Data overview: “Landmark” fields

options(scipen = 1000000, digits = 2)

library(pacman)

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

# 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()),
                  show_col_types = FALSE)

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

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 = toupper(gender),
         operation = toupper(operation),
         processing_disposition = toupper(processing_disposition),
         citizenship_country = toupper(citizenship_country))

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

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)),
         citizenship_country = as.factor(toupper(citizenship_country)))

methods <- arr %>% 
  count(arrest_method) %>% 
  arrange(desc(n))

top_methods <- methods %>% 
  filter(n > 10000)

arr <- arr %>% 
  mutate(arrest_method_short =
           case_when(arrest_method %in%
                       unlist(top_methods$arrest_method) ~
                       as.character(arrest_method), 
                     TRUE ~ 
                       "All others"))

This notebook provides an overview of landmark and apprehension_landmark fields respectively associated with ICE encounter and arrest records (the removals data does not contain a similar field).

We argue that it is important to exercise caution when interpreting these values, as they cannot be relied upon as precise locations of these events. Many records instead encode the ICE entity associated with the event; attempts to geolocate these events to the state or county level are likely to be inaccurate.

These fields appear to be composed of semi-structured data. It seems likely that values are generated via some form of auto-completion, given propagation of minor typographical errors in repeated entries, for example, the string “PORTALND NON-DETAINED ARREST” appears 446 times in the arrests dataset, while “PORTLAND NON-DETAINED ARREST” appears 0 times.

Most values appear a small number of times; a few values appear many times, especially in the encounters data.

enc_landmarks <- enc %>% 
  filter(!is.na(landmark)) %>% 
  group_by(landmark) %>% 
  summarize(n = n(),
            n_aor = n_distinct(aor),
            type = "encounters") %>% 
  arrange(desc(n))

arr_landmarks <- arr %>% 
  filter(!is.na(apprehension_landmark)) %>% 
  group_by(apprehension_landmark) %>% 
  summarize(n = n(),
            n_aor = n_distinct(aor),
            type = "arrests") %>% 
  rename(landmark = apprehension_landmark) %>% 
  arrange(desc(n))

dat <- rbind(enc_landmarks, arr_landmarks)

b1 <- dat %>%
  ggplot(aes(x = type, y = log(n), color = type)) +
  geom_boxplot() +
  labs(title = "Landmark string frequency by dataset")

b1

Encounters

The Encounters dataset contains 12549 distinct landmark values; or 21125 distinct combinations of aor and landmark.

Missingness

A total of 1528342 or 27.66% of encounter records are missing landmark values; see below for an overview of missingness over time and across ICE areas of responsibility.

p1 <- enc %>%
  mutate(null_landmark = is.na(landmark)) %>% 
  count(null_landmark, fy) %>% 
  ggplot(aes(x = fy, y = n, fill = null_landmark)) +
  geom_col(position="fill") +
  scale_y_continuous(labels = scales::percent) +
  labs(title = "Proportion of encounters missing `landmark` value")

p1

p2 <- enc %>%
  mutate(null_landmark = is.na(landmark)) %>% 
  count(null_landmark, fy, aor) %>% 
  ggplot(aes(x = fy, y = n, fill = null_landmark)) +
  geom_col(position="fill") +
  scale_y_continuous(labels = scales::percent) +
  scale_x_discrete(breaks=seq(2012, 2022, 4)) +
  theme(axis.text.x = element_text(angle = 90, vjust = 1, hjust=1)) +
  facet_wrap(~aor) + 
  labs(title = "Proportion of encounters missing `landmark` value")

p2

Most common encounter `landmark` values

See below a table of the ten most common encounter landmark values. Note the top two values are associated with ICE “Interoperability” centers in Los Angeles, CA (Los Angeles AOR), and Batavia, NY (Buffalo AOR), which utilized nationwide databases to identify potential targets for ICE enforcement actions in the context of “Secure Communities” and other ICE programs; it is possible that these targets may not even be located in the respective states or AORs of these interoperability centers. See the Encounters notebook for an overview of rates of encounters per AOR, which shows that the LOS and BUF regions had extremely high rates of encounters during periods when these landmark values predominate.

