Skip to contents

Assign anonymised points to local geographies

Source: R/ingest-geography.R
sl_assign_geography.Rd

Uses British National Grid (EPSG:27700). Missing or invalid coordinates are retained with NA assignments. On shared polygon edges, ties are resolved by code and flagged ambiguous. Boundary distances describe anonymised snap points, not the unknown true locations; resulting error may be systematic. The boundary-sensitive share uses assigned events with measured distances. Missing-coordinate and unassigned shares use all supplied events.

Usage

sl_assign_geography(
  records,
  boundaries,
  types = names(boundaries),
  threshold = 50
)

Arguments

records

Contract-bearing records.

boundaries

A named list of sf layers, or one sf layer with metadata.

types

Names of the layers to assign; defaults to supplied layers.

threshold

Boundary-sensitive distance in metres.

Value

Records with geography codes, per-layer distances and flags, and an updated contract. No event rows are dropped or duplicated.

See also

sl_boundaries(), sl_location_quality()

Other ingest: sl_archive_download(), sl_archive_index(), sl_archive_snapshot(), sl_boundaries(), sl_cache_clear(), sl_cache_dir(), sl_contract(), sl_exposure(), sl_list_versions(), sl_population(), sl_population_crosstab(), sl_read_records(), sl_sample(), sl_select_version()

Examples

b <- readRDS(system.file("extdata", "sample-boundaries.rds",
  package = "searchlight"
))
x <- sl_sample()[1:3, ]
b$msoa21 <- b$msoa21[b$msoa21$geography_code %in% x$msoa21, ]
sl_assign_geography(x, b, "msoa21")
#> # A tibble: 3 × 38
#>   type    date                part_of_a_policing_o…¹ policing_operation latitude
#>   <fct>   <dttm>              <lgl>                  <chr>                 <dbl>
#> 1 Person… 2026-05-01 00:03:00 FALSE                  NA                     53.8
#> 2 Person… 2026-05-01 00:15:00 FALSE                  NA                     53.8
#> 3 Person… 2026-05-01 00:30:00 FALSE                  NA                     53.7
#> # ℹ abbreviated name: ¹​part_of_a_policing_operation
#> # ℹ 33 more variables: longitude <dbl>, gender <fct>, age_range <fct>,
#> #   self_defined_ethnicity_raw <chr>, officer_defined_ethnicity <chr>,
#> #   legislation <chr>, object_of_search <chr>, outcome <chr>,
#> #   outcome_linked_to_object_of_search <chr>,
#> #   removal_of_more_than_just_outer_clothing <chr>, date_raw <chr>,
#> #   force_id <chr>, month <chr>, ethnicity_5 <fct>, ethnicity_19 <fct>, …