pacman::p_load(tidyverse, lubridate, here, skimr, yaml, ggplot2, ggalt, gplots,
sf, rnaturalearth, rnaturalearthdata, maps, tools, RColorBrewer
)
inputfile <- here::here("write/input/uw-chr-i213-public.csv.gz")
i213 <- read_delim(
inputfile, delim = "|",
col_types = cols(
# .default = col_character(),
source = col_character(),
sex = col_character(),
cmplxn = col_character(),
country_of_citizenship = col_factor(),
year = col_double(),
month = col_double(),
day = col_double(),
hour = col_double(),
minute = col_double(),
fy = col_double(),
age = col_double(),
accompanied_juvenile_flag = col_double(),
unaccompanied_juvenile_flag = col_double(),
custody_redetermination_flag = col_double()
))i213 <- i213 %>%
mutate(across(where(is.character), ~ na_if(.,"")))
i213$date <- as.Date(i213$date)
i213$month <- as.Date(cut(i213$date, breaks='month'))
i213 <- i213 %>%
mutate(lawsuit = case_when(
str_detect(installment, "2023") ~ 2,
TRUE ~ 1))
i213$installment <- factor(i213$installment, levels = c("ICE 2020-05",
"ICE 2020-06",
"ICE 2020-07",
"ICE 2020-08",
"ICE 2020-09",
"ICE 2020-10",
"ICE 2020-11",
"ICE 2020-12",
"ICE 2021-01",
"ICE 2021-06",
"ICE 2023-Pts.1-2",
"ICE 2023-Pt.3",
"ICE 2023-Pt.4",
"ICE 2023-Pt.5",
"ICE 2023-Pt.6",
"ICE 2023-Pt.7",
"ICE 2023-Pt.8",
"ICE 2023-Pt.9",
"ICE 2023-Pt.10"))
good_values <- c("ALBINO",
"BLACK",
"DARK BROWN",
"DARK",
"FAIR",
"LIGHT BROWN",
"LIGHT",
"MEDIUM BROWN",
"MEDIUM",
"OLIVE",
"RUDDY",
"SALLOW",
"YELLOW")
i213 <- i213 %>% mutate(cmplxn = case_when(
cmplxn %in% good_values ~ cmplxn,
is.na(cmplxn) ~ NA_character_,
TRUE ~ "OTHER"))
i213$office <- str_match(i213$location_code, "SEA|POO|BLW|SPW")
i213 <- i213 %>%
mutate(method_short = case_when(
str_detect(method_location_apprehension, "PB") ~ "PB",
str_detect(method_location_apprehension, "CFD") ~ "CFD",
str_detect(method_location_apprehension, "CST") ~ "CST",
str_detect(method_location_apprehension, "CLC") ~ "CLC",
str_detect(method_location_apprehension, "NCA") ~ "NCA",
str_detect(method_location_apprehension, "LEA") ~ "LEA",
str_detect(method_location_apprehension, "OA") ~ "OA",
str_detect(method_location_apprehension, "OTF") ~ "OTF",
str_detect(method_location_apprehension, "TCB") ~ "TCB",
str_detect(method_location_apprehension, "ISP") ~ "ISP",
str_detect(method_location_apprehension, "REP") ~ "REP",
str_detect(method_location_apprehension, "L") ~ "L",
str_detect(method_location_apprehension, "O|0") ~ "O",
is.na(method_location_apprehension) ~ NA_character_,
TRUE ~ NA_character_),
method_verbose = case_when(
str_detect(method_location_apprehension, "PB") ~ "Patrol Border",
str_detect(method_location_apprehension, "CFD") ~ "CAP Federal",
str_detect(method_location_apprehension, "CST") ~ "CAP State",
str_detect(method_location_apprehension, "CLC") ~ "CAP Local",
str_detect(method_location_apprehension, "NCA") ~ "Non-Custodial Arrest",
str_detect(method_location_apprehension, "LEA") ~ "Law Enforcement Assist",
str_detect(method_location_apprehension, "OA") ~ "Other Agency",
str_detect(method_location_apprehension, "OTF") ~ "Task Force",
str_detect(method_location_apprehension, "TCB") ~ "Transportation Check",
str_detect(method_location_apprehension, "ISP") ~ "Inspection",
str_detect(method_location_apprehension, "REP") ~ "Reprocessed",
str_detect(method_location_apprehension, "L") ~ "Located",
str_detect(method_location_apprehension, "O|0") ~ "Other",
is.na(method_location_apprehension) ~ NA_character_,
TRUE ~ NA_character_),
method_int_border = case_when(
str_detect(method_location_apprehension, "PB") ~ "Border",
str_detect(method_location_apprehension, "CFD") ~ "Interior",
str_detect(method_location_apprehension, "CST") ~ "Interior",
str_detect(method_location_apprehension, "CLC") ~ "Interior",
str_detect(method_location_apprehension, "NCA") ~ "Interior",
str_detect(method_location_apprehension, "LEA") ~ "Interior",
str_detect(method_location_apprehension, "OA") ~ "Interior",
str_detect(method_location_apprehension, "OTF") ~ "Interior",
str_detect(method_location_apprehension, "TCB") ~ "Interior",
str_detect(method_location_apprehension, "ISP") ~ "Border",
str_detect(method_location_apprehension, "L") ~ "Interior",
str_detect(method_location_apprehension, "REP") ~ "Other",
str_detect(method_location_apprehension, "O|0") ~ "Other",
is.na(method_location_apprehension) ~ NA_character_,
TRUE ~ NA_character_))Descriptive analysis of UWCHR I-213 collection
As part of its “Human Rights At Home” and “Immigrant Rights Observatory” research initiatives, the University of Washington Center for Human Rights (UWCHR) has obtained a collection of I-213 “Record of Deportable/Inadmissible Alien” forms via Freedom of Information Act (FOIA) requests to the US Department of Homeland Security (DHS). These internal forms documenting apprehensions by US Immigration and Customs Enforcement (ICE) and Customs and Border Protection (CBP) are an important source of qualitative and quantitative information regarding immigration enforcement practices and patterns.