Note also the inclusion of landmarks associated with enforcement programs at either the state or AOR-level (“SECURE COMMUNITIES NEW YORK”). Finally, other values are denoted as “GENERAL AREA, NON-SPECIFIC”; these will be discussed in more detail below in the context of the arrests dataset.

dat <- enc %>% 
  filter(!is.na(landmark)) %>% 
  count(landmark) %>% 
  arrange(desc(n)) %>% 
  head(10)

knitr::kable(dat)

landmark	n
SC INTEROPERABILITY LAFO	534558
BATAVIA INTEROPERABILITY REGIONAL CENTER NY STATE	155265
CHICAGO DEPORT CENTER SECURE COMMUNITIES, IL	68959
CAP - MARICOPA COUNTY SHERIFFS OFFICE JAIL	48899
WSM GENERAL AREA, NON-SPECIFIC	45155
HARRIS COUNTY JAIL, HOUSTON, TX	35808
GCJ GENERAL AREA, NON-SPECIFIC	35749
SECURE COMMUNITIES NEW YORK	35147
DALLAS COUNTY GENERAL AREA	29991
TEXAS DEPT OF CRIMINAL JUSTICE, WALKER CO	27724

Arrests

The Arrests dataset contains 10619 distinct apprehension_landmark values; or 14622 distinct combinations of aor and apprehension_landmark.

Missingness

A total of 55999 or 3.22% of arrest records are missing apprehension_landmark values; see below for an overview of missingness over time and among ICE areas of responsibility.

Note especially that specific categories of arrests, including “ERO Reprocessed Arrest” and “Inspections” are significantly more likely to be missing apprehension_landmark values.

p1 <- arr %>%
  mutate(null_landmark = is.na(apprehension_landmark)) %>% 
  count(null_landmark, fy) %>% 
  ggplot(aes(x = fy, y = n, fill = null_landmark)) +
  geom_col(position="fill") +
  scale_y_continuous(labels = scales::percent) +
  labs(title = "Proportion of arrests missing `apprehension_landmark` value")

p1

p2 <- arr %>%
  mutate(null_landmark = is.na(apprehension_landmark)) %>% 
  count(null_landmark, fy, aor) %>% 
  ggplot(aes(x = fy, y = n, fill = null_landmark)) +
  geom_col(position="fill") +
  scale_y_continuous(labels = scales::percent) +
  scale_x_discrete(breaks=seq(2012, 2022, 4)) +
  theme(axis.text.x = element_text(angle = 90, vjust = 1, hjust=1)) +
  facet_wrap(~aor) +
  labs(title = "Proportion of arrests missing `apprehension_landmark` value",
       subtitle = "By ICE Area of Responsibility (AOR)")

p2

p3 <- arr %>%
  mutate(null_landmark = is.na(apprehension_landmark)) %>% 
  count(null_landmark, fy, arrest_method_short) %>% 
  ggplot(aes(x = fy, y = n, fill = null_landmark)) +
  geom_col(position="fill") +
  scale_y_continuous(labels = scales::percent) +
  scale_x_discrete(breaks=seq(2012, 2022, 4)) +
  theme(axis.text.x = element_text(angle = 90, vjust = 1, hjust=1)) +
  facet_wrap(~arrest_method_short) +
  labs(title = "Proportion of arrests missing `apprehension_landmark` value",
       subtitle = "By arrest method")

p3

Most common arrests `apprehension_landmark` values

See below a table of the ten most common arrest apprehension_landmark values. In contrast with the encounters landmarks described above, these appear to map more reliably to county or state-level landmarks; however, see below for a discussion of concerns related to geolocation of these values.

dat <- arr %>% 
  filter(!is.na(apprehension_landmark)) %>% 
  count(apprehension_landmark) %>% 
  arrange(desc(n)) %>% 
  head(10)

knitr::kable(dat)

apprehension_landmark	n
HARRIS COUNTY JAIL, HOUSTON, TX	29191
DALLAS COUNTY GENERAL AREA	27236
LOS ANGELES COUNTY GENERAL AREA, NON-SPECIFIC	25106
TEXAS DEPT OF CRIMINAL JUSTICE, WALKER CO	25093
CAP - MARICOPA COUNTY SHERIFFS OFFICE JAIL	21254
LOS ANGELES COUNTY JAIL, LOS ANGELES, CA	19391
NDD - 26 FEDERAL PLAZA NY, NY	17915
ICE ERO NEWARK	16517
VAL VERDE COUNTY JAIL	15593
DAL COUNTY JAIL	13846

Problematic `apprehension_landmark` values

ICE Field Offices and programs

However, note the value “ICE ERO NEWARK”, which denotes an arrest associated with Enforcement and Removal Operations out of the Newark Field Office; this Field Office has jurisdiction over the entire state of New Jersey, and we believe it would likely be inaccurate to associate these arrests with the city of Newark or Essex County, NJ. Additionally, the value “NDD - 26 FEDERAL PLAZA NY, NY” denotes the “non-detained docket” of ICE’s New York City Field Office; it is unclear that this should be taken as the precise location of an arrest, versus an administrative category.

Values denoting ICE field offices or programs (e.g. “FUGITIVE OPERATIONS”, “STREET ARREST”) rather than locations are also common throughout the dataset; attempts to geolocate and interpret these values as precise arrest locations will likely lead to systematic over-representation of geographic divisions associated with ICE field offices.

