# Spatial filtering

#### Callum Waite & Shandiya Balasubramaniam

#### 2023-10-13

Biodiversity queries to the ALA usually require some spatial filtering. Let’s see how spatial data are stored in the ALA and a few different methods to spatially filter data with galah and other packages.

Most records in the ALA contain location data in the form of two key fields: `decimalLatitude` and `decimalLongitude`. As expected, these fields are the decimal coordinates of each occurrence, with south and west values denoted by negatives. While there may be some uncertainty in these values (see field `coordinateUncertaintyInMeters`), they are generally very accurate.

While these are very important and useful fields, very rarely will we encounter situations that require queries directly calling `decimalLatitude` and `decimalLongitude`. Instead, the ALA and galah have a number of features that make spatial queries simpler.

### Contextual and spatial layers

Often we want to filter results down to some commonly defined spatial regions, such as states, LGAs or IBRA/IMCRA regions. The ALA contains a large range (>100) of contextual and spatial layers, in-built as searchable and queriable fields. They are denoted by names beginning with `"cl"`, followed by an identifying number that may be up to 6 digits long. These fields are each based on shapefiles, and contain the names of the regions in these layers that each record lies in.

We strongly recommend using `search_fields()` to check whether a contextual layer already exists in the ALA that matches what you require before proceeding with other methods of spatial filtering. These fields are all able to be queried with `filter()`and so they are generally easier to use.

Suppose we are interested in querying records of the Red-Necked Avocet (Recurvirostra novaehollandiae) in a particular protected wetlands, the Coorong wetlands in South Australia. We can search the ALA fields for wetlands.

    library(galah)
    library(tidyverse)
    library(gt)
    library(sf)

    galah_config(email = "your_email_here", verbose = FALSE)

    search_fields("wetlands")

    ## # A tibble: 1 × 3
    ##   id    description                     type
    ##   <chr> <chr>                           <chr>
    ## 1 cl901 Directory of Important Wetlands fields

Our search identifies that layer `cl901` seems to match what we are looking for. We can then either view all possible values in the field with `show_values()`, or search again for our particular field.

    search_fields("cl901") |> search_values("coorong")

    ## • Showing values for 'cl901'.

    ## # A tibble: 1 × 1
    ##   cl901
    ##   <chr>
    ## 1 The Coorong, Lake Alexandrina & Lake Albert

We can filter all occurrences for exact matches with this value, `"Lake Eyre"`. Our `galah` query can be built as follows:

    galah_call() |>
      identify("Recurvirostra novaehollandiae") |>
      filter(cl901 == "The Coorong, Lake Alexandrina & Lake Albert") |>
      collect() |>
      head(5) |>
      gt::gt()

recordID | scientificName | taxonConceptID | decimalLatitude | decimalLongitude | eventDate | occurrenceStatus | dataResourceName
--- | --- | --- | --- | --- | --- | --- | ---
00630f58-ee78-4f69-ba39-718b0a1c3356 | Recurvirostra novaehollandiae | https://biodiversity.org.au/afd/taxa/c69e7308-527a-429d-a80d-143bd20b5100 | -35.59293 | 139.0218 | 2008-11-18 | PRESENT | SA Fauna
00705077-abc2-4f28-aedd-11a479ec2d82 | Recurvirostra novaehollandiae | https://biodiversity.org.au/afd/taxa/c69e7308-527a-429d-a80d-143bd20b5100 | -35.52833 | 138.8089 | 2016-03-06 | PRESENT | BirdLife Australia, Birdata
0071444e-ddf2-45dc-9a10-c6399686e306 | Recurvirostra novaehollandiae | https://biodiversity.org.au/afd/taxa/c69e7308-527a-429d-a80d-143bd20b5100 | -35.57719 | 138.9923 | 2008-01-01 | PRESENT | SA Fauna
008a2e6c-26b4-4f4e-a428-b0330db53466 | Recurvirostra novaehollandiae | https://biodiversity.org.au/afd/taxa/c69e7308-527a-429d-a80d-143bd20b5100 | -35.52831 | 138.8280 | 2017-11-19 17:15:00 | PRESENT | eBird Australia
00b11f13-0100-4471-b18b-7e9736ddeeaf | Recurvirostra novaehollandiae | https://biodiversity.org.au/afd/taxa/c69e7308-527a-429d-a80d-143bd20b5100 | -36.18001 | 139.6589 | 2017-08-05 13:25:00 | PRESENT | eBird Australia

