
Number of rows	5525050
Number of columns	14
_______________________
Column type frequency:
character	9
Date	1
factor	2
numeric	2
________________________
Group variables	None

Variable type: character

skim_variable	n_missing	complete_rate	min	max	n_unique
landmark	1528342	0.72	1	80	12548
operation	3792221	0.31	1	128	621
processing_disposition	108732	0.98	5	44	52
citizenship_country	36296	0.99	4	31	251
gender	10	1.00	4	7	3
hashid	0	1.00	40	40	5525050
area_of_responsibility	223996	0.96	25	37	26
year_mth	0	1.00	4	16	136
fy	0	1.00	4	4	12

Variable type: Date

skim_variable	n_missing	complete_rate	min	max	median	n_unique
event_date	0	1	2011-10-01	2023-01-29	2016-08-09	4139

Variable type: factor

skim_variable	n_missing	complete_rate	ordered	n_unique	top_counts
aor	223996	0.96	FALSE	25	LOS: 764534, MIA: 465778, NYC: 346856, DAL: 331541
month	0	1.00	TRUE	12	Oct: 510157, Nov: 506096, Jan: 471728, Aug: 471617

Variable type: numeric

skim_variable	n_missing	complete_rate	mean	sd	p0	p25	p50	p75	p100	hist
id	0	1	2762524.50	1594944.70	0	1381262	2762524	4143787	5525049	▇▇▇▇▇
year	0	1	2016.34	3.45	2011	2013	2016	2019	2023	▇▅▆▃▅

Field definitions

Datasets were released without any data dictionary or field descriptions; in cases where this information is not self-explanatory, we have attempted to provide citations of relevant sources providing context.

Original dataset fields

aor: ICE Area of Responsibility associated with encounter
event_date: Date of encounter
landmark: Landmark or entity associated with encounter
operation: Operation associated with encounter
processing_disposition: Status of removal proceedings associated with event
citizenship_country: Country of citizenship of encountered individual
gender: Gender of encountered individual
encounter_threat_level: Fully redacted in original dataset
alien_file_number: Unique individual identifier for encountered individual, fully redacted in original dataset

Additional fields created by UWCHR

id: Sequential record identifier (not individual identifier)
hashid: Unique record hash (not individual identifier)
year: Calendar year derived from event_date
month: Abbreviated month derived from event_date
year_mth: Calendar year and month derived from event_date
fy: U.S. government fiscal year (Oct.-Sept.) derived from event_date

Total encounters

p1 <- enc %>%
  filter(event_date >= "2011-10-01",
         event_date <= "2022-09-30") %>% 
  group_by(fy) %>% 
  summarize(n = n()) %>% 
  ggplot(aes(x = fy, y = n)) +
  geom_line(aes(group=1)) +
  ylim(0, NA) +
  labs(title = "Total nationwide ICE encounters per FY") +
  theme_minimal()

p1

# p2 <- enc %>%
#   group_by(year_mth) %>%
#   summarize(n = n()) %>%
#   ggplot(aes(x = year_mth, y = n)) +
#   geom_line(aes(group=1)) +
#   ylim(0, NA) +
#   labs(title = "Total nationwide ICE encounters per month")
# 
# p2
# 
# p3 <- enc %>%
#   group_by(fy, month) %>%
#   summarize(n = n()) %>%
#   ggplot(aes(x = month, y = n, color = fy, group = fy)) +
#   geom_line() +
#   ylim(0, NA) +
#   scale_color_viridis_d() +
#   labs(title = "Total nationwide ICE encounters per month")
# 
# p3

Basic demographics

Gender

Note increasing proportion of encounters involving females:

p1 <- enc %>%
  filter(event_date >= "2011-10-01",
         event_date <= "2022-09-30") %>% 
  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 encounters, % by gender") +
  theme_minimal()

p1

Country of citizenship

Total ICE encounters by country of citizenship:

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

p1 <- enc %>%
  filter(event_date >= "2011-10-01",
         event_date <= "2022-09-30") %>% 
  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 encounters by country of citizenship (top 15)") +
  theme_minimal()

ggplotly(p1)

U.S. citizens and “unknown” nationality

Decreasing proportion of ICE encounters of U.S. citizens/and “unknown” nationality. These categories are suggestive of encounters involving people not amenable for removal by ICE.

