  A robust algorithm to detect coherent structures with sparse Lagrangian
particle tracking data, using Voronoi tessellation and techniques from spectral
graph theory, is proposed and tested. Neighbouring tracer particles are
naturally identified through the Voronoi tessellation of the tracers'
distribution. The method examines the 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 novel approach proves to be successful at identifying
coherence with realistic and very sparse experimental data. Specifically, it is
shown for the first time that coherent structures are identifiable for mean
inter-particle distances of the order of the largest length scales in the flow.