For example, 1749 records with apprehension_landmark value “SEA CAP”, denoting Criminal Alien Program arrests out of the Seattle field office, should likely not be interpreted as associated with the city of Seattle or King County, as these arrests could have taken place anywhere in the Seattle Area of Responsibility.

ldmk_aor_count <- arr %>%
  group_by(apprehension_landmark) %>%
  summarize(n = n(),
            n_aor = n_distinct(aor)) %>% 
  arrange(desc(n_aor))

select_nonspecific <- c("LICENSING UNIT/STATE POLICE", "287g", "at-large", "California Healthcare Facility", "CALIFORNIA HIGHWAY PATROL", "CAP ACI", "CIS REFERRAL", "FEDERAL DETENTION CENTER (FDC)", "FIELD ARREST", "FTC CI (Federal Transfer Center)", "FTM-JCART", "FTM-VCAS", "FUG - NON FUGITIVE", "FUGITIVE OPERATIONS", "FUGITIVE SOUTH TEAM ARRESTS", "FUGOP", "FUGOPS", "STREET ARREST", "STREET ARRESTS", "U.S. Marshalls Service", "U.S. Marshals", "U.S. Marshals Service", "U.S. MARSHALS SERVICE", "U.S. PROBATION OFFICE", "UNITED STATES MARSHALL SERVICE", "UNITED STATES PROBATION & PAROLE", "UNITED STATES PROBATION", "US 281 TO FM 493 EXP 83 NORTH TO FM 490", "US DISTRICT COURT", "US MARSHALLS", "US Marshals TF", "USCIS ARREST", "USCIS REFERRALS")

dat <- arr %>% 
  filter(apprehension_landmark %in% select_nonspecific) %>% 
  group_by(apprehension_landmark) %>%
  summarize(n = n(),
            n_aor = n_distinct(aor)) %>% 
  arrange(desc(n)) %>% 
  head(15)

knitr::kable(dat)

apprehension_landmark	n	n_aor
FUGITIVE OPERATIONS	9616	13
FUGITIVE SOUTH TEAM ARRESTS	1504	6
FEDERAL DETENTION CENTER (FDC)	1231	3
CAP ACI	462	3
FUGOPS	218	2
UNITED STATES MARSHALL SERVICE	210	2
US DISTRICT COURT	174	2
FTM-VCAS	111	2
FTC CI (Federal Transfer Center)	103	1
FUG - NON FUGITIVE	86	1
STREET ARREST	76	3
FTM-JCART	63	1
California Healthcare Facility	49	1
FIELD ARREST	39	1
287g	36	1

“GENERAL AREA, NON-SPECIFIC”

nonspecific_str <- unique(arr$apprehension_landmark[grep('[A-Z]{3} GENERAL AREA, NON-SPECIFIC', arr$apprehension_landmark)])

aor_dat <- arr %>% 
  mutate(nonspecific = apprehension_landmark %in% nonspecific_str) %>% 
  count(nonspecific, fy, aor)

method_dat <- arr %>% 
  mutate(nonspecific = apprehension_landmark %in% nonspecific_str) %>% 
  count(nonspecific, fy, arrest_method_short)

A significant number of apprehension_landmark values include the phrase “GENERAL AREA, NON-SPECIFIC”; when excluding values that also describe a state or county by name, these amount to 293824 records or 16.88% of the arrests dataset.

The majority of these values follow a regular format of a three-letter alphabetic code followed by “GENERAL AREA, NON-SPECIFIC”. We believe that these three-letter alphabetic codes denote ICE field offices or sub-field offices; this observation is derived from our analysis of DHS I-213 “Record of Deportable/Inadmissible Alien” forms for the Seattle Area of Responsibility, which include a “Location Code” field which appears to encode the DHS field office and sub-field office associated with each apprehension, e.g. “SEA/RIC” for Seattle field office, Richland, WA sub-field office. Although some maps of ICE sub-field offices have been published, we are unaware of any comprehensive source of information regarding interpretation of sub-field office codes or their respective jurisdictions. Our analysis of I-213 forms for the Seattle AOR suggests that sub-field offices may conduct arrests in multiple counties or states.

Crucially, the quantity of records with these explicitly non-specific apprehension_landmark values varies over time and between ICE AORs and programs (by arrest_method), as observed below. Note this does not include other apprehension_landmark values which cannot be precisely geolocated.