Under FOIA, UWCHR initially requested all I-213s produced by both CBP and ICE in the state of Washington from 2012-2017; when the agencies failed to adhere to the requirements of FOIA—first claiming privacy waivers were needed for every individual form and then claiming that as law enforcement agencies, they were exempt from providing such records—UWCHR sued in September 2018. UWCHR underwent separate settlement negotiations with each sub-agency. In these discussions, CBP agreed to provide a sample of all documents produced by staff in the agency’s Blaine and Spokane sectors from January 1, 2012 to September 30, 2017, totaling 165 records; and ICE agreed to provide copies of all documents produced from January 1, 2019 to March 31, 2020 by staff from the agency’s Seattle field office (including records from the Seattle and Portland sub-offices as well as the Blaine and Spokane Border Patrol Sectors, see below for details), totaling 3887.
UWCHR later submitted an identical FOIA request to ICE for April 1, 2020, through November 30, 2021, and underwent a second lawsuit and settlement, resulting in the release of an additional 2895 records. As noted below, this second lawsuit production does not include any forms with location codes corresponding to the Blaine or Spokane Border Patrol Sectors, and presents other characteristics which may preclude direct comparison of trends between the two lawsuit productions.
The resulting PDF forms were scraped and cleaned in a separate
private repository developed by the Human Rights Data Analysis Group
(HRDAG) and maintained by UWCHR. Cleaning and hand-coding of resulting
data is ongoing; the following is a preliminary descriptive analysis of
the collection. Maps of apprehension locations by I-213
at_near value and at_near county per capita
were previously published for UWCHR’s August 2021 report “Protecting
Immigrant Rights: Is Washington’s Law Working?”, see: https://uwchr.github.io/i-213-analysis/map-wa.html.
I-213 structure
Each record is made up of an I-213 “Record of Deportable/Inadmissible Alien” form collecting subject identification, biometrics, and apprehension characteristics data in a structured format (typographical variations may be introduced in the scraping process, we have attempted to standardize values in the cleaning process); followed by a semi-structured “Narrative” field containing additional description of the subject and apprehension, usually continued from the I-213 on one or more I-831 “Continuation Page” forms. Personally identifying information such as names and unique identifiers are redacted; narratives also include redactions under various FOIA exemptions. A small number of forms are fully redacted.
“Narrative” field contents are excluded from the public version of
this dataset because they may contain sensitive information. Dataset
fields following the naming convention mentions_* reflect
simple str_detect() results for keywords in the “Narrative”
text as described in the repository README.
While I-213 form contents are relatively structured, some fields may be left blank and the logic or meaning of codes used in some fields is obscure. For more details, see this ICE “I-213 Preparation” document released via FOIA; note that in some cases field contents differ from those described here.
Sample I-213 form
The following is a typical I-213 form representing an apprehension upon entry at Sea-Tac International Airport. Note redacted and blank I-213 fields, as well as fields filled with “See Narrative”. Most but not all text fields are scraped for the dataset analyzed here. Tick boxes are not scraped.
Sample I-831 form
The following is a typical I-831 “Continuation Page” associated with the above I-213 form, which continues the “Narrative” field. Other text fields here are not scraped, but we note that the “Title” field might provide details regarding the official responsible for the apprehension, though this information is also included in the narrative in this case.
I-213 narratives follow a semi-regular structure; we have scraped
at_near values from the narrative where the “At Near” field
reads “See Narrative”; and we have attempted to scrape “Current Criminal
Charges” and “Current Administrative Charges” as
charge_code_words, with lots of OCR inconsistencies. It
appears that I-213 narratives are at least in part machine-generated
rather than entered by hand: note the “null:” pointer preceding the
description of the apprehension below.
Record source
UWCHR’s I-213 collection consists of records produced by both CBP and ICE via FOIA litigation, as described above. CBP records are a sample (every 20th record) of all forms produced by staff in the agency’s Blaine and Spokane sectors from January 1, 2012 to September 30, 2017; ICE records include all documents produced from January 1, 2019 to November 30, 2021 by staff from the agency’s Seattle field office.
As seen below, some ICE records fall out of the window of time
covered by the FOIA settlement; closer scrutiny of these records often
shows that the I-213 “Date/Time” field (scraped as year,
month, day, hour,
minute fields; also converted into fiscal year
fy) sometimes relates to a prior apprehension, with the
date_of_action field more accurately reflecting the date of
the apprehension described by the I-213 narrative. Missing “Date/Time”
values have also been filled in with date_of_action values
where appropriate. This cleaning is ongoing in a separate private
repository; cleaning methods and data replacements available for review
upon request. For most analysis below we will restrict the dataset to
records dated January 1, 2019 and after.
A small number of records with missing source data
represent fully-redacted forms produced by ICE; these are excluded in
the following analysis. As CBP records are not intended to be
comprehensive, and because CBP apprehensions which result in individuals
being transferred to ICE custody are represented in the ICE I-213
collection, CBP records are also excluded from the following
analysis.
i213 <- i213 %>%
mutate(across(where(is.character), ~ na_if(.,"")))
ann <- i213 %>%
filter(!is.na(source)) %>%
count(source, year) %>%
pivot_wider(names_from = source, values_from = n,
values_fill = list(n = 0)) %>%
arrange(year)
cbp <- i213 %>%
filter(source == "cbp")
i213 <- i213 %>%
filter(source != "cbp")
ann## # A tibble: 15 × 3
## year cbp ice
## <dbl> <int> <int>
## 1 2008 0 1
## 2 2010 0 4
## 3 2011 0 1
## 4 2012 1 2
## 5 2013 20 1
## 6 2014 20 3
## 7 2015 9 0
## 8 2016 25 2
## 9 2017 24 5
## 10 2018 41 34
## 11 2019 25 3028
## 12 2020 0 1499
## 13 2021 0 2161
## 14 2022 0 6
## 15 NA 0 35ICE record release installments
Records were released by ICE to UWCHR in installments. Following UWCHR’s first FOIA lawsuit, nine initial monthly installments were released from May 2020 to January 2021. Subsequent UWCHR research revealed additional I-213 forms which should have been disclosed per the terms of the FOIA lawsuit settlement; in response, ICE released a final installment in June 2021. Records resulting from the second lawsuit were released in eight installments during 2023. Each ICE installment was released as a continuous PDF file. During the file scraping process, each installment was assigned a unique six-digit hash value for identification purposes.
