qs_clean_coordinates.RmdThe clean_coordinates function enables a fast, automated and reproducible flagging of potentially erroneous occurrence coordinates based on geographic gazetteers. The function can flag records based on known problems common to biological collections.
Individual test can be switched on and off by a logical flag (see ?clean_coordinates) and distance thresholds for all tests can also be adapted. Custom gazetteers for the cleaning can be provided for all tests (for a higher level of detail). See here for a detailed tutorial on how to clean occurrence records using CoordinateCleaner.
Please find a detailed tutorial on how to clean occurrence records (e.g. from GBIF) here and how to clean fossil data (e.g. from PBDB) here.
clean_coordinates wraps around multiple tests for common error sources in species distribution records. Individual test can be included or excluded from a run with the tests argument of clean_coordinates, e.g. "seas" switches the seas test off. Most basic tests are switched on by defaults, but some more complex are switched off by default.
library(CoordinateCleaner)
## Registered S3 method overwritten by 'dplyr':
## method from
## as.data.frame.tbl_df tibble
## Registered S3 methods overwritten by 'ggplot2':
## method from
## [.quosures rlang
## c.quosures rlang
## print.quosures rlang
exmpl <- data.frame(species = sample(letters, size = 250, replace = TRUE),
decimallongitude = runif(250, min = 42, max = 51),
decimallatitude = runif(250, min = -26, max = -11),
countries = "MDG")
#run all tests
dat <- clean_coordinates(exmpl, tests = c("capitals", "centroids", "countries", "equal", "gbif", "institutions", "outliers", "seas", "urban", "zeros"), countries = "countries")
## Testing coordinate validity
## Flagged 0 records.
## Testing equal lat/lon
## Flagged 0 records.
## Testing zero coordinates
## Flagged 0 records.
## Testing country capitals
## Flagged 0 records.
## Testing country centroids
## Flagged 0 records.
## Testing sea coordinates
## OGR data source with driver: ESRI Shapefile
## Source: "C:\Users\alexander.zizka\AppData\Local\Temp\Rtmpqc5aFR", layer: "ne_50m_land"
## with 1420 features
## It has 3 fields
## Integer64 fields read as strings: scalerank
## Flagged 156 records.
## Testing urban areas
## No reference for urban areas found.
## Using rnaturalearth to download.
## OGR data source with driver: ESRI Shapefile
## Source: "C:\Users\alexander.zizka\AppData\Local\Temp\Rtmpqc5aFR", layer: "ne_50m_urban_areas"
## with 2143 features
## It has 4 fields
## Integer64 fields read as strings: scalerank
## Flagged 0 records.
## Testing country identity
## Flagged 156 records.
## Testing geographic outliers
## Flagged 3 records.
## Testing GBIF headquarters, flagging records around Copenhagen
## Flagged 0 records.
## Testing biodiversity institutions
## Flagged 0 records.
## Flagged 158 of 250 records, EQ = 0.63.
#only run the validity test
dat <- clean_coordinates(exmpl, tests = c(""))
## Testing coordinate validity
## Flagged 0 records.
## Flagged 0 of 250 records, EQ = 0.| Test | Function | Background | Default |
|---|---|---|---|
| capitals | radius around capitals | georeferenced from location description | on |
| centroids | radius around country and province centroids | geo-referenced from description | on |
| countries | coordinates in the right country | switched lon/lat, data entry errors | off |
| duplicates | records from one species with identical coordinates | repetitive observation of identical individual, same voucher from multiple data sources, genetic data | off |
| gbif | radius around GBIF headquarters | data entry errors, falsely geo-referenced | on |
| institutions | radius around biodiversity institutions | falsely geo-referenced, zoo or garden records | on |
| outliers | records far away from all other records of this species | various | off |
| seas | in the sea | switched lon/lat | on |
| urban | within urban area | cultivated/captivity | off |
| validity | outside reference coordinate system | missing data, data entry errors | on |
| zeros | plain zeros, lat = lon | missing data, data entry errors | on |
capitals, centroids and institutions
The capitals, centroids and institutions test use a radius around gazetteers to flag coordinates. You can change this radius for each test using the .rad arguments. The radius is specified in decimal degrees. This means that the actual size of the in meters will vary slightly depending on latitude.
