Worked example: juvenile arrests from state to street
This vignette works a single question all the way down the geographic ladder: how many people under 18 are arrested, and where?
It starts with all 50 states, narrows to one city police department, and ends with every department in that city’s metropolitan area. Along the way it shows what the CDE API can and cannot answer, because both matter when you are building an analysis you intend to defend.
Every figure here comes from live API calls made on 2026-10-06; see Reproducing this.
1. What counts as a juvenile arrest?
The CDE reports arrestee age in buckets, split by sex. Pull them for one state and look at what comes back:
oh <- get_arrest_demographics(state_abb = "OH", from = "01-2023", to = "12-2023")
unique(oh$demographic_type)
#> [1] "Arrestee Sex" "Arrestee Race" "Male Arrests By Age"
#> [4] "Female Arrests By Age"
age <- oh[grepl("By Age", oh$demographic_type), ]
sort(unique(age$demographic_value))
#> [1] "10-12" "11-12" "13-14" "15"
#> [5] "16" "17" "18" "19"
#> [9] "20" "21" "22" "23"
#> [13] "24" "25-29" "30-34" "35-39"
#> [17] "40-44" "45-49" "50-54" "55-59"
#> [21] "60-64" "65 and over" "Adult Other" "Juvenile Other"
#> [25] "Under 10" "Under 11"A juvenile is anyone under 18, which here means every bucket up to
and including 17, plus the explicit
Juvenile Other category.
Two traps are worth knowing about before you sum anything.
The API emits two bucket vintages at once. You will
see both Under 10 and Under 11, and both
10-12 and 11-12. These overlap. In practice
only one vintage is populated and the other is zero-filled, so including
all of them is safe — but if you hand-pick a subset you can easily
double count. Check before you trust it:
overlap <- age[age$demographic_value %in% c("Under 10", "Under 11", "10-12", "11-12"), ]
stats::aggregate(count ~ demographic_value, data = overlap, FUN = sum)
#> demographic_value count
#> 1 10-12 2159
#> 2 11-12 0
#> 3 Under 10 131
#> 4 Under 11 0Adult Other is not a juvenile bucket
despite sitting next to Juvenile Other alphabetically.
With that established, a small helper:
JUVENILE_BUCKETS <- c(
"Under 10", "Under 11", "10-12", "11-12",
"13-14", "15", "16", "17", "Juvenile Other"
)
juvenile_summary <- function(demographics) {
age <- demographics[grepl("By Age", demographics$demographic_type), ]
data.frame(
juvenile = sum(age$count[age$demographic_value %in% JUVENILE_BUCKETS]),
total = sum(age$count)
)
}
oh_summary <- juvenile_summary(oh)
oh_summary
#> juvenile total
#> 1 22260 187953Ohio reported 22,260 arrests of under-18s in 2023, out of 187,953 arrests in total.
2. All 50 states
get_arrest_demographics() takes a state abbreviation, so
the national picture is one call per state:
states <- datasets::state.abb
state_juvenile <- do.call(rbind, lapply(states, function(s) {
d <- get_arrest_demographics(state_abb = s, from = "01-2023", to = "12-2023")
cbind(state = s, juvenile_summary(d))
}))
state_juvenile$share <- state_juvenile$juvenile / state_juvenile$total
head(state_juvenile[order(-state_juvenile$share), ], 5)
#> state juvenile total share
#> 49 WI 28896 199010 0.1451987
#> 50 WY 2676 19001 0.1408347
#> 27 NE 6642 48243 0.1376780
#> 26 MT 4152 30319 0.1369438
#> 34 ND 4050 32876 0.1231902Why share, and not a rate per capita
Mapping raw counts would mostly draw a population map — California would win every year. The obvious fix is arrests per 100,000 juveniles, but the CDE does not publish a juvenile population, and inventing one from another source introduces a denominator whose vintage and geography may not match.
