uscogdata/man/cog_geographic_rollup.Rd

% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/rollup.R
\name{cog_geographic_rollup}
\alias{cog_geographic_rollup}
\title{Aggregate spending across state/county/city layers for a place}
\usage{
cog_geographic_rollup(
  govids,
  category,
  years,
  per_capita = FALSE,
  adjust_to_year = NULL,
  expenditure_concept = c("primary", "direct", "total"),
  coverage = c("all", "census", "consistent")
)
}
\arguments{
\item{govids}{Named list with any non-empty subset of elements named
`state`, `county`, `city`. Each element is a character vector of
`canonical_govid` values. At least one layer required.}

\item{category}{Single category name or character vector (passed through
to [cog_spending()]), or the reserved `"All Categories"` for one summed
row per `(year, canonical_govid, subtype)` covering every category in the
concept's scope. `"All Categories"` is the efficient way to build a
geographic total: without it a caller must issue one rollup per category
and sum the results themselves.}

\item{years}{Integer vector of years.}

\item{per_capita}{If `TRUE`, per-capita uses each gov's own per-year
population from `gov_population_yearly`. Govs with missing population
are excluded from the result.}

\item{adjust_to_year}{Integer base year for CPI-U conversion, or `NULL`.}

\item{expenditure_concept}{`"primary"` (default), `"direct"`, or
`"total"` -- see [cog_spending()] for the three concepts. `"total"` is
refused here because combining Total across multiple layers of
government double-counts intergovernmental transfers (a state's payment
to a school district is the same dollar the district reports as its own
Direct spending); `"primary"` and `"direct"` combine safely.}

\item{coverage}{How to handle the Census of Governments survey cycle,
  which is a **complete census only in years ending in 2 and 7** -- every
  other year is a sample, and the sample varies enormously (on the bundled
  fixture, Wisconsin's 608-city universe reports 597 governments in FY2012
  and 112 in FY2019).

  * `"all"` (default) -- every unit that reported that year. Unchanged
    behaviour, so existing code keeps working.
  * `"census"` -- census years only. Aborts if the requested range holds
    none, rather than silently returning nothing.
  * `"consistent"` -- only units reporting in *every* requested year, giving
    a balanced panel.

  Regardless of mode, `provenance$coverage` always carries per-year
  `n_units_expected`, `n_units_collected`, `n_units_reporting` and
  `is_census_year`, and `provenance$coverage_mode` records the mode.
  `is_census_year` is a statement about the **survey calendar**, never a
  claim of completeness: FY1967 is a census year in which only 97 of
  Wisconsin's 608 cities report. `n_units_reporting` is
  category-conditional and is not a response rate on its own -- see
  "Reading `coverage`" below for what each counter answers.}
}
\value{
Tibble with columns `year`, `layer`, `canonical_govid`, `gov_name`,
  `spend_subtype`, `category`, `amt_nominal`, optional `amt_real` /
  `amt_per_capita_nominal` / `amt_per_capita_real`, optional `pop_source`,
  `codes_included`, `aggregate_fallback`, `scope_note`, `notes`. Carries a
  `provenance` attribute with `verb = "cog_geographic_rollup"`, `layers`,
  and `rollup$included_govids` / `rollup$excluded_govids`.
}
\description{
Wraps [cog_spending()], tags each row with its layer, and attaches a
human-readable `scope_note` documenting geographic-scope caveats (e.g.
"county totals include areas outside the listed city"). Useful for
"place portraits" that compare a city to the surrounding county and
containing state on one set of axes.
}
\details{
When `per_capita = TRUE`, rows whose government has no observed
population in that year (`pop_source == "unavailable"`) are dropped from
the result. The dropped govids are recorded in
`provenance$rollup$excluded_govids`. This excludes special districts
(gov type 4) and school districts (gov type 5) from per-capita rollups
by design — see `vignette('population-denominators')`.
}
\section{Reading `coverage`}{

`provenance$coverage` carries three per-year counters:

  * `n_units_expected` -- how many governments you asked about.
  * `n_units_collected` -- how many of those appear in the corpus at all
    that year (in ANY category), separating sampling from real zeros.
  * `n_units_reporting` -- how many have rows for the SPECIFIC category you
    requested. This is always <= n_units_collected: a government can be
    collected but have no rows for "Police" because it contracts policing
    to the county sheriff, not because it wasn't surveyed.

**`n_units_reporting` is category-conditional** and therefore **not a
response rate**: `n_units_reporting / n_units_expected` conflates sampling
(never collected) with real zeros (collected but spends nothing in your
category). Use `n_units_collected / n_units_expected` for the true
collection rate, and `n_units_reporting / n_units_collected` for category
participation among collected units.

In FY2022 — a complete census year — Georgia reports 393 of 567 cities for
`category = "Police"`; the 174-city gap is overwhelmingly cities that
contract policing to the county sheriff, not non-response.

The comparison that *is* valid is the same category across a census year
(ending in 2 or 7) and a sample year, where the real-zero component is
roughly constant and the difference reflects the survey cycle. `is_census_year`
marks which is which.
}