clean_coordinates(exmpl, capitals_rad = 0.1)## Testing coordinate validity## Flagged 0 records.## Testing equal lat/lon## Flagged 0 records.## Testing zero coordinates## Flagged 0 records.## Testing country capitals## Flagged 0 records.## Testing country centroids## Flagged 0 records.## Testing sea coordinates## OGR data source with driver: ESRI Shapefile
## Source: "C:\Users\alexander.zizka\AppData\Local\Temp\Rtmpqc5aFR", layer: "ne_50m_land"
## with 1420 features
## It has 3 fields
## Integer64 fields read as strings: scalerank## Flagged 156 records.## Testing geographic outliers## Flagged 3 records.## Testing GBIF headquarters, flagging records around Copenhagen## Flagged 0 records.## Testing biodiversity institutions## Flagged 0 records.## Flagged 158 of 250 records, EQ = 0.63.## species decimallongitude decimallatitude val equ zer cap cen
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## sea otl gbf inst summary
## 1 FALSE TRUE TRUE TRUE FALSE
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## 214 FALSE TRUE TRUE TRUE FALSE
## 215 FALSE TRUE TRUE TRUE FALSE
## 216 TRUE TRUE TRUE TRUE TRUE
## 217 FALSE TRUE TRUE TRUE FALSE
## 218 TRUE TRUE TRUE TRUE TRUE
## 219 FALSE TRUE TRUE TRUE FALSE
## 220 FALSE TRUE TRUE TRUE FALSE
## 221 FALSE TRUE TRUE TRUE FALSE
## 222 TRUE TRUE TRUE TRUE TRUE
## 223 TRUE TRUE TRUE TRUE TRUE
## 224 TRUE TRUE TRUE TRUE TRUE
## 225 FALSE TRUE TRUE TRUE FALSE
## 226 FALSE TRUE TRUE TRUE FALSE
## 227 FALSE TRUE TRUE TRUE FALSE
## 228 FALSE TRUE TRUE TRUE FALSE
## 229 TRUE TRUE TRUE TRUE TRUE
## 230 FALSE TRUE TRUE TRUE FALSE
## 231 FALSE TRUE TRUE TRUE FALSE
## 232 FALSE TRUE TRUE TRUE FALSE
## 233 FALSE TRUE TRUE TRUE FALSE
## 234 TRUE TRUE TRUE TRUE TRUE
## 235 TRUE TRUE TRUE TRUE TRUE
## 236 FALSE TRUE TRUE TRUE FALSE
## 237 TRUE TRUE TRUE TRUE TRUE
## 238 FALSE TRUE TRUE TRUE FALSE
## 239 TRUE TRUE TRUE TRUE TRUE
## 240 TRUE TRUE TRUE TRUE TRUE
## 241 TRUE TRUE TRUE TRUE TRUE
## 242 FALSE TRUE TRUE TRUE FALSE
## 243 TRUE FALSE TRUE TRUE FALSE
## 244 FALSE TRUE TRUE TRUE FALSE
## 245 FALSE TRUE TRUE TRUE FALSE
## 246 TRUE TRUE TRUE TRUE TRUE
## 247 FALSE TRUE TRUE TRUE FALSE
## 248 FALSE TRUE TRUE TRUE FALSE
## 249 FALSE TRUE TRUE TRUE FALSE
## 250 FALSE TRUE TRUE TRUE FALSE| Test | Default radius [°] | Default radius (lat 0°/45°) [km] |
|---|---|---|
| capitals | 0.05 | 5.5/4 |
| centroids | 0.01 | 1.1/0.8 |
| gbif | 1 | 63.0 |
| institutions | 0.001 | 0.1/0.08 |
| zeros | 0.5 | 55.6 |
You can use custom gazetteers for all CleanCoordinates tests, via the .ref arguments of the function. For example the capitals.ref argument controls the reference for the capitals test. Customized reference data must follow the same format as the default reference for the same test. You can check the structure of gazetteers via their documentation or by looking at the gazetteer (e.g. head(capitals)). For example:
#check the format of the default capitals reference
head(countrtyref) #a data.frame with four columns: ISO3, capital, longitude, latitude
#create new reference data set from scratch. For real analysis you
#probably want to load the alternative file from a .txt file
my.cap <- data.frame(ISO3 = LETTERS[1:10],
capital = letters[1:10],
capital.longitude = runif(10, -180, 180),
capital.latitude = runif(10, -90, 90))
flags <- clean_coordinates(exmpl, capitals.ref = my.cap)In this way test can be completely customized, you could for example provide a gazetteer with the locations of hardware stores (in the capitals format) if you want to flag records around hardware stores.
Classes of the default gazetteers of clean_coordinates.
| Test | Default gazetteer | Class | Argument |
|---|---|---|---|
| capitals | countryref | data.frame |
capitals.ref |
| centroids | countryref | data.frame |
centroids.ref |
| countrycheck | rnaturalearth::ne_countries(scale = “medium”) | SpatialPolygonsDataFrame |
country.ref |
| institutions | institutions | data.frame |
inst.ref |
| seas | landmass | SpatialPolygonsDataFrame |
seas.ref |
| urban | rnaturalearth::ne_download(scale = ‘medium’, type = ‘urban_areas’) | SpatialPolygonsDataFrame |
urban.ref |
You cane easily summarize the results of clean_coordinates either with the report option or via summary. If report == T the summary is written to the working directory as a .txt file, if report is a character, it is the path to which the summary file will be written, Alternatively, you can get a summary of the number of records flagged with summary.
#via the report option
flags <- clean_coordinates(exmpl, report = T)
## Testing coordinate validity
## Flagged 0 records.
## Testing equal lat/lon
## Flagged 0 records.
## Testing zero coordinates
## Flagged 0 records.
## Testing country capitals
## Flagged 0 records.
## Testing country centroids
## Flagged 0 records.
## Testing sea coordinates
## OGR data source with driver: ESRI Shapefile
## Source: "C:\Users\alexander.zizka\AppData\Local\Temp\Rtmpqc5aFR", layer: "ne_50m_land"
## with 1420 features
## It has 3 fields
## Integer64 fields read as strings: scalerank
## Flagged 156 records.
## Testing geographic outliers
## Flagged 3 records.
## Testing GBIF headquarters, flagging records around Copenhagen
## Flagged 0 records.
## Testing biodiversity institutions
## Flagged 0 records.
## Flagged 158 of 250 records, EQ = 0.63.
#via summary
summary(flags)
## decimallatitude val equ zer
## 0 0 0 0
## cap cen sea otl
## 0 0 156 3
## gbf inst summary
## 0 0 158