DDP vs ICE FY25 annual statistics

Comparing the Deportation Data Project’s daily detention counts with ICE’s published annual statistics to identify unreported facilities and track population changes

Author

Carwil Bjork-James

Published

April 2, 2026

Note

This report was generated using AI under general human direction. At the time of generation, the contents have not been comprehensively reviewed by a human analyst.

Code

library(dplyr)
library(tidyr)
library(ggplot2)
library(scales)
library(knitr)
library(sf)
library(leaflet)
library(leafpop)

Code

# Pre-computed data from the ice-detention pipeline.
# Run copy-data.sh to refresh from the pipeline project.
ice_fy25          <- readRDS("data/ice_fy25.rds")
ddp_fy25          <- readRDS("data/ddp_fy25.rds")
ddp_fy25_keyed    <- readRDS("data/ddp_fy25_keyed.rds")
daily_totals      <- readRDS("data/daily_totals.rds")
unmatched         <- readRDS("data/unmatched.rds")
daily_unmatched   <- readRDS("data/daily_unmatched.rds")
monthly_unmatched <- readRDS("data/monthly_unmatched.rds")
peak_fy25         <- readRDS("data/peak_fy25.rds")
biden_unmatched   <- readRDS("data/biden_unmatched.rds")
trump_unmatched   <- readRDS("data/trump_unmatched.rds")
daily_unreported  <- readRDS("data/daily_unreported.rds")

Throughout this report, facility-level summaries distinguish between facilities with ADP ≥ 2 and those with ADP < 2. Facilities averaging fewer than two detainees per day—typically hospitals, occasional-use hold rooms, and other transient placements—account for many DDP facility codes but negligible population. Separating them keeps attention on facilities with substantive detention activity.

Aggregate comparison

The DDP (Deportation Data Project) provides daily facility-level detention counts. To compare with the ICE FY25 annual statistics, we average DDP daily counts from October 1, 2024 through September 24, 2025—the date stamp on the FY25 spreadsheet.

Code

tibble(
  Source      = c("ICE FY25 annual stats", "DDP daily average"),
  Facilities  = c(nrow(ice_fy25), nrow(ddp_fy25)),
  `Total ADP` = c(round(sum(ice_fy25$ice_adp, na.rm = TRUE)),
                   round(sum(ddp_fy25$ddp_adp)))
) |>
  kable()

Source	Facilities	Total ADP
ICE FY25 annual stats	189	47549
DDP daily average	853	50130

The DDP covers 853 facility codes while the ICE spreadsheet lists 189 facilities. The DDP’s higher total reflects the many smaller facilities (hold rooms, federal prisons, medical facilities) not included in the ICE annual statistics.

Daily population over time

Code

fy_breaks <- as.Date(c("2023-10-01", "2024-10-01", "2025-10-01"))
fy_labels <- data.frame(
  x = as.Date(c("2024-04-01", "2025-04-01", "2025-10-08")),
  label = c("FY24", "FY25", "FY26")
)

ggplot(daily_totals, aes(x = date, y = total_pop)) +
  geom_line(linewidth = 0.3) +
  geom_vline(xintercept = fy_breaks, linetype = "dashed", alpha = 0.4) +
  geom_text(data = fy_labels, aes(x = x, y = max(daily_totals$total_pop) * 1.04,
                                   label = label), inherit.aes = FALSE, size = 3) +
  scale_y_continuous(labels = comma) +
  labs(x = NULL, y = "Total detained population")

Figure 1: Total ICE detained population per day (DDP data, all facilities)

Facility-level matching

We join the two datasets through canonical facility IDs. Each DDP facility code is mapped to a canonical ID via detloc_lookup_full, which preserves all DETLOC variants from every source (DMCP, DDP, hold/ERO, Vera). Some canonical facilities have multiple codes—for example, Central Arizona Florence Correctional Complex uses CCADCAZ (DMCP) and CCAFLAZ (DDP), and Delaney Hall uses ESSEXNJ (DMCP) and DHDFNJ (DDP). Using the full lookup ensures all codes contribute ADP to the correct facility.

Code

# Aggregate DDP by canonical_id for matched facilities.
ddp_by_canonical <- ddp_fy25_keyed |>
  filter(in_ice_fy25) |>
  group_by(canonical_id) |>
  summarise(ddp_adp = sum(ddp_adp), .groups = "drop")

comparison <- ice_fy25 |>
  group_by(canonical_id) |>
  summarise(
    canonical_name = first(canonical_name),
    ice_adp = sum(ice_adp, na.rm = TRUE),
    .groups = "drop"
  ) |>
  full_join(ddp_by_canonical, by = "canonical_id") |>
  mutate(
    in_ice = !is.na(ice_adp),
    in_ddp = !is.na(ddp_adp),
    diff   = ddp_adp - ice_adp,
    pct_diff = diff / ice_adp * 100
  )

matched <- comparison |> filter(in_ice & in_ddp)

tibble(
  Category = c("In both sources", "ICE only (no DDP match)",
               "Matched: ICE total ADP", "Matched: DDP total ADP",
               "Matched: aggregate difference"),
  Value = c(
    sum(comparison$in_ice & comparison$in_ddp),
    sum(comparison$in_ice & !comparison$in_ddp),
    round(sum(matched$ice_adp, na.rm = TRUE)),
    round(sum(matched$ddp_adp, na.rm = TRUE)),
    paste0(round(sum(matched$ddp_adp, na.rm = TRUE) -
                   sum(matched$ice_adp, na.rm = TRUE)),
           " (", round((sum(matched$ddp_adp, na.rm = TRUE) /
                           sum(matched$ice_adp, na.rm = TRUE) - 1) * 100, 2), "%)")
  )
) |>
  kable()

