UK Ward Boundaries for Mapping • wardboundaries

Overview

wardboundaries is an R data package for mapping UK ward boundaries.

The main objects in the package are sf tibbles of ward boundaries, obtained from the ONS Open Geography Portal.

All the original variables and observations are retained and a geometry column of type MULTIPOLYGON has been added
LAD codes and names have been added to datasets where required, as I believe they will prove useful in most use cases

The data is ready to be joined to measurements and plotted with e.g. ggplot2 or leaflet.

Current datasets

20m resolution

countries_dec2022_20m
lad_dec2022_20m
lad_may2023_20m
lad_dec2023_20m
wards_dec2022_20m
wards_may2023_20m
wards_dec2023_20m

200m resolution

wards_dec2011
wards_dec2019
wards_dec2020
wards_dec2021
wards_dec2022
wards_may2023
wards_dec2023

500m resolution

countries_dec2022
lad_dec2022
lad_may2023

Installation

devtools::install_github("jimgar/wardboundaries")

Use and examples

Access

Accessing a dataset can be done two ways.

By namespace, without loading the whole library

wardboundaries::wards_dec2022
#> Simple feature collection with 8483 features and 12 fields
#> Geometry type: MULTIPOLYGON
#> Dimension:     XY
#> Bounding box:  xmin: -8.649996 ymin: 49.88234 xmax: 1.763571 ymax: 60.86087
#> Geodetic CRS:  WGS 84
#> # A tibble: 8,483 × 13
#>    WD22CD    WD22NM   WD22NMW LAD22CD LAD22NM  BNG_E  BNG_N  LONG   LAT OBJECTID
#>    <chr>     <chr>    <chr>   <chr>   <chr>    <int>  <int> <dbl> <dbl>    <int>
#>  1 E05000650 Astley … " "     E08000… Bolton  370670 412906 -2.44  53.6        1
#>  2 E05000651 Bradshaw " "     E08000… Bolton  374590 413058 -2.39  53.6        2
#>  3 E05000652 Breight… " "     E08000… Bolton  374437 409641 -2.39  53.6        3
#>  4 E05000653 Bromley… " "     E08000… Bolton  372113 414150 -2.42  53.6        4
#>  5 E05000654 Crompton " "     E08000… Bolton  371741 410493 -2.43  53.6        5
#>  6 E05000655 Farnwor… " "     E08000… Bolton  373564 406162 -2.40  53.6        6
#>  7 E05000656 Great L… " "     E08000… Bolton  371959 408204 -2.42  53.6        7
#>  8 E05000657 Halliwe… " "     E08000… Bolton  370828 409676 -2.44  53.6        8
#>  9 E05000658 Harper … " "     E08000… Bolton  371512 406080 -2.43  53.6        9
#> 10 E05000659 Heaton … " "     E08000… Bolton  367961 409240 -2.49  53.6       10
#> # ℹ 8,473 more rows
#> # ℹ 3 more variables: Shape__Area <dbl>, Shape__Length <dbl>,
#> #   geometry <MULTIPOLYGON [°]>

Or by loading the whole library

library(wardboundaries)

wards_dec2022
#> Simple feature collection with 8483 features and 12 fields
#> Geometry type: MULTIPOLYGON
#> Dimension:     XY
#> Bounding box:  xmin: -8.649996 ymin: 49.88234 xmax: 1.763571 ymax: 60.86087
#> Geodetic CRS:  WGS 84
#> # A tibble: 8,483 × 13
#>    WD22CD    WD22NM   WD22NMW LAD22CD LAD22NM  BNG_E  BNG_N  LONG   LAT OBJECTID
#>    <chr>     <chr>    <chr>   <chr>   <chr>    <int>  <int> <dbl> <dbl>    <int>
#>  1 E05000650 Astley … " "     E08000… Bolton  370670 412906 -2.44  53.6        1
#>  2 E05000651 Bradshaw " "     E08000… Bolton  374590 413058 -2.39  53.6        2
#>  3 E05000652 Breight… " "     E08000… Bolton  374437 409641 -2.39  53.6        3
#>  4 E05000653 Bromley… " "     E08000… Bolton  372113 414150 -2.42  53.6        4
#>  5 E05000654 Crompton " "     E08000… Bolton  371741 410493 -2.43  53.6        5
#>  6 E05000655 Farnwor… " "     E08000… Bolton  373564 406162 -2.40  53.6        6
#>  7 E05000656 Great L… " "     E08000… Bolton  371959 408204 -2.42  53.6        7
#>  8 E05000657 Halliwe… " "     E08000… Bolton  370828 409676 -2.44  53.6        8
#>  9 E05000658 Harper … " "     E08000… Bolton  371512 406080 -2.43  53.6        9
#> 10 E05000659 Heaton … " "     E08000… Bolton  367961 409240 -2.49  53.6       10
#> # ℹ 8,473 more rows
#> # ℹ 3 more variables: Shape__Area <dbl>, Shape__Length <dbl>,
#> #   geometry <MULTIPOLYGON [°]>

