Introducing sftrack: A framework for movement data in R

Matthew Boone, Rocio Joo, Mathieu Basille
Ecology Society of America conference 2020

History of movement data


  • Tracking data
    • discrete points
    • home range analysis, resource selection functions, etc…
  • Movement data
    • series of steps
    • Hidden-Markov Models, step selection functions, etc…

History of movement data


  • Proliferation of study in the last 20 years.
  • Devices: gps, satellite, accelerometer, light loggers, cameras, telemetry, etc…
  • Data sizes increasing rapidly with more sensors
figJoo, R., Picardi, S., Boone, M., Clay, T., Patrick, S., Romero-Romero, V., Basille, M., A Decade of Movement Ecology, 2020. arXiv:2006.00110

Software used in the field of Movement Ecology

1Joo, R., Picardi, S., Boone, M., Clay, T., Patrick, S., Romero-Romero, V., Basille, M., A Decade of Movement Ecology, 2020. arXiv:2006.00110

Depends and Suggests of movement packages in R

figJoo, R., Boone, M.E., Clay T.A., Patrick S.C., Clusella-Trullas, S., Basille, M. Navigating through the R packages for movement, Journal of Animal Ecology. 2019. doi: 10.1111/1365-2656.13116

Need for a new defining class

  • Many classes to work with.
  • Methods written in S4.
  • Documentation of classes and methods can be limited.
  • more time = less research

`sf` package

Pebesma, E. Simple Features for R: Standardized Support for Spatial Vector Data, The R Journal. 2019. doi: 10.32614/RJ-2018-009

simple features framework —>

easy mapping and spatial analysis

Goals for `sftrack`

Build a movement framework that the community will buy into

Flexible enough to contain ever expanding structures of movement models

Make methods and documentation transparent

Fully compatible with sf, tidyverse, and ggplot

Goals for `sftrack`

Goals for `sftrack`

Our goals are not to replace any of these existing packages. But to create a class that is flexible enough to be used by all these packages.

Format of an `sftrack`


  • S3, data.frame model
  • sf geometry column
  • grouping class with a required 'id' field
  • time (as.POSIXct or integer)
  • error column (optional)

`sftrack` - sftraj class

`sftrack` - reading & inputting

  animal_id          timestamp           longitude         latitude         fix           
 Length:17447       Length:17447       Min.   :-80.89   Min.   : 0.00   Length:17447      
 Class :character   Class :character   1st Qu.:-80.28   1st Qu.: 0.00   Class :character  
 Mode  :character   Mode  :character   Median :-80.27   Median :26.07   Mode  :character  
                                       Mean   :-54.89   Mean   :17.83                     
                                       3rd Qu.:  0.00   3rd Qu.:26.07                     
                                       Max.   :  0.00   Max.   :27.89                     

devtools::install_github("mablab/sftrack")
raccoon <- read.csv('my_data/raccoon_data.csv')

summary(raccoon)

Prepare

library(lubridate)
raccoon$timestamp <- ymd_hms(raccoon$timestamp)
# lat/long crs
wsg <- 'EPSG:4326'

Create

my_sftrack <- as_sftrack(raccoon, burst = 'animal_id', time = 'timestamp', 
                         coords = c('longitude', 'latitude'), crs=wsg, zeroNA = TRUE)

`sftrack` - plotting works with `sf` methods

plot(my_sftrack, axes = T, graticule = TRUE, pch = 4, lwd=3.8)

plot of chunk unnamed-chunk-5

`sftrack` - manipulating like a data.frame

my_sftrack <- my_sftrack[c('TTP-058','TTP-041'),]
print(my_sftrack, n_row=12)

Sftrack with 4769 features and 7 fields (2236 empty geometries) 
Geometry : "geometry" (XY, crs: WGS 84) 
Timestamp : "timestamp" (POSIXct in UTC) 
Burst : "burst" (*id*) 
-------------------------------
      animal_id           timestamp longitude latitude fix         burst                   geometry
10891   TTP-041 2019-01-15 17:02:30   0.00000  0.00000  NO (id: TTP-041)                POINT EMPTY
3351    TTP-041 2019-01-16 17:02:30 -80.27812 26.06610  3D (id: TTP-041)  POINT (-80.27812 26.0661)
3352    TTP-041 2019-01-17 17:02:30 -80.27830 26.06561  3D (id: TTP-041)  POINT (-80.2783 26.06561)
3353    TTP-041 2019-01-18 17:02:30 -80.27835 26.06566  3D (id: TTP-041) POINT (-80.27835 26.06566)
3354    TTP-041 2019-01-19 00:02:30 -80.27531 26.06607  3D (id: TTP-041) POINT (-80.27531 26.06607)
3355    TTP-041 2019-01-19 01:02:05 -80.27620 26.06573  3D (id: TTP-041)  POINT (-80.2762 26.06573)
3356    TTP-041 2019-01-19 02:02:05 -80.27636 26.06859  3D (id: TTP-041) POINT (-80.27636 26.06859)
10892   TTP-041 2019-01-19 03:02:30   0.00000  0.00000  NO (id: TTP-041)                POINT EMPTY
10893   TTP-041 2019-01-19 04:02:30   0.00000  0.00000  NO (id: TTP-041)                POINT EMPTY
10894   TTP-041 2019-01-19 05:02:30   0.00000  0.00000  NO (id: TTP-041)                POINT EMPTY
15584   TTP-041 2019-01-19 06:02:30 -80.27873 26.06819  2D (id: TTP-041) POINT (-80.27873 26.06819)
10895   TTP-041 2019-01-19 07:02:30   0.00000  0.00000  NO (id: TTP-041)                POINT EMPTY