Category	Value
In both sources	188
ICE only (no DDP match)	0
Matched: ICE total ADP	47549
Matched: DDP total ADP	48587
Matched: aggregate difference	1038 (2.18%)

Facility-level agreement

Code

both_nonzero <- matched |> filter(ice_adp > 0, ddp_adp > 0)

ggplot(both_nonzero, aes(x = ice_adp, y = ddp_adp)) +
  geom_abline(slope = 1, intercept = 0, linetype = "dashed", alpha = 0.5) +
  geom_point(alpha = 0.6) +
  scale_x_log10(labels = comma) +
  scale_y_log10(labels = comma) +
  labs(x = "ICE FY25 ADP (annual stats)", y = "DDP mean daily population")

Figure 2: FY25 ADP comparison by facility (log scale). Dashed line = perfect agreement.

Code

tibble(
  `Agreement band` = c("Within 10%", "Within 25%", "Within 50%", ">50% difference"),
  Facilities = c(
    sum(abs(both_nonzero$pct_diff) <= 10),
    sum(abs(both_nonzero$pct_diff) > 10 & abs(both_nonzero$pct_diff) <= 25),
    sum(abs(both_nonzero$pct_diff) > 25 & abs(both_nonzero$pct_diff) <= 50),
    sum(abs(both_nonzero$pct_diff) > 50)
  )
) |>
  kable()

Agreement band	Facilities
Within 10%	115
Within 25%	26
Within 50%	30
>50% difference	17

Among the 188 matched facilities with nonzero ADP in both sources, 115 agree within 10%. The log-scale correlation is 0.998.

Absolute ADP differences

Code

abs_diff <- abs(both_nonzero$diff)
tibble(
  `Difference band` = c("< 5", "5–25", "25–100", "100–500", "> 500"),
  Facilities = c(
    sum(abs_diff < 5),
    sum(abs_diff >= 5 & abs_diff < 25),
    sum(abs_diff >= 25 & abs_diff < 100),
    sum(abs_diff >= 100 & abs_diff < 500),
    sum(abs_diff >= 500)
  ),
  `Total excess ADP` = c(
    round(sum(both_nonzero$diff[abs_diff < 5])),
    round(sum(both_nonzero$diff[abs_diff >= 5 & abs_diff < 25])),
    round(sum(both_nonzero$diff[abs_diff >= 25 & abs_diff < 100])),
    round(sum(both_nonzero$diff[abs_diff >= 100 & abs_diff < 500])),
    round(sum(both_nonzero$diff[abs_diff >= 500]))
  )
) |>
  kable()

Table 1: Distribution of absolute ADP differences (DDP − ICE) among matched facilities

Difference band	Facilities	Total excess ADP
< 5	126	146
5–25	54	432
25–100	7	329
100–500	1	131
> 500	0	0

The net difference across all 188 matched facilities is 1038 ADP (DDP minus ICE). Positive values indicate DDP counts higher than ICE.

Code

# Log-modulus transform: sign(x) * log10(|x| + 1)
log_mod <- function(x) sign(x) * log10(abs(x) + 1)
log_mod_inv <- function(x) sign(x) * (10^abs(x) - 1)

ggplot(both_nonzero, aes(x = diff)) +
  geom_histogram(bins = 50) +
  scale_x_continuous(
    transform = scales::new_transform("logmod", log_mod, log_mod_inv),
    breaks = c(-1000, -100, -10, 0, 10, 100, 1000),
    labels = comma
  ) +
  labs(x = "ADP difference (DDP \u2212 ICE)", y = "Facilities")

Figure 3: Distribution of facility-level ADP differences (DDP − ICE) among matched facilities (log-modulus scale)

Table 2: ICE FY25 facilities with no DDP match

Code

ice_only <- comparison |> filter(in_ice & !in_ddp)
if (nrow(ice_only) > 0) {
  ice_only |>
    select(canonical_id, canonical_name, ice_adp) |>
    mutate(ice_adp = round(ice_adp)) |>
    arrange(desc(ice_adp)) |>
    kable(col.names = c("ID", "Facility", "ICE ADP"))
}

All ICE FY25 facilities have a matching DDP facility code.

Unmatched facilities

663 of 853 DDP facility codes do not appear in the ICE FY25 annual statistics. These are facilities where ICE holds detainees but which are excluded from the published spreadsheet.

By facility type

Facility types are drawn from the Vera Institute’s classification system. “Dedicated” facilities are purpose-built or contractually dedicated to ICE detention. “Non-Dedicated” are county jails and other facilities that also hold ICE detainees. “Hold/Staging” are short-term ERO processing rooms. “Federal” includes BOP prisons and USMS facilities.

