readme.md
In Chrisjb/rgeoportal: Fetch UK Boundary Files
rgeoportal
Installing rgeoportal
Install from github using devtools:
library(devtools)
install_github('Chrisjb/rgeoportal')
get_boundary
get_boundary(boundary_type = c("administrative", "census", "electoral",
"eurostat", "health", "other", "postcodes"),
boundary_name = NA,
bbox = NA,
redius = NA,
point = NA,
names_like = NA,
names_equal = NA,
custom_polygon = NA)
boundary_type
We can get the boundary names available within each boundary type by
specifying boundary_type with no boundary_name option:
library(rgeoportal)
census_boundaries <- get_boundary(boundary_type = 'census')
head(census_boundaries)
## name
## 2 Built_Up_Area_Sub_Divisions_December_2011_Boundaries_V2
## 4 Built_Up_Areas_December_2011_Boundaries_V2
## 6 Census_Merged_Local_Authority_Districts_December_2011_Boundaries
## 8 Census_Merged_Wards_December_2011_Boundaries
## 10 Lower_Super_Output_Areas_December_2001_Boundaries
## 12 Lower_Super_Output_Areas_December_2001_Centroids
administrative <- get_boundary(boundary_type = 'administrative')
head(administrative)
## name
## 2 Combined_Authorities_December_2018_Boundaries_EN_BFC
## 4 Combined_Authorities_December_2018_Boundaries_EN_BFE
## 6 Combined_Authorities_December_2018_Boundaries_EN_BGC
## 8 Combined_Authorities_December_2018_Boundaries_EN_BUC
## 10 Combined_Authorities_December_2019_Boundaries_EN_BFC
## 12 Combined_Authorities_December_2019_Boundaries_EN_BFE
Some of the boundary names come with a range of suffixes:
- BFC: Full resolution - clipped to the coastline (Mean High Water
mark).
- BFE: Full resolution - extent of the realm (usually this is the Mean
Low Water mark but in some cases boundaries extend beyond this to
include off shore islands).
- BGC: Generalised (20m) - clipped to the coastline (Mean High Water
mark).
- BUC: Ultra Generalised (500m) - clipped to the coastline (Mean High
Water mark). Contains both Ordnance Survey and ONS Intellectual
Property Rights.
Note that most boundaries have a maximim record count of 200. Large
requests will typically be limited to 200 observations.
boundary_name
We can select one of the boundary names returned from the above request
and set it as the boundary_name:
merged_auth <- get_boundary(boundary_type = 'census',
boundary_name='Census_Merged_Local_Authority_Districts_December_2011_Boundaries')
head(merged_auth)
## Simple feature collection with 6 features and 6 fields
## geometry type: MULTIPOLYGON
## dimension: XY
## bbox: xmin: -2.832457 ymin: 53.30503 xmax: -0.7884345 ymax: 54.72717
## CRS: 4326
## # A tibble: 6 x 7
## objectid cmlad11cd cmlad11nm cmlad11nmw st_area.shape. st_length.shape.
## <int> <chr> <chr> <chr> <dbl> <dbl>
## 1 1 E41000001 Hartlepo… " " 93892901. 68997.
## 2 2 E41000002 Middlesb… " " 53861837. 44918.
## 3 3 E41000003 Redcar a… " " 244915895. 93271.
## 4 4 E41000004 Stockton… " " 203920097. 155580.
## 5 5 E41000005 Darlingt… " " 197473286. 105743.
## 6 6 E41000006 Halton " " 79115070. 76288.
## # … with 1 more variable: geometry <MULTIPOLYGON [°]>
library(ggplot2)
ggplot() +
geom_sf(data=merged_auth)
Specifying the request
In general we don’t want to request more data than we need. Many
boundaries also limit the amount of polygons any given request will
return to 200. There are several ways to request only the subset of the
total boundary data we need:
- bbox: return only results in the specified bbox
- radius: return only results within a given distance of a specified
point
- names_like: return only results where the polygon name contains the
given string(s)
- names_equal: return only results where the polygon name matches the
given string(s)
- custom_polygon: return only results in the specified polygon
bbox
One way to request a subset of boundary data is to add a bbox to the
request. We can set a bounding box in the form ‘xmin,ymin,xmax,ymax’ or
as an sf bbox as generated by sf::st_bbox().
