  A novel algorithm to detect coherent structures with sparse Lagrangian
particle tracking data, using Voronoi tessellation and techniques from spectral
graph theory, is tested. Neighbouring tracer particles are naturally identified
through the Voronoi tessellation of the tracers' distribution. The method
examines the \textit{neighbouring time} of tracer trajectories, defined as the
total flow time two Voronoi cells share a common Voronoi edge, by converting
this information into a Cartesian distance in the graph representation of the
Voronoi diagram. Coherence is assigned to groups of Voronoi cells whose
neighbouring time remains high throughout the time interval of analysis. The
technique is first tested on the two-dimensional synthetic data of a
double-gyre flow, and then with challenging, large-scale three-dimensional
Lagrangian particle tracking data behind a bluff body at high Reynolds number.
The tested technique proves to be successful at identifying coherence with
realistic experimental data. Specifically, it is shown that coherent tracer
motion is identifiable for mean inter-particle distances of the order of the
largest length scales in the flow.