p2 <- enc %>%
  filter(event_date >= "2011-10-01",
         event_date <= "2022-09-30") %>% 
  mutate(citizenship_country = case_when(
    citizenship_country %in% 
      c("UNITED STATES", "UNKNOWN") ~ 
      citizenship_country,
    TRUE ~ 
      "ALL OTHERS"
  )) %>% 
  count(fy, citizenship_country) %>% 
  ggplot(aes(x=fy, y=n, fill=citizenship_country)) +
  geom_col(position="fill") +
  labs(title = "Total ICE encounters, proportion U.S./UNKNOWN") +
  theme_minimal()

p2

Total encounters per AOR

Below is an interactive chart of total ICE encounters per FY by AOR. Note significant quantity of encounters with missing (“NA”) AOR; trend of missing values does not parallel overall trends.

p1 <- enc %>% 
  filter(event_date >= "2011-10-01",
         event_date <= "2022-09-30") %>% 
  group_by(fy, aor) %>% 
  summarize(n = n()) %>% 
  ggplot(aes(x = as.factor(fy), y=n, color=aor, group=aor)) +
  geom_line() +
  labs(title = "Total ICE encounters per FY by AOR") +
  theme_minimal()

ggplotly(p1)

# p2 <- enc %>%
#   group_by(year_mth, aor) %>% 
#   summarize(n = n()) %>% 
#   ggplot(aes(x = year_mth, y=n, color=aor, group=aor)) +
#   geom_line() +
#   labs(title = "Total ICE encounters per month by AOR")
# 
# ggplotly(p2)

Percent change

Percent change in encounters per FY nationally and by AOR.

natl_pct_chg <- enc %>%
  filter(event_date >= "2011-10-01",
         event_date <= "2022-09-30") %>% 
  group_by(fy) %>%
  summarize(n = n()) %>% 
  mutate(pct_change = (n/lag(n) - 1))

p1 <- natl_pct_chg %>% 
  filter(!is.na(pct_change)) %>% 
  ggplot(aes(x = fy, y = pct_change)) +
  geom_col() +
  scale_y_continuous(labels = scales::percent) +
  labs(title="FY % change in total ICE encounters") +
  theme_minimal()

p1

aor_pct_chg <- enc %>%
  filter(event_date >= "2011-10-01",
         event_date <= "2022-09-30") %>% 
  filter(!is.na(aor)) %>% 
  group_by(fy, aor) %>%
  summarize(n = n()) %>% 
  group_by(aor) %>% 
  arrange(fy, .by_group=TRUE) %>% 
  mutate(pct_change = (n/lag(n) - 1))

p2 <- aor_pct_chg %>% 
  filter(!is.na(pct_change)) %>% 
  ggplot(aes(x = fy, y = pct_change)) +
  geom_col() +
  scale_y_continuous(labels = scales::percent) +
  scale_x_discrete(breaks=seq(2012, 2022, 2)) +
  facet_wrap(~aor)  +
  labs(title="FY % change in total ICE encounters per AOR") +
  theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1)) +
  theme_minimal()

p2

Operation

Note high proportion of records missing operation data.

ops <- enc %>% 
  count(operation) %>% 
  arrange(desc(n))

top_ops <- ops %>% 
  filter(n > 10000)

enc <- enc %>% 
  mutate(operation_short = case_when(
    operation %in% unlist(top_ops$operation) ~ as.character(operation), 
    TRUE ~ "ALL OTHERS"))

p1 <- enc %>% 
  filter(event_date >= "2011-10-01",
           event_date <= "2022-09-30") %>% 
  group_by(fy, operation_short) %>% 
  summarize(n = n()) %>% 
  ggplot(aes(x = as.factor(fy), y=n, fill=operation_short)) +
  geom_col() +
  scale_x_discrete(breaks=seq(2012, 2022, 2)) +
  labs(title = "Total ICE encounters per FY by operation") +
  theme_minimal()

ggplotly(p1)

Some patterns are more visible at month-to-month level. Note large encounter totals for “Operation Horizon” during November 2021, February-March 2022; note also increasing proportion of encounters related to southwest border.

p2 <- enc %>% 
  filter(event_date >= "2019-10-01"
         # !is.na(operation)
         ) %>% 
  group_by(year_mth, operation_short) %>% 
  summarize(n = n()) %>% 
  ggplot(aes(x = year_mth, y=n, fill=operation_short)) +
  geom_col() +
  labs(title = "Total ICE encounters per month by operation, FY20-22") +
  theme_minimal()

ggplotly(p2)