One way to get a bounding box is to use st_bbox on another set of
polygons:
my_auths <- merged_auth %>% filter(cmlad11nm %in% c('Wandsworth'))
my_bbox <- st_bbox(my_auths)
msoa_in_bbox <- get_boundary(boundary_type = 'census',
boundary_name='Middle_Super_Output_Areas_December_2011_Boundaries',
bbox = my_bbox)
head(msoa_in_bbox)
## Simple feature collection with 6 features and 6 fields
## geometry type: MULTIPOLYGON
## dimension: XY
## bbox: xmin: -0.2275311 ymin: 51.47271 xmax: -0.1907725 ymax: 51.49169
## CRS: 4326
## # A tibble: 6 x 7
## objectid msoa11cd msoa11nm msoa11nmw st_area.shape. st_length.shape.
## <int> <chr> <chr> <chr> <dbl> <dbl>
## 1 371 E020003… Hammers… Hammersm… 399794. 6279.
## 2 372 E020003… Hammers… Hammersm… 437502. 4776.
## 3 373 E020003… Hammers… Hammersm… 407860. 4479.
## 4 374 E020003… Hammers… Hammersm… 531673. 3639.
## 5 375 E020003… Hammers… Hammersm… 628264. 5733.
## 6 376 E020003… Hammers… Hammersm… 347891. 3901.
## # … with 1 more variable: geometry <MULTIPOLYGON [°]>
ggplot() +
geom_sf(data=msoa_in_bbox) +
geom_sf(data = st_as_sfc(my_bbox), colour = 'black', fill=NA)
We can also specify a bbox from a user specified string:
msoa_in_bbox_2 <- get_boundary(boundary_type = 'census',
boundary_name='Middle_Super_Output_Areas_December_2011_Boundaries',
bbox = '-2.65,51.41,-2.52,51.50')
head(msoa_in_bbox)
## Simple feature collection with 6 features and 6 fields
## geometry type: MULTIPOLYGON
## dimension: XY
## bbox: xmin: -0.2275311 ymin: 51.47271 xmax: -0.1907725 ymax: 51.49169
## CRS: 4326
## # A tibble: 6 x 7
## objectid msoa11cd msoa11nm msoa11nmw st_area.shape. st_length.shape.
## <int> <chr> <chr> <chr> <dbl> <dbl>
## 1 371 E020003… Hammers… Hammersm… 399794. 6279.
## 2 372 E020003… Hammers… Hammersm… 437502. 4776.
## 3 373 E020003… Hammers… Hammersm… 407860. 4479.
## 4 374 E020003… Hammers… Hammersm… 531673. 3639.
## 5 375 E020003… Hammers… Hammersm… 628264. 5733.
## 6 376 E020003… Hammers… Hammersm… 347891. 3901.
## # … with 1 more variable: geometry <MULTIPOLYGON [°]>
ggplot() +
geom_sf(data=msoa_in_bbox_2) +
coord_sf(xlim = c(-2.65,-2.52), ylim =c(51.41, 51.50))
radius
Another way to filter the request is to get boundaries within a set
radius of a given point. If radius is set, point must be set too.
point should be a longitude latitude point in the form ‘lng,lat’
radius is specified in meters
ward_in_radius <- get_boundary(boundary_type = 'admin',
boundary_name='Wards_May_2019_Boundaries_UK_BFC',
point = '-2.58791,51.45451',
radius = 2000)
names_like
If we don’t want to list the exact names of the wards, we can do partial
matching by specifying names_like. This is especially useful for
boundaries where the area name forms a predictable pattern such as msoa
names which all start with their local authority name.
Specifying names_like will return polygons that (partially) match the
name of the polygon
msoa_dartford <- get_boundary(boundary_type = 'census',
boundary_name='Middle_Super_Output_Areas_December_2011_Boundaries',
names_like = c('Dartford'))
head(msoa_dartford)
