Exercise 2_1_1: Working with OSM Data
Stefan Jünger & Anne-Kathrin Stroppe
Introduction to Geospatial Techniques for Social Scientists in R
1
Pick a city of the world to your liking and retrieve its bounding box.
Then plot it: What do you see?
The function you may search for is part of the
data
package. Have a look at the slides.
boothbay <- osmdata::getbb("Boothbay")
plot(boothbay)
# We see two points, which build the extremes of the bounding box.2
Please choose a couple of building types you are interested in and set
them as key and value pairs. You can find a list of building types in
the Overpass
API documentation. But don’t forget to set the timeout query
parameters using the
osmdata::opq() function first.
First, specify the bounding box like before; second, the query
parameters; and third, the key and value pairs. Try using a pipe
workflow, as it makes it easier.
boothbay <-
osmdata::getbb("Boothbay") |>
osmdata::opq(timeout = 25*100) |>
osmdata::add_osm_feature(
key = "building",
value = c("house", "residential", "apartments", "detached", "bungalow")
)3
Download the data using the
osmadata::osmdata_sf() function
and extract only the polygons.
The downloaded data is a list. The polygons are a named list element
that you can extract with its name
osm_polygons, just like
a variable in a data table.
boothbay <-
osmdata::getbb("Boothbay") |>
osmdata::opq(timeout = 25*100) |>
osmdata::add_osm_feature(
key = "building",
) |>
osmdata::osmdata_sf()
boothbay <- boothbay$osm_polygons4
Take some time to browse through the data. Depending on your chosen
building type, you may find some interesting information. You can also
plot the data you have just downloaded.
You may consider converting the data into a tibble using
tibble::as_tibble() and maybe a sf::st_as_sf()
afterward for a nicer browsing experience.
library(tmap)
boothbay <-
boothbay |>
tibble::as_tibble() |>
sf::st_as_sf()
boothbay## Simple feature collection with 2232 features and 86 fields
## Geometry type: POLYGON
## Dimension: XY
## Bounding box: xmin: -69.69212 ymin: 43.7545 xmax: -69.51428 ymax: 43.94156
## Geodetic CRS: WGS 84
## # A tibble: 2,232 × 87
## osm_id name `addr:city` `addr:county` `addr:hamlet` `addr:housenumber` `addr:locality` `addr:postcode` `addr:state` `addr:street`
## <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
## 1 234813057 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 2 234813059 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 3 234813061 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 4 234813064 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 5 235324281 Southp… Southport <NA> <NA> 1032 <NA> 04576 <NA> Hendricks Hi…
## 6 235324664 Robins… Southport <NA> <NA> 20 <NA> 04576 ME Hendricks Hi…
## 7 235326155 Southp… Southport <NA> <NA> 443 <NA> 04576 <NA> Hendricks Hi…
## 8 307439615 <NA> Boothbay <NA> <NA> 92 <NA> 04537 ME Isle of Spri…
## 9 307439616 <NA> Boothbay <NA> <NA> 96 <NA> 04537 ME Isle of Spri…
## 10 307439617 <NA> Boothbay <NA> <NA> 60 <NA> 04537 ME Isle of Spri…
## # ℹ 2,222 more rows
## # ℹ 77 more variables: `addr:unit` <chr>, amenity <chr>, area <chr>, barrier <chr>, beauty <chr>, brand <chr>, `brand:wikidata` <chr>,
## # building <chr>, `building:colour` <chr>, `building:levels` <chr>, `building:material` <chr>, check_date <chr>, club <chr>,
## # company <chr>, craft <chr>, cuisine <chr>, denomination <chr>, description <chr>, ele <chr>, emergency <chr>, `fuel:diesel` <chr>,
## # `fuel:octane_89` <chr>, `gnis:county_id` <chr>, `gnis:created` <chr>, `gnis:feature_id` <chr>, `gnis:state_id` <chr>, golf <chr>,
## # guest_house <chr>, healthcare <chr>, `healthcare:speciality` <chr>, heritage <chr>, `heritage:operator` <chr>,
## # `heritage:website` <chr>, historic <chr>, house <chr>, information <chr>, layer <chr>, leisure <chr>, lit <chr>, man_made <chr>, …tmap_mode(("view"))## tmap mode set to interactive viewingtm_shape(boothbay) +
tm_polygons()