Incorporates an sf object containing features
with POINT geometry into a landscape network (LSN), which is a
topological data model of streams/rivers represented as a
directional graph embedded in 2-D geographic space. Point
locations are 'snapped' to the closest edge location and new
information is generated describing the geographic and
topological location relative to other features in the LSN.
sites_to_lsn(
sites,
edges,
snap_tolerance,
save_local = TRUE,
lsn_path = NULL,
file_name = NULL,
overwrite = FALSE,
verbose = TRUE
)An sf object with POINT geometry, typically
representing observed data or prediction locations.
An sf object with LINESTING geometry created
using lines_to_lsn.
Numeric distance in map units >= 0. Sites that
are <= this distance from an edge feature will snapped to the
closest location on the edge. When the distance between the site
and all edges is > snap_tolerance, the point feature is not
snapped or included in the lsn_path.
Logical indicating whether the outputs should be
saved to a local directory in GeoPackage format. Defaults to
TRUE.
Pathname to the LSN. This is typically a directory created by
lines_to_lsn. Required if save_local = TRUE.
Filename for output sites, which are saved to lsn_path
in GeoPackage format (must include the .gpkg extension). Required if save_local = TRUE.
Logical indicating whether the outputs saved to
lsn_path should overwrite existing files if they
exist. Defaults to FALSE.
Logical indicating whether progress messages should
be printed to the console. Defaults to TRUE
An sf object with POINT geometry containing the
features from sites that were found within the
snap_tolerance distance to the closest edge. In addition to
the original columns in sites, three new columns are
added: rid, ratio, and snapdist (see Details). If
save_local = TRUE, the sf object is also saved to
lsn_path.
The sites_to_lsn function is used to incorporate
observed and prediction sites into the LSN. The output is an
sf object with POINT geometry, which contains only the
point features from sites that were found less than the
snap_tolerance distance from the closest edge
feature. When a sites point feature meets these
requirements, it is moved (i.e. snapped) to the closest location
on an edge feature. Three new columns are also added: rid, ratio,
and snapdist. The rid column contains the rid for the edge the
site has been snapped to. The second column, ratio,
represents the proportional length of the edge found between the
downstream end node for the edge and the updated/snapped site
location. The snapdist is the distance in map units that the site
was moved. If the distance between a point feature and the
closest edge is greater than or equal to the
snap_tolerance, the feature is not included in the output.
The snap_tolerance must always be \(\ge 0\) and must be large
enough to snap all of the point features to the edges. Using
snap_tolerance = 0 is not recommended, even when the
sites features intersect the edge features. Instead, a
very small snap_tolerance value is recommended to ensure
that differences in the precision of the x and y coordinates and
the line location do not result in unsnapped point features.
Note that the sites and edges
must have the same projection.
# Get temporary directory, where the example LSN will be stored
# locally.
temp_dir <- tempdir()
# Build the LSN. When working with your own data, lsn_path will be
# a local folder of your choice rather than a temporary directory.
edges<- lines_to_lsn(
streams = MF_streams,
lsn_path = temp_dir,
snap_tolerance = 1,
check_topology = FALSE,
overwrite = TRUE,
verbose = FALSE
)
# # Incorporate observed sites, MF_obs, into LSN
obs<- sites_to_lsn(
sites = MF_obs,
edges = edges,
save_local = FALSE,
snap_tolerance = 100,
overwrite = TRUE,
verbose = FALSE
)
# Notice that 3 new columns have been added to obs
names(obs)
#> [1] "STREAMNAME" "COMID" "AREAWTMAP" "SLOPE" "ELEV_DEM"
#> [6] "Source" "Summer_mn" "MaxOver20" "C16" "C20"
#> [11] "C24" "FlowCMS" "AirMEANc" "AirMWMTc" "rcaAreaKm2"
#> [16] "h2oAreaKm2" "geometry" "rid" "ratio" "snapdist"
# Incorporate prediction dataset, MF_preds, into LSN
preds<- sites_to_lsn(sites = MF_preds,
edges = edges,
save_local = FALSE,
snap_tolerance = 1,
overwrite = TRUE,
verbose = FALSE
)