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alan-barzilay avatar alan-barzilay commented on August 15, 2024 1

Thank you so much for your help! I managed to make it work so I will close my issue :)

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munterfi avatar munterfi commented on August 15, 2024

Hi @alan-barzilay, thanks for your interest in the eRTG3D package.

Since movement in 2-D is a special case of movement in 3-D, the algorithm also supports two-dimensional simulations (see the Trajectory simulations in 2-D vignette). To create trajectories in 2-D just set the third dimension to zero:

library(eRTG3D)

# Dummy dataset
head(niclas)
#>         x       y        z
#> 1 2556476 1188336 1283.736
#> 2 2558565 1189297 1359.580
#> 3 2560477 1189861 1369.713
#> 4 2562431 1190668 1391.752
#> 5 2563809 1190657 1806.566
#> 6 2567940 1192930 1764.062

# Set z dim to zero
niclas$z <- 0

And then simulate the trajectories using the wrapper function reproduce.track.3d:

sim <- reproduce.track.3d(niclas, n.sim = 100, parallel = TRUE)
#>   |TLD cube dimensions: 9 x 1 x 3
#>   |Simulate UERW with 46500 steps
#>   |======================================================================| 100%
#>   |Elapsed time: 1.7s
#>   |Extracting Q probabilities for 31 steps
#>   |ForkCluster (unix): Running 15 processes in parallel
#>   |======================================================================| 100%
#>   |Elapsed time: 0.5s
#>   |Simulate 100 CERWs with 31 steps
#>   |ForkCluster (unix): Running 15 processes in parallel
#>   |======================================================================| 100%
#>   |Elapsed time: 2.1s
#>   |Dead end tracks removed (n = 30, proportion: 0.3)

Visualize results:

plot3d.densities(niclas, sim)

dens

plot2d(niclas, sim, titleText = "2-D trajectory simulation")

plot2d

For further information on the individual steps to simulate trajectories check also the standard workflow vignette of the package.

Session info 
devtools::session_info()
#> ─ Session info ───────────────────────────────────────────────────────────────
#>  setting  value                       
#>  version  R version 4.0.5 (2021-03-31)
#>  os       macOS Big Sur 10.16         
#>  system   x86_64, darwin17.0          
#>  ui       X11                         
#>  language (EN)                        
#>  collate  en_US.UTF-8                 
#>  ctype    en_US.UTF-8                 
#>  tz       Europe/Zurich               
#>  date     2021-05-16                  
#> 
#> ─ Packages ───────────────────────────────────────────────────────────────────
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#>  cachem        1.0.4   2021-02-13 [1] CRAN (R 4.0.2)
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#>  ellipsis      0.3.2   2021-04-29 [1] CRAN (R 4.0.2)
#>  eRTG3D      * 0.6.3   2020-07-26 [1] CRAN (R 4.0.2)
#>  evaluate      0.14    2019-05-28 [1] CRAN (R 4.0.0)
#>  fansi         0.4.2   2021-01-15 [1] CRAN (R 4.0.2)
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#>  memoise       2.0.0   2021-01-26 [1] CRAN (R 4.0.2)
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#> [1] /Library/Frameworks/R.framework/Versions/4.0/Resources/library

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alan-barzilay avatar alan-barzilay commented on August 15, 2024

@munterfinger Thank you for your detailed response! I don't know how I missed that vignette, thank you.

My understanding of eRTG3D is that it can generate artificial trajectories given an example trajectory and information about the environment, right? For my use case I just need to generate random trajectories between 2 points, the idea is to simply simulate someone going from point A to point B so I don't really have an example trajectory to use as a base and the environment could be anything.

From your experience with this project, do you believe it could be a good fit for my use case or should I look for another solution? I originally mistook this tool as an implementation of this paper that seems closer to my objective, but I would rather not have to implement something from scratch haha
I was thinking about creating a simple trajectory going straight from point A to point B to test with eRTG3D, but it seems this would create trajectories that are to much "well behaved".