Code

unmatched |>
  group_by(type_grouped, adp_class) |>
  summarise(
    Facilities = n(),
    `FY25 ADP` = round(sum(ddp_adp)),
    .groups = "drop"
  ) |>
  arrange(desc(`FY25 ADP`)) |>
  kable(col.names = c("Facility type", "ADP class", "Facilities", "FY25 ADP"))

Table 3: DDP facilities not in ICE FY25, by Vera facility type and ADP class

Facility type	ADP class	Facilities	FY25 ADP
Hold/Staging	ADP ≥ 2	77	1103
Federal	ADP ≥ 2	10	206
Dedicated	ADP ≥ 2	2	186
Hold/Staging	ADP < 2	101	60
Medical	ADP ≥ 2	7	37
Medical	ADP < 2	221	32
Non-Dedicated	ADP < 2	147	25
Non-Dedicated	ADP ≥ 2	6	22
Federal	ADP < 2	75	18
Other/Unknown	ADP ≥ 2	1	5
Family/Youth	ADP ≥ 2	1	3
Dedicated	ADP < 2	6	1
Family/Youth	ADP < 2	4	0
Other/Unknown	ADP < 2	4	0
Unclassified	ADP < 2	1	0

The 663 unmatched facilities account for a full-year ADP of roughly 1,699. Of those, 559 facility codes average fewer than 2 detainees per day, contributing 136 ADP in total.

Code

dedicated_unmatched <- unmatched |>
  filter(type_grouped == "Dedicated")

if (nrow(dedicated_unmatched) > 0) {
  dedicated_unmatched |>
    select(detention_facility_code, detention_facility, state, ddp_adp, adp_class) |>
    mutate(ddp_adp = round(ddp_adp)) |>
    arrange(desc(ddp_adp)) |>
    kable(col.names = c("DETLOC", "Facility name", "State", "FY25 ADP", "ADP class"))
}

Table 4: Dedicated facilities in DDP but absent from ICE FY25

DETLOC	Facility name	State	FY25 ADP	ADP class
FLDSSFS	FLORIDA SOFT-SIDED FACILITY-SOUTH	FL	170	ADP ≥ 2
FLBAKCI	BAKER C. I.	FL	16	ADP ≥ 2
BERNANM	BERNALILLO COUNTY DET CTR	NM	0	ADP < 2
BIINCCO	BI INCORORATED, GEO GROUP COMPANY	CO	0	ADP < 2
BPC	BOSTON SPC	MA	0	ADP < 2
CVANXCA	CENTRAL VALLEY ANNEX	CA	0	ADP < 2
FLDADCI	DADE CORRECTIONAL INST	FL	0	ADP < 2
LPAPSAZ	LA PALMA CORR CTR APSO	AZ	0	ADP < 2

8 Dedicated facilities appear in DDP but not in ICE FY25, contributing 186 ADP.

Daily and monthly detention in unmatched facilities

Code

ggplot(daily_unmatched, aes(x = date, y = total_pop)) +
  geom_line(linewidth = 0.3) +
  scale_y_continuous(labels = comma) +
  scale_x_date(date_labels = "%b %Y", date_breaks = "2 months") +
  labs(x = NULL, y = "Detained population (unmatched facilities)")

Figure 4: Daily detained population in facilities not in ICE FY25 annual statistics

Code

monthly_totals <- monthly_unmatched |>
  group_by(month) |>
  summarise(daily_avg = sum(n_detained) / n_distinct(date), .groups = "drop")

ggplot(monthly_totals, aes(x = month, y = daily_avg)) +
  geom_col() +
  scale_y_continuous(labels = comma) +
  scale_x_date(date_labels = "%b %Y", date_breaks = "2 months") +
  labs(x = NULL, y = "Average daily population")

Figure 5: Average daily population in unmatched facilities, by month

Code

monthly_by_type <- monthly_unmatched |>
  group_by(month, type_grouped) |>
  summarise(daily_avg = sum(n_detained) / n_distinct(date), .groups = "drop")

ggplot(monthly_by_type, aes(x = month, y = daily_avg, fill = type_grouped)) +
  geom_col() +
  scale_y_continuous(labels = comma) +
  scale_x_date(date_labels = "%b %Y", date_breaks = "2 months") +
  labs(x = NULL, y = "Average daily population", fill = "Facility type")

Figure 6: Average daily population in unmatched facilities, by month and type

Last 60 days of FY25

The final 60 days of the FY25 data window (July 27 – September 24, 2025) capture the period of peak detention.

Code

last60_start <- as.Date("2025-07-27")
last60_end   <- as.Date("2025-09-24")

last60 <- monthly_unmatched |>
  filter(date >= last60_start, date <= last60_end)

last60_by_type <- last60 |>
  group_by(type_grouped) |>
  summarise(
    Facilities = n_distinct(detention_facility_code),
    `Daily avg` = round(sum(n_detained) / n_distinct(date)),
    .groups = "drop"
  ) |>
  arrange(desc(`Daily avg`))

last60_total <- sum(last60_by_type$`Daily avg`)

last60_by_type |>
  rename(`Facility type` = type_grouped) |>
  kable()

Table 5: Average daily population in unmatched facilities, last 60 days of FY25

Facility type	Facilities	Daily avg
Hold/Staging	178	1987
Dedicated	8	787
Federal	85	419
Medical	228	97
Non-Dedicated	153	81
Other/Unknown	5	8
Family/Youth	5	5
Unclassified	1	0

In the last 60 days, 3,384 people per day were held in facilities absent from the ICE FY25 annual statistics.