p1 <- aor_dat %>% 
  ggplot(aes(x = fy, y = n, fill = nonspecific)) +
  geom_col(position="fill") +
  scale_y_continuous(labels = scales::percent) +
  scale_x_discrete(breaks=seq(2012, 2022, 4)) +
  theme(axis.text.x = element_text(angle = 90, vjust = 1, hjust=1)) +
  facet_wrap(~aor) +
  labs(title = "Proportion of arrests with non-specific `apprehension_landmark` value",
       subtitle = "By ICE area of responsibility (`aor`)")

p1

p2 <- method_dat %>% 
  ggplot(aes(x = fy, y = n, fill = nonspecific)) +
  geom_col(position="fill") +
  scale_y_continuous(labels = scales::percent) +
  scale_x_discrete(breaks=seq(2012, 2022, 4)) +
  theme(axis.text.x = element_text(angle = 90, vjust = 1, hjust=1)) +
  facet_wrap(~arrest_method_short) +
  labs(title = "Proportion of arrests with non-specific `apprehension_landmark` value",
       subtitle = "By select `arrest_method`")

p2

Text analysis

Although we caution against interpreting landmark values as precise encounter/arrest locations, we do not suggest that these fields are not important for quantitative or qualitative analysis. For example, see below a very simple example of textual analysis suggestive of a trend of decreasing arrests involving local and county jails nationwide, alongside a corresponding decrease in arrests via ICE’s “CAP Local Incarceration” program.

arr <- arr %>% 
  mutate(
    landmark_type = case_when(
      str_detect(apprehension_landmark, "COUNTY JAIL") ~ "COUNTY JAIL/SHERIFF",
      str_detect(apprehension_landmark, "PARISH JAIL") ~ "COUNTY JAIL/SHERIFF",
      str_detect(apprehension_landmark, "CO\\.? JAIL") ~ "COUNTY JAIL/SHERIFF",
      str_detect(apprehension_landmark, "SHERIFF|SHERRIFF") ~ "COUNTY JAIL/SHERIFF",
      str_detect(apprehension_landmark, "COUNTY PRISON") ~ "COUNTY JAIL/SHERIFF",
      str_detect(apprehension_landmark, "CITY JAIL") ~ "CITY JAIL/POLICE",
      str_detect(apprehension_landmark, "POLICE") ~ "CITY JAIL/POLICE",
      str_detect(apprehension_landmark, "PD") ~ "CITY JAIL/POLICE",
      str_detect(apprehension_landmark, "STATE PRISON") ~ "STATE PRISON/JAIL",
      str_detect(apprehension_landmark, "STATE JAIL") ~ "STATE PRISON/JAIL",
      str_detect(apprehension_landmark, "DEPT\\.? OF CORRECTIONS") ~ "STATE PRISON/JAIL",
      str_detect(apprehension_landmark, "DEPARTMENT OF CORRECTIONS") ~ "STATE PRISON/JAIL",
      str_detect(apprehension_landmark, "\\bDOC\\b") ~ "STATE PRISON/JAIL",
      str_detect(apprehension_landmark, "FEDERAL") ~ "FEDERAL PRISON",
      str_detect(apprehension_landmark, "\\bBOP\\b") ~ "FEDERAL PRISON",
      str_detect(apprehension_landmark, "FCI") ~ "FEDERAL PRISON",
      TRUE ~ "ALL OTHERS"
      ))

p1 <- arr %>% 
  count(fy, landmark_type) %>% 
  ggplot(aes(x = fy, y = n, fill = landmark_type)) +
  geom_col(position="fill")
  
p1

p2 <- arr %>% 
  mutate(cap_methods = case_when(
    str_detect(arrest_method, "CAP") ~ arrest_method,
    !str_detect(arrest_method, "CAP") ~ "All other arrest methods"
  )) %>% 
  count(fy, cap_methods) %>% 
  ggplot(aes(x = fy, y = n, fill = cap_methods)) +
  geom_col(position="fill")
  
p2

Increase in values connoting “non-detained docket” arrests (“NDD”):

arr <- arr %>% 
  mutate(
    landmark_type = case_when(
      str_detect(apprehension_landmark, "NONDETAINED") ~ "NDD",
      str_detect(apprehension_landmark, "NON DETAINED") ~ "NDD",
      str_detect(apprehension_landmark, "NDD") ~ "NDD",
      str_detect(apprehension_landmark, "NON\\-DETAINED") ~ "NDD",
      TRUE ~ "ALL OTHERS"
      ))

p1 <- arr %>% 
  count(fy, landmark_type) %>% 
  ggplot(aes(x = fy, y = n, fill = landmark_type)) +
  geom_col(position="fill")
  
p1

p2 <- arr %>% 
  filter(!is.na(aor),
         !aor == "HQ",
         !is.na(apprehension_landmark)) %>% 
  count(fy, aor, landmark_type) %>% 
  ggplot(aes(x = fy, y = n, fill = landmark_type)) +
  geom_col(position="fill") +
  facet_wrap(~aor) +
  scale_x_discrete(breaks=seq(2012, 2022, 4))
  
p2

p3 <- arr %>% 
  filter(!is.na(aor),
         !aor == "HQ",
         !is.na(apprehension_landmark)) %>% 
  count(fy, arrest_method_short, landmark_type) %>% 
  ggplot(aes(x = fy, y = n, fill = landmark_type)) +
  geom_col(position="fill") +
  facet_wrap(~arrest_method_short) +
  scale_x_discrete(breaks=seq(2012, 2022, 4))
  
