`rsyncrosim`: introduction to spatial data

This vignette will cover incorporating spatial data into SyncroSim models using the rsyncrosim package within the SyncroSim software framework. For an overview of SyncroSim and rsyncrosim, as well as a basic usage tutorial for rsyncrosim, see the Introduction to rsyncrosim vignette. To learn how to use iterations in the rsyncrosim interface, see the rsyncrosim: introduction to uncertainty vignette. To learn how to link models using pipelines in the rsyncrosim interface, see the rsyncrosim: introduction to pipelines vignette.

SyncroSim Package: helloworldSpatial

To demonstrate how to use spatial data in the rsyncrosim interface, we will be using the helloworldSpatial SyncroSim package. helloworldSpatial was designed to be a simple package to show off some key functionalities of SyncroSim, including the ability to use both spatial and non-spatial data.

The package takes 3 inputs, mMean, mSD, and a spatial raster file of intercept (b) values. For each iteration, a value m, representing the slope, is sampled from a normal distribution with mean of mMean and standard deviation of mSD. These values are run through 2 models to produce both spatial and non-spatial outputs.

Infographic of helloworldSpatial package

For more details on the different features of the helloworldSpatial SyncroSim package, consult the SyncroSim Enhancing a Package: Integrating Spatial Data tutorial.

Setup

Install SyncroSim

Before using rsyncrosim you will first need to download and install the SyncroSim software. Versions of SyncroSim exist for both Windows and Linux.

Note: this tutorial was developed using rsyncrosim version 2.0. To use rsyncrosim version 2.0 or greater, SyncroSim version 3.0 or greater is required.

Installing and loading R packages

You will need to install the rsyncrosim R package, either using CRAN or from the rsyncrosim GitHub repository. Versions of rsyncrosim are available for both Windows and Linux. You may need to install the terra package from CRAN as well.

In a new R script, load the necessary packages. This includes the rsyncrosim and terra R packages.

# Load R packages
library(rsyncrosim)  # package for working with SyncroSim
library(terra)       # package for working with spatial data

Connecting R to SyncroSim using session()

Finish setting up the R environment for the rsyncrosim workflow by creating a SyncroSim Session object. Use the session() function to connect R to your installed copy of the SyncroSim software.

mySession <- session("path/to/install_folder")      # Create a Session based SyncroSim install folder
mySession <- session()                              # Using default install folder (Windows only)
mySession                                           # Displays the Session object

## class               : Session
## filepath [character]: C:/Program Files/SyncroSim Studio
## silent [logical]    : TRUE
## printCmd [logical]  : FALSE
## condaFilepath [NULL]:

Use the version() function to ensure you are using the latest version of SyncroSim.

version(mySession)

## [1] "3.0.9"

Installing SyncroSim packages using installPackage()

Install helloworldSpatial using the rynscrosim function installPackage(). This function takes a package name as input and then queries the SyncroSim package server for the specified package.

# Install helloworldSpatial
installPackage("helloworldSpatial")

## Package <helloworldSpatial v2.0.0> installed

helloworldSpatial should now be included in the package list returned by the packages() function in rsyncrosim:

# Get list of installed packages
packages()

##                name version
## 1 helloworldSpatial   2.0.0
##                                                     description
## 1 Example demonstrating how to use spatial data with an R model
##                                                                                 location
## 1 C:\\Users\\birch\\AppData\\Local\\SyncroSim Studio\\Packages\\helloworldSpatial\\2.0.0
##   status
## 1     OK

Create a modeling workflow

When creating a new modeling workflow from scratch, we need to create objects of the following scopes:

• Library
• Projects
• Scenarios

For more information on these scopes, see the Introduction to rsyncrosim vignette.

Set up library, project, and scenario

# Create a new library
myLibrary <- ssimLibrary(name = "helloworldLibrary.ssim",
                         session = mySession,
                         package = "helloworldSpatial",
                         overwrite = TRUE)

## Package <helloworldSpatial v2.0.0> added

# Open the default project
myProject = rsyncrosim::project(ssimObject = myLibrary, project = "Definitions")

# Create a new scenario (associated with the default project)
myScenario = scenario(ssimObject = myProject, scenario = "My spatial scenario")

View model inputs using datasheet()

View the datasheets associated with your new scenario using the datasheet() function from rsyncrosim.