`sftrack` - manipulating like a data.frame


library(OpenStreetMap)

# Get bounding box from sf geometry
bbox <- st_bbox(my_sftrack)
lower_right <- bbox[c(2,3)] + c(-0.01,0.2)
upper_left <- bbox[c(4,1)] + c(0.01,-0.2)

# download open street map
map <- openmap(upper_left,lower_right,
               zoom=11, type='osm')

# project map
map <- openproj(map)



plot(map)
plot(my_sftrack, add= T, pch=4, lwd=2, col = 'black')

plot of chunk unnamed-chunk-8

`sftrack` - acts just like an 'sf' object


utm_17 <- '+proj=utm +zone=17 +ellps=WGS84 +datum=WGS84 +units=m +no_defs'
my_sftrack <- st_transform(my_sftrack, crs = utm_17)

# Make a polygon within study
poly_pts <- list(
  rbind(
  c(572300, 2883500),
  c(572600, 2883500),
  c(572600, 2883700),
  c(572300, 2883700),
  c(572300, 2883500)
  )
)
polygon <- st_sfc(st_polygon(poly_pts), crs=utm_17)



# What points are within a polygon?

plot(polygon, axes= T, graticule = TRUE, expandBB = c(1.5,1.5,1.5,1.5), col =sf.colors(alpha=0.2))
plot(my_sftrack, add =T)

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`sftrack` - acts just like an 'sf' object


answer <- st_within(my_sftrack, polygon, sparse=FALSE)
sub_sftrack <- my_sftrack[answer, ]
head(sub_sftrack)

Sftrack with 6 features and 7 fields (0 empty geometries) 
Geometry : "geometry" (XY, crs: +proj=utm +zone=17 +ellps=WGS84 +datum=WGS84 +units=m +no_defs) 
Timestamp : "timestamp" (POSIXct in UTC) 
Burst : "burst" (*id*) 
-------------------------------
      animal_id           timestamp longitude latitude fix         burst                 geometry
3382    TTP-041 2019-01-24 02:02:09 -80.27637 26.07026  3D (id: TTP-041) POINT (572377.1 2883665)
15601   TTP-041 2019-01-28 20:02:30 -80.27576 26.06974  2D (id: TTP-041) POINT (572438.2 2883608)
3425    TTP-041 2019-02-03 19:02:14 -80.27701 26.07041  3D (id: TTP-041) POINT (572313.6 2883681)
3426    TTP-041 2019-02-03 20:02:08 -80.27466 26.07010  3D (id: TTP-041) POINT (572548.8 2883649)
3482    TTP-041 2019-02-13 02:02:09 -80.27630 26.06926  3D (id: TTP-041) POINT (572385.2 2883555)
17035   TTP-041 2019-02-22 02:02:20 -80.27497 26.06999  3D (id: TTP-041) POINT (572517.3 2883636)

`sftrack` - calculate common parameters

step_calc <- step_metrics(my_sftrack)
head(step_calc)

          dx         dy       dist    dt abs_angle  rel_angle        speed
1         NA         NA         NA 86400        NA         NA           NA
2  18.008768  53.593639  56.538428 86400 1.2466258         NA 6.543800e-04
3   4.727009   4.625484   6.613601 86400 0.7745432 -0.4720826 7.654631e-05
4 303.425593  47.756375 307.160807 25200 0.1561101 -0.6184331 1.218892e-02
5  89.014047  38.261403  96.888779  3575 0.4059593  0.2498492 2.710176e-02
6  17.864152 316.988342 317.491318  3600 1.5145000  1.1085407 8.819203e-02
                   sftrack_id
1 TTP-041_2019-01-15 17:02:30
2 TTP-041_2019-01-16 17:02:30
3 TTP-041_2019-01-17 17:02:30
4 TTP-041_2019-01-18 17:02:30
5 TTP-041_2019-01-19 00:02:30
6 TTP-041_2019-01-19 01:02:05

summary(step_calc)