p3

Top 5 `apprehension_landmark` per `area_of_responsibility`

tab1 <- arr %>% count(area_of_responsibility, apprehension_landmark) %>% arrange(area_of_responsibility, desc(n)) %>% group_by(area_of_responsibility) %>%  slice_head(n=5)

knitr::kable(tab1)

area_of_responsibility	apprehension_landmark	n
Atlanta Area of Responsibility	ATLANTA, GA	12221
Atlanta Area of Responsibility	GWINNETT COUNTY JAIL - 287(G)	11782
Atlanta Area of Responsibility	D. RAY JAMES	7027
Atlanta Area of Responsibility	MECKLENBURG COUNTY, NC	5507
Atlanta Area of Responsibility	COBB COUNTY JAIL - 287(G)	3724
Baltimore Area of Responsibility	BALTIMORE CITY MD	2883
Baltimore Area of Responsibility	NON DETAINED ENCOUNTERS AT BAL ERO	2279
Baltimore Area of Responsibility	PRINCE GEORGE’S COUNTY MD	1258
Baltimore Area of Responsibility	MONTGOMERY COUNTY MD	1167
Baltimore Area of Responsibility	BALTIMORE COUNTY MD	1019
Boston Area of Responsibility	NON-DETAINED AND JUVENILE	8896
Boston Area of Responsibility	FUGITIVE OPERATIONS MA	4896
Boston Area of Responsibility	POM GENERAL AREA, NON-SPECIFIC	1974
Boston Area of Responsibility	HAR GENERAL AREA, NON-SPECIFIC	1657
Boston Area of Responsibility	HILLSBOROUGH COUNTY NH	1580
Buffalo Area of Responsibility	WENDE CORRECTIONAL FACILITY	5046
Buffalo Area of Responsibility	ERIE COUNTY	1392
Buffalo Area of Responsibility	ORLEANS CORRECTIONAL FACILITY	975
Buffalo Area of Responsibility	NA	760
Buffalo Area of Responsibility	FEDERAL CORRECTIONAL INSTITUTE RAY BROOK	711
Chicago Area of Responsibility	CHI GENERAL AREA, NON-SPECIFIC	8604
Chicago Area of Responsibility	INP GENERAL AREA, NON-SPECIFIC	7243
Chicago Area of Responsibility	MARION COUNTY SHERIFF’S OFFICE, INDIANAPOLIS, INDIANA	4237
Chicago Area of Responsibility	NA	3901
Chicago Area of Responsibility	LOUISVILLE FUGITIVE OPS ARREST	3344
Dallas Area of Responsibility	DALLAS COUNTY GENERAL AREA	26586
Dallas Area of Responsibility	DAL COUNTY JAIL	13744
Dallas Area of Responsibility	BOP GILES W DALBY	7670
Dallas Area of Responsibility	GPC CI (Great Plains Correctional Center)	5371
Dallas Area of Responsibility	OKLAHOMA CITY ICE ERO SUB-OFFICE	5189
Denver Area of Responsibility	DENVER COUNTY	4762
Denver Area of Responsibility	COLORADO DEPARTMENT OF CORRECTIONS	3297
Denver Area of Responsibility	CENTENNIAL, COLORADO	2591
Denver Area of Responsibility	ADAMS COUNTY JAIL	2459
Denver Area of Responsibility	DENVER JUSTICE CENTER	1674
Detroit Area of Responsibility	CLM GENERAL AREA, NON-SPECIFIC	9305
Detroit Area of Responsibility	DETROIT, MI	3583
Detroit Area of Responsibility	NORTHEAST OHIO CORRECTIONAL CENTER	2193
Detroit Area of Responsibility	CUYAHOGA COUNTY JAIL	1628
Detroit Area of Responsibility	FRANKLIN COUNTY JAIL	1562
El Paso Area of Responsibility	NA	3235
El Paso Area of Responsibility	EPC GENERAL AREA, NON-SPECIFIC	2110
El Paso Area of Responsibility	REEVES COUNTY DETENTION CENTER 3 100 W County Rd 470 Pecos Texas 79772	1626