Comparison of the installments follows. We note that date distribution of the installments is erratic but that the center of the date distributions of the first nine installments roughly advances chronologically through the timeline; while the final installment “back-fills” prior months. Note that the overall monthly distribution of records is relatively consistent until the onset of the COVID-19 pandemic in 2020. Comparison of the installments along other lines, such as geographic distribution, is possible but not particularly instructive; see “compare-installments.Rmd”. Comparison of the records between lawsuit production periods is more useful; see “compare-lawsuits.Rmd”.
i213$date <- as.Date(i213$date)
data <- i213 %>%
select(date, source, installment, lawsuit) %>%
filter(date >= '2019-01-01',
date <= '2021-12-31',
source == 'ice')
data$month <- as.Date(cut(data$date, breaks='month'))
p1 <- data %>% ggplot(aes(x = month)) +
geom_bar() +
# scale_x_date(labels = scales::date_format("%b"),
# breaks = "3 month") +
facet_wrap(~installment) +
theme(axis.text.x = element_text(angle = 90,
vjust = 0.5,
hjust = 1)) +
labs(title = "I-213 installment distribution")
p1
p2 <- data %>% ggplot(aes(x = month, fill=installment)) +
geom_bar(width=10) +
#scale_x_date(labels = scales::date_format("%b"),
# breaks = "3 month") +
theme(axis.text.x = element_text(angle = 90,
vjust = 0.5,
hjust = 1)) +
labs(title = "I-213 installment distribution")
p2
p3 <- data %>% ggplot(aes(x = month, fill=as.factor(lawsuit))) +
geom_bar(width=10) +
#scale_x_date(labels = scales::date_format("%b"),
# breaks = "3 month") +
theme(axis.text.x = element_text(angle = 90,
vjust = 0.5,
hjust = 1)) +
labs(title = "I-213 distribution by lawsuit production")
p3
Demographics: Overview
Here we display tables for basic demographic characteristics for subjects of the ICE I-213 forms: ten most common “Country of Citizenship” values; “Age” and “Sex” breakdowns (excluding missing values); and “Complexion”.
“Complexion” values are decoded following “The Border’s Long Shadow” (ACLU of Michigan, 2021).
i213 %>% count(country_of_citizenship, sort=TRUE) %>%
mutate(pct = n/sum(n) * 100) %>%
head(10)## # A tibble: 10 × 3
## country_of_citizenship n pct
## <fct> <int> <dbl>
## 1 MEXICO 3394 50.0
## 2 HONDURAS 501 7.39
## 3 GUATEMALA 497 7.33
## 4 NICARAGUA 313 4.62
## 5 VENEZUELA 282 4.16
## 6 INDIA 255 3.76
## 7 EL SALVADOR 242 3.57
## 8 CUBA 168 2.48
## 9 CHINA 96 1.42
## 10 HAITI 91 1.34i213 %>%
filter(!is.na(age), !is.na(sex)) %>%
mutate(age_group = cut(age,
breaks = c(0, 18, 25, 40, 60, Inf),
right = FALSE)) %>%
count(sex, age_group) %>%
pivot_wider(names_from = sex, values_from = n)## # A tibble: 5 × 4
## age_group F M U
## <fct> <int> <int> <int>
## 1 [0,18) 213 241 NA
## 2 [18,25) 190 647 1
## 3 [25,40) 537 2422 3
## 4 [40,60) 188 1171 2
## 5 [60,Inf) 21 67 NAi213 %>%
count(cmplxn, sort = TRUE)## # A tibble: 14 × 2
## cmplxn n
## <chr> <int>
## 1 MEDIUM 3728
## 2 <NA> 1130
## 3 MEDIUM BROWN 735
## 4 LIGHT BROWN 530
## 5 LIGHT 226
## 6 FAIR 218
## 7 DARK 59
## 8 DARK BROWN 58
## 9 BLACK 49
## 10 OTHER 23
## 11 OLIVE 20
## 12 RUDDY 3
## 13 SALLOW 2
## 14 YELLOW 1Age/Sex
Distribution of age/sex over full ICE collection. Adult males make up vast majority of population:
pop_data <- i213 %>%
filter(!is.na(age), !is.na(sex), sex %in% c("M", "F")) %>%
mutate(age_group = cut(age,
breaks = c(0, 18, 25, 40, 60, Inf),
right = FALSE),
state = case_when(!state %in% c('WA', 'OR') ~ "ALL OTHERS",
TRUE ~ state)) %>%
count(sex, age_group) %>%
mutate(prop = n / sum(n)) %>%
ungroup() %>%
mutate(n = case_when(sex == "F" ~ -n,
TRUE ~ n),
prop = case_when(sex == "F" ~ -prop,
TRUE ~ prop))
pop_prop_range <- range(pop_data$prop)
pop_prop_range_breaks <- pretty(pop_prop_range, 10)
p1 <- pop_data %>%
ggplot(aes(x = prop, y = age_group, fill=sex)) +
geom_col() +
scale_y_discrete(limits=rev) +
scale_x_continuous(breaks = pop_prop_range_breaks,
labels = function(x) scales::percent(abs(x))) +
labs(title = "I-213 forms age/sex distribution")
p1
Distribution of age/sex by ICE lawsuit production. Note significantly greater proportion of females and juveniles in second lawsuit collection:
pop_data <- i213 %>%
filter(!is.na(age), !is.na(sex), sex %in% c("M", "F")) %>%
mutate(age_group = cut(age,
breaks = c(0, 18, 25, 40, 60, Inf),
right = FALSE)) %>%
group_by(sex, age_group, lawsuit) %>%
