Concordance between postal areas and local health districts.

Contains the other administrative geometries which intersect with the local health district boundaries, along with the size of the intersection.

Usage

poa_lhd_concordance

Format

An object of class data.frame with 779 rows and 5 columns.

Source

Computed using the same source datasets as lhd and poa_nsw.

Details

For geographic regions used by the Australian Bureau of Statistics (ABS), the ABS publishes correspondence files. These files compare how two different types of regions align with each other. The Australian Government Department of Health and Aged Care published analogous concordance files for primary health networks (PHNs). These are useful for mapping between different types of administrative districts. There does not appear to be a publicly available set of concordance files for New South Wales local health district geographies.

The concordance was computed here by intersecting the ABS geometries with the local health district geometries. The fraction of the ABS geometry's area included in the intersection is reported in the column FRAC_INCLUDED. Any intersection with FRAC_INCLUDED at least 0.01% was retained. Area computations were performed in crs_gda2020_albers() (EPSG 9473 equal area Albers) coordinates at the original reolution of the source data.

Note that postal areas are not precisely the same as postcodes used by Australia Post, however they are very similar.

Examples

library(dplyr)
#> 
#> Attaching package: ‘dplyr’
#> The following objects are masked from ‘package:stats’:
#> 
#>     filter, lag
#> The following objects are masked from ‘package:base’:
#> 
#>     intersect, setdiff, setequal, union

# postcodes that overlap with Murrumbidgee LHD
poa_lhd_concordance |>
  filter(LHD_NAME == "Murrumbidgee Local Health District", FRAC_INCLUDED > 0.005) |>
  arrange(desc(FRAC_INCLUDED)) |>
  pull(POA_NAME_2021)
#>  [1] "2585" "2587" "2588" "2590" "2640" "2641" "2642" "2643" "2644" "2645"
#> [11] "2646" "2647" "2649" "2650" "2651" "2652" "2653" "2655" "2656" "2658"
#> [21] "2659" "2660" "2661" "2663" "2665" "2666" "2668" "2669" "2678" "2680"
#> [31] "2681" "2700" "2701" "2702" "2703" "2705" "2706" "2707" "2710" "2712"
#> [41] "2713" "2714" "2716" "2720" "2722" "2725" "2726" "2727" "2729" "2730"
#> [51] "2731" "2732" "2733" "2734" "2735" "2736" "2803" "2586" "2675" "2672"
#> [61] "2594" "2671" "2711" "2721" "2808" "2611" "3644" "2807" "2629" "2627"
#> [71] "2794" "2582" "2878" "2583" "3707" "2715" "3691"

# extract the main LHD for each postcode
poa_lhd_concordance |>
  arrange(desc(FRAC_INCLUDED)) |>
  slice_head(n = 1, by = POA_NAME_2021) |>
  mutate(postcode = POA_NAME_2021, lhd = LHD_NAME, .keep = "none") |>
  as_tibble()
#> # A tibble: 646 × 2
#>    postcode lhd                                       
#>    <chr>    <chr>                                     
#>  1 2016     Sydney Local Health District              
#>  2 2019     South Eastern Sydney Local Health District
#>  3 2021     South Eastern Sydney Local Health District
#>  4 2022     South Eastern Sydney Local Health District
#>  5 2023     South Eastern Sydney Local Health District
#>  6 2025     South Eastern Sydney Local Health District
#>  7 2031     South Eastern Sydney Local Health District
#>  8 2032     South Eastern Sydney Local Health District
#>  9 2033     South Eastern Sydney Local Health District
#> 10 2035     South Eastern Sydney Local Health District
#> # ℹ 636 more rows