       dx                 dy                 dist                dt          abs_angle     
 Min.   :   0.000   Min.   :    0.000   Min.   :    0.00   Min.   :  872   Min.   :0.0003  
 1st Qu.:   1.191   1st Qu.:    1.316   1st Qu.:    4.20   1st Qu.: 3584   1st Qu.:0.4701  
 Median :  23.989   Median :   28.200   Median :   61.41   Median : 3600   Median :0.9192  
 Mean   :  89.687   Mean   :  126.776   Mean   :  174.98   Mean   : 5561   Mean   :0.8712  
 3rd Qu.:  96.840   3rd Qu.:  154.804   3rd Qu.:  235.78   3rd Qu.: 3600   3rd Qu.:1.2716  
 Max.   :9821.837   Max.   :14427.086   Max.   :14427.40   Max.   :86400   Max.   :1.5699  
 NA's   :2237       NA's   :2237        NA's   :2237       NA's   :2       NA's   :2794    
   rel_angle          speed         sftrack_id       
 Min.   :-1.502   Min.   :0.0000   Length:4769       
 1st Qu.:-0.390   1st Qu.:0.0011   Class :character  
 Median :-0.009   Median :0.0220   Mode  :character  
 Mean   :-0.005   Mean   :0.0629                     
 3rd Qu.: 0.369   3rd Qu.:0.0768                     
 Max.   : 1.511   Max.   :7.7028                     
 NA's   :3228     NA's   :2237

`sftrack` - filtering


# Filter out by travel distance

my_sftrack <- my_sftrack[!is.na(step_calc$dist) &step_calc$dist<200,]

library(OpenStreetMap)
map_zoom <- 
  openmap(c(26.078,-80.292),c(26.062,-80.262),
          zoom=15, type='osm')
# project to UTM 17
map_zoom <- openproj(map_zoom, utm_17)



plot(map_zoom)
plot(my_sftrack, add= T, pch=4, lwd=2)

plot of chunk unnamed-chunk-15

`sftrack` - sftraj class


my_sftraj <- as_sftraj(my_sftrack)
head(my_sftraj)

Sftraj with 6 features and 7 fields (0 empty geometries) 
Geometry : "geometry" (XY, crs: +proj=utm +zone=17 +ellps=WGS84 +datum=WGS84 +units=m +no_defs) 
Timestamp : "timestamp" (POSIXct in UTC) 
Burst : "burst" (*id*) 
-------------------------------
      animal_id           timestamp longitude latitude fix         burst
3351    TTP-041 2019-01-16 17:02:30 -80.27812 26.06610  3D (id: TTP-041)
3352    TTP-041 2019-01-17 17:02:30 -80.27830 26.06561  3D (id: TTP-041)
3354    TTP-041 2019-01-19 00:02:30 -80.27531 26.06607  3D (id: TTP-041)
3356    TTP-041 2019-01-19 02:02:05 -80.27636 26.06859  3D (id: TTP-041)
15584   TTP-041 2019-01-19 06:02:30 -80.27873 26.06819  2D (id: TTP-041)
3357    TTP-041 2019-01-19 08:02:30 -80.27837 26.06565  3D (id: TTP-041)
                            geometry
3351  LINESTRING (572205.1 288320...
3352  LINESTRING (572187.1 288315...
3354  LINESTRING (572485.8 288320...
3356  LINESTRING (572378.9 288348...
15584 LINESTRING (572142.7 288343...
3357  LINESTRING (572180.1 288315...



plot(my_sftraj, graticule = TRUE, key.pos=4, main = 'Tree Tops Park Raccoons')

plot of chunk unnamed-chunk-17

`sftrack` - dynamic grouping


data('raccoon',package='sftrack')

raccoon$timestamp <- ymd_hms(raccoon$timestamp)

# create a  new month category
raccoon$month <- month(raccoon$timestamp)

burst = c(id = 'animal_id', month = 'month')

my_sftraj <- as_sftraj(raccoon, burst = burst, 
                       time = 'timestamp', coords = c('longitude', 'latitude'),
                       crs=wsg, zeroNA = TRUE)

# Check out what group is active
active_burst(my_sftraj)

[1] "id"    "month"



plot(my_sftraj, graticule = TRUE)

plot of chunk unnamed-chunk-19

`sftrack` - dynamic grouping

active_burst(my_sftraj) <- 'id'
plot(my_sftraj, graticule = TRUE)

plot of chunk unnamed-chunk-20

Core components yet to be installed


  • tibble methods
  • expanded ggplot methods (limited currently)
  • base plot functions need to be expanding to link completely with sf plot methods.
  • Consensus on naming
  • Standards, DBS connections? WKT?

How can you help?


  • Install and try it out! github.com/mablab/sftrack
  • Need input from users + developers
  • Need feedback on final specifications of the framework
    • specifically on sftraj class and grouping structure

Thank You





@birderboone
github/birderboone
mablab.org