# View all datasheets associated with a library, project, or scenario
datasheet(myScenario)

##       scope                                    name             displayName
## 23 scenario                  core_DistributionValue           Distributions
## 24 scenario              core_ExternalVariableValue      External Variables
## 25 scenario                           core_Pipeline                Pipeline
## 26 scenario             core_SpatialMultiprocessing Spatial Multiprocessing
## 27 scenario        helloworldSpatial_InputDatasheet                  Inputs
## 28 scenario helloworldSpatial_IntermediateDatasheet    Intermediate Outputs
## 29 scenario       helloworldSpatial_OutputDatasheet                 Outputs
## 30 scenario            helloworldSpatial_RunControl             Run Control

From the list of datasheets above, we can see that there are four datasheets specific to the helloworldSpatial package, including an Inputs datasheet, an Intermediate Outputs datasheet, an Outputs datasheet, and a Run Control datasheet.

Configure model inputs using datasheet() and addRow()

Currently our input scenario datasheets are empty! We need to add some values to our Inputs datasheet, Run Control datasheet, and Pipeline datasheet so we can run our model.

Inputs datasheet

First, assign the contents of the Inputs datasheet to a new data frame variable using datasheet(), then check the columns that need input values.

# Load Inputs datasheet to a new R data frame
myInputDataframe <- datasheet(myScenario,
                              name = "helloworldSpatial_InputDatasheet")

# Check the columns of the input data frame
str(myInputDataframe)

## 'data.frame':    0 obs. of  3 variables:
##  $ mMean              : num 
##  $ mSD                : num 
##  $ InterceptRasterFile: chr

The Inputs datasheet requires three values:

• mMean : the mean of a normal distribution that will determine the slope of the linear equation.
• mSD : the standard deviation of a normal distribution that will determine the slope of the linear equation.
• InterceptRasterFile : the file path to a raster image, in which each cell of the image will be an intercept in the linear equation.

In this example, the external file we are using for the InterceptRasterFile is a simple 5x5 raster TIF file generated using the raster package in R. The file used in this vignette can be found here.

InterceptRasterFile input image

Add these values to a new data frame, then use the addRow() function from rsyncrosim to update the input data frame

# Create input data and add it to the input data frame
myInputRow <- data.frame(mMean = 0, 
                         mSD = 4,
                         InterceptRasterFile = "path/to/input-raster.tif")
myInputDataframe <- addRow(myInputDataframe, myInputRow)

# Check values
myInputDataframe

##   mMean mSD      InterceptRasterFile
## 1     0   4 path/to/input-raster.tif

Finally, save the updated R data frame to a SyncroSim datasheet using saveDatasheet().

# Save input R data frame as a SyncroSim datasheet
saveDatasheet(ssimObject = myScenario, 
              data = myInputDataframe,
              name = "helloworldSpatial_InputDatasheet")

## Datasheet <helloworldSpatial_InputDatasheet> saved

Run Control datasheet

The Run Control datasheet sets the number of iterations and the minimum and maximum time steps for our model. We’ll assign the contents of this datasheet to a new data frame variable as well and then add then update the information in the data frame using addRow(). We need to specify data for the following four columns:

• MaximumIteration : total number of iterations to run the model for.
• MinimumTimestep : the starting time point of the simulation.
• MaximumTimestep : the end time point of the simulation.

Note: A fourth hidden column, MinimumIteration, also exists in the Run Control datasheet (default=1).

