Places that start with “bad” (but not “badger”!)
The theme for today’s #30DayMapChallenge: Bad maps. Inspired by this map of German town’s starting with “Bad” (designating a spa town), I thought I’d do something similar for the US.
We get places names from the USGS Gazetteer files. Be warned that these are large files that will take a while to download and read.
library(tidyverse)
library(sf)
library(tmap)
url <- "https://prd-tnm.s3.amazonaws.com/StagedProducts/GeographicNames/FullModel/Gazetteer_National_GDB.zip"
zip_file <- tempfile(fileext = ".zip")
options(timeout=180) #file is large and times out with default timeout
download.file(url, zip_file, mode = "wb")
temp_dir <- tempdir()
places_dir <- unzip(zipfile = zip_file, exdir = temp_dir)
gazetteer_places <- st_read(paste0(temp_dir, "\\Gazetteer_National_GDB.gdb"), layer = "Gaz_Names") #layer with namesReading layer `Gaz_Names' from data source
`C:\Users\user1\AppData\Local\Temp\RtmpkZQ702\Gazetteer_National_GDB.gdb'
using driver `OpenFileGDB'Reading layer `Gaz_Features' from data source
`C:\Users\user1\AppData\Local\Temp\RtmpkZQ702\Gazetteer_National_GDB.gdb'
using driver `OpenFileGDB'
Simple feature collection with 980622 features and 19 fields
Geometry type: MULTIPOINT
Dimension: XYZ
Bounding box: xmin: -179.4681 ymin: -85.37423 xmax: 179.9833 ymax: 71.41745
z_range: zmin: 0 zmax: 0
Geodetic CRS: NAD83all_places <- gazetteer_geo |> right_join(gazetteer_places, by = "feature_id")Now we start filtering for places starting with “Bad”:
bad_places <- all_places |>
filter(str_detect(feature_name, "^[Bb]ad"))This gives us 640 places, but just looking at the names of the first few shows that they’re not all “bad” places:
head(bad_places$feature_name)[1] "Bad River" "Badger Creek"
[3] "Badger Mountain Division" "Bad River Indian Reservation"
[5] "Bad River Reservation" "Badwater Creek" Two out of the 6 are not “bad” but “Badger.” After browsing through the full list of included names, I came with this heuristic to eliminate most “non-bad” names, such as “Baden,” “Badoff,” or “Badito.”
bad_places <- bad_places |>
filter(!str_detect(feature_name, "^[Bb]ad[degiou]"))
bad_places |>
st_drop_geometry() |>
select(feature_name, feature_class) |>
DT::datatable()What type of places are most commonly bad?
bad_places |>
st_drop_geometry() |>
group_by(feature_class) |>
tally(sort = T) |>
DT::datatable()Streams!
Let’s map all the 209 bad places:
You can click on the points to display the name. We could add the names right on the map, but as bad places cluster together (and in the case of rivers and streams, the same feature shows up multiple times), this makes for a messy map:
That’s all for today.
For attribution, please cite this work as
Kliems (2023, Nov. 4). Harald Kliems: #30DayMapChallenge: A "bad" map of the US. Retrieved from https://haraldkliems.netlify.app/posts/2023-11-04-30daymapchallenge-a-bad-map-of-the-us/
BibTeX citation
@misc{kliems2023#30daymapchallenge:,
author = {Kliems, Harald},
title = {Harald Kliems: #30DayMapChallenge: A "bad" map of the US},
url = {https://haraldkliems.netlify.app/posts/2023-11-04-30daymapchallenge-a-bad-map-of-the-us/},
year = {2023}
}