summarize(n = n()) %>%
group_by(lawsuit) %>%
mutate(prop = n / sum(n)) %>%
ungroup() %>%
mutate(n = case_when(sex == "F" ~ -n,
TRUE ~ n),
prop = case_when(sex == "F" ~ -prop,
TRUE ~ prop))
pop_prop_range <- range(pop_data$prop)
pop_prop_range_breaks <- pretty(pop_prop_range, 10)
p1 <- pop_data %>%
ggplot(aes(x = prop, y = age_group, fill=sex)) +
geom_col() +
scale_y_discrete(limits=rev) +
scale_x_continuous(breaks = pop_prop_range_breaks,
labels = function(x) scales::percent(abs(x))) +
facet_wrap(~lawsuit) +
labs(title = "I-213 forms age/sex distribution",
subtitle = "Subsets by lawsuit production")
p1
Country ranking
Change in rank of most frequent country_of_citizenship
values by quarter. Note reduction of Mexican nationality in latter
portion of time period (second lawsuit production):
my_theme <- function() {
# Colors
color.background = "white"
color.text = "#22211d"
# Begin construction of chart
theme_bw(base_size=16) +
# Format background colors
theme(panel.background = element_rect(fill=color.background, color=color.background)) +
theme(plot.background = element_rect(fill=color.background, color=color.background)) +
theme(panel.border = element_rect(color=color.background)) +
theme(strip.background = element_rect(fill=color.background, color=color.background)) +
# Format the grid
theme(panel.grid.major.y = element_blank()) +
theme(panel.grid.minor.y = element_blank()) +
theme(axis.ticks = element_blank()) +
# Format the legend
theme(legend.position = "none") +
# Format title and axis labels
theme(plot.title = element_text(color=color.text, size=20, face = "bold")) +
theme(axis.title.x = element_text(size=14, color="black", face = "bold")) +
theme(axis.title.y = element_text(size=14, color="black", face = "bold", vjust=1.25)) +
theme(axis.text.x = element_text(size=10, vjust=0.5, hjust=0, color = color.text)) +
theme(axis.text.y = element_text(size=10, color = color.text)) +
theme(strip.text = element_text(face = "bold")) +
# Plot margins
theme(plot.margin = unit(c(0.35, 0.2, 0.3, 0.35), "cm"))
}
show.top.n <- 10
rankings <- i213 %>%
filter(date >= '2019-01-01',
date <= '2021-12-31') %>%
mutate(country = substr(country_of_citizenship, start = 1, stop = 3)) %>%
count(quarter, country) %>%
arrange(quarter, desc(n), country) %>%
group_by(quarter) %>%
mutate(ranking = row_number(),
quarter = as.character(quarter))
rankings <- rankings %>%
mutate(flag = ifelse(country %in% c("MEX", "IND", "HON", "GUA", "NIC", "EL ", "VEN", "CHI"), TRUE, FALSE),
country_col = if_else(flag == TRUE, country, "zzz"))
p1 <- rankings %>%
ggplot(aes(x = quarter, y = ranking, group = country)) +
geom_line(aes(color = country_col, alpha=1), linewidth = 2) +
geom_point(aes(color = country_col, alpha=1), size = 4) +
geom_point(color = "#FFFFFF", size = 1) +
scale_y_reverse(breaks = 1:show.top.n) +
geom_text(data = rankings %>% filter(quarter == '2019.2'),
aes(label = country, x = 0) , hjust = 0, nudge_x=-.1, fontface = "bold", color = "#888888", size = 4) +
geom_text(data = rankings %>% filter(quarter == '2022.1'),
aes(label = country, x = 14) , hjust = 1.5, fontface = "bold", color = "#888888", size = 4) +
coord_cartesian(ylim = c(show.top.n,1)) +
theme(legend.position = "none") +
labs(x = "Quarter",
y = "Rank",
title = "SEA AOR I-213s",
subtitle = "Country of citizenship rank") +
my_theme() +
scale_color_manual(values = c("#F70020","#191A1A","#FB9701","#1A7D00","#072C8F", "yellow", 'purple', "lightblue", "grey"))
p1
Apprehension location: “At/Near”
Most forms include an “At/Near” value indicating the location of the apprehension; in some cases the “At/Near” field includes a reference to “See Narrative”, in which case we extract this value from the narrative field or complete by hand. In most cases these values follow a “City, State” format; sometimes codes for specific points of entry (e.g. “PHY” for the Pacific Highway POE in Blaine, WA; or “SEA” for Seatle-Tacoma International Airport in Sea-Tac, WA) or names of specific institutions such as jails or correctional facilities. We have standardized values to “City, State” format and include latitude and longitude values via Google Maps API.
Note that Seattle, WA and Portland, OR are sites of ICE field offices and may include forms processed in those offices but apprehended elsewhere; the same may be true of forms associated with the Northwest Detention Center in Tacoma, WA.
