In avisserquinn/OSMtidy: OSMtidy is an R package for anyone who needs to tidy Open Street Map data into a concise yet complex geospatial dataset with a consistent naming convention.

library(OSMtidy)

knitr::opts_chunk$set(echo = TRUE, out.height = "75%", out.width = "75%")

1. Introduction

If you've not done so already, you can install OSMtidy using the package devtools: devtools::install_github("avisserquinn/OSMtidy").

This vignette provides an example of how to prepare a shapefile for use in OSMtidy. If you don't have a shapefile, OSMtidy includes example data from the UK's Ordnance Survey.

2. Example data

The example data is a shapefile of wards across the UK. The data is from the UK's Ordinance Survey Open Data (https://www.ordnancesurvey.co.uk/business-government/products/boundaryline) and is made available under the UK Government’s OGL licence; see http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/ for details.

Access the file path of the example data with the code below.

files <- 
  system.file("extdata", "", package = "OSMtidy") %>% 
  list.files(pattern = "district_borough_unitary_ward_region", full.names = TRUE)
files

3. Reading in a shapefile

You can read in a shapefile to R using a function from the R package sf, st_read(). As a dependency of OSMtidy, sf should already be installed and loaded.

If using the example data, you can use the filepath saved above. Alternatively, you can paste the filepath in as a string, e.g. "myShapefile.shp".

shp <- st_read(files[[3]]) # The third element in the vector has the extension .shp

shp

At the top of the output it says that this is a Simple feature collection with 7125 features and 15 fields. Each feature represents a ward in the UK. We need to choose one location to focus on - in these vignettes we will look at a ward in the City of Edinburgh (Scotland) called Leith Walk.

4. Tidying the shapefile

In the next code chunk, we reduce the data down to the location of interest using tidyverse notation.

shp <- 
  shp %>%
  filter(str_detect(FILE_NAME, "CITY_OF_EDINBURGH")) %>% 
  filter(str_detect(NAME, "Leith Walk")) %>%
  select(geometry)
shp

Because the shapefile was imported to R as a spatial feature, using sf, we can plot it in ggplot using geom_sf().

shp %>% 
  ggplot() + 
  geom_sf() + 
  theme_void()

You can simplify the jagged outlines of the ward using st_simplify(). The final step is to transform the projection to EPSG:4326 (https://epsg.io/4326) using st_transform().

shp <- 
  shp %>% 
  st_simplify(dTolerance = 25) %>% 
  st_transform(4326)
shp

shp %>% 
  ggplot() + 
  geom_sf() + 
  theme_void()

5. Export the shapefile

The shapefile is now ready for use in OSMtidy. Shapefiles can be exported using the sf function st_write().

shp %>% 
  st_write("exampleEdinburgh.shp", 
           delete_dsn = TRUE, 
           delete_layer = TRUE, 
           quiet = TRUE)

Now you’re ready to run OSMtidy! See Vignette 2 for a general walkthrough of the OSMtidy workflow.

avisserquinn/OSMtidy documentation built on June 3, 2023, 7:30 a.m.