# Load Run Control datasheet to an R data frame
runSettings <- datasheet(myScenario, name = "helloworldSpatial_RunControl")

# Check the columns of the Run Control data frame
str(runSettings)

## 'data.frame':    0 obs. of  3 variables:
##  $ MinimumTimestep : num 
##  $ MaximumTimestep : num 
##  $ MaximumIteration: num

# Create Run Control data and add it to the Run Control data frame
runSettingsRow <- data.frame(MaximumIteration = 5,
                             MinimumTimestep = 1,
                             MaximumTimestep = 10)

runSettings <- addRow(runSettings, runSettingsRow)

# Check values
runSettings

##   MinimumTimestep MaximumTimestep MaximumIteration
## 1               1              10                5

# Save Run Control R data frame to a SyncroSim datasheet
saveDatasheet(ssimObject = myScenario, 
              data = runSettings,
              name = "helloworldSpatial_RunControl")

## Datasheet <helloworldSpatial_RunControl> saved

Pipeline datasheet

The helloworldSpatial package uses pipelines to link the output of one model to the input of a second model. To learn more about pipelines, see the rsyncrosim: introduction to pipelines vignette and the SyncroSim Enhancing a Package: Linking Models tutorial.

To implement pipelines in our package, we need to specify the order in which to run the transformers (i.e. models) in our pipeline by editing the Pipeline datasheet. The Pipeline datasheet is part of the built-in SyncroSim core, so we access it using the “core_” prefix with the datasheet() function.

From viewing the structure of the Pipeline datasheet we know that the StageNameId is a factor with two levels:

• Hello World Spatial 1 (R)
• Hello World Spatial 2 (R)

We will set the data for this datasheet such that Hello World Spatial 1 (R) is run first, then Hello World Spatial 2 (R). This way, the output from Hello World Spatial 1 (R) is used as the input for Hello World Spatial 2 (R).

# Load Pipeline datasheet to an R data frame
myPipelineDataframe <- datasheet(myScenario, name = "core_Pipeline")

# Check the columns of the Pipeline data frame
str(myPipelineDataframe)

## 'data.frame':    0 obs. of  2 variables:
##  $ StageNameId: Factor w/ 2 levels "Hello World Spatial 1 (R)",..: 
##  $ RunOrder   : num

# Create Pipeline data and add it to the Pipeline data frame
myPipelineRow <- data.frame(StageNameId = c("Hello World Spatial 1 (R)", 
                                            "Hello World Spatial 2 (R)"),
                            RunOrder = c(1, 2))

myPipelineDataframe <- addRow(myPipelineDataframe, myPipelineRow)

# Check values
myPipelineDataframe

##                 StageNameId RunOrder
## 1 Hello World Spatial 1 (R)        1
## 2 Hello World Spatial 2 (R)        2

# Save Pipeline R data frame to a SyncroSim datasheet
saveDatasheet(ssimObject = myScenario, data = myPipelineDataframe,
              name = "core_Pipeline")

## Datasheet <core_Pipeline> saved

Run scenarios

Setting run parameters with run()

We will now run our scenario using the run() function in rsyncrosim.

If we have a large model and we want to parallelize the run using multiprocessing, we can modify the library-scoped “core_Multiprocessing” datasheet. Since we are using five iterations in our model, we will set the number of jobs to five so each multiprocessing core will run a single iteration.

# Load list of available library-scoped datasheets
datasheet(myLibrary)

##      scope                      name             displayName
## 1  library               core_Backup                  Backup
## 2  library             core_JlConfig                   Julia
## 3  library      core_Multiprocessing         Multiprocessing
## 4  library               core_Option                 Options
## 5  library core_ProcessorGroupOption Processor Group Options
## 6  library  core_ProcessorGroupValue  Processor Group Values
## 7  library             core_PyConfig                  Python
## 8  library              core_RConfig                       R
## 9  library              core_Setting                Settings
## 10 library        core_SpatialOption         Spatial Options
## 11 library            core_SysFolder                 Folders

# Load the library-scoped multiprocessing datasheet
multiprocess <- datasheet(myLibrary, name = "core_Multiprocessing")

# Check required inputs
str(multiprocess)

## 'data.frame':    1 obs. of  4 variables:
##  $ EnableMultiprocessing  : logi FALSE
##  $ MaximumJobs            : num 7
##  $ EnableMultiScenario    : logi FALSE
##  $ EnableCopyExternalFiles: logi NA

# Enable multiprocessing
multiprocess$EnableMultiprocessing <- TRUE

# Set maximum number of jobs to 5
multiprocess$MaximumJobs <- 5

# Save multiprocessing configuration
saveDatasheet(ssimObject = myLibrary, 
              data = multiprocess, 
              name = "core_Multiprocessing")

## Datasheet <core_Multiprocessing> saved

Now, when we run our scenario, it will use the desired multiprocessing configuration.