Peak FY25 population by facility

Code

peak_fy25 |>
  arrange(desc(peak_pop)) |>
  head(20) |>
  select(detention_facility, state, type_grouped, peak_pop, peak_date, mean_pop) |>
  kable(col.names = c("Facility", "State", "Type", "Peak", "Peak date", "Mean ADP"))

Table 6: Top 20 unmatched facilities by FY25 peak detained population

Facility	State	Type	Peak	Peak date	Mean ADP
EL PASO SOFT SIDED FACILITY	TX	Hold/Staging	1606	2025-08-12	243.2
FLORIDA SOFT-SIDED FACILITY-SOUTH	FL	Dedicated	1482	2025-07-30	170.3
KROME HOLD ROOM	FL	Hold/Staging	600	2025-02-17	36.5
URSULA CENTRALIZED PROCESSING CNTR	TX	Federal	463	2025-07-11	31.6
LOS CUST CASE	CA	Hold/Staging	455	2025-07-11	58.7
BAKER C. I.	FL	Dedicated	294	2025-09-24	15.7
RICHMOND SUB-OFFICE HOLD	VA	Hold/Staging	277	2025-09-07	16.2
BROADVIEW SERVICE STAGING	IL	Hold/Staging	245	2025-09-20	31.7
NYC HOLD ROOM	NY	Hold/Staging	228	2025-06-05	40.9
DALLAS F.O. HOLD	TX	Hold/Staging	225	2025-09-18	53.1
MONTGOMERY HOLD RM	TX	Hold/Staging	221	2025-08-21	50.0
BOSTON HOLDROOM	MA	Hold/Staging	206	2025-05-13	18.6
AZ REM OP COORD CENTER (AROCC)	AZ	Hold/Staging	195	2025-04-30	37.4
MIAMI STAGING FACILITY	FL	Hold/Staging	176	2025-01-31	16.5
ATLANTA DIST. HOLD RM	GA	Hold/Staging	168	2025-06-25	42.1
OLDHAM COUNTY JAIL	KY	Federal	156	2025-09-17	63.1
SANTA ANA DRO HOLDROOM	CA	Hold/Staging	150	2025-06-11	11.3
SAN ANTONIO DRO HOLD ROOM	TX	Hold/Staging	145	2025-06-04	19.6
INDIANAPOLIS HOLD ROOM	IN	Hold/Staging	130	2025-09-19	12.3
WASHINGTON FIELD OFFICE	VA	Hold/Staging	127	2025-08-23	17.3

Code

peak_fy25 |>
  filter(peak_pop > 0) |>
  ggplot(aes(x = peak_pop)) +
  geom_histogram(bins = 40) +
  scale_x_log10(labels = comma) +
  labs(x = "Peak detained population (FY25)", y = "Facilities")

Figure 7: Distribution of FY25 peak population among unmatched facilities (log scale, facilities with peak > 0)

Map of unmatched facilities

Code

map_data <- peak_fy25 |>
  filter(peak_pop > 0, !is.na(latitude), !is.na(longitude))

map_sf <- st_as_sf(map_data, coords = c("longitude", "latitude"), crs = 4326)

type_colors <- c(
  "Dedicated"     = "#e41a1c",
  "Non-Dedicated" = "#377eb8",
  "Federal"       = "#4daf4a",
  "Hold/Staging"  = "#ff7f00",
  "Family/Youth"  = "#984ea3",
  "Medical"       = "#a65628",
  "Other/Unknown" = "#999999",
  "Unclassified"  = "#333333"
)

map_sf$type_grouped <- factor(map_sf$type_grouped, levels = names(type_colors))
present_levels <- levels(droplevels(map_sf$type_grouped))
pal <- colorFactor(palette = type_colors[present_levels], domain = present_levels)

# sqrt scale for area-proportional circle sizing
radii <- scales::rescale(sqrt(map_sf$peak_pop), to = c(3, 20))

leaflet(map_sf) |>
  addProviderTiles("CartoDB.Positron") |>
  addCircleMarkers(
    radius      = radii,
    color       = "black",
    weight      = 1,
    fillColor   = ~pal(type_grouped),
    fillOpacity = 0.7,
    label       = ~paste0(detention_facility, " (", state, ": peak ", peak_pop, ")"),
    popup       = ~paste0(
      "<b>", detention_facility, "</b><br>",
      state, " | ", type_grouped, "<br>",
      "Peak: ", peak_pop, " (", peak_date, ")<br>",
      "Mean ADP: ", mean_pop
    )
  ) |>
  addLegend(
    pal = pal, values = ~type_grouped,
    title = "Facility type", position = "bottomright"
  )

Figure 8: DDP facilities not in ICE FY25 annual statistics, sized by FY25 peak population

Unreported detention: Biden final quarter vs early Trump

The preceding sections establish which DDP facilities are absent from the ICE FY25 annual statistics. Here we compare the unreported population—detainees held at those unmatched facilities—during the final three months of the Biden administration versus a comparable window under the Trump administration.