## Simple feature collection with 6 features and 6 fields
## geometry type: POLYGON
## dimension: XY
## bbox: xmin: 0.1967327 ymin: 51.43022 xmax: 0.326364 ymax: 51.47966
## CRS: 4326
## # A tibble: 6 x 7
## objectid msoa11cd msoa11nm msoa11nmw st_area.shape. st_length.shape.
## <int> <chr> <chr> <chr> <dbl> <dbl>
## 1 4919 E020050… Dartfor… Dartford… 6562445. 11923.
## 2 4920 E020050… Dartfor… Dartford… 7581994. 26428.
## 3 4921 E020050… Dartfor… Dartford… 2507711. 8580.
## 4 4922 E020050… Dartfor… Dartford… 1628033. 10428.
## 5 4923 E020050… Dartfor… Dartford… 1565851. 6611.
## 6 4924 E020050… Dartfor… Dartford… 4940014. 11088.
## # … with 1 more variable: geometry <POLYGON [°]>
names_equal
If we know the exact name(s) of the polygon we want to return, we should
specify names_equal:
dg <- get_boundary(boundary_type = 'admin',
boundary_name='Local_Authority_Districts_December_2019_Boundaries_UK_BFC',
names_equal = c('Dartford', 'Gravesham'))
head(dg)
## Simple feature collection with 2 features and 10 fields
## geometry type: POLYGON
## dimension: XY
## bbox: xmin: 0.1491599 ymin: 51.32501 xmax: 0.4911962 ymax: 51.47966
## CRS: 4326
## # A tibble: 2 x 11
## objectid lad19cd lad19nm lad19nmw bng_e bng_n long lat st_area.shape.
## <int> <chr> <chr> <chr> <int> <int> <dbl> <dbl> <dbl>
## 1 130 E07000… Dartfo… " " 556167 172917 0.245 51.4 72725577.
## 2 132 E07000… Graves… " " 566969 169114 0.399 51.4 98991345.
## # … with 2 more variables: st_length.shape. <dbl>, geometry <POLYGON [°]>
custom_polygon
There is also the option to use a custom polygon and return boundaries
that intersect the given polygon.
We may need to simplify the polygon before sending the request:
custom_poly <- ward_in_radius %>% filter(wd19nm == 'Clifton')
custom_poly_simp <- rmapshaper::ms_simplify(custom_poly)
## Registered S3 method overwritten by 'geojsonlint':
## method from
## print.location dplyr
lsoa_in_poly <- get_boundary(boundary_type = 'census',
boundary_name='Lower_Super_Output_Areas_December_2011_Boundaries',
custom_polygon = custom_poly_simp)
ggplot() +
geom_sf(data = st_intersection(lsoa_in_poly, custom_poly)) +
geom_sf(data =custom_poly, color = 'red', fill=NA )
## although coordinates are longitude/latitude, st_intersection assumes that they are planar
## Warning: attribute variables are assumed to be spatially constant throughout all
## geometries
Chrisjb/rgeoportal documentation built on Sept. 29, 2020, 12:46 a.m.
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rgeoportal
Installing rgeoportal
Install from github using devtools:
library(devtools)
install_github('Chrisjb/rgeoportal')
get_boundary
get_boundary(boundary_type = c("administrative", "census", "electoral",
"eurostat", "health", "other", "postcodes"),
boundary_name = NA,
bbox = NA,
redius = NA,
point = NA,
names_like = NA,
names_equal = NA,
custom_polygon = NA)
boundary_type
We can get the boundary names available within each boundary type by
specifying boundary_type with no boundary_name option:
library(rgeoportal)
census_boundaries <- get_boundary(boundary_type = 'census')
head(census_boundaries)
## name
## 2 Built_Up_Area_Sub_Divisions_December_2011_Boundaries_V2
## 4 Built_Up_Areas_December_2011_Boundaries_V2
## 6 Census_Merged_Local_Authority_Districts_December_2011_Boundaries
## 8 Census_Merged_Wards_December_2011_Boundaries
## 10 Lower_Super_Output_Areas_December_2001_Boundaries
## 12 Lower_Super_Output_Areas_December_2001_Centroids
administrative <- get_boundary(boundary_type = 'administrative')
head(administrative)
## name
## 2 Combined_Authorities_December_2018_Boundaries_EN_BFC
## 4 Combined_Authorities_December_2018_Boundaries_EN_BFE
## 6 Combined_Authorities_December_2018_Boundaries_EN_BGC
## 8 Combined_Authorities_December_2018_Boundaries_EN_BUC
## 10 Combined_Authorities_December_2019_Boundaries_EN_BFC
## 12 Combined_Authorities_December_2019_Boundaries_EN_BFE
Some of the boundary names come with a range of suffixes:
- BFC: Full resolution - clipped to the coastline (Mean High Water mark).
- BFE: Full resolution - extent of the realm (usually this is the Mean Low Water mark but in some cases boundaries extend beyond this to include off shore islands).
- BGC: Generalised (20m) - clipped to the coastline (Mean High Water mark).
- BUC: Ultra Generalised (500m) - clipped to the coastline (Mean High Water mark). Contains both Ordnance Survey and ONS Intellectual Property Rights.
