bcmaps layers and point data

2024-01-23

We often want to be able to add point data to a map. This vignette will run through a simple example of converting a data.frame of latitude/longitude pairs into an sf points layer, and reprojecting it so that the points can be overlaid on a layer from the bcmaps package.

For this example, say we have done surveys for a species across B.C., and we want to be able to add the presences and absences to a map of British Columbia.

First, load the packages we will need:

library(sf)
library(bcmaps)

We will create a mock dataframe of locations of species presence/absences (in real life these would probably be in a csv or Excel file that we would import):

set.seed(42)
spp <- data.frame(site_num = LETTERS[1:10], spp_present = sample(c("yes", "no"), 10, replace = TRUE),
                 lat = runif(10, 49, 60), long = runif(10, -128, -120),
                 stringsAsFactors = FALSE)
head(spp)
#>   site_num spp_present      lat      long
#> 1        A         yes 54.03516 -120.7677
#> 2        B         yes 56.91023 -126.8903
#> 3        C         yes 59.28139 -120.0889
#> 4        D         yes 51.80972 -120.4267
#> 5        E          no 54.08522 -127.3405
#> 6        F          no 59.34016 -123.8863

Next we convert this to an sf points layer using the sf package:

spp <- st_as_sf(spp, coords = c("long", "lat"))
summary(spp)
#>    site_num         spp_present           geometry 
#>  Length:10          Length:10          POINT  :10  
#>  Class :character   Class :character   epsg:NA: 0  
#>  Mode  :character   Mode  :character
plot(spp["spp_present"])

Map of randomly generated points of two colours representing species absent, and species present.

In order to overlay these points on other spatial layers, they need to use the same Coordinate Reference System (CRS). Coordinate systems and projections in R can be confusing. There is a great reference on using them here: https://www.nceas.ucsb.edu/sites/default/files/2020-04/OverviewCoordinateReferenceSystems.pdf.

We know that our occurrence data are in decimal degrees in NAD83, so we will assign the corresponding proj4string. You can specify the projection using a full proj4 string ("+proj=longlat +ellps=GRS80 +towgs84=0,0,0,0,0,0,0 +no_defs") or the EPSG code (4269). We will use the EPSG code because it’s shorter and less likely to make a typo with.

spp <- st_set_crs(spp, 4326)

All of the maps in the bcmaps package are in BC Albers projection (EPSG:3005), which is the B.C. government standard. It is an ‘equal area’ projection, meaning that the size of areas is not distorted, and thus is suitable for analyses on large areas.

If we look at the proj4string for bc_bound_layer and our spp_df, we see that they are different:

