Matthew Strimas-Mackey

One of the first things I took on when I started at the Cornell Lab of
Ornithology was creating the [auk R
package](https://cornelllabofornithology.github.io/auk/) for accessing
eBird data. The entire eBird dataset can be downloaded as a massive text
file, called the eBird Basic Dataset (EBD), and auk pulls out manageable
chunks of the dataset based on various spatial, temporal, or taxonomic
filters. I'm often asked "how do I extract data from within a polygon?"
(usually "polygon" is replaced by "shapefile", but I try to avoid that
word since there's [good reasons to stop using
shapefiles](http://switchfromshapefile.org/)). Rather than answer these
questions individually, I thought I'd do a quick post about how to do
this with auk. Note that, at the time of posting, this requires auk
version 0.4.1 or higher, which can be installed with:

::: {.highlight}
``` {.chroma tabindex="0"}
install.packages("auk")
```
:::

For more details on auk and eBird data in general, including how to get
access to the EBD, it's worth reading the first two chapters of the
[eBird Best Practices
book](https://cornelllabofornithology.github.io/ebird-best-practices/).
For the sake of speed and smaller file size, I'll be working on a subset
of the EBD containing all Northern Bobwhite records from 2019, which I
obtained using the [EBD custom download
form](https://cornelllabofornithology.github.io/ebird-best-practices/ebird.html#ebird-size-custom),
and you can [access
here](https://github.com/mstrimas/strimasdotcom/raw/master/content/post/2020-04-02-extracting-ebird-data-polygon/ebd_norbob_201901_201912_relFeb-2020.zip).
However, everything I'll show in this post works equally as well (just a
lot slower!) on the full EBD. For this example, let's say we want to
extract all records from within a polygon defining [Bird Conservation
Region](https://nabci-us.org/resources/bird-conservation-regions/) 27
([Southeastern Coastal
Plains](https://nabci-us.org/resources/bird-conservation-regions-map/#bcr27)).
A GeoPackage of this region is available on the GitHub repository for
the eBird Best Practices book, [download
it](https://github.com/CornellLabofOrnithology/ebird-best-practices/raw/master/data/gis-data.gpkg),
place it in the `data/` subdirectory of your RStudio project, then load
it into R with:

::: {.highlight}
``` {.chroma tabindex="0"}
library(sf)library(auk)library(dplyr)poly <- read_sf("data/gis-data.gpkg", layer = "bcr")
```
:::

If you have a shapefile, replace `"data/gis-data.gpkg"` with the path to
your shapefile and omit `layer = "bcr"`. Now that we have a polygon,
extracting eBird data is a two step process:

1.  Extract data from the EBD that's within a bounding box containing
    the polygons using the function `auk_bbox()`. This is necessary
    because due to the way auk works under the hood, it can only filter
    to ranges of latitudes and longitudes.
2.  Import the resulting data into R and further subset it to just the
    observations that fall within the polygon.

Fortunately, step 1 is made easier by `auk_bbox()` accepting spatial
`sf` or `raster` objects and automatically calculating the bounding box
for you. For example,

::: {.highlight}
``` {.chroma tabindex="0"}
auk_ebd("data/ebd_norbob_201901_201912_relFeb-2020.txt") %>%   auk_bbox(poly)
```
:::

``` {tabindex="0"}
Input   EBD: /home/maelle/Documents/ropensci/roweb2/content/technotes/2020-04-16-extracting-ebird-data-from-a-polygon/data/ebd_norbob_201901_201912_relFeb-2020.txt Output   Filters not executedFilters   Species: all  Countries: all  States: all  BCRs: all  Bounding box: Lon -91.6 - -75.5; Lat 29.3 - 37.3  Date: all  Start time: all  Last edited date: all  Protocol: all  Project code: all  Duration: all  Distance travelled: all  Records with breeding codes only: no  Complete checklists only: no
```

Notice that the output of the above command says
`Bounding box: Lon -91.6 - -75.5; Lat 29.3 - 37.3`, which are the bounds
of the smallest square that contains the polygon. Let's follow the
method [outlined in the Best Practices
book](https://cornelllabofornithology.github.io/ebird-best-practices/ebird.html#ebird-extract)
to extract some data! We'll get all observations on complete checklists
from May to August inside the bounding box of the polygon:

::: {.highlight}
``` {.chroma tabindex="0"}
f_out <- "data/ebd_norbob_poly.txt"auk_ebd("data/ebd_norbob_201901_201912_relFeb-2020.txt") %>%   # define filters  auk_bbox(poly) %>%   auk_date(c("*-05-01", "*-08-31")) %>%   auk_complete() %>%   # compile and run filters  auk_filter(f_out)
```
:::

The results were output to a file, which you can read in with
`read_ebd()`.