Juvenile share of all arrests avoids that entirely. It is self-normalising, uses only figures from the same response, and answers a question people actually ask: of everyone arrested here, what fraction were children?
library(ggplot2)
state_sf <- tigris::states(cb = TRUE, resolution = "20m", progress_bar = FALSE)
state_sf <- state_sf[state_sf$STUSPS %in% state_juvenile$state, ]
state_sf <- merge(state_sf, state_juvenile, by.x = "STUSPS", by.y = "state")
# shift_geometry() repositions Alaska and Hawaii; without it a CONUS-centred
# projection throws them into the corners at the wrong scale.
state_sf <- tigris::shift_geometry(state_sf)
ggplot(state_sf) +
geom_sf(aes(fill = share), colour = "white", linewidth = 0.15) +
scale_fill_viridis_c(
labels = scales::percent, name = "Under-18\nshare",
option = "magma", direction = -1
) +
labs(
title = "Under-18 share of all reported arrests, 2023",
subtitle = "Juvenile arrests as a share of each state's reported total",
caption = "Source: FBI Crime Data Explorer via the fbiCDE R package"
) +
theme_void(base_size = 11) +
theme(plot.title = element_text(face = "bold"))
plot of chunk choropleth
Read this map carefully
The spread runs from about 1.6% to 14.5% — a 9-fold difference. That is far too wide to be credible as a real difference in how often children commit crimes, so something other than behaviour is driving most of it. The next step is to find out what.
The obvious suspect is incomplete reporting, so check it. The arrest
response reports, for every month, the state’s population
and the participated_population covered by agencies that
reported that month. Their ratio over the year is the share of the
state’s people whose arrests are in the count:
coverage <- do.call(rbind, lapply(state_juvenile$state, function(s) {
a <- get_arrest_count(state_abb = s, from = "01-2023", to = "12-2023")
data.frame(state = s,
coverage = sum(a$participated_population) / sum(a$population))
}))
state_juvenile <- merge(state_juvenile, coverage, by = "state")
summary(state_juvenile$coverage)
#> Min. 1st Qu. Median Mean 3rd Qu. Max.
#> 0.6373 0.9505 0.9893 0.9599 0.9973 1.0000
coverage_cor <- cor(state_juvenile$share, state_juvenile$coverage)
round(coverage_cor, 3)
#> [1] -0.012This is not the same as NIBRS participation, the share of a state’s agencies that report through NIBRS. Agencies still on the older summary system report arrests too: Pennsylvania has under a fifth of its agencies on NIBRS, yet its arrests cover 96% of its population.
Coverage is high almost everywhere — the median state reports for 98.9% of its population — and its correlation with juvenile share is -0.01, so coverage is not the explanation. Kentucky reports the lowest juvenile share in the country with 100% of its population covered:
cols <- c("state", "juvenile", "total", "share", "coverage")
head(state_juvenile[order(state_juvenile$share), cols], 5)
#> state juvenile total share coverage
#> 17 KY 3267 202329 0.01614697 1.0000000
#> 49 WV 669 32484 0.02059475 0.9061234
#> 2 AL 3679 142285 0.02585656 0.9668559
#> 5 CA 30956 778357 0.03977095 0.9808664
#> 34 NY 16125 372558 0.04328185 0.9906259What the data does establish is narrow but useful: the spread is not explained by how completely a state’s agencies report. It does not tell us what does explain it.
One plausible hypothesis is structural – states differ in whether a juvenile taken into custody is recorded as an arrest in the UCR sense at all, since juvenile justice systems are separate and their dispositions differ. That is a hypothesis this data cannot confirm, and it is offered as a direction to investigate rather than a finding.
The defensible conclusion is the conservative one: this map should not be read as a ranking of juvenile offending, and any use of it needs to establish what each state actually counts before comparing them.
Always keep coverage beside the number:
low <- state_juvenile[state_juvenile$coverage < 0.9, cols]
low[order(low$coverage), ]
#> state juvenile total share coverage
#> 25 MS 2711 59999 0.04518409 0.6373427
#> 9 FL 15284 234394 0.06520645 0.8296835
#> 29 NE 6642 48243 0.13767801 0.8491194
#> 15 IN 10512 134022 0.07843488 0.8580829
#> 50 WY 2676 19001 0.14083469 0.8831401
#> 41 SD 4710 46954 0.10031094 0.8860466
#> 32 NM 2278 52293 0.04356224 0.89138803. Arrests by offence
Which offences are juveniles arrested for? The arrest endpoint
addresses one offence by a numeric code, and
get_arrest_demographics() takes an offence name and fetches
its codes, so the age breakdown is available offence by offence.
