ssn_import: Import 'SSN' object

In SSN2: Spatial Modeling on Stream Networks

Import SSN object

Description

This function reads spatial data from a .ssn folder and creates an SSN object.

Usage

ssn_import(
  path,
  include_obs = TRUE,
  predpts,
  format_additive = FALSE,
  names_additive = NULL,
  overwrite = FALSE
)

Arguments

path	Filepath to the .ssn directory. See details.
include_obs	default = TRUE. Logical indicating whether observed sites should be included in the SSN object.
predpts	Vector of shapefile basenames for prediction sites found within the .ssn folder.
format_additive	Logical indicating whether the columns containing the addtive function values should be formated for SSN2. Default = FALSE.
names_additive	Character vector of column names in observed and prediction site datasets containing additive function values. Must be defined if format_additive = TRUE. Default = NULL.
overwrite	default = FALSE. If TRUE, overwrite existing binaryID.db files.

Details

The importSSN function imports spatial data from a .ssn folder to create an SSN object. The information contained in the .ssn folder can be generated using a number of proprietary and open source software tools:

• The Spatial Tools for the Analysis of River Systems (STARS) tools for ArcGIS Desktop versions 9.3x-10.8x (Peterson and Ver Hoef 2014). This custom ArcGIS toolset is designed to work with existing streams data in vector format.

• The openSTARS package (Kattwinkel et al. 2020) extends the functionality of the STARS toolset, which makes use of R and GRASS GIS. It is open source and designed to derive streams in raster format from a digital elevation model (DEM).

• The SSNbler package (currently in development as of September 2023) is an open source version of the STARS toolset, which makes use of the functionality found in the sf package to process streams data in vector format.

When spatial data are processed using one of these software tools, a .ssn directory is output which contains all of the spatial, topological and attribute data needed to fit a spatial statistical stream network model to streams data. This includes:

• An edges shapefile of lines that represent the stream network.

• A sites shapefile of points where observed data were collected on the stream network.

• Prediction sites shapefile(s) of locations where predictions will be made.

• netID.dat files for each distinct network, which store the topological relationships of the line segments in edges.

A more detailed description of the .ssn directory and its contents is provided in Peterson and Ver Hoef (2014).

The ssn_import imports the edges, observed sites, and prediction sites as sf data.frame objects. A new column named 'netgeom' is created to store important data that represents topological relationships in a spatial stream network model. These data are stored in character format, which is less likely to be inadvertantly changed by users. See ssn_get_netgeom for a more detailed description of the format and contents of 'netgeom'.

The information contained in the netID text files is imported into an SQLite database, binaryID.db, which is stored in the .ssn directory. This information is used internally by ssn_create_distmat, ssn_lm and ssn_glm to calculate the data necessary to fit a spatial statistical model to stream network data. If overwrite = TRUE (overwrite = FALSE is the default) and a binaryID.db file already exists within the .ssn directory, it will be overwriten when the SSN object is created.

At a minimum, an SSN object must always contain streams, which are referred to as edges. The SSN object would also typically contain a set of observed sites, where measurements have been collected and only one observed dataset is permitted. When include_obs=FALSE, an SSN object is created without observations. This option provides flexibility for users who would like to simulate data on a set of artifical sites on an existing stream network. Note that observation sites must be included in the SSN object in order to fit models using ssn_lm or ssn_glm. The SSN object may contain multiple sets of prediction points (or none), which are stored as separate shapefiles in the .ssn directory. The ssn_import_predpts function allows users to import additional sets of prediction sites to a an existing SSN object.

Value

ssn_import returns an object of class SSN, which is a list with four elements containing:

• edges: An sf data.frame containing the stream network, with an additional 'netgeom' column.

• obs: An sf data.frame containing observed site locations, with an additional 'netgeom' column. NA if include_obs = FALSE.

• preds: A list of sf data.frames containing prediction site locations. The names of the preds list correspond to the basenames of the prediction site shapefiles (without the .shp extension) specified in predpts. Empty list if predpts is not provided.

• path: The local file to the .ssn directory associated with the SSN object.

References

Kattwinkel, M., Szocs, E., Peterson, E., and Schafer, R.B. (2020) Preparing GIS data for analysis of stream monitoring data: The R package openSTARS. PLOS One 15(9), e0239237. Peterson, E., and Ver Hoef, J.M. (2014) STARS: An ArcGIS toolset used to calculate the spatial information needed to fit spatial statistical stream network models to stream network data. Journal of Statistical Software 56(2), 1–17.

See Also

ssn_get_netgeom

Examples

## Create local temporary copy of MiddleFork04.ssn found in
# SSN2/lsndata folder. Only necessary for this example.
copy_lsn_to_temp()

## Import SSN object with no prediction sites
mf04 <- ssn_import(paste0(tempdir(), "/MiddleFork04.ssn"),
  overwrite = TRUE
)

## Import SSN object with 3 sets of prediction sites
mf04p <- ssn_import(paste0(tempdir(), "/MiddleFork04.ssn"),
  predpts = c(
    "pred1km.shp",
    "CapeHorn.shp",
    "Knapp.shp"
  ),
  overwrite = TRUE
)

SSN2 documentation built on May 29, 2024, 4:41 a.m.