“At/Near” values outside the Pacific Northwest may include subjects apprehended elsewhere and processed by the Seattle ICE Field Office, but I-213 narratives confirm that most apprehensions represented in the dataset occurred in the Pacific Northwest.
i213 %>% count(at_near, sort = TRUE) %>%
filter(n >= 20)## # A tibble: 47 × 2
## at_near n
## <chr> <int>
## 1 <NA> 795
## 2 SEATTLE, WA 725
## 3 PORTLAND, OR 602
## 4 SEATAC, WA 528
## 5 TACOMA, WA 402
## 6 BLAINE, WA 209
## 7 SALEM, OR 202
## 8 SUMAS, WA 169
## 9 EUGENE, OR 166
## 10 EPHRATA, WA 125
## # ℹ 37 more rowsi213 %>%
count(state, sort = TRUE)## # A tibble: 15 × 2
## state n
## <chr> <int>
## 1 WA 4306
## 2 OR 1508
## 3 <NA> 798
## 4 ID 48
## 5 AK 37
## 6 TX 35
## 7 CA 25
## 8 AZ 10
## 9 PA 7
## 10 MT 2
## 11 NC 2
## 12 CO 1
## 13 FL 1
## 14 KY 1
## 15 NM 1rankings <- i213 %>%
filter(date >= '2019-01-01',
date <= '2021-12-31') %>%
count(quarter, at_near) %>%
arrange(quarter, desc(n), at_near) %>%
group_by(quarter) %>%
mutate(ranking = row_number(),
quarter = as.character(quarter)) %>%
filter(ranking <= 5)Mapping apprehension location
We can map records by their “At/Near” values in a variety of ways. For more examples, see: https://uwchr.github.io/i-213-analysis/map-wa.html
world <- ne_countries(scale = "medium", returnclass = "sf")
states <- st_as_sf(map("state", crs = 4326, plot = FALSE, fill = TRUE))
states <- cbind(states, st_coordinates(st_point_on_surface(states)))
states$ID <- toTitleCase(as.character(states$ID))
pnw_coords <- coord_sf(xlim = c(-130, -112.5), ylim = c(41, 50), expand = FALSE, crs = 4326)
data_at_near <- i213 %>%
group_by(at_near, lon, lat, lawsuit) %>%
summarise(n = n()) %>%
na.omit()
data_at_near <- st_as_sf(data_at_near, coords = c("lon", "lat"),
crs = 4326, agr = "constant")
ggplot(data = world) +
geom_sf() +
geom_sf(data = states, fill = NA) +
geom_sf(data = data_at_near, aes(size=n), shape = 21, fill = "red", alpha=.5) +
pnw_coords +
ggtitle('I-213 forms by "at_near" latitude/longitude')
Apprehension location by DHS office
I-213 forms include “Location Code” field designating the DHS office and sub-office responsible for the apprehension. Values consist of two three-letter codes separated by a forward-slash (“/”). No public data dictionary translating these codes is known, but they appear to be geographically consistent, as seen below. This analysis suggests that the first part of the code designates the local ICE sub-field office or Border Patrol station, while the second part of the code designates the regional ICE field office or Border Patrol sector, e.g. “RIC/SEA” for Seattle ICE Field Office, Richland, WA Sub-Field Office (largely corresponding a published map of ICE Enforcement and Removal Operations field offices); “EUG/POO” for Portland ICE Office, Eugene, OR Sub-Field Office; “SMS/BLW” for Blaine Sector Border Patrol, Sumas Station; “SPK/SPW” for Spokane Sector Border Patrol, Spokane Station (roughly corresponding to a published table of Border Patrol sectors and stations).
Plotting location codes (with added jitter for visual effect) and simplified codes for DHS offices (with lines encircling points to suggest jurisdictional demarcations) demonstrates the geographic consistency of these values. Some ICE field offices and Border Patrol sectors span multiple counties and states, while others are more localized. Note that the Seattle ICE field office covers the majority of the Seattle “Area of Responsibility” (WA, OR, AK; Alaska records fall under the Anchorage Sub-Field Office, “ANC/ANC”); note also the difference in coverage between the relatively localized Blaine Sector Border Patrol (“BLW”) versus the wide geographic range of the Spokane Sector (“SPW”). Note also the overlap of the Seattle, Portland, and Spokane jurisdictions in Central Washington.
i213 %>% count(location_code, sort = TRUE) %>%
filter(n >= 20)## # A tibble: 19 × 2
## location_code n
## <chr> <int>
## 1 SEA/SEA 2089
## 2 TAC/SEA 758
## 3 <NA> 601
## 4 POO/POO 584
## 5 POO 474
## 6 EUG/POO 374
## 7 SPK/SPW 263
## 8 RIC/SEA 211
## 9 SMS/BLW 204
## 10 YAK/SEA 193
## 11 BLS/BLW 174
## 12 SPO/SEA 156
## 13 WNT/SEA 123
## 14 MED/SEA 95
## 15 ORV/SPW 91
## 16 BLH/BLW 66
## 17 PHY/SEA 51
## 18 ANC/ANC 37
## 19 BLA/SEA 30i213$office <- str_match(i213$location_code, "SEA|POO|BLW|SPW")
loc_codes <- i213 %>%
group_by(location_code) %>%
summarize(n = n()) %>%
arrange(desc(n)) %>%
na.omit()
loc_codes_county <- i213 %>%
group_by(location_code) %>%
summarize(n_counties = n_distinct(county),
n_state = n_distinct(state)) %>%
arrange(desc(n_counties)) %>%
na.omit()
top <- unlist(loc_codes[1:20, 'location_code'])
sites <- i213 %>% select(lon, lat, location_code, office) %>%
filter(location_code %in% top) %>%
na.omit()
sites <- st_as_sf(sites, coords = c("lon", "lat"),
crs = 4326, agr = "constant")
sites <- cbind(sites, st_coordinates(sites))
# Exclude small number of records outside geographic bounds of Pacific Northwest
sites <- sites[sites$X < -110,]
sites <- sites[sites$X > -130,]
sites <- sites[sites$Y < 50,]
sites <- sites[sites$Y > 42,]
sites <- sites %>%
na.omit()
m1 <- ggplot() +
geom_sf(data = world) +
geom_sf(data = states, fill = NA) +
geom_jitter(data = sites, size = 2, shape = 23, width=.25, height=.25, aes(x = X, y = Y, fill = location_code)) +
pnw_coords +
ggtitle('I-213 forms by approx. "at_near" latitude/longitude')
m2 <- ggplot() +
geom_sf(data = world) +
geom_sf(data = states, fill = NA) +
geom_point(data = sites, size = 2, shape = 23, aes(x = X, y = Y, fill = office)) +
geom_encircle(data = sites, aes(x = X, y = Y, group=office, col=office)) +
pnw_coords +
ggtitle('I-213 forms by "at_near" latitude/longitude')
m1
m2
I-213 records from April 2020 through November 2021 (second lawsuit production) do not include any forms with location codes “BLW” or “SPW”:
p1 <- i213 %>%
filter(source == 'ice') %>%
count(lawsuit, office) %>%
ggplot(aes(x = office, y=n, fill=as.factor(lawsuit))) +
geom_col() +
facet_wrap(~lawsuit) +
labs(title = "I-213 form office code by lawsuit production")
p1
“Method of Location/Apprehension”
The method_location_apprehension field consists of a set
of regular alphanumeric codes that apparently describe how the subject
of the form was identified or apprehended. No single source defining
these codes is known to be publicly available but several are mentioned
in various contexts and others can be tentatively decoded using context
clues in I-213 narratives. The ten most common codes follow:
i213 %>%
filter(!is.na(method_location_apprehension)) %>%
count(method_location_apprehension, sort = TRUE) %>%
head(10)## # A tibble: 10 × 2
## method_location_apprehension n
## <chr> <int>
## 1 NCA NA 1018
## 2 REP 520.3 790
## 3 CLC NA 750
## 4 L NA 719
## 5 CST NA 505
## 6 PB UNKNOWN 490
## 7 CFD NA 476
## 8 ISP NA 342
## 9 O NA 275
## 10 O UNKNOWN 155As seen above, most of the codes consist of a primary alphabetic code followed by “NA” or “UNKNOWN”, but others include secodary alpha-numeric codes. In some cases, the significance of these is unknown (e.g. “CLC 511.2.2”, “L 13D”). However, the primary codes can be standardized into a dozen main categories, most of which can be tentatively decoded.