El Paso Area of Responsibility	ABQ GENERAL AREA, NON-SPECIFIC	1350
El Paso Area of Responsibility	EL PASO COUNTY DETENTION FACILITY	1158
HQ Area of Responsibility	PRINCE WILLIAM/MANASSAS REGIONAL JAIL - VA	11
HQ Area of Responsibility	NA	9
HQ Area of Responsibility	LOS ANGELES COUNTY GENERAL AREA, NON-SPECIFIC	4
HQ Area of Responsibility	CAP-PADOC SCI-CAMP HILL PA STATE	4
HQ Area of Responsibility	ABQ GENERAL AREA, NON-SPECIFIC	3
Harlingen Area of Responsibility	HLG GENERAL AREA, NON-SPECIFIC	2782
Harlingen Area of Responsibility	SEGOVIA STATE JAIL- PRE-RELEASE, TX	1643
Harlingen Area of Responsibility	Coastal Bend Detention Center	1055
Harlingen Area of Responsibility	HIDALGO COUNTY JAIL, EDINBURG, TXN - TX1080000	469
Harlingen Area of Responsibility	LRD GENERAL AREA, NON-SPECIFIC	348
Houston Area of Responsibility	HARRIS COUNTY JAIL, HOUSTON, TX	29060
Houston Area of Responsibility	TEXAS DEPT OF CRIMINAL JUSTICE, WALKER CO	24917
Houston Area of Responsibility	BOP FEDERAL DETENTION CENTER, HOUSTON, TX	11799
Houston Area of Responsibility	MTG GENERAL AREA, NON-SPECIFIC	10273
Houston Area of Responsibility	HPC GENERAL AREA, NON-SPECIFIC	8870
Los Angeles Area of Responsibility	LOS ANGELES COUNTY GENERAL AREA, NON-SPECIFIC	24862
Los Angeles Area of Responsibility	LOS ANGELES COUNTY JAIL, LOS ANGELES, CA	19271
Los Angeles Area of Responsibility	ORANGE COUNTY JAIL - INTAKE RELEASE CENTER	6485
Los Angeles Area of Responsibility	SBD GENERAL AREA, NON-SPECIFIC	6259
Los Angeles Area of Responsibility	SAA GENERAL AREA, NON-SPECIFIC	6080
Miami Area of Responsibility	KROME SPC	4114
Miami Area of Responsibility	FUGITIVE OPERATIONS	3822
Miami Area of Responsibility	MIA GENERAL AREA, NON-SPECIFIC	3808
Miami Area of Responsibility	MIAMI FUGITIVE OPERATIONS	3711
Miami Area of Responsibility	287G COLLIER FMY ERO PROGRAM	3383
New Orleans Area of Responsibility	NSV GENERAL AREA, NON-SPECIFIC	4619
New Orleans Area of Responsibility	NOL GENERAL AREA, NON-SPECIFIC	3936
New Orleans Area of Responsibility	BHM GENERAL AREA, NON-SPECIFIC	3359
New Orleans Area of Responsibility	JNA GENERAL AREA, NON-SPECIFIC	3024
New Orleans Area of Responsibility	FUGITIVE OPERATIONS TN STATE	2642
New York City Area of Responsibility	NDD - 26 FEDERAL PLAZA NY, NY	17651
New York City Area of Responsibility	FUGITIVE OPERATIONS NY STATE	6893
New York City Area of Responsibility	CAP - SUFFOLK COUNTY JAIL NY STATE	2253
New York City Area of Responsibility	CIP GENERAL AREA, NON-SPECIFIC	2211
New York City Area of Responsibility	CAP - NASSAU COUNTY JAIL NY STATE	2096
Newark Area of Responsibility	ICE ERO NEWARK	16392
Newark Area of Responsibility	HUDSON COUNTY JAIL	2017
Newark Area of Responsibility	ESSEX COUNTY JAIL	1789
Newark Area of Responsibility	ICE ERO MOUNT LAUREL	1599
Newark Area of Responsibility	NEWARK PD	1509
Philadelphia Area of Responsibility	USBOP CI-MOSHANNON VALLEY	7317
Philadelphia Area of Responsibility	CAP-YRK GENERAL AREA, NON-SPECIFIC PA STATE	3665