### galah_geolocate()

While server-side spatial information is useful, there are likely to be cases where the shapefile or region you wish to query will not be pre-loaded as a contextual layer in the ALA. In this case, shapefiles can be introduced to the filtering process using the {sf} package and the `galah_geolocate()` function. Shapefiles can be provided as an `sf` object, whether that is by importing them with `sf::st_read()` or taking a `POLYGON` or `MULTIPOLYGON` character string and transforming them with `sf::st_as_sfc()`.

For instance, we might interested in species occurrences in King George Square, Brisbane. We can take the `MULTIPOLYGON` object for the square (as sourced from the Brisbane City Council) and transform it into `sfc` and then `sf` objects.

    king_george_sq <- "MULTIPOLYGON(((153.0243 -27.46886, 153.0242 -27.46896, 153.0236 -27.46837, 153.0239 -27.46814, 153.0239 -27.46813, 153.0242 -27.46789, 153.0244 -27.46805, 153.0245 -27.46821, 153.0246 -27.46828, 153.0247 -27.46835, 153.0248 -27.46848, 153.0246 -27.4686, 153.0246 -27.46862, 153.0245 -27.46871, 153.0243 -27.46886)))" |>
      sf::st_as_sfc() |>
      sf::st_as_sf()

We can provide this `MULTIPOLYGON` in our filter as the argument of `galah_geolocate()` to assess which species have been recorded in King George Square.

    galah_call() |>
      galah_geolocate(king_george_sq) |>
      select(decimalLatitude,
             decimalLongitude,
             eventDate,
             scientificName,
             vernacularName) |>
      collect() |>
      head(10) |>
      gt::gt()

decimalLatitude | decimalLongitude | eventDate | scientificName | vernacularName
--- | --- | --- | --- | ---
-27.46862 | 153.0243 | 2006-03-29 23:32:00 | Threskiornis moluccus | Australian White Ibis
-27.46861 | 153.0244 | 2023-12-01 23:12:02 | Cosmophasis baehrae | NA
-27.46855 | 153.0239 | 2024-02-03 10:45:00 | Mediastinia | NA
-27.46851 | 153.0238 | 2023-04-08 07:49:00 | Entomyzon cyanotis | Blue-faced Honeyeater
-27.46842 | 153.0241 | 2022-08-30 01:29:00 | Threskiornis moluccus | Australian White Ibis
-27.46833 | 153.0241 | 2022-07-24 14:43:00 | Threskiornis moluccus | Australian White Ibis

There is a second argument of `galah_geolocate()` called `type`, which defaults to value `"polygon"`. By setting the `type` argument to `"bbox"`, the provided `POLYGON` or `MULTIPOLYGON` will be converted into the smallest bounding box (rectangle) that contains the `POLYGON`. In this case, records will be included that may not exactly lie inside the provided shape.

    galah_call() |>
      galah_geolocate(king_george_sq, type = "bbox") |>
      select(decimalLatitude,
             decimalLongitude,
             eventDate,
             scientificName,
             vernacularName) |>
      collect() |>
      head(10) |>
      gt::gt()

    ## Data returned for bounding box:
    ## xmin = 153.0236 xmax = 153.0248 ymin = -27.46896 ymax = -27.46789

decimalLatitude | decimalLongitude | eventDate | scientificName | vernacularName
--- | --- | --- | --- | ---
-27.46889 | 153.0244 | 2015-10-09 | Burhinus (Burhinus) grallarius | Bush Stone-curlew
-27.46882 | 153.0236 | 2019-04-21 04:40:00 | Threskiornis moluccus | Australian White Ibis
-27.46882 | 153.0236 | 2005-08-28 12:18:00 | Threskiornis moluccus | Australian White Ibis
-27.46862 | 153.0243 | 2006-03-29 23:32:00 | Threskiornis moluccus | Australian White Ibis
-27.46861 | 153.0244 | 2023-12-01 23:12:02 | Cosmophasis baehrae | NA
-27.46855 | 153.0239 | 2024-02-03 10:45:00 | Mediastinia | NA
-27.46851 | 153.0238 | 2023-04-08 07:49:00 | Entomyzon cyanotis | Blue-faced Honeyeater
-27.46842 | 153.0241 | 2022-08-30 01:29:00 | Threskiornis moluccus | Australian White Ibis
-27.46833 | 153.0241 | 2022-07-24 14:43:00 | Threskiornis moluccus | Australian White Ibis
-27.46806 | 153.0239 | NA | Syntrichia laevipila | Screw Moss