Several of the codes are described in an ICE document obtained via FOIA by the American Immigration Council (see page 44). Three of the codes refer to various jurisdictions of ICE’s Criminal Alien Program (CAP): “CLC”, “CST” and “CFD”, referring to CAP Local (city and county jails and correctional facilities), CAP State (state prisons and corrections), and CAP Federal (federal prisons) respectively. The same document defines “NCA” as “Non-Custodial Arrest” for street arrests; and “L” as “Located” for arrests of fugitives. Review of I-213 narratives is largely consistent with these interpretations. (Other codes mentioned in this document are not represented in the UWCHR I-213 collection.)
The code “OA” is defined as “Other Agency” in reports by the ACLU of Michigan and the American Immigration Council, and reportedly involves apprehensions carried out in support of or with the support of other law enforcement agencies. Review of I-213 narratives is largely consistent with this interpretation; for example, some arrests at courthouses in Central Washington involving collaboration with local law enforcement are coded “OA”.
Other codes are tentatively deciphered as follows based on context clues in I-213 narratives and other information:
- “PB”: Likely “Patrolling Border”; appears to involve apprehensions of suspected unauthorized border-crossers at or near the physical border.
- “ISP”: Likely “Inspection”; appears to involve apprehensions during inspection at authorized points of entry (mostly Sea-Tac Int’l Airport and border crossings in Blaine, WA).
- “TCB”: Likely “Transportation/Bus Check”; almost all records involve immigration checks on board buses in the Spokane, WA area.
- “OTF”: Likely “Task Force”; appears to involve apprehensions during joint task force operations with local law enforcement.
- “LEA”: Likely “Law Enforcement Assistance”; appears to involve direct requests for assistance by local law enforcement.
- “O”: Unknown, possibly “Other”.
For a comparison of these codes with keywords mentioned in I-213 narratives, see: https://uwchr.github.io/i-213-analysis/heatmaps.html
We can examine these “Method of Location/Apprehension” categories along various axes including over time, by “Location Code” as discussed above. We note for example that the Seattle and Portland ICE offices have a very different distribution of categories compared to the Blaine or Spokane Border Patrol Sectors; and that the Blaine Border Patrol Sector and Spokane Border Patrol Sector also differ, with Spokane’s distribution more suggestive of reliance on collaboration with local law enforcement.
Comparison of proportion of these categories by county is also interesting: note high proportion of “PB” (“Patrolling Border”) apprehensions in Northern border counties. Note also the correspondence of the categories “CFD” (“CAP Federal”) and “CST” (“CAP State”) with locations of major prisons: e.g. the Federal Detention Center, SeaTac in King County; or Coyote Ridge Corrections Center in Connel, WA in Franklin County.
i213$date <- as.Date(i213$date)
data <- i213 %>%
select(date, state, source, method_location_apprehension, method_short, method_verbose, method_int_border, kww_in_force, office, county) %>%
filter(!is.na(state),
date >= '2019-01-01' & date <= '2022-01-01',
source == 'ice')
data$month <- as.Date(cut(data$date, breaks='month'))
p1 <- data %>%
ggplot(aes(x = month)) +
geom_bar() +
# scale_x_date(labels = scales::date_format("%b"),
# breaks = "1 month") +
facet_wrap(~method_short) +
theme(axis.text.x = element_text(angle = 90,
vjust = 0.5,
hjust = 1)) +
labs(title = "I-213 Method of location/apprehension dist.")
p1
# p2 <- data %>%
# ggplot(aes(x = method_short)) +
# geom_bar(aes(fill= method_short)) +
# facet_wrap(~office) +
# theme(axis.text.x = element_text(angle = 90,
# vjust = 0.5,
# hjust = 1))
#
# p2
priority_counties <- read_yaml('../../shared/hand/priority_counties.yaml')
priority_counties <- append(priority_counties, 'Whatcom County')
p3 <- data %>%
filter(county %in% priority_counties) %>%
ggplot(aes(x = county, fill= method_verbose)) +
geom_bar(position = "fill") +
scale_y_continuous() +
theme(axis.text.x = element_text(angle = 90,
vjust = 0.5,
hjust = 1)) +
labs(title = "I-213 Method of location/apprehension prop.",
subtitle = "WA priority counties")
p3
# p4 <- data %>%
# filter(!county %in% priority_counties) %>%
# ggplot(aes(x = county, fill= method_verbose)) +
# geom_bar(position = "fill") +
# scale_y_continuous() +
# theme(axis.text.x = element_text(angle = 90,
# vjust = 0.5,
# hjust = 1))
#
# p4
# p5 <- data %>%
# filter(county %in% priority_counties) %>%
# ggplot(aes(x = county, fill= method_int_border)) +
# geom_bar() +
# theme(axis.text.x = element_text(angle = 90,
# vjust = 0.5,
# hjust = 1)) +
# labs(main= "I-213 Method of location/apprehension dist.")
#
# p5Note significant differences in categorization of I-213 forms by lawsuit production. It is an open question whether this is a result of changes in enforcement practices over time; or different criteria for record production via FOIA.