Code

biden_from <- as.Date("2024-10-20")
biden_to   <- as.Date("2025-01-19")
trump_from <- as.Date("2025-07-17")
trump_to   <- as.Date("2025-10-15")

Biden final quarter: 2024-10-20 to 2025-01-19 (92 days)
Trump recent quarter: 2025-07-17 to 2025-10-15 (91 days)

Aggregate unreported population

Code

bind_rows(biden_unmatched, trump_unmatched) |>
  filter(peak_pop > 0) |>
  group_by(period, adp_class) |>
  summarise(
    Facilities = n(),
    `Total ADP` = round(sum(mean_pop)),
    .groups = "drop"
  ) |>
  arrange(adp_class, period) |>
  kable()

Table 7: Unreported DDP population by period (facilities not in ICE FY25)

period	adp_class	Facilities	Total ADP
Biden	ADP < 2	268	95
Trump	ADP < 2	302	128
Biden	ADP ≥ 2	50	396
Trump	ADP ≥ 2	138	3372

Code

bind_rows(biden_unmatched, trump_unmatched) |>
  filter(peak_pop > 0, adp_class == "ADP \u2265 2") |>
  group_by(period) |>
  summarise(
    Facilities = n(),
    `Total ADP` = round(sum(mean_pop)),
    `Mean ADP per facility` = round(sum(mean_pop) / n(), 1),
    .groups = "drop"
  ) |>
  kable()

Table 8: Unreported population totals (ADP ≥ 2 facilities only)

period	Facilities	Total ADP	Mean ADP per facility
Biden	50	396	7.9
Trump	138	3372	24.4

By facility type

Code

type_comp <- bind_rows(biden_unmatched, trump_unmatched) |>
  filter(peak_pop > 0, adp_class == "ADP \u2265 2") |>
  group_by(period, type_grouped) |>
  summarise(
    Facilities = n(),
    ADP = round(sum(mean_pop)),
    .groups = "drop"
  )

type_comp |>
  pivot_wider(names_from = period,
              values_from = c(Facilities, ADP),
              values_fill = 0,
              names_glue = "{period} {.value}") |>
  mutate(
    `ADP change` = `Trump ADP` - `Biden ADP`,
    `% change` = if_else(
      `Biden ADP` == 0, NA_real_,
      round((`Trump ADP` / `Biden ADP` - 1) * 100, 1)
    )
  ) |>
  arrange(desc(`Trump ADP`)) |>
  kable(col.names = c("Facility type",
                       "Biden fac.", "Trump fac.",
                       "Biden ADP", "Trump ADP",
                       "ADP change", "% change"))

Table 9: Unreported facilities by type and period (ADP ≥ 2 only)

Facility type	Biden fac.	Trump fac.	Biden ADP	Trump ADP	ADP change	% change
Hold/Staging	37	91	258	1848	1590	616.3
Dedicated	0	2	0	985	985	NA
Federal	3	16	92	393	301	327.2
Non-Dedicated	2	16	14	76	62	442.9
Medical	6	11	26	60	34	130.8
Other/Unknown	1	1	3	6	3	100.0
Family/Youth	1	1	2	4	2	100.0

Code

bind_rows(biden_unmatched, trump_unmatched) |>
  filter(peak_pop > 0, adp_class == "ADP < 2") |>
  group_by(period, type_grouped) |>
  summarise(
    Facilities = n(),
    ADP = round(sum(mean_pop), 1),
    .groups = "drop"
  ) |>
  pivot_wider(names_from = period,
              values_from = c(Facilities, ADP),
              values_fill = 0,
              names_glue = "{period} {.value}") |>
  arrange(desc(`Trump Facilities`)) |>
  kable(col.names = c("Facility type",
                       "Biden fac.", "Trump fac.",
                       "Biden ADP", "Trump ADP"))

Table 10: Transient facilities (ADP < 2) by type and period

Facility type	Biden fac.	Trump fac.	Biden ADP	Trump ADP
Medical	81	120	15.1	36.7
Hold/Staging	117	73	59.2	48.2
Non-Dedicated	41	58	10.3	21.6
Federal	25	43	10.0	17.6
Dedicated	2	4	0.0	1.4
Family/Youth	1	2	0.1	0.9
Other/Unknown	1	2	0.0	1.5

Facilities active in only one period

Code

biden_active <- biden_unmatched |> filter(peak_pop > 0, adp_class == "ADP \u2265 2")
trump_active <- trump_unmatched |> filter(peak_pop > 0, adp_class == "ADP \u2265 2")

biden_only_codes <- setdiff(biden_active$detention_facility_code,
                             trump_active$detention_facility_code)
trump_only_codes <- setdiff(trump_active$detention_facility_code,
                             biden_active$detention_facility_code)