Philadelphia Area of Responsibility	PHI GENERAL AREA, NON-SPECIFIC	2988
Philadelphia Area of Responsibility	NA	2858
Philadelphia Area of Responsibility	PENNSYLVANIA VCAS	2218
Phoenix Area of Responsibility	CAP - MARICOPA COUNTY SHERIFFS OFFICE JAIL	21188
Phoenix Area of Responsibility	CAP - LOWER BUCKEYE JAIL	6220
Phoenix Area of Responsibility	FUGITIVE OPERATIONS GENERAL	5125
Phoenix Area of Responsibility	NA	4329
Phoenix Area of Responsibility	CAP PIMA COUNTY JAIL AZ STATE	3180
Salt Lake City Area of Responsibility	SLC GENERAL AREA, NON-SPECIFIC	7210
Salt Lake City Area of Responsibility	NEVADA SOUTHERN DETENTION CENTER	5020
Salt Lake City Area of Responsibility	CLARK COUNTY DETENTION CENTER	4779
Salt Lake City Area of Responsibility	LVG GENERAL AREA, NON-SPECIFIC	4545
Salt Lake City Area of Responsibility	SALT LAKE COUNTY ADULT DETENTION CENTER - UT	2936
San Antonio Area of Responsibility	VAL VERDE COUNTY JAIL	15477
San Antonio Area of Responsibility	HIDALGO COUNTY JAIL, EDINBURG, TXN - TX1080000	11508
San Antonio Area of Responsibility	SNA GENERAL AREA, NON-SPECIFIC	11475
San Antonio Area of Responsibility	NA	10043
San Antonio Area of Responsibility	HLG GENERAL AREA, NON-SPECIFIC	9460
San Diego Area of Responsibility	SAN DIEGO INSPECTIONS	8123
San Diego Area of Responsibility	SAN DIEGO FUGOPS	7666
San Diego Area of Responsibility	SAN DIEGO CENTRAL JAIL	3400
San Diego Area of Responsibility	CALIPATRIA STATE PRISON	3149
San Diego Area of Responsibility	NA	2404
San Francisco Area of Responsibility	TAFT FEDERAL CORRECTIONAL INSTITUTION	7212
San Francisco Area of Responsibility	FRE GENERAL AREA, NON-SPECIFIC	4530
San Francisco Area of Responsibility	KERN COUNTY JAIL LERDO	4328
San Francisco Area of Responsibility	ALAMEDA COUNTY JAIL - SANTA RITA	4273
San Francisco Area of Responsibility	FUGITIVE OPERATIONS	3764
Seattle Area of Responsibility	SEATTLE NON-DETAINED DOCKET	2869
Seattle Area of Responsibility	POO CAP NON-CUSTODIAL ARREST	1933
Seattle Area of Responsibility	SEA CAP	1728
Seattle Area of Responsibility	FRANKLIN CO. JAIL	1559
Seattle Area of Responsibility	WASHINGTON COUNTY JAIL	1312
St. Paul Area of Responsibility	SPM GENERAL AREA, NON-SPECIFIC	5743
St. Paul Area of Responsibility	HENNEPIN COUNTY ADULT DETENTION CENTER, MN	2872
St. Paul Area of Responsibility	OMAHA NE NON-FUGITIVE ARREST	1820
St. Paul Area of Responsibility	DOUGLAS COUNTY JAIL, NE	1674
St. Paul Area of Responsibility	OMA GENERAL AREA, NON-SPECIFIC	1588
Washington Area of Responsibility	WAS GENERAL AREA, NON-SPECIFIC	6194
Washington Area of Responsibility	NORTHERN VIRGINIA AREA	5325
Washington Area of Responsibility	PRINCE WILLIAM/MANASSAS REGIONAL JAIL - VA	4288
Washington Area of Responsibility	FAIRFAX COUNTY JAIL - VA	2748
Washington Area of Responsibility	RCM GENERAL AREA, NON-SPECIFIC	1986
NA	NA	5662
NA	PRINCE WILLIAM/MANASSAS REGIONAL JAIL - VA	358
NA	JAK GENERAL AREA, NON-SPECIFIC	228
NA	DALLAS COUNTY GENERAL AREA	221
NA	XLS GENERAL AREA, NON-SPECIFIC	218

