Edge-based locality sensitive hashing for efficient geo-fