Efficient Pathway Identification from Geospatial Trajectories

In the earth-physics community Lagrangian trajectories are used within multiple contexts – analyzing the spreading of pollutants in the air or studying the connectivity between two ocean regions of interest. Huge amounts of data are generated reporting the geo position and other variables e.g. temperature, depth or salinity for particles spreading in the ocean. As state-of-the-art, these experiments are analyzed and visualized by binning the particle positions to pre-defined rectangular boxes. For each box a particle density is computed which then yields a probability map to visualize major pathways. Identifying the main pathways directly still remains a challenge when huge amounts of particles and variables are involved.

We propose a novel method that focuses on linking the net fluctuation of particles between adaptable hexagonal grid cells. For very small areas the rectangular boxing does not imply big differences in area or shape, though when gridding larger areas it introduces rather large distortions. Using hexagons instead provides multiple advantages, such as constant distances between the centers of neighboring boxes or more possibilities of movement due to 6 edges instead of 4 with a lower number of neighbors at the same time (6 instead of 9). The net fluctuation can be viewed as transition strength between the cells.Through this network perspective, the density of the transition strength can be visualized clearly. The main pathways are the transitions with the highest net fluctuation. Thus, simple statistical filtering can be used to reveal the main pathways. The combination of network analysis and adaptable hexagonal grid cells yields a surprisingly time and resource efficient way to identify main pathways.