3. Tidy data
The first step is to select a single ward area to focus on. Here we look at a ward in the City of Edinburgh (Scotland) called Leith Walk.
shp <- st_read("district_borough_unitary_ward_region.shp", quiet = TRUE)
shp
#> Simple feature collection with 7125 features and 15 fields
#> geometry type: MULTIPOLYGON
#> dimension: XY
#> bbox: xmin: 5512.998 ymin: 5333.603 xmax: 655989 ymax: 1220302
#> proj4string: +proj=tmerc +lat_0=49 +lon_0=-2 +k=0.9996012717 +x_0=400000 +y_0=-100000 +datum=OSGB36 +units=m +no_defs
#> First 10 features:
#> NAME AREA_CODE DESCRIPTIO
#> 1 Chiltern Rise Ward DIW District Ward
#> 2 Lacey Green, Speen and the Hampdens Ward DIW District Ward
#> 3 Booker and Cressex Ward DIW District Ward
#> 4 Abbey Ward DIW District Ward
#> 5 Sands Ward DIW District Ward
#> 6 Disraeli Ward DIW District Ward
#> 7 Terriers and Amersham Hill Ward DIW District Ward
#> 8 Greater Hughenden Ward DIW District Ward
#> 9 Tylers Green and Loudwater Ward DIW District Ward
#> 10 The Risboroughs Ward DIW District Ward
#> FILE_NAME NUMBER NUMBER0 POLYGON_ID UNIT_ID CODE HECTARES
#> 1 BUCKINGHAMSHIRE_COUNTY 1 1 51815 11853 E05002679 2245.296
#> 2 BUCKINGHAMSHIRE_COUNTY 2 2 51826 11864 E05002689 2322.664
#> 3 BUCKINGHAMSHIRE_COUNTY 3 3 51819 11858 E05002676 262.429
#> 4 BUCKINGHAMSHIRE_COUNTY 4 4 125171 11788 E05002674 455.674
#> 5 BUCKINGHAMSHIRE_COUNTY 5 5 51822 11861 E05002695 337.526
#> 6 BUCKINGHAMSHIRE_COUNTY 6 6 51918 11784 E05002680 213.448
#> 7 BUCKINGHAMSHIRE_COUNTY 7 7 125173 11810 E05002697 276.751
#> 8 BUCKINGHAMSHIRE_COUNTY 8 8 126851 42959 E05002683 1879.171
#> 9 BUCKINGHAMSHIRE_COUNTY 9 9 117730 11750 E05002701 719.226
#> 10 BUCKINGHAMSHIRE_COUNTY 10 10 128834 11865 E05002699 1065.502
#> AREA TYPE_CODE DESCRIPT0 TYPE_COD0 DESCRIPT1
#> 1 0 VA CIVIL VOTING AREA <NA> <NA>
#> 2 0 VA CIVIL VOTING AREA <NA> <NA>
#> 3 0 VA CIVIL VOTING AREA <NA> <NA>
#> 4 0 VA CIVIL VOTING AREA <NA> <NA>
#> 5 0 VA CIVIL VOTING AREA <NA> <NA>
#> 6 0 VA CIVIL VOTING AREA <NA> <NA>
#> 7 0 VA CIVIL VOTING AREA <NA> <NA>
#> 8 0 VA CIVIL VOTING AREA <NA> <NA>
#> 9 0 VA CIVIL VOTING AREA <NA> <NA>
#> 10 0 VA CIVIL VOTING AREA <NA> <NA>
#> geometry
#> 1 MULTIPOLYGON (((477425.3 19...
#> 2 MULTIPOLYGON (((481498.6 19...
#> 3 MULTIPOLYGON (((483096.8 19...
#> 4 MULTIPOLYGON (((485369 1910...
#> 5 MULTIPOLYGON (((481944.2 19...
#> 6 MULTIPOLYGON (((486440 1933...
#> 7 MULTIPOLYGON (((488140.6 19...
#> 8 MULTIPOLYGON (((483481.9 19...
#> 9 MULTIPOLYGON (((491787 1907...
#> 10 MULTIPOLYGON (((482000.6 20...In the next code chunk, we reduce the data down to the location of interest.
shp <-
shp %>%
filter(str_detect(FILE_NAME, "CITY_OF_EDINBURGH")) %>%
filter(str_detect(NAME, "Leith Walk")) %>%
select(geometry)
shp
#> Simple feature collection with 1 feature and 0 fields
#> geometry type: MULTIPOLYGON
#> dimension: XY
#> bbox: xmin: 325186.8 ymin: 674419.7 xmax: 327500.8 ymax: 676416
#> proj4string: +proj=tmerc +lat_0=49 +lon_0=-2 +k=0.9996012717 +x_0=400000 +y_0=-100000 +datum=OSGB36 +units=m +no_defs
#> geometry
#> 1 MULTIPOLYGON (((327500.8 67...
shp %>% ggplot() + geom_sf()
Now we can simplify the jagged outlines of the ward and trasnform the projection to epsg 4326.
shp <- shp %>% st_simplify(dTolerance = 25) %>% st_transform(4326)
shp
#> Simple feature collection with 1 feature and 0 fields
#> geometry type: POLYGON
#> dimension: XY
#> bbox: xmin: -3.199973 ymin: 55.95726 xmax: -3.162921 ymax: 55.97507
#> CRS: EPSG:4326
#> geometry
#> 1 POLYGON ((-3.162921 55.9633...
shp %>% ggplot() + geom_sf()
The final step is to export the shapefile using the sf function st_write(). When using OSMtidy, all the associated shapefile files need to be placed in the shapefile folder in the OSMtidy directory.
shp %>% st_write("exampleEdinburgh.shp", delete_dsn = TRUE, delete_layer = TRUE, quiet = TRUE)
#> Warning in CPL_write_ogr(obj, dsn, layer, driver,
#> as.character(dataset_options), : GDAL Error 1: exampleEdinburgh.shp does not
#> appear to be a file or directory.Now you’re ready to run OSMtidy! See Vignette 2 for a walkthrough.