Code

if (length(biden_only_codes) > 0) {
  biden_unmatched |>
    filter(detention_facility_code %in% biden_only_codes) |>
    arrange(desc(mean_pop)) |>
    select(detention_facility, state, type_grouped, mean_pop, peak_pop) |>
    mutate(mean_pop = round(mean_pop, 1)) |>
    kable(col.names = c("Facility", "State", "Type", "Biden ADP", "Biden peak"))
}

Table 11: Unreported facilities with ADP ≥ 2 active under Biden but not Trump

Facility	State	Type	Biden ADP	Biden peak
KROME HOLD ROOM	FL	Hold/Staging	21.3	116
NYE COUNTY SHERIFF-PAHRUMP	NV	Non-Dedicated	12.0	69
GUAYNABO MDC (SAN JUAN)	PR	Federal	6.6	12
PECOS HOLD ROOM	TX	Hold/Staging	2.7	27
BALDWIN COUNTY COR. CENTER	AL	Non-Dedicated	2.0	14

5 unreported facilities (ADP ≥ 2) were active in the Biden period but fell below the threshold or had zero population in the Trump period.

Code

if (length(trump_only_codes) > 0) {
  trump_unmatched |>
    filter(detention_facility_code %in% trump_only_codes) |>
    arrange(desc(mean_pop)) |>
    select(detention_facility, state, type_grouped, mean_pop, peak_pop) |>
    mutate(mean_pop = round(mean_pop, 1)) |>
    kable(col.names = c("Facility", "State", "Type", "Trump ADP", "Trump peak"))
}