The CDE names offences at three levels of detail (offence names,
categories and breakdowns), and within one level the counts do not
overlap, so ranking offences means ranking one level. Of the 34 offence
names, two (Rape and Runaway) have no arrests
in the API, and so no code; rape arrests are reported as
Rape (Legacy). That leaves:
offense_names <- intersect(list_ucr_arrest_offenses(level = "name"),
ucr_arrest_offense_codes$name)
length(offense_names)
#> [1] 32by_offense <- do.call(rbind, lapply(offense_names, function(o) {
d <- get_arrest_demographics(state_abb = "OH", offense = o,
from = "01-2023", to = "12-2023")
cbind(offense = o, juvenile_summary(d))
}))
by_offense$share <- by_offense$juvenile / by_offense$total
# Against the all-offence figures from section 1:
rbind(by_offence = c(juvenile = sum(by_offense$juvenile),
total = sum(by_offense$total)),
all_offences = c(juvenile = oh_summary$juvenile,
total = oh_summary$total))
#> juvenile total
#> by_offence 22317 188836
#> all_offences 22260 187953
by_offense <- by_offense[order(-by_offense$juvenile), ]
rownames(by_offense) <- NULL
head(by_offense, 10)
#> offense juvenile total share
#> 1 All Other Offenses 6987 48673 0.14354981
#> 2 Simple Assault 5288 35930 0.14717506
#> 3 Disorderly Conduct 2144 9151 0.23429133
#> 4 Larceny 1666 19500 0.08543590
#> 5 Drug Possession 920 19163 0.04800918
#> 6 Vandalism 910 3669 0.24802398
#> 7 Stolen Property 694 2413 0.28760879
#> 8 Aggravated Assault 616 6374 0.09664261
#> 9 Motor Vehicle Theft 555 1321 0.42013626
#> 10 Weapons 525 4695 0.11182109The two do not quite agree. The offence names partition all arrests, but the all-offence demographics leave out the arrests filed under the five “(Unspecified)” offence codes, such as “Drug Abuse Violations (Unspecified)”: 883 arrests in Ohio, 57 of them juvenile. The per-offence figures include them, so where completeness matters, sum by offence.
All Other Offenses leads with 6,987 juvenile arrests, 14% of all its arrests. Among offences with at least 500 arrests, the most juvenile is Motor Vehicle Theft: 42% of its arrests were of under-18s.
top$offense <- factor(top$offense, levels = rev(top$offense))
ggplot(top, aes(x = juvenile, y = offense)) +
geom_col(fill = "#4c72b0") +
geom_text(aes(label = scales::percent(share, accuracy = 1)),
hjust = -0.15, size = 3.4) +
scale_x_continuous(labels = scales::comma,
expand = expansion(mult = c(0, 0.12))) +
labs(
title = "Ohio's most common under-18 arrest offences, 2023",
subtitle = "Juvenile arrests; labels give the juvenile share of each offence's arrests",
x = "Arrests of under-18s", y = NULL
) +
theme_minimal(base_size = 11) +
theme(plot.title = element_text(face = "bold"),
panel.grid.major.y = element_blank())
plot of chunk offense-plot
Two names need care. All Other Offenses is the catch-all
for everything without its own line. And drug arrests are split:
Drug Possession and Drug Sale/Manufacturing
hold almost all of them, while Drug Abuse Violations is
only the remainder classed as neither (880 in Ohio). The category
"Drug/Narcotic Offenses" gives the total, 20,997; a
category spanning several codes costs one request per code.
