WCSE 2018 ISBN: 978-981-11-7861-0
DOI: 10.18178/wcse.2018.06.025

A Grid-based Marine Traffic Hotspot Detection Algorithm on SpatialHadoop

Bao Lei, Yang Le

Abstract— Owe to the establishment of modern navigation and communication networks, maritime vessel trajectory data becomes increasingly available. These data sets are always huge and usually heavily skewed, finding hot spots area among maritime traffic data is critical for real time applications ranging from military surveillance to transportation management.In this paper, we proposed a grid-based hotspot detection algorithm on maritime traffic data. By gridding the spatial area into small buckets, it take the vessels density on each bucket into further calculation instead of the location of each single moving vessel. In order to handle the big data set, the computation apply the SpatialHadoop framework which can establish the R-tree spatial index to enhance location data handling performance on MapReduce programming. Experiments on real AIS data shows that the method propose is effective and fast.

Index Terms— location big data, hotspot detection, trajectory data mining, SpatialHadoop

Bao Lei
Computer Science Department, Wuhan East Lake College, CHINA
Yang Le
College of Electronics Engineering ,Navy University of Engineering, CHINA


Cite: Bao Lei, Yang Le, "A Grid-based Marine Traffic Hotspot Detection Algorithm on SpatialHadoop," Proceedings of 2018 the 8th International Workshop on Computer Science and Engineering, pp. 141-145, Bangkok, 28-30 June, 2018.