## --- load toy baseline (relationship-defined) ---
acs_path <- system.file("extdata", "toy_acs_zcta_hennepin.csv", package = "geoDeltaAudit")
stopifnot(nchar(acs_path) > 0)
acs_zcta_hennepin <- readr::read_csv(acs_path, show_col_types = FALSE) %>%
janitor::clean_names() %>%
dplyr::mutate(zcta = stringr::str_pad(as.character(.data$zcta), 5, pad = "0"))
# Toy assoc: 1:1 ZCTA -> ZIP (same 5-digit IDs)
zcta_zip_hennepin <- acs_zcta_hennepin %>%
dplyr::distinct(.data$zcta) %>%
dplyr::transmute(zcta = .data$zcta, zip = .data$zcta) %>%
dplyr::distinct()
assoc_structure <- zcta_zip_hennepin %>%
dplyr::summarise(
n_rows = dplyr::n(),
n_zctas = dplyr::n_distinct(.data$zcta),
n_zips = dplyr::n_distinct(.data$zip)
)
assoc_structure## # A tibble: 1 × 3
## n_rows n_zctas n_zips
## <int> <int> <int>
## 1 74 74 74
unmapped <- acs_zcta_hennepin %>%
dplyr::anti_join(zcta_zip_hennepin %>% dplyr::distinct(.data$zcta), by = "zcta")
fanout_stats <- zcta_zip_hennepin %>%
dplyr::count(.data$zcta, name = "n_zip") %>%
dplyr::summarise(
min = min(.data$n_zip),
median = median(.data$n_zip),
mean = mean(.data$n_zip),
max = max(.data$n_zip)
)
list(
n_unmapped_zctas = nrow(unmapped),
fanout = fanout_stats
)## $n_unmapped_zctas
## [1] 0
##
## $fanout
## # A tibble: 1 × 4
## min median mean max
## <int> <dbl> <dbl> <int>
## 1 1 1 1 1
This vignette shows how geoDeltaAudit separates
data values from geographic transformation
rules.
The maps above visualize how identical source values can yield different spatial memberships depending on whether boundaries are defined by relationships or geometry. The numerical audit steps in other vignettes quantify the downstream effects of these choices.
This vignette is intentionally visual and descriptive. It does not perform transformations or inference.