5 Searching for Sites

There are several ways to find sites in neotoma2 using the R package (Dominguez-Vidaña and Goring 2023). We think of sites as being primarily spatial objects. sites have names, locations, and are found within the context of geopolitical units. Within the API and in the package, the site itself does not have associated information about taxa, dataset types or ages. The site is the container into which we add that information. So, when we search for sites we can search by:

Parameter Description
sitename A valid site name (case insensitive) using % as a wildcard.
siteid A unique numeric site id from the Neotoma Database
loc A bounding box vector, geoJSON or WKT string.
altmin Lower altitude bound for sites.
altmax Upper altitude bound for site locations.
database The constituent database from which the records are pulled.
datasettype The kind of dataset (see get_tables(datasettypes))
datasetid Unique numeric dataset identifier in Neotoma
doi A valid dataset DOI in Neotoma
gpid A unique numeric identifier, or text string identifying a geopolitical unit in Neotoma
keywords Unique sample keywords for records in Neotoma.
contacts A name or numeric id for individuals associuated with sites.
taxa Unique numeric identifiers or taxon names associated with sites.

All sites in Neotoma contain one or more datasets. It’s worth noting that the results of these search parameters may be slightly unexpected. For example, searching for sites by sitename, latitude, or altitude will return all of the datasets for the particular site. Searching for terms such as datasettype, datasetid or taxa will return the site, but the only datasets returned will be those matching the dataset-specific search terms. We’ll see this later.

5.1 Site names: sitename="%Lago%"

We may know exactly what site we’re looking for (“Lago Grande di Monticchio”), or have an approximate guess for the site name (for example, we know it’s something like “Lago Grande”, or “Grande Lago Grande”, but we’re not sure how it was entered specifically), or we may want to search all sites that have a specific term, for example, Lago.

We use the general format: get_sites(sitename="%Lago%") for searching by name.

PostgreSQL (and the API) uses the percent sign as a wildcard. So "%Lago%" would pick up “Lago Grande di Monticchio” for us (and picks up “Lago di Martignano” and “Lago Padule”). Note that the search query is also case insensitive, so you could simply write "%LAGO%".

5.1.1 Code

lac_sites <- neotoma2::get_sites(sitename = "%Lago %")

5.1.2 Result

5.2 3.1.2. Location: loc=c()

The original neotoma package used a bounding box for locations, structured as a vector of latitude and longitude values: c(xmin, ymin, xmax, ymax). The neotoma2 R package supports both this simple bounding box, but also more complex spatial objects, using the sf package. Using the sf package allows us to more easily work with raster and polygon data in R, and to select sites from more complex spatial objects. The loc parameter works with the simple vector, WKT, geoJSON objects and native sf objects in R.

As an example of searching for sites using a location, we’ve created a rough representation of Italy as a polygon. To work with this spatial object in R we also transformed the geoJSON element to an object for the sf package. There are many other tools to work with spatial objects in R. Regardless of how you get the data into R, neotoma2 works with almost all objects in the sf package.

geoJSON <- '{"coordinates":
      [8.22, 44.13],
      [12.44, 41.72],
      [15.86, 37.82],
      [18.61, 39.99],
      [12.20, 45.39],
      [13.62, 45.86],
      [13.45, 46.46],
      [12.21, 47.11],
      [10.41, 46.69],
      [6.99, 45.97],
      [6.87, 44.34],
      [8.22, 44.13]

italy_sf <- geojsonsf::geojson_sf(geoJSON)

# Note here we use the `all_data` flag to capture all the sites within the polygon.
# We're using `all_data` here because we know that the site information is relatively small
# for Italy. If we were working in a new area or with a new search we would limit the
# search size.
italy_sites <- neotoma2::get_sites(loc = italy_sf, all_data = TRUE)

You can always simply plot() the sites objects, but you will lose some of the geographic context. The plotLeaflet() function returns a leaflet() map, and allows you to further customize it, or add additional spatial data (like our original bounding polygon, sa_sf, which works directly with the R leaflet package):

5.2.1 Code

neotoma2::plotLeaflet(italy_sites) %>% 
  leaflet::addPolygons(map = ., 
                       data = italy_sf, 
                       color = "green")

5.2.2 Result


Dominguez-Vidaña, Socorro, and Simon J. Goring. 2023. “Neotoma2: An r Package to Access Data from the Neotoma Paleoecology Database.” Journal of Open Source Software 8 (91): 5561. https://doi.org/10.21105/joss.05561.