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munterfi avatar munterfi commented on August 15, 2024

Hi, if I understand correctly, you have no ground truth movement data and thus no movement properties of the mover?

Therefore you have to define the properties of the mover, which could be done by correlated random walks. In the example below a correlated random walk with 0.9 correlation in turn angle and a correlation of 1 in lift angle (only 2-D) is generated. You can vary the movement behavior by changing the turn angle correlation and mean step length (meanStep):

library(eRTG3D)
set.seed(1234)

# Define your origin A and destination B
orig <- c(50,20,0)
dest <- c(100, 50, 0)
dist_total <- sqrt(sum((dest-orig)^2))

# Correlated random walk 
crw <- sim.crw.3d(
  nStep = 1000,
  rTurn = 0.9,
  rLift = 1,
  meanStep = 5,
  start = orig
)

# Set third dim to zero
crw$z <- 0
plot3d.densities(crw)

unnamed-chunk-1-1

plot2d(crw)

unnamed-chunk-1-2

Now extract the movement properties of the mover:

# Movement characteristics (P)
P <- get.track.densities.3d(crw)
#>   |TLD cube dimensions: 25 x 1 x 20
plot3d.tldCube(P$tldCube)

p

As the algorithm assumes constant time between the individual locations (not often the case in real GPS data…) you can easily extract the number of steps needed to reach the destination with the given movement properties using a distance approach (alternative would be time). In addition allow the mover some more flexibility by multiplying the number of steps by a factor greater 1 (increases the number of successful simulated trajectories):

# Get number of steps
flexibility <- 1.1
crw <- track.properties.3d(crw)
dist_per_step <- mean(crw$d, na.rm = TRUE)
sim_locs <- round(dist_total/dist_per_step)*flexibility

Set the boundary conditions: Initial azimuth and lift angle (pi/2 in the 2-D case):

# Boundary conditions
a0 <- crw$a[1]
g0 <- crw$g[1]

Then extract the attraction term Q for ‘sim_locs’ steps from a long (f times the sim_locs) unconditional empirical random walk with movement characteristics P:

f <- 1500
uerw <- sim.uncond.3d(
  sim_locs*f,
  start = orig, 
  a0 = a0,
  g0 = g0,
  densities = P)
#>   |Simulate UERW with 33000 steps
#>   |======================================================================| 100%
#>   |Elapsed time: 1.9s
Q <- qProb.3d(uerw, sim_locs, parallel = FALSE)
#>   |Extracting Q probabilities for 22 steps
#>   |======================================================================| 100%
#>   |Elapsed time: 0.6s

Finally you can simulate the conditional empirical random walks between A and B:

# Conditional empirical random walks between A and B
cerw_list <- n.sim.cond.3d(
  n.sim = 100, sim_locs,
  start = orig, end = dest,
  a0 = a0, g0 = g0,
  densities = P, qProbs = Q,
  parallel = TRUE
)
#>   |Simulate 100 CERWs with 22 steps
#>   |ForkCluster (unix): Running 15 processes in parallel
#>   |======================================================================| 100%
#>   |Elapsed time: 2.1s

# Remove trajectories, which did not reach the destination
cerw_list <- filter.dead.ends(cerw_list)
#>   |Dead end tracks removed (n = 40, proportion: 0.4)

# Visualize result
plot3d.densities(crw, cerw_list)

unnamed-chunk-6-1

plot2d(cerw_list)

unnamed-chunk-6-2

Let me know if this solves the issue?

Session info 
devtools::session_info()
#> ─ Session info ───────────────────────────────────────────────────────────────
#>  setting  value                       
#>  version  R version 4.0.5 (2021-03-31)
#>  os       macOS Big Sur 10.16         
#>  system   x86_64, darwin17.0          
#>  ui       X11                         
#>  language (EN)                        
#>  collate  en_US.UTF-8                 
#>  ctype    en_US.UTF-8                 
#>  tz       Europe/Zurich               
#>  date     2021-05-16                  
#> 
#> ─ Packages ───────────────────────────────────────────────────────────────────
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