4. One agency: Columbus Police Department
Agency-level queries use an ORI, the CDE’s 9-character agency identifier. Look it up rather than guessing:
agencies <- fbi_api_agencies
agencies[agencies$state_abbr == "OH" &
agencies$agency_name == "Columbus Police Department",
c("ori", "agency_name", "agency_type_name", "county_name")]
#> ori agency_name agency_type_name
#> 12198 OHCOP0000 Columbus Police Department City
#> county_name
#> 12198 DELAWARE; FAIRFIELD; FRANKLINTwo things in that row are worth pausing on.
OHCOP0000 has letters where you might expect digits.
That is normal — many ORIs do — and it is a poor guide to what kind of
agency you have. Ohio uses the same shape for OHCIP0000
(Cincinnati Police), OHCLP0000 (Cleveland Police) and
OHOHP0000, which is the State Highway Patrol. Use
agency_type_name, not the ORI, to tell them apart.
Second, county_name lists three
counties. Columbus straddles Delaware, Fairfield and Franklin. That
matters in section 5.
Before trusting a year’s total, check that the agency reported every month:
cbus_months <- get_arrest_count("OHCOP0000", from = "01-2023", to = "12-2023")
nrow(cbus_months)
#> [1] 12
range(cbus_months$count)
#> [1] 568 711All twelve months are there, so what follows is not a partial year.
columbus <- get_arrest_demographics(
ori = "OHCOP0000", from = "01-2023", to = "12-2023"
)
juvenile_summary(columbus)
#> juvenile total
#> 1 751 7693The full age profile, rather than just the total:
cbus_age <- columbus[grepl("By Age", columbus$demographic_type), ]
cbus_age <- stats::aggregate(count ~ demographic_value, data = cbus_age, FUN = sum)
cbus_age[cbus_age$demographic_value %in% JUVENILE_BUCKETS, ]
#> demographic_value count
#> 1 10-12 49
#> 2 11-12 0
#> 3 13-14 217
#> 4 15 166
#> 5 16 157
#> 6 17 162
#> 24 Juvenile Other 0
#> 25 Under 10 0
#> 26 Under 11 05. Every department in the Columbus area
A city police department is not the same thing as a city. Columbus PD covers the incorporated city; the surrounding metro contains dozens of other agencies, each reporting separately.
metro_agencies() resolves a metropolitan statistical
area to the union of its member counties’ agencies:
metro <- metro_agencies("Columbus, OH")
nrow(metro)
#> [1] 96
table(metro$agency_class)
#>
#> campus county_primary municipal special
#> 5 10 77 4Note Columbus PD appears once, even though it
polices three counties that are all in this metro. Deduplication matters
here: without it the city’s arrests would be counted three times. Its
row is attributed to the first of those counties, and
agency_county_names keeps all three:
sum(metro$ori == "OHCOP0000")
#> [1] 1
metro[metro$ori == "OHCOP0000", c("county_name", "agency_county_names")]
#> county_name agency_county_names
#> 11 DELAWARE DELAWARE; FAIRFIELD; FRANKLINNow fan out over the agencies the package’s geography functions query
by default — sheriffs, city police and campus police — leaving out
transit, special-purpose and state agencies. There is no arrest version
of get_metro_crime_detail(), so this loops directly, and
keeps a record of any agency whose request fails rather than dropping it
silently:
members <- metro[metro$agency_class %in% c("county_primary", "municipal", "campus"), ]
failed <- character(0)
metro_juvenile <- do.call(rbind, lapply(seq_len(nrow(members)), function(i) {
d <- tryCatch(
get_arrest_demographics(ori = members$ori[i], from = "01-2023", to = "12-2023"),
error = function(e) {
failed <<- c(failed, members$ori[i])
NULL
}
)
if (is.null(d)) return(NULL)
cbind(
agency = members$agency_name[i],
class = members$agency_class[i],
juvenile_summary(d)
)
}))
nrow(metro_juvenile)
#> [1] 92
failed
#> character(0)
sum(metro_juvenile$total > 0)
#> [1] 65Not every agency reports. Of the 92 agencies queried, 27 returned no arrests at all — which may mean a genuine zero or may mean they did not report, and the two are not the same claim. Keep them visible rather than dropping them silently.
