stopdetection - Stop Detection in Timestamped Trajectory Data using Spatiotemporal Clustering

Trajectory data formed by human or animal movement is often marked by periods of movement interspersed with periods of standing still. It is often of interest to researchers to separate geolocation trajectories of latitude/longitude points by clustering consecutive locations to produce a model of this behavior. This package implements the Stay Point detection algorithm originally described in Ye (2009) <doi:10.1109/MDM.2009.11> that uses time and distance thresholds to characterize spatial regions as 'stops'. This package also implements the concept of merging described in Montoliu (2013) <doi:10.1007/s11042-011-0982-z> as stay point region estimation, which allows for clustering of temporally adjacent stops for which distance between the midpoints is less than the provided threshold. GPS-like data from various sources can be used, but the temporal thresholds must be considered with respect to the sampling interval, and the spatial thresholds must be considered with respect to the measurement error.

Last updated 2 years ago

4.00 score 2 stars 6 scripts 228 downloads

topdowntimeratio - Top-Down Time Ratio Segmentation for Coordinate Trajectories

Data collected on movement behavior is often in the form of time- stamped latitude/longitude coordinates sampled from the underlying movement behavior. These data can be compressed into a set of segments via the Top- Down Time Ratio Segmentation method described in Meratnia and de By (2004) <doi:10.1007/978-3-540-24741-8_44> which, with some loss of information, can both reduce the size of the data as well as provide corrective smoothing mechanisms to help reduce the impact of measurement error. This is an improvement on the well-known Douglas-Peucker algorithm for segmentation that operates not on the basis of perpendicular distances. Top-Down Time Ratio segmentation allows for disparate sampling time intervals by calculating the distance between locations and segments with respect to time. Provided a trajectory with timestamps, tdtr() returns a set of straight- line segments that can represent the full trajectory. McCool, Lugtig, and Schouten (2022) <doi:10.1007/s11116-022-10328-2> describe this method as implemented here in more detail.

Last updated 2 years ago

2.70 score 4 scripts 197 downloads