# Run the first scenario we created
myResultScenario <- run(myScenario)

## [1] "Running scenario [1] My spatial scenario"

After the scenario has been run, a results scenario is created that contains results in the output datasheets.

View results

The next step is to view the output datasheets added to the result scenario when it was run.

Viewing non-spatial results with datasheet()

First, we will view the non-spatial results within the results scenarios. For each step in the pipeline, We can load the result tables using the datasheet() function.

# Load results of first transformer in the pipeline
resultsSummary <- datasheet(myResultScenario,
                            name = "helloworldSpatial_IntermediateDatasheet")

# View results table of first transformer in the pipeline
head(resultsSummary)

##   Iteration Timestep         y        OutputRasterFile
## 1         1        1  67.68919 rasterMap_iter1_ts1.tif
## 2         1        2 139.71237 rasterMap_iter1_ts2.tif
## 3         1        3 211.73555 rasterMap_iter1_ts3.tif
## 4         1        4 283.75874 rasterMap_iter1_ts4.tif
## 5         1        5 355.78192 rasterMap_iter1_ts5.tif
## 6         1        6 427.80510 rasterMap_iter1_ts6.tif

# Load results of second transformer in the pipeline
resultsSummary2 <- datasheet(myResultScenario,
                             name = "helloworldSpatial_OutputDatasheet")

# View results table of second transformer in the pipeline
head(resultsSummary2)

##   Iteration Timestep       yCum
## 1         1        1   67.68919
## 2         1        2  207.40156
## 3         1        3  419.13711
## 4         1        4  702.89585
## 5         1        5 1058.67777
## 6         1        6 1486.48287

From viewing these datasheets, we can see that the spatial output is contained within the IntermediateDatasheet, in the column called OutputRasterFile.

Viewing spatial results with datasheetSpatRaster()

For spatial results, we want to load the results as raster images. To do this, we will use the datasheetSpatRaster() function from rsyncrosim. The first argument is the result scenario object. Next, we specify the name of the datasheet containing raster images using the datasheet argument, and the column pertaining to the raster images using the column argument. The results contain many raster images, since we have a raster for each combination of iteration and timestep. We can use the iteration and timestep arguments to specify a single raster image or a subset of raster images we want to view.

# Load raster files for first result scenario with timestep and iteration
rasterMaps <- datasheetSpatRaster(
  myResultScenario,
  datasheet = "helloworldSpatial_IntermediateDatasheet",
  column = "OutputRasterFile",
  iteration = 1,
  timestep = 5
  )

# View results
rasterMaps

## class       : SpatRaster 
## dimensions  : 5, 5, 1  (nrow, ncol, nlyr)
## resolution  : 0.4, 0.4  (x, y)
## extent      : -1, 1, -1, 1  (xmin, xmax, ymin, ymax)
## coord. ref. : lon/lat WGS 84 (EPSG:4326) 
## source      : rasterMap_iter1_ts5.tif 
## name        : rasterMap_iter1_ts5 
## min value   :            11.77882 
## max value   :            16.52308

plot(rasterMaps[[1]])

Viewing spatial results in SyncroSim Studio

To create maps using the results scenario we just generated, open the current library in SyncroSim Studio and sync the updates from rsyncrosim using the “refresh” button in the upper toolbar (circled in red below). All the updates made in rsyncrosim should appear in SyncroSim Studio. We can now add the results scenario to the Results Viewer and create our maps. For more information on generating map in SyncroSim Studio, see the SyncroSim tutorials on creating and customizing maps

Using rsyncrosim with SyncroSim Studio to map spatial data