Table 12: Unreported facilities with ADP ≥ 2 active under Trump but not Biden

Facility	State	Type	Trump ADP	Trump peak
FLORIDA SOFT-SIDED FACILITY-SOUTH	FL	Dedicated	808.4	1482
EL PASO SOFT SIDED FACILITY	TX	Hold/Staging	326.1	1606
BAKER C. I.	FL	Dedicated	176.1	667
OLDHAM COUNTY JAIL	KY	Federal	132.1	156
URSULA CENTRALIZED PROCESSING CNTR	TX	Federal	84.6	365
MIAMI (MIRAMAR) HOLD ROOM	FL	Hold/Staging	34.7	94
BROOKS COUNTY JAIL (CONTRACT)	TX	Federal	21.1	90
BOSTON HOLDROOM	MA	Hold/Staging	20.4	43
SANTA ANA DRO HOLDROOM	CA	Hold/Staging	19.5	96
CHARLOTTE HOLD ROOM	NC	Hold/Staging	17.6	48
SOUTH TEXAS/PEARSALL HOLD ROOM	TX	Hold/Staging	16.0	54
SFR HOLD ROOM	CA	Hold/Staging	15.6	44
TURNER GUIFORD KNIGHT (TGK) JAIL	FL	Non-Dedicated	14.9	27
KANSAS CITY HOLDROOM	MO	Hold/Staging	13.0	60
WESTERN TENNESSEE DET. FAC.	TN	Federal	12.6	74
OMAHA FO HOLD	NE	Hold/Staging	12.2	59
JACKSONVILLE SUB-OFFICE	FL	Hold/Staging	12.2	34
PHI DISTRICT OFFICE	PA	Hold/Staging	11.1	27
MIAMI CORRECTIONAL CENTER	IN	Non-Dedicated	11.1	95
PRAIRIELAND SUBOFFICE HOLD ROOM	TX	Hold/Staging	10.9	81
TALLAHASSEE HOLD ROOM	FL	Hold/Staging	10.5	52
STUART HOLD ROOM	FL	Hold/Staging	10.3	53
CHARLESTON, SC HOLD ROOM	SC	Hold/Staging	9.5	42
FORT MYERS ERO HOLDROOM	FL	Hold/Staging	7.6	23
CENTRAL ISLIP HOLD ROOM	NY	Hold/Staging	7.5	27
SACRAMENTO HOLD	CA	Hold/Staging	7.4	35
VAL VERDE DETENTION CENTER	TX	Federal	7.4	28
MARTIN COUNTY JAIL	FL	Non-Dedicated	7.4	24
DENVER HOLD ROOM	CO	Hold/Staging	6.5	21
SEATTLE FIELD OFFICE HOLD ROOM	WA	Hold/Staging	6.5	21
CHRISTIAN COUNTY JAIL	KY	Federal	6.4	51
WESTMINSTER CUSTODY	CA	Hold/Staging	6.0	7
ST. LOUIS HOLDROOM	MO	Hold/Staging	5.9	36
LAS VEGAS HOLD ROOM	NV	Hold/Staging	5.7	17
RALEIGH HOLD ROOM	NC	Hold/Staging	5.6	13
HARTFORD OFFICE	CT	Hold/Staging	5.5	35
BROWARD COUNTY JAIL	FL	Non-Dedicated	5.4	14
LITTLE ROCK HOLD ROOM	AR	Hold/Staging	5.3	18
RIO VISTA BEHV HLTH HSP	TX	Medical	5.2	14
KNOXVILLE HOLD ROOM	TN	Hold/Staging	5.1	20
ERO HOLD ROOM (SPRINGFIELD MO)	MO	Hold/Staging	5.0	62
BIRMINGHAM HOLD ROOM	AL	Hold/Staging	4.7	18
OSCEOLA COUNTY JAIL	FL	Non-Dedicated	4.5	12
VICTOR VALLEY GLOBAL MED CENTER	CA	Medical	4.5	9
EASTERN REGIONAL JAIL	WV	Federal	4.5	23
HCA HOUSTON HC CONROE	TX	Medical	4.3	10
HOUSTON FO HOLDROOM	TX	Hold/Staging	4.3	15
LUBBOCK HOLD ROOM	TX	Hold/Staging	4.2	22
CHATTANOOGA HOLD ROOM	TN	Hold/Staging	4.0	12
SW VIRGINIA REG JAIL AUTH-ABG FAC	VA	Non-Dedicated	4.0	47
SIOUX COUNTY JAIL	IA	Federal	4.0	11
SAN ANGELO HOLDROOM	TX	Hold/Staging	3.8	12
YORK HOLD ROOM	PA	Hold/Staging	3.8	14
NEW HANOVER CO DET CENTER	NC	Non-Dedicated	3.7	10
PITTSBURGH HOLDROOM	PA	Hold/Staging	3.7	17
NORFOLK SUB-OFFICE HOLD	VA	Hold/Staging	3.7	16
EL PASO COUNTY DETENTION FACILITY	TX	Non-Dedicated	3.6	19
PORTLAND DISTRICT OFFICE	OR	Hold/Staging	3.6	28
STOCKTON STAGING FACILITY	CA	Hold/Staging	3.6	15
DORCHESTER CO DET CTR	SC	Federal	3.6	10
PLATTE COUNTY JAIL	WY	Federal	3.6	12
CHAVEZ DET CRT	NM	Non-Dedicated	3.5	16
IMPERIAL SUB OFFICE HOLD ROOM	CA	Hold/Staging	3.4	21
MILWAUKEE HOLDROOM	WI	Hold/Staging	3.3	27
RAPP SEC CENTER	VA	Federal	3.3	15
IRWIN COUNTY DETENTION CENTER	GA	Non-Dedicated	3.2	72
TOM GREEN CTY JAIL	TX	Federal	3.2	11
MOBILE HOLDROOM	AL	Hold/Staging	3.1	21
FINNEY COUNTY JAIL	KS	Non-Dedicated	3.0	10
ROSWELL HOLD ROOM	NM	Hold/Staging	3.0	16
AMARILLO HOLD ROOM	TX	Hold/Staging	2.8	17
HCA FLORIDA KENDALL HOSPITAL	FL	Medical	2.8	6
LAWRENCE CO. JAIL, SD	SD	Non-Dedicated	2.7	11
SALISBURY HOLD ROOM	MD	Hold/Staging	2.7	23
GRAND RAPIDS HOLDROOM	MI	Hold/Staging	2.7	9
PROVO HOLD ROOM	UT	Hold/Staging	2.7	10
NEWBURGH HOLDING	NY	Hold/Staging	2.7	10
ALBUQUERQUE HOLD ROOM	NM	Hold/Staging	2.6	15
MIDLAND, TX ERO HOLDROOM	TX	Hold/Staging	2.5	7
TULSA HOLD ROOM	OK	Hold/Staging	2.5	16
YAKIMA SUB-OFFICE	WA	Hold/Staging	2.5	11
NEW YORK PRESBYTERIAN HOSPITAL	NY	Medical	2.4	5
CHESAPEAKE CITY JAIL	VA	Non-Dedicated	2.3	25
SARASOTA COUNTY JAIL	FL	Non-Dedicated	2.3	6
CEDAR RAPIDS HOLD ROOM	IA	Hold/Staging	2.2	17
DOVER HOLDROOM	DE	Hold/Staging	2.1	6
FREDERICK HOLDROOM	CO	Hold/Staging	2.1	7
KARNES CTY CORR CTR	TX	Federal	2.1	10
CASCADE COUNTY JAIL, MT	MT	Federal	2.1	5
PIKE COUNTY ERO HOLD ROOM	PA	Hold/Staging	2.1	7
OGDEN HOLD ROOM	UT	Hold/Staging	2.0	13
GARVIN COUNTY JAIL	OK	Non-Dedicated	2.0	17
HILLSBOROUGH COUNTY JAIL	FL	Non-Dedicated	2.0	12

93 unreported facilities (ADP ≥ 2) were active in the Trump period but not the Biden period.

Code

if (length(trump_only_codes) > 0) {
  trump_unmatched |>
    filter(detention_facility_code %in% trump_only_codes) |>
    group_by(type_grouped) |>
    summarise(
      Facilities = n(),
      `Total ADP` = round(sum(mean_pop)),
      .groups = "drop"
    ) |>
    arrange(desc(`Total ADP`)) |>
    kable(col.names = c("Facility type", "Facilities", "Total ADP"))
}

Table 13: New unreported facilities under Trump (ADP ≥ 2), by type

Facility type	Facilities	Total ADP
Dedicated	2	985
Hold/Staging	56	706
Federal	14	290
Non-Dedicated	16	76
Medical	5	19