p1 <- i213 %>%
filter(source == 'ice') %>%
count(lawsuit, method_short) %>%
ggplot(aes(x = method_short, y=n, fill=method_short)) +
geom_col() +
facet_wrap(~lawsuit) +
labs(title = "I-213 Method of location/apprehension dist.",
subtitle = "Comparisin of lawusit productions")
p1
OR
Focus on OR records:
data <- i213 %>%
select(date, state, source, method_location_apprehension, method_short, method_verbose, kww_in_force, office, county) %>%
filter(!is.na(state),
date >= '2019-01-01',
date <= '2021-12-31',
source == 'ice')
data$month <- as.Date(cut(data$date, breaks='month'))
p1 <- data %>%
filter(!is.na(state),
state == 'OR') %>%
ggplot(aes(x = month)) +
geom_bar() +
scale_x_date(labels = scales::date_format("%b"),
breaks = "1 month") +
facet_wrap(~method_short) +
theme(axis.text.x = element_text(angle = 90,
vjust = 0.5,
hjust = 1)) +
labs(title = "OR I-213 forms by method of location/apprehension")
p1
p3 <- data %>%
filter(!is.na(state),
state == 'OR') %>%
ggplot(aes(x = county, fill= method_verbose)) +
geom_bar() +
theme(axis.text.x = element_text(angle = 90,
vjust = 0.5,
hjust = 1)) +
labs(title = "OR I-213 forms by county, method of location/apprehension")
p3
p5 <- data %>%
filter(!is.na(state),
state == 'OR') %>%
ggplot(aes(x = county, fill= method_verbose)) +
geom_bar(position = "fill") +
scale_y_continuous(labels = scales::percent) +
theme(axis.text.x = element_text(angle = 90,
vjust = 0.5,
hjust = 1)) +
labs(title = "OR I-213 forms by county, proportion method of location/apprehension")
p5
p4 <- data %>%
filter(!is.na(state),
state %in% c("OR", "WA")) %>%
ggplot(aes(x = state, fill= method_verbose)) +
geom_bar() +
theme(axis.text.x = element_text(angle = 90,
vjust = 0.5,
hjust = 1)) +
labs(title = "OR I-213 forms by method of location/apprehension")
p4
p6 <- data %>%
filter(!is.na(state),
state %in% c("OR", "WA")) %>%
ggplot(aes(x = state, fill= method_verbose)) +
geom_bar(position = "fill") +
scale_y_continuous(labels = scales::percent) +
theme(axis.text.x = element_text(angle = 90,
vjust = 0.5,
hjust = 1)) +
labs(title = "I-213 forms by proportion method of location/apprehension",
subtitle = "WA/OR comparison")
p6
p7 <- data %>%
filter(!is.na(state),
state == 'OR') %>%
group_by(month, method_verbose) %>%
summarize(n = n()) %>%
ggplot(aes(x = month, y=n, fill= method_verbose)) +
geom_col() +
theme(axis.text.x = element_text(angle = 90,
vjust = 0.5,
hjust = 1)) +
facet_wrap(~method_verbose) +
labs(title = "OR I-213 forms by method of location/apprehension")
p7
Country of origin
A few views of most common “Country of Origin” categories.
top <- i213 %>%
count(country_of_citizenship, sort = TRUE)
top <- head(as.character(top$country_of_citizenship), 10)
i213 <- i213 %>%
filter(!is.na(country_of_citizenship)) %>%
mutate(country_short = case_when(country_of_citizenship %in% top ~ country_of_citizenship,
TRUE ~ "ALL OTHERS"))
p1 <- i213 %>%
ggplot(aes(x = country_short, fill= country_short)) +
geom_bar() +
facet_wrap(~office) +
theme(axis.text.x = element_text(angle = 90,
vjust = 0.5,
hjust = 1))
p1
p2 <- i213 %>%
ggplot(aes(x = method_short, fill= method_short)) +
geom_bar() +
facet_wrap(~country_short) +
theme(axis.text.x = element_text(angle = 90,
vjust = 0.5,
hjust = 1))
p2
i213$month <- as.Date(cut(i213$date, breaks='month'))
p4 <- i213 %>%
filter(!is.na(state),
state %in% c('WA','OR'),
date >= '2019-01-01',
date <= '2022-01-01',
source == 'ice') %>%
count(month, state, country_short) %>%
ggplot(aes(x = month, y = n, fill=country_short)) +
geom_col() +
facet_wrap(~state) +
theme(axis.text.x = element_text(angle = 90,
vjust = 0.5,
hjust = 1))
p4
imm_record and crim_record
i213 <- i213 %>%
mutate(imm_record = case_when(str_detect(imm_record, "NEGATIVE") ~ "NEGATIVE",
str_detect(imm_record, "NBGATIVB") ~ "NEGATIVE",
str_detect(imm_record, "POSITIVE") ~ "POSITIVE",
TRUE ~ NA_character_))
i213 %>%
count(lawsuit,imm_record) %>%
arrange(desc(n))## # A tibble: 6 × 3
## lawsuit imm_record n
## <dbl> <chr> <int>
## 1 1 <NA> 1469
## 2 2 POSITIVE 1447
## 3 2 NEGATIVE 1436
## 4 1 NEGATIVE 1369
## 5 1 POSITIVE 1048
## 6 2 <NA> 2i213 %>%
filter(state %in% c('WA', 'OR')) %>%
count(state,imm_record) %>%
arrange(desc(n))## # A tibble: 6 × 3
