Re-represent each trajectory spatially using a constant number of points so that adjacent points on the trajectory become equidistant to each other.

mt_spatialize(data, use = "trajectories", dimensions = c("xpos", "ypos"), save_as = "sp_trajectories", n_points = 20)

data | a mousetrap data object created using one of the mt_import
functions (see mt_example for details). Alternatively, a trajectory
array can be provided directly (in this case |
---|---|

use | a character string specifying which trajectory data should be used. |

dimensions | a character string specifying which trajectory variables should be used. Can be of length 2 or 3 for two-dimensional or three-dimensional data. |

save_as | a character string specifying where the resulting trajectory data should be stored. |

n_points | an integer or vector of integers specifying the number of points used to represent the spatially rescaled trajectories. If a single integer is provided, the number of points will be constant across trajectories. Alternatively, a vector of integers can provided that specify the number of points for each trajectory individually. |

A mousetrap data object (see mt_example) with an additional
array (by default called `sp_trajectories`

) containing the spatialized
trajectories. If a trajectory array was provided directly as `data`

,
only the spatialized trajectories will be returned.

`mt_spatialize`

is used to emphasize the trajectories' shape. Usually,
the vast majority of points of a raw or a time-normalized trajectory lie
close to the start and end point. `mt_spatialize`

re-distributes these
points so that the spatial distribution is uniform across the entire
trajectory. `mt_spatialize`

is mainly used to improve the results of
clustering (in particular mt_cluster) and visualization.

KH2017 <- mt_spatialize(data=KH2017, dimensions = c('xpos','ypos'), n_points = 20)