Note that most boundaries have a maximim record count of 200. Large requests will typically be limited to 200 observations.
boundary_name
We can select one of the boundary names returned from the above request
and set it as the boundary_name:
merged_auth <- get_boundary(boundary_type = 'census',
boundary_name='Census_Merged_Local_Authority_Districts_December_2011_Boundaries')
head(merged_auth)
## Simple feature collection with 6 features and 6 fields
## geometry type: MULTIPOLYGON
## dimension: XY
## bbox: xmin: -2.832457 ymin: 53.30503 xmax: -0.7884345 ymax: 54.72717
## CRS: 4326
## # A tibble: 6 x 7
## objectid cmlad11cd cmlad11nm cmlad11nmw st_area.shape. st_length.shape.
## <int> <chr> <chr> <chr> <dbl> <dbl>
## 1 1 E41000001 Hartlepo… " " 93892901. 68997.
## 2 2 E41000002 Middlesb… " " 53861837. 44918.
## 3 3 E41000003 Redcar a… " " 244915895. 93271.
## 4 4 E41000004 Stockton… " " 203920097. 155580.
## 5 5 E41000005 Darlingt… " " 197473286. 105743.
## 6 6 E41000006 Halton " " 79115070. 76288.
## # … with 1 more variable: geometry <MULTIPOLYGON [°]>
library(ggplot2)
ggplot() +
geom_sf(data=merged_auth)
Specifying the request
In general we don’t want to request more data than we need. Many boundaries also limit the amount of polygons any given request will return to 200. There are several ways to request only the subset of the total boundary data we need:
- bbox: return only results in the specified bbox
- radius: return only results within a given distance of a specified point
- names_like: return only results where the polygon name contains the given string(s)
- names_equal: return only results where the polygon name matches the given string(s)
- custom_polygon: return only results in the specified polygon
bbox
One way to request a subset of boundary data is to add a bbox to the
request. We can set a bounding box in the form ‘xmin,ymin,xmax,ymax’ or
as an sf bbox as generated by sf::st_bbox().
One way to get a bounding box is to use st_bbox on another set of
polygons:
my_auths <- merged_auth %>% filter(cmlad11nm %in% c('Wandsworth'))
my_bbox <- st_bbox(my_auths)
msoa_in_bbox <- get_boundary(boundary_type = 'census',
boundary_name='Middle_Super_Output_Areas_December_2011_Boundaries',
bbox = my_bbox)
head(msoa_in_bbox)
## Simple feature collection with 6 features and 6 fields
## geometry type: MULTIPOLYGON
## dimension: XY
## bbox: xmin: -0.2275311 ymin: 51.47271 xmax: -0.1907725 ymax: 51.49169
## CRS: 4326
## # A tibble: 6 x 7
## objectid msoa11cd msoa11nm msoa11nmw st_area.shape. st_length.shape.
## <int> <chr> <chr> <chr> <dbl> <dbl>
## 1 371 E020003… Hammers… Hammersm… 399794. 6279.
## 2 372 E020003… Hammers… Hammersm… 437502. 4776.
## 3 373 E020003… Hammers… Hammersm… 407860. 4479.
## 4 374 E020003… Hammers… Hammersm… 531673. 3639.
## 5 375 E020003… Hammers… Hammersm… 628264. 5733.
## 6 376 E020003… Hammers… Hammersm… 347891. 3901.
## # … with 1 more variable: geometry <MULTIPOLYGON [°]>
ggplot() +
geom_sf(data=msoa_in_bbox) +
geom_sf(data = st_as_sfc(my_bbox), colour = 'black', fill=NA)
We can also specify a bbox from a user specified string:
msoa_in_bbox_2 <- get_boundary(boundary_type = 'census',
boundary_name='Middle_Super_Output_Areas_December_2011_Boundaries',
bbox = '-2.65,51.41,-2.52,51.50')
head(msoa_in_bbox)