bc_bound_layer <- bc_bound()
st_crs(bc_bound_layer)
#> Coordinate Reference System:
#>   User input: NAD83 / BC Albers 
#>   wkt:
#> PROJCRS["NAD83 / BC Albers",
#>     BASEGEOGCRS["NAD83",
#>         DATUM["North American Datum 1983",
#>             ELLIPSOID["GRS 1980",6378137,298.257222101,
#>                 LENGTHUNIT["metre",1]]],
#>         PRIMEM["Greenwich",0,
#>             ANGLEUNIT["degree",0.0174532925199433]],
#>         ID["EPSG",4269]],
#>     CONVERSION["British Columbia Albers",
#>         METHOD["Albers Equal Area",
#>             ID["EPSG",9822]],
#>         PARAMETER["Latitude of false origin",45,
#>             ANGLEUNIT["degree",0.0174532925199433],
#>             ID["EPSG",8821]],
#>         PARAMETER["Longitude of false origin",-126,
#>             ANGLEUNIT["degree",0.0174532925199433],
#>             ID["EPSG",8822]],
#>         PARAMETER["Latitude of 1st standard parallel",50,
#>             ANGLEUNIT["degree",0.0174532925199433],
#>             ID["EPSG",8823]],
#>         PARAMETER["Latitude of 2nd standard parallel",58.5,
#>             ANGLEUNIT["degree",0.0174532925199433],
#>             ID["EPSG",8824]],
#>         PARAMETER["Easting at false origin",1000000,
#>             LENGTHUNIT["metre",1],
#>             ID["EPSG",8826]],
#>         PARAMETER["Northing at false origin",0,
#>             LENGTHUNIT["metre",1],
#>             ID["EPSG",8827]]],
#>     CS[Cartesian,2],
#>         AXIS["(E)",east,
#>             ORDER[1],
#>             LENGTHUNIT["metre",1]],
#>         AXIS["(N)",north,
#>             ORDER[2],
#>             LENGTHUNIT["metre",1]],
#>     USAGE[
#>         SCOPE["Province-wide spatial data management."],
#>         AREA["Canada - British Columbia."],
#>         BBOX[48.25,-139.04,60.01,-114.08]],
#>     ID["EPSG",3005]]
st_crs(spp)
#> Coordinate Reference System:
#>   User input: EPSG:4326 
#>   wkt:
#> GEOGCRS["WGS 84",
#>     ENSEMBLE["World Geodetic System 1984 ensemble",
#>         MEMBER["World Geodetic System 1984 (Transit)"],
#>         MEMBER["World Geodetic System 1984 (G730)"],
#>         MEMBER["World Geodetic System 1984 (G873)"],
#>         MEMBER["World Geodetic System 1984 (G1150)"],
#>         MEMBER["World Geodetic System 1984 (G1674)"],
#>         MEMBER["World Geodetic System 1984 (G1762)"],
#>         MEMBER["World Geodetic System 1984 (G2139)"],
#>         ELLIPSOID["WGS 84",6378137,298.257223563,
#>             LENGTHUNIT["metre",1]],
#>         ENSEMBLEACCURACY[2.0]],
#>     PRIMEM["Greenwich",0,
#>         ANGLEUNIT["degree",0.0174532925199433]],
#>     CS[ellipsoidal,2],
#>         AXIS["geodetic latitude (Lat)",north,
#>             ORDER[1],
#>             ANGLEUNIT["degree",0.0174532925199433]],
#>         AXIS["geodetic longitude (Lon)",east,
#>             ORDER[2],
#>             ANGLEUNIT["degree",0.0174532925199433]],
#>     USAGE[
#>         SCOPE["Horizontal component of 3D system."],
#>         AREA["World."],
#>         BBOX[-90,-180,90,180]],
#>     ID["EPSG",4326]]

So let’s transform the dataframe of species presence/absences into the same CRS as bc_bound_layer:

spp <- transform_bc_albers(spp)

Now we can overlay the points on the provincial boundary dataset:

plot(spp["spp_present"], graticule = TRUE, reset = FALSE)
plot(st_geometry(bc_bound_layer), add = TRUE)

The same random coloured points as before, now overlaid on a map of British Columbia.

Now we want to know what ecoregion of the province each of these observations was in. We can use the ecoregions data from bcmaps, and the st_join function from the sf package to extract ecoregions from the point data and add that information:

ecoreg <- ecoregions(ask = FALSE)
st_join(spp, ecoreg["ECOREGION_NAME"])
#> Simple feature collection with 10 features and 3 fields
#> Geometry type: POINT
#> Dimension:     XY
#> Bounding box:  xmin: 912519.3 ymin: 600149.1 xmax: 1383324 ymax: 1642418
#> Projected CRS: NAD83 / BC Albers
#>    site_num spp_present                   ECOREGION_NAME                 geometry
#> 1         A         yes CENTRAL CANADIAN ROCKY MOUNTAINS  POINT (1341578 1016164)
#> 2         B         yes    BOREAL MOUNTAINS AND PLATEAUS POINT (945861.3 1324757)
#> 3         C         yes                HAY-SLAVE LOWLAND  POINT (1337001 1602713)
#> 4         D         yes               COLUMBIA HIGHLANDS POINT (1383324 770393.7)
#> 5         E          no       EASTERN HAZELTON MOUNTAINS POINT (912519.3 1009950)
#> 6         F          no                   MUSKWA PLATEAU  POINT (1120431 1596971)
#> 7         G          no                   MUSKWA PLATEAU  POINT (1063173 1642418)
#> 8         H          no        THOMPSON-OKANAGAN PLATEAU POINT (1373357 600149.1)
#> 9         I         yes                     FRASER BASIN  POINT (1102484 1025860)
#> 10        J          no CENTRAL CANADIAN ROCKY MOUNTAINS  POINT (1297769 1139361)