## state imm_record n
## <chr> <chr> <int>
## 1 WA NEGATIVE 1798
## 2 WA POSITIVE 1558
## 3 WA <NA> 946
## 4 OR POSITIVE 761
## 5 OR <NA> 450
## 6 OR NEGATIVE 297i213 %>%
filter(date >= '2019-01-01',
date <= '2021-12-31') %>%
count(imm_record, quarter) %>%
ggplot(aes(x=as.factor(quarter), y=n, fill=imm_record)) +
geom_col()
i213 %>%
filter(date >= '2019-01-01',
date <= '2021-12-31') %>%
count(imm_record, office, quarter) %>%
ggplot(aes(x=as.factor(quarter), y=n, fill=imm_record)) +
geom_col() +
facet_wrap(~office)
i213 %>%
filter(date >= '2019-01-01',
date <= '2021-12-31',
state %in% c('WA', 'OR') | is.na(state)) %>%
count(imm_record, state, quarter) %>%
ggplot(aes(x=as.factor(quarter), y=n, fill=imm_record)) +
geom_col() +
facet_wrap(~state)
i213 %>%
count(imm_record, method_short) %>%
ggplot(aes(x=as.factor(method_short), y=n, fill=imm_record)) +
geom_col()
# i213 %>%
# filter(date >= '2019-01-01',
# date <= '2021-12-31',
# state %in% c('WA', 'OR'),
# !is.na(imm_record),
# !is.na(method_short)) %>%
# count(imm_record, state, method_short, quarter) %>%
# ggplot(aes(x=as.factor(quarter), y=n, fill=imm_record)) +
# geom_col(position='fill') +
# facet_grid(method_short ~ state)i213 <- i213 %>%
mutate(crim_record = case_when(str_detect(crim_record, "See Narrative") ~ "See Narrative",
str_detect(crim_record, "None Known") ~ "None Known",
TRUE ~ NA_character_))
i213 %>%
count(lawsuit,crim_record) %>%
arrange(desc(n))## # A tibble: 6 × 3
## lawsuit crim_record n
## <dbl> <chr> <int>
## 1 1 <NA> 1510
## 2 1 See Narrative 1264
## 3 2 None Known 1183
## 4 1 None Known 1112
## 5 2 See Narrative 1109
## 6 2 <NA> 593i213 %>%
filter(state %in% c('WA', 'OR')) %>%
count(state,crim_record) %>%
arrange(desc(n))## # A tibble: 6 × 3
## state crim_record n
## <chr> <chr> <int>
## 1 WA None Known 1758
## 2 WA See Narrative 1427
## 3 WA <NA> 1117
## 4 OR See Narrative 816
## 5 OR <NA> 522
## 6 OR None Known 170i213 %>%
count(crim_record, method_short) %>%
ggplot(aes(x=as.factor(method_short), y=n, fill=crim_record)) +
geom_col()
# i213 %>%
# filter(date >= '2019-01-01',
# date <= '2021-12-31',
# state %in% c('WA', 'OR'),
# !is.na(crim_record),
# !is.na(method_short)) %>%
# count(crim_record, state, method_short, quarter) %>%
# ggplot(aes(x=as.factor(quarter), y=n, fill=crim_record)) +
# geom_col(position='fill') +
# facet_grid(method_short ~ state)
#
# i213 %>%
# filter(date >= '2019-01-01',
# date <= '2021-12-31') %>%
# count(crim_record, office, method_short) %>%
# ggplot(aes(x=n, y=method_short, fill=crim_record)) +
# geom_col() +
# facet_grid(~ office)
i213 %>%
count(crim_record, imm_record) %>%
ggplot(aes(x=crim_record, y=n, fill=imm_record)) +
geom_col(position='fill')
Mapping “mentions_*”
A few passes at mapping records that mention keywords.
Note I-213s with narratives mentioning keyword “courthouse” include high concentration in central WA; see 2019 UWCHR report on courthouse arrests in WA state.
data <- i213 %>%
group_by(mentions_courthouse, lon, lat) %>%
summarise(n = n()) %>%
filter(mentions_courthouse == TRUE) %>%
na.omit()
data <- st_as_sf(data, coords = c("lon", "lat"),
crs = 4326, agr = "constant")
ggplot(data = world) +
geom_sf() +
geom_sf(data = states, fill = NA) +
geom_sf(data = data, aes(size=n), shape = 21, fill = "red", alpha=.5) +
pnw_coords +
ggtitle('I-213 forms mentioning "courthouse" by "at_near" latitude/longitude')
data <- i213 %>%
group_by(mentions_juvenile, lon, lat) %>%
summarise(n = n()) %>%
filter(mentions_juvenile == TRUE) %>%
na.omit()
data <- st_as_sf(data, coords = c("lon", "lat"),
crs = 4326, agr = "constant")
ggplot(data = world) +
geom_sf() +
geom_sf(data = states, fill = NA) +
geom_sf(data = data, aes(size=n), shape = 21, fill = "red", alpha=.5) +
pnw_coords +
ggtitle('I-213 forms mentioning "juvenile" by "at_near" latitude/longitude')
data <- i213 %>%
group_by(mentions_bus, lon, lat) %>%
summarise(n = n()) %>%
filter(mentions_bus == TRUE) %>%
na.omit()
data <- st_as_sf(data, coords = c("lon", "lat"),
crs = 4326, agr = "constant")
ggplot(data = world) +
geom_sf() +
geom_sf(data = states, fill = NA) +
geom_sf(data = data, aes(size=n), shape = 21, fill = "red", alpha=.5) +
pnw_coords +
ggtitle('I-213 forms mentioning "bus" by "at_near" latitude/longitude')