## Simple feature collection with 6 features and 6 fields
## geometry type: MULTIPOLYGON
## dimension: XY
## bbox: xmin: -0.2275311 ymin: 51.47271 xmax: -0.1907725 ymax: 51.49169
## CRS: 4326
## # A tibble: 6 x 7
## objectid msoa11cd msoa11nm msoa11nmw st_area.shape. st_length.shape.
## <int> <chr> <chr> <chr> <dbl> <dbl>
## 1 371 E020003… Hammers… Hammersm… 399794. 6279.
## 2 372 E020003… Hammers… Hammersm… 437502. 4776.
## 3 373 E020003… Hammers… Hammersm… 407860. 4479.
## 4 374 E020003… Hammers… Hammersm… 531673. 3639.
## 5 375 E020003… Hammers… Hammersm… 628264. 5733.
## 6 376 E020003… Hammers… Hammersm… 347891. 3901.
## # … with 1 more variable: geometry <MULTIPOLYGON [°]>
ggplot() +
geom_sf(data=msoa_in_bbox_2) +
coord_sf(xlim = c(-2.65,-2.52), ylim =c(51.41, 51.50))
radius
Another way to filter the request is to get boundaries within a set
radius of a given point. If radius is set, point must be set too.
point should be a longitude latitude point in the form ‘lng,lat’
radius is specified in meters
ward_in_radius <- get_boundary(boundary_type = 'admin',
boundary_name='Wards_May_2019_Boundaries_UK_BFC',
point = '-2.58791,51.45451',
radius = 2000)
names_like
If we don’t want to list the exact names of the wards, we can do partial
matching by specifying names_like. This is especially useful for
boundaries where the area name forms a predictable pattern such as msoa
names which all start with their local authority name.
Specifying names_like will return polygons that (partially) match the
name of the polygon
msoa_dartford <- get_boundary(boundary_type = 'census',
boundary_name='Middle_Super_Output_Areas_December_2011_Boundaries',
names_like = c('Dartford'))
head(msoa_dartford)
## Simple feature collection with 6 features and 6 fields
## geometry type: POLYGON
## dimension: XY
## bbox: xmin: 0.1967327 ymin: 51.43022 xmax: 0.326364 ymax: 51.47966
## CRS: 4326
## # A tibble: 6 x 7
## objectid msoa11cd msoa11nm msoa11nmw st_area.shape. st_length.shape.
## <int> <chr> <chr> <chr> <dbl> <dbl>
## 1 4919 E020050… Dartfor… Dartford… 6562445. 11923.
## 2 4920 E020050… Dartfor… Dartford… 7581994. 26428.
## 3 4921 E020050… Dartfor… Dartford… 2507711. 8580.
## 4 4922 E020050… Dartfor… Dartford… 1628033. 10428.
## 5 4923 E020050… Dartfor… Dartford… 1565851. 6611.
## 6 4924 E020050… Dartfor… Dartford… 4940014. 11088.
## # … with 1 more variable: geometry <POLYGON [°]>
names_equal
If we know the exact name(s) of the polygon we want to return, we should
specify names_equal:
dg <- get_boundary(boundary_type = 'admin',
boundary_name='Local_Authority_Districts_December_2019_Boundaries_UK_BFC',
names_equal = c('Dartford', 'Gravesham'))
head(dg)
## Simple feature collection with 2 features and 10 fields
## geometry type: POLYGON
## dimension: XY
## bbox: xmin: 0.1491599 ymin: 51.32501 xmax: 0.4911962 ymax: 51.47966
## CRS: 4326
## # A tibble: 2 x 11
## objectid lad19cd lad19nm lad19nmw bng_e bng_n long lat st_area.shape.
## <int> <chr> <chr> <chr> <int> <int> <dbl> <dbl> <dbl>
## 1 130 E07000… Dartfo… " " 556167 172917 0.245 51.4 72725577.
## 2 132 E07000… Graves… " " 566969 169114 0.399 51.4 98991345.
## # … with 2 more variables: st_length.shape. <dbl>, geometry <POLYGON [°]>
custom_polygon
There is also the option to use a custom polygon and return boundaries that intersect the given polygon.
We may need to simplify the polygon before sending the request:
custom_poly <- ward_in_radius %>% filter(wd19nm == 'Clifton')
custom_poly_simp <- rmapshaper::ms_simplify(custom_poly)
## Registered S3 method overwritten by 'geojsonlint':
## method from
## print.location dplyr
lsoa_in_poly <- get_boundary(boundary_type = 'census',
boundary_name='Lower_Super_Output_Areas_December_2011_Boundaries',
custom_polygon = custom_poly_simp)
ggplot() +
geom_sf(data = st_intersection(lsoa_in_poly, custom_poly)) +
geom_sf(data =custom_poly, color = 'red', fill=NA )
## although coordinates are longitude/latitude, st_intersection assumes that they are planar
## Warning: attribute variables are assumed to be spatially constant throughout all
## geometries
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