TrajDataMining

Diego Monteiro

2017-11-23

TrajDataMining

A new method to detect a pattern called Partners in trajectories. This algorithm identifies pairs of trajecories whose objects saty together during certain periods. This method is avaliable in the TrajDataMining package. It differs from existing moving together patterns because its implementation is highly parallelizable and its clustering method is based on distance time series analysis.

Example data gives

library(TrajDataMining)
library(ggplot2)
library(sp)
library(magrittr)
suppressMessages(library(rgdal))

Plot of A1 data example

A1 track its a trajectory of sea elephant

  df <- data.frame(x=A1@sp@coords[,1],y=A1@sp@coords[,2])

  ggplot(df,aes(x=df$x,y=df$y))+geom_path(aes(group = 1), arrow = arrow(),color='blue')+ggtitle("elephant sea Trajectory")+xlab("X")+ ylab("Y")+theme_classic()

Douglas Peucker Method

Douglas-Peucker which reduces trajectories by preserving spatial precisions.

# get the maximum distance
 max <- max(A1@connections$distance)

 douglasp <- douglasPeucker(A1,max)
 
 df <- data.frame(x=douglasp@sp@coords[,1],y=douglasp@sp@coords[,2])
 ggplot(df,aes(x=df$x,y=df$y))+geom_path(aes(group = 1), arrow = arrow(),color='blue')+ggtitle("Method Douglas Peucker")+xlab("X")+ ylab("Y")+theme_classic()

Speed Filter

A speed filter that filters out trajectory observations whose speeds are above a user-defined maximum velocity ( ETIENNE , 2011).

# get the maximum distance
  sf <- speedFilter(A1,0.01)
## [1] "2008-01-23 21:32:44"
## [1] "2008-01-24 01:31:26"
## [1] "2008-01-24 03:12:00"
## [1] "2008-01-24 11:24:42"
## [1] "2008-07-21 04:30:30"
## [1] "2008-07-21 11:22:30"
## [1] "2008-07-22 01:09:49"
## [1] "2008-07-25 13:19:46"
## [1] "2008-07-28 11:40:23"
## [1] "2008-07-29 01:23:43"
## [1] "2008-07-31 09:15:41"
## [1] "2008-11-19 12:38:34"
  df <- data.frame(x=sf@sp@coords[,1],y=sf@sp@coords[,2])

 ggplot(df,aes(x=df$x,y=df$y))+geom_path(aes(group = 1), arrow = arrow(),color='blue')+ggtitle("Method Speed Filter")+xlab("X")+ ylab("Y")+theme_classic()

Partner

A method, named Partner, that identifies objects that are moving together. We propose this method to recognize trajectories that stay together, based on trajectory distance time series analysis.

For to send this data for the database use the class DataSourceInfo.

  data <- FALSE
  
  partner(A1,A2,110792,2277,0,data)
##             begintime             endtime id1 id2
## 1 2008-02-04 21:26:54 2008-11-11 06:58:44   1   2