reporting <- metro_juvenile[metro_juvenile$total > 0, ]
reporting$share <- reporting$juvenile / reporting$total
top10 <- head(reporting[order(-reporting$juvenile), ], 10)
top10[, c("agency", "class", "juvenile", "total", "share")]
#> agency class juvenile total share
#> 10 Columbus Police Department municipal 751 7693 0.09762121
#> 28 Whitehall Police Department municipal 444 4306 0.10311194
#> 25 Grove City Police Department municipal 419 1808 0.23174779
#> 19 Reynoldsburg Police Department municipal 152 1203 0.12635079
#> 2 Delaware Police Department municipal 149 573 0.26003490
#> 1 Delaware County Sheriff's Office county_primary 123 640 0.19218750
#> 31 Groveport Police Department municipal 106 170 0.62352941
#> 23 Gahanna Police Department municipal 89 601 0.14808652
#> 26 Hilliard Police Department municipal 78 603 0.12935323
#> 9 Westerville Police Department municipal 77 490 0.15714286top10$agency <- factor(top10$agency, levels = rev(top10$agency))
ggplot(top10, aes(x = juvenile, y = agency)) +
geom_col(fill = "#55a868") +
scale_x_continuous(labels = scales::comma) +
labs(
title = "Under-18 arrests in the Columbus, OH metro, 2023",
subtitle = "Ten agencies reporting the most juvenile arrests",
x = "Under-18 arrests", y = NULL
) +
theme_minimal(base_size = 11) +
theme(plot.title = element_text(face = "bold"),
panel.grid.major.y = element_blank())
plot of chunk metro-plot
The payoff
metro_total <- sum(reporting$juvenile)
city_total <- reporting$juvenile[reporting$agency == "Columbus Police Department"]
city_share <- city_total / metro_total
c(metro = metro_total, city = city_total, city_share = round(city_share, 3))
#> metro city city_share
#> 3207.000 751.000 0.234Columbus PD accounts for 23% of juvenile arrests in its own metropolitan area. An analysis that queried only the city department would miss 77% of them. That is the argument for the geographic layer: the boundary you query is a methodological choice, and the CDE will happily let you pick the wrong one without complaint.
Note also that share and volume rank differently. Some small suburban agencies report juvenile shares several times the city’s, on much smaller totals:
# Restrict to agencies with enough arrests for a share to mean anything.
sizable <- reporting[reporting$total >= 100, ]
head(sizable[order(-sizable$share), c("agency", "juvenile", "total", "share")], 5)
#> agency juvenile total
#> 31 Groveport Police Department 106 170
#> 65 Madison County Sheriff's Office 50 185
#> 2 Delaware Police Department 149 573
#> 43 Madison Township Police Department, Franklin County 65 280
#> 25 Grove City Police Department 419 1808
#> share
#> 31 0.6235294
#> 65 0.2702703
#> 2 0.2600349
#> 43 0.2321429
#> 25 0.2317478Caveats worth carrying forward
- A zero is not the same as “did not report”. The
arrest endpoint does not distinguish them;
get_county_crime_detail()and its siblings do, via areportedflag, and every series function returns theparticipated_populationthat shows how much of a population reported. Prefer those when the distinction matters. - Juvenile share is not a clean behavioural measure. The spread across states is not credible as pure behavioural variation, and reporting coverage does not explain it. What does remains an open question, so do not rank states on this figure.
- Offence names have codes, not URLs. The API
addresses one offence by a numeric code; an offence name in the URL is
an error. The package maps names to codes
(
ucr_arrest_offense_codes), and a name spanning several codes is their sum. - Metro membership is attribution, not spatial truth. An agency is included because it is attributed to a member county, not because its jurisdiction has been intersected with the metro boundary.
Reproducing this
Every figure here comes from live API calls. The vignette is
precomputed — vignettes/juvenile-arrests.Rmd.orig holds the
source and is knitted to .Rmd by
data-raw/precompute_vignettes.R — so building the package
never touches the network. Re-run that script to refresh against current
data; the numbers will move as agencies backfill.