Facilities active in both periods

Code

both_codes <- intersect(biden_active$detention_facility_code,
                         trump_active$detention_facility_code)

both_compare <- biden_unmatched |>
  filter(detention_facility_code %in% both_codes) |>
  select(detention_facility_code, detention_facility, state, type_grouped,
         biden_adp = mean_pop, biden_peak = peak_pop) |>
  inner_join(
    trump_unmatched |>
      filter(detention_facility_code %in% both_codes) |>
      select(detention_facility_code,
             trump_adp = mean_pop, trump_peak = peak_pop),
    by = "detention_facility_code"
  ) |>
  mutate(
    adp_change = trump_adp - biden_adp,
    adp_pct_change = if_else(biden_adp == 0, NA_real_,
                             (trump_adp / biden_adp - 1) * 100)
  )

Code

ggplot(both_compare, aes(x = biden_adp, y = trump_adp, color = type_grouped)) +
  geom_abline(slope = 1, intercept = 0, linetype = "dashed", alpha = 0.5) +
  geom_point(alpha = 0.7) +
  scale_x_log10(labels = comma) +
  scale_y_log10(labels = comma) +
  labs(x = "Biden final quarter ADP", y = "Trump recent quarter ADP",
       color = "Facility type")

Figure 9: Mean ADP at unreported facilities: Biden vs Trump (facilities with ADP ≥ 2 in both periods, log scale)

Code

both_compare |>
  arrange(desc(adp_change)) |>
  head(15) |>
  transmute(
    Facility = detention_facility, State = state, Type = type_grouped,
    `Biden ADP` = round(biden_adp),
    `Trump ADP` = round(trump_adp),
    Change = round(adp_change)
  ) |>
  kable()

Table 14: Top 15 unreported facilities by ADP increase (Biden → Trump)

Facility	State	Type	Biden ADP	Trump ADP	Change
DALLAS F.O. HOLD	TX	Hold/Staging	16	104	88
LOS CUST CASE	CA	Hold/Staging	7	89	82
ATLANTA DIST. HOLD RM	GA	Hold/Staging	9	89	80
BROADVIEW SERVICE STAGING	IL	Hold/Staging	9	89	80
MONTGOMERY HOLD RM	TX	Hold/Staging	30	96	66
NYC HOLD ROOM	NY	Hold/Staging	5	67	62
AZ REM OP COORD CENTER (AROCC)	AZ	Hold/Staging	16	70	54
BALTIMORE HOLD ROOM	MD	Hold/Staging	2	47	45
WASHINGTON FIELD OFFICE	VA	Hold/Staging	7	44	37
SAN ANTONIO DRO HOLD ROOM	TX	Hold/Staging	5	36	31
RICHMOND SUB-OFFICE HOLD	VA	Hold/Staging	5	34	29
ORLANDO HOLD ROOM	FL	Hold/Staging	6	34	28
TAMPA HOLD ROOM	FL	Hold/Staging	5	33	27
SND DISTRICT STAGING	CA	Hold/Staging	9	35	27
INDIANAPOLIS HOLD ROOM	IN	Hold/Staging	4	31	26

Code

both_compare |>
  arrange(adp_change) |>
  head(15) |>
  transmute(
    Facility = detention_facility, State = state, Type = type_grouped,
    `Biden ADP` = round(biden_adp),
    `Trump ADP` = round(trump_adp),
    Change = round(adp_change)
  ) |>
  kable()

Table 15: Top 15 unreported facilities by ADP decrease (Biden → Trump)

Facility	State	Type	Biden ADP	Trump ADP	Change
MCALLEN HOLD ROOM	TX	Hold/Staging	13	5	-7
COLUMBIA CARE CENTER	SC	Medical	3	2	-1
LARKIN BEHAVIORAL HEALTH SVCS	FL	Medical	8	8	0
MIAMI STAGING FACILITY	FL	Hold/Staging	11	12	1
SANTA MARIA SUB OFFICE ERO	CA	Hold/Staging	3	4	1
TUCSON INS HOLD ROOM	AZ	Hold/Staging	2	3	1
WACO DRO HOLD ROOM	TX	Hold/Staging	3	5	1
SALT LAKE COUNTY JAIL	UT	Federal	8	10	1
NW REG JUV DET CTR	VA	Family/Youth	2	4	2
LARKIN HOSPITAL	FL	Medical	4	6	2
KROME/MIAMI HUB	FL	Hold/Staging	4	7	3
FT LAUDERDALE BEHAVOR HLTH CTR	FL	Medical	4	6	3
JACKSON MS HOLD ROOM	MS	Hold/Staging	2	5	3
JFK INTERNATIONAL AP - T7	NY	Other/Unknown	3	6	3
VENTURA CUSTODY CASE	CA	Hold/Staging	3	7	3

Daily unreported population by period

Code

ggplot(daily_unreported, aes(x = date, y = total_pop, fill = type_grouped)) +
  geom_col() +
  facet_wrap(~period, scales = "free_x") +
  scale_y_continuous(labels = comma) +
  scale_x_date(date_labels = "%b %d") +
  labs(x = NULL, y = "Unreported detained population", fill = "Facility type")

Figure 10: Daily unreported detention population during both comparison periods, by facility type (ADP ≥ 2 facilities only)