Diagnose anonymised location quality

Snap-point concentration is the share at the most common reported coordinate within an LSOA, among records with usable coordinates in that LSOA. It is not the concentration at true incident locations. Missing LSOA assignments remain a separate group. Per-geography boundary diagnostics remain in the contract.

Usage

sl_location_quality(records)

Arguments

records

Contract-bearing records, optionally with assigned LSOAs.

Value

A force-month-LSOA tibble with missingness, concentration and boundary sensitivity, carrying the contract.

See also

sl_assign_geography(), sl_timestamp_quality()

Other audit: sl_benchmark(), sl_coverage(), sl_coverage_compare(), sl_quality(), sl_timestamp_quality()

Examples

sl_location_quality(sl_sample()[1:100, ])
#> # A tibble: 59 × 7
#>    force_id month lsoa21 n_records missing_coordinate_s…¹ snap_point_concentra…²
#>    <chr>    <chr> <chr>      <int>                  <dbl>                  <dbl>
#>  1 west-yo… 2026… E0101…         1                      0                      1
#>  2 west-yo… 2026… E0101…         2                      0                      1
#>  3 west-yo… 2026… E0101…         1                      0                      1
#>  4 west-yo… 2026… E0101…         1                      0                      1
#>  5 west-yo… 2026… E0101…         1                      0                      1
#>  6 west-yo… 2026… E0101…         2                      0                      1
#>  7 west-yo… 2026… E0101…         2                      0                      1
#>  8 west-yo… 2026… E0101…         2                      0                      1
#>  9 west-yo… 2026… E0101…         1                      0                      1
#> 10 west-yo… 2026… E0101…         1                      0                      1
#> # ℹ 49 more rows
#> # ℹ abbreviated names: ¹​missing_coordinate_share, ²​snap_point_concentration
#> # ℹ 1 more variable: boundary_sensitive_share <dbl>