::: {.highlight}
``` {.chroma tabindex="0"}
ebd <- read_ebd("data/ebd_norbob_poly.txt")
```
:::

The data are now in a data frame and it's time to proceed to step 2:
further subset the data to only keep points within the polygon. First
we'll convert this data frame to a spatial `sf` object using the
`latitude` and `longitude` columns, then well use `st_within()` to
identify the points within the polygon, and use this to subset the data
frame. Note that we have to be careful with our coordinate reference
system here: `crs = 4326` specifies that the EBD data are in
unprojected, lat-long coordinates and we use `st_transform()` to ensure
the polygons and points are in the coordinate reference system.

::: {.highlight}
``` {.chroma tabindex="0"}
# convert to sf objectebd_sf <- ebd %>%   select(longitude, latitude) %>%   st_as_sf( coords = c("longitude", "latitude"), crs = 4326)# put polygons in same crspoly_ll <- st_transform(poly, crs = st_crs(ebd_sf))# identify points in polygonin_poly <- st_within(ebd_sf, poly_ll, sparse = FALSE)
```
:::

``` {tabindex="0"}
although coordinates are longitude/latitude, st_within assumes that they are planar
```

::: {.highlight}
``` {.chroma tabindex="0"}
# subset data frameebd_in_poly <- ebd[in_poly[, 1], ]
```
:::

Finally, let's create a simple map showing the EBD observations before
(in black) and after (in green) subsetting the data to be within the
polygon.

::: {.highlight}
``` {.chroma tabindex="0"}
par(mar = c(0, 0, 0, 0))plot(poly %>% st_geometry(), col = "grey40", border = NA)plot(ebd_sf, col = "black", pch = 19, cex = 0.5, add = TRUE)plot(ebd_sf[in_poly[, 1], ],      col = "forestgreen", pch = 19, cex = 0.5,      add = TRUE)legend("top",        legend = c("All observations", "After spatial subsetting"),        col = c("grey40", "forestgreen"),        pch = 19,       bty = "n",       ncol = 2)
```
:::

<figure>
<img src="plot-1.png"
alt="map of all occurrences, with the polygon in grey, and the points kept after spatial subsetting in green" />
</figure>

Looks like it worked! We got just the points within the polygon as
intended. Two final notes:

1.  If you're working with the full EBD (a 200+ GB file), you'll need to
    follow step 1 and subset the data using auk prior to importing
    into R. However, if you've used the custom download form to get an
    EBD subset, your file is likely small enough that you can read the
    data directly into R with `read_ebd()` and skip straight to step 2.
2.  If your intention is to eventually [zero-fill the
    EBD](https://cornelllabofornithology.github.io/ebird-best-practices/ebird.html#ebird-zf)
    to produce presence-absence data you'll need to include the sampling
    event data file in the `auk_ebd()`, subset both the EBD and sampling
    event data files separately to points within the polygon, the
    combine them together and zero-fill with `auk_zerofill()`.

auk users are welcome to submit issues on GitHub with bug reports or
feature requests, or to [email me
directly](https://github.com/CornellLabofOrnithology/auk/blob/88cf9a370adb8d7a106f07cd45003033c73edc8e/DESCRIPTION#L8).
Should you be interested in contributing to auk, there is also a
[vignette specifically for adding functionality to the
package](https://docs.ropensci.org/auk/articles/development.html).


One of the first things I took on when I started at the Cornell Lab of Ornithology was creating the auk R package for accessing eBird data. The entire eBird dataset can be downloaded as a massive text file, called the eBird Basic Dataset (EBD), and auk pulls out manageable chunks of the dataset based on various spatial, temporal, or taxonomic filters.

Publicaciones de Rogue Scholar

Extracting eBird Data from a Polygon