#### Large shapefiles

The `type` argument with option `"bbox"` is provided because `sf` objects with >500 vertices will not be accepted by the ALA. In the event you have a large shapefile, using `type = "bbox"` will at least enable an initial reduction of the data that is downloaded, before finer filtering to the actual shapefile will obtain the desired set of occurrences. Alternatively, one can also perform the `"bbox"` reduction before passing the shape to `galah_geolocate()` by using `sf::st_bbox()`.

A common situation for this to occur is when a shapefile with multiple shapes is provided, where we are interested in grouping our results by each shape. Here is a mock workflow using a subset of a shapefile of all 2,184 Brisbane parks.

Let’s say we are interested in knowing which parks in the Brisbane postcode 4075 have the most occurrences of the Scaly-Breasted Lorikeet, Trichoglossus chlorolepidotus, since 2020. We can download the entire shapefile from the above link, and perform our filtering and summarising as follows:

    brisbane_parks <- sf::st_read("path/to/Park___Locations.shp") |>
      sf::st_make_valid() |>
      filter(POST_CODE == 4075)

    # Convert shapefile to a bounding box
    brisbane_parks_bbox <- brisbane_parks |> sf::st_bbox()

    # Find all occurrences of Trichoglossus chlorolepidotus in the bounding box in 2022
    lorikeet_brisbane <- galah_call() |>
      filter(scientificName == "Trichoglossus chlorolepidotus",
             year >= 2020) |>
      galah_geolocate(brisbane_parks_bbox, type = "bbox") |>
      collect()

    ## Data returned for bounding box:
    ## xmin = 152.96331 xmax = 152.99668 ymin = -27.57737 ymax = -27.51606

    # Filter records down to only those in the shapefile polygons
    lorikeet_brisbane |>
      # Create a point geometry based on the occurrence coordinates
      sf::st_as_sf(coords = c("decimalLongitude", "decimalLatitude"), crs = sf::st_crs(brisbane_parks), remove = FALSE) |>
      # identify which park each occurrence sits in with st_intersects()
      mutate(intersection = sf::st_intersects(geometry, brisbane_parks) |> as.integer(),
             park = ifelse(is.na(intersection), NA, brisbane_parks$PARK_NAME[intersection])) |>
      # Filter out occurrences that did not occur in a park
      filter(!is.na(park)) |>
      # Drop the geometry column
      sf::st_drop_geometry() |>
      # Summarise the top 10 parks for lorikeet sightings in 2022
      group_by(park) |>
      summarise(counts = n()) |>
      arrange(desc(counts)) |>
      head(10)

    ## # A tibble: 10 × 2
    ##    park                           counts
    ##    <chr>                           <int>
    ##  1 SHERWOOD ARBORETUM                276
    ##  2 NYUNDARE-BA PARK                  271
    ##  3 FAULKNER PARK                      51
    ##  4 STRICKLAND TERRACE PARK            33
    ##  5 FORT ROAD BUSHLAND                 31
    ##  6 GRACEVILLE RIVERSIDE PARKLANDS     31
    ##  7 BENARRAWA RESERVE                  19
    ##  8 NOSWORTHY PARK                     16
    ##  9 HORACE WINDOW RESERVE               9
    ## 10 GRACEVILLE AVENUE PARK              6

Some shapefiles cover large geographic areas with the caveat that even the bounding box doesn’t restrict the number of records to a value that can be downloaded easily. In this case, we recommend more nuances and detailed methods that can be performed using looping techniques. One of our ALA Labs blog posts, Hex maps for species occurrence data, has been written detailing how to approach larger problems such as this.