Processing disposition

disps <- enc %>% 
  count(processing_disposition) %>% 
  arrange(desc(n))

top_disps <- disps %>% 
  filter(n > 10000)

enc <- enc %>% 
  mutate(disp_short = case_when(
    processing_disposition %in%
      unlist(top_disps$processing_disposition) ~
      as.character(processing_disposition), 
    TRUE ~
      "ALL OTHERS"))

p1 <- enc %>% 
filter(event_date >= "2011-10-01",
         event_date <= "2022-09-30") %>% 
  group_by(fy, disp_short) %>% 
  summarize(n = n()) %>% 
  ggplot(aes(x = as.factor(fy), y=n, fill=disp_short)) +
  geom_col() +
  scale_x_discrete(breaks=seq(2012, 2022, 4)) +
  labs(title = "Total ICE encounters per FY by processing disposition") +
  theme_minimal()

ggplotly(p1)

Proportion “not amenable to removal”

Here we calculate the proportion of encounters with processing_disposition of “NOT AMENABLE TO REMOVAL” OR “FOREIGN BORN USC” (U.S. citizen). Higher proportion of these categories is suggestive of un-targeted enforcement practices.

not_amenable = c("NOT AMENABLE TO REMOVAL", "FOREIGN BORN USC")

fy_total <- enc %>% 
  filter(event_date >= "2011-10-01",
         event_date <= "2022-09-30") %>% 
  group_by(fy) %>% 
  summarize(n_total = n())

fy_not_amenable <- enc %>% 
  filter(event_date >= "2011-10-01",
         event_date <= "2022-09-30") %>% 
  filter(disp_short %in% not_amenable) %>% 
  group_by(fy) %>% 
  summarize(n_not_amenable = n())

dat <- left_join(fy_not_amenable, fy_total, by=c('fy')) %>% 
  mutate(pct_not_amenable = n_not_amenable/n_total)

p1 <- dat %>% 
  ggplot(aes(x = fy, y = pct_not_amenable)) +
  geom_col() +
  scale_y_continuous(labels = scales::percent) +
  labs(title = "% ICE encounters not amenable to removal or USC") +
  theme_minimal()
  

p1

fy_aor_total <- enc %>% 
  filter(event_date >= "2011-10-01",
         event_date <= "2022-09-30") %>% 
  group_by(fy, aor) %>% 
  summarize(n_total = n())

fy_aor_not_amenable <- enc %>%
  filter(event_date >= "2011-10-01",
         event_date <= "2022-09-30") %>% 
  filter(disp_short %in% not_amenable) %>% 
  group_by(fy, aor) %>% 
  summarize(n_not_amenable = n())

dat <- left_join(fy_aor_not_amenable, fy_aor_total, by=c('fy', 'aor')) %>% 
  mutate(pct_not_amenable = n_not_amenable/n_total)

p2 <- dat %>% 
  ggplot(aes(x = fy, y = pct_not_amenable, fill = aor)) +
  geom_col() +
  scale_y_continuous(labels = scales::percent) +
  scale_x_discrete(breaks=seq(2012, 2022, 2)) +
  labs(title = "% ICE encounters not amenable to removal or USC") +
  facet_wrap(~aor) +
  theme_minimal()

p2

Landmark

Overview of most common encounter landmark values gives a sense of the diversity of this category; note inclusion of general values denoting the entity responsible for the encounter rather than a precise geographic location. Note also high proportion of records missing landmark values.

landmarks <- enc %>% 
  count(landmark) %>% 
  arrange(desc(n))

p1 <- enc %>% 
  filter(event_date >= "2011-10-01",
         event_date <= "2022-09-30") %>% 
  mutate(landmark = case_when(landmark %in%
                                head(landmarks$landmark, 15) ~
                                as.character(landmark), 
                              TRUE ~
                                "ALL OTHERS")) %>% 
  group_by(fy, landmark) %>% 
  summarize(n = n()) %>% 
  ggplot(aes(x = as.factor(fy), y=n, fill=landmark)) +
  geom_col() +
  labs(title = "Total ICE encounters per FY by landmark (top 15)") +
  theme_minimal()

ggplotly(p1)

For discussion of ICE’s definition of “encounters”, see American Immigration Council, “Changing Patterns of Interior Immigration Enforcement in the United States, 2016 - 2018”, July 2019: https://www.americanimmigrationcouncil.org/research/interior-immigration-enforcement-united-states-2016-2018 filter(event_date <= “2022-09-30”) %>%↩︎

ICE ERO-LESA nationwide encounters data, FY12-22

UWCHR

2024-06-01