Map with ggplot2

Perhaps you have some sort of demographic data for each ward. Plotting this as a choropleth with ggplot2 is nice and straightforward

# Make some random numbers that represent a demographic dataset
demo_data <- tibble::tibble(
  WD22CD = dplyr::pull(wardboundaries::wards_dec2022, WD22CD),
  number_of_ducks = sample(
    150:1500, nrow(wardboundaries::wards_dec2022),
    replace = TRUE
  )
)

demo_data
#> # A tibble: 8,483 × 2
#>    WD22CD    number_of_ducks
#>    <chr>               <int>
#>  1 E05000650            1036
#>  2 E05000651            1110
#>  3 E05000652             621
#>  4 E05000653             876
#>  5 E05000654             891
#>  6 E05000655             527
#>  7 E05000656             892
#>  8 E05000657             192
#>  9 E05000658            1031
#> 10 E05000659             968
#> # ℹ 8,473 more rows

# Join the datasets together
ducks_per_ward <- dplyr::left_join(
  wardboundaries::wards_dec2022,
  demo_data,
  by = dplyr::join_by(WD22CD)
)

# Plot
ducks_per_ward |>
  ggplot2::ggplot() +
  ggplot2::geom_sf(ggplot2::aes(fill = number_of_ducks), colour = NA) +
  ggplot2::theme_void()

The following plot isn’t exactly pretty, but it shows how easy it is to focus in on a particular local authority and add labels

# Plot
ducks_per_ward |>
  dplyr::filter(LAD22NM == "Vale of White Horse") |>
  ggplot2::ggplot() +
  ggplot2::geom_sf(
    ggplot2::aes(fill = number_of_ducks),
    colour = NA,
    show.legend = FALSE
  ) +
  ggplot2::geom_sf_label(ggplot2::aes(label = number_of_ducks)) +
  ggplot2::theme_void()

Map with leaflet

The following example is mainly cribbed from Leaflet for R. With a couple of format helpers and not much code, it’s possible to create an interactive version of the map

# Format helpers
pal <- leaflet::colorNumeric("Greens", domain = ducks_per_ward$number_of_ducks)
thousands <- scales::label_comma()

labels <- sprintf(
  "<strong>%s</strong><br/>%s",
  ducks_per_ward$WD22NM, thousands(ducks_per_ward$number_of_ducks)
) |> lapply(htmltools::HTML)

# Leaflet map
leaflet::leaflet() |>
  leaflet::addTiles() |>
  leaflet::addPolygons(
    data = ducks_per_ward,
    fillColor = ~ pal(number_of_ducks),
    weight = 0.1,
    opacity = 0.9,
    color = "white",
    dashArray = "3",
    fillOpacity = 0.9,
    highlightOptions = leaflet::highlightOptions(
      weight = 1,
      color = "yellow",
      dashArray = "",
      fillOpacity = 0.9,
      bringToFront = TRUE
    ),
    label = labels,
    labelOptions = leaflet::labelOptions(
      style = list("font-weight" = "normal", padding = "3px 8px"),
      textsize = "15px",
      direction = "auto"
    )
  ) |>
  leaflet::addLegend(
    pal = pal,
    values = ducks_per_ward$number_of_ducks,
    opacity = 0.9,
    title = NULL,
    position = "topleft"
  )

Further resources

geographr

If your needs are suited to my example maps above then wardboundaries works just fine.

If you have more complex requirements then I recommend checking out geographr. They go far beyond the simple local authority / ward scenario and have boundaries for larger hierarchies, lookup tables, NHS point data, etc. Each dataset in geographr is slimmed down to the essential variables, so you may have to stitch different datasets together. Another important note is that the polygons are highly simplified, whereas wardboundaries offers up to 20m resolution.

Trafford lab API post

A couple of weeks before making this package I knew next to nothing about geospatial data or how to get hold of it. I still don’t, but Trafford Council’s Data lab wrote a very helpful Medium post that explains how to work with the ONS Open Geography Portal’s API to get data in practice. If you have a niche interest this resource will help you understand how to create a specific query for your needs and read the data straight into R with sf.

R map books

Spatial Data Science (with applications in R) “introduces and explains the concepts underlying spatial data: points, lines, polygons, rasters, coverages, geometry attributes, data cubes, reference systems, as well as higher-level concepts including how attributes relate to geometries and how this affects analysis”
Geocomputation with R “is for people who want to analyze, visualize and model geographic data with open source software”

Credits

Developed by Jim Gardner.

Hex image cropped from Invocation 6 by Helvetica Blanc.

Largely modelled on and inspired by geographr.

See LICENCE