Top 5 `apprehension_landmark` per `arrest_method_short`

tab2 <- arr %>% count(arrest_method_short, apprehension_landmark) %>% arrange(arrest_method_short, desc(n)) %>% group_by(arrest_method_short) %>%  slice_head(n=5)

knitr::kable(tab2)

arrest_method_short	apprehension_landmark	n
287(g) Program	GWINNETT COUNTY JAIL - 287(G)	11829
287(g) Program	HARRIS COUNTY JAIL, HOUSTON, TX	4854
287(g) Program	PRINCE WILLIAM/MANASSAS REGIONAL JAIL - VA	4548
287(g) Program	COBB COUNTY JAIL - 287(G)	3717
287(g) Program	287G COLLIER FMY ERO PROGRAM	3339
All others	CIS REFERAL, MIAMI	1784
All others	LVG GENERAL AREA, NON-SPECIFIC	492
All others	NA	484
All others	MIAMI ASYLUM REFFERAL	479
All others	JAK GENERAL AREA, NON-SPECIFIC	416
CAP Federal Incarceration	VAL VERDE COUNTY JAIL	15506
CAP Federal Incarceration	BOP FEDERAL DETENTION CENTER, HOUSTON, TX	11597
CAP Federal Incarceration	BOP GILES W DALBY	7711
CAP Federal Incarceration	USBOP CI-MOSHANNON VALLEY	7450
CAP Federal Incarceration	TAFT FEDERAL CORRECTIONAL INSTITUTION	7096
CAP Local Incarceration	HARRIS COUNTY JAIL, HOUSTON, TX	24240
CAP Local Incarceration	CAP - MARICOPA COUNTY SHERIFFS OFFICE JAIL	21007
CAP Local Incarceration	LOS ANGELES COUNTY JAIL, LOS ANGELES, CA	17441
CAP Local Incarceration	DAL COUNTY JAIL	13656
CAP Local Incarceration	DALLAS COUNTY GENERAL AREA	12068
CAP State Incarceration	TEXAS DEPT OF CRIMINAL JUSTICE, WALKER CO	24997
CAP State Incarceration	WENDE CORRECTIONAL FACILITY	5182
CAP State Incarceration	CALIPATRIA STATE PRISON	3460
CAP State Incarceration	COLORADO DEPARTMENT OF CORRECTIONS	3096
CAP State Incarceration	KROME SPC	2994
ERO Reprocessed Arrest	NA	32674
ERO Reprocessed Arrest	PIC GENERAL AREA, NON-SPECIFIC	1567
ERO Reprocessed Arrest	SAN DIEGO INSPECTIONS	1281
ERO Reprocessed Arrest	DHD GENERAL AREA, NON-SPECIFIC	1130
ERO Reprocessed Arrest	HLG GENERAL AREA, NON-SPECIFIC	888
Inspections	NA	5219
Inspections	SAN DIEGO INSPECTIONS	4700
Inspections	PIT GENERAL AREA, NON-SPECIFIC	66
Inspections	HLG DISTRICT OFFICE	60
Inspections	SNA GENERAL AREA, NON-SPECIFIC	27
Law Enforcement Agency Response Unit	LEAR - ARIZONA DEPARTMENT OF PUBLIC SAFETY	1725
Law Enforcement Agency Response Unit	LEAR - PHOENIX POLICE DEPARTMENT	1482
Law Enforcement Agency Response Unit	LOS ANGELES COUNTY JAIL, LOS ANGELES, CA	612
Law Enforcement Agency Response Unit	NA	474
Law Enforcement Agency Response Unit	LEAR - OFFICE OF INVESTIGATIONS	428
Located	FUGITIVE OPERATIONS	6662
Located	LOS ANGELES COUNTY GENERAL AREA, NON-SPECIFIC	4959
Located	FUGITIVE OPERATIONS CA STATE	4693
Located	SBD GENERAL AREA, NON-SPECIFIC	4094
Located	FUGITIVE OPERATIONS MA	3386
Non-Custodial Arrest	ICE ERO NEWARK	16376
Non-Custodial Arrest	NDD - 26 FEDERAL PLAZA NY, NY	15545
Non-Custodial Arrest	DALLAS COUNTY GENERAL AREA	10087
Non-Custodial Arrest	NON-DETAINED AND JUVENILE	9288
Non-Custodial Arrest	LOS ANGELES COUNTY GENERAL AREA, NON-SPECIFIC	6814
Other efforts	CHI GENERAL AREA, NON-SPECIFIC	2585
Other efforts	Miramar ICE/ERO Sub-Office	1304
Other efforts	DALLAS COUNTY GENERAL AREA	1074
Other efforts	SEATTLE NON-DETAINED DOCKET	994
Other efforts	NDD - 26 FEDERAL PLAZA NY, NY	780
Probation and Parole	INP GENERAL AREA, NON-SPECIFIC	670
Probation and Parole	LOS ANGELES COUNTY GENERAL AREA, NON-SPECIFIC	474
Probation and Parole	KROME SPC	441
Probation and Parole	SAN DIEGO FUGOPS	436
Probation and Parole	VENTURA FUGITIVE OPERATIONS	426

ICE ERO-LESA enforcement events FY12-22: Analysis of ‘landmark’ fields

UWCHR

2024-10-31

Data overview: “Landmark” fields

Encounters

Missingness

Most common encounter `landmark` values

Arrests

Missingness

Most common arrests `apprehension_landmark` values

Problematic `apprehension_landmark` values

ICE Field Offices and programs

“GENERAL AREA, NON-SPECIFIC”

Text analysis

Top 5 `apprehension_landmark` per `area_of_responsibility`

Top 5 `apprehension_landmark` per `arrest_method_short`

ICE ERO-LESA enforcement events FY12-22: Analysis of ‘landmark’ fields

UWCHR

2024-10-31

Data overview: “Landmark” fields

Encounters

Missingness

Most common encounter landmark values

Arrests

Missingness

Most common arrests apprehension_landmark values

Problematic apprehension_landmark values

ICE Field Offices and programs

“GENERAL AREA, NON-SPECIFIC”

Text analysis

Top 5 apprehension_landmark per area_of_responsibility

Top 5 apprehension_landmark per arrest_method_short

Most common encounter `landmark` values

Most common arrests `apprehension_landmark` values

Problematic `apprehension_landmark` values

Top 5 `apprehension_landmark` per `area_of_responsibility`

Top 5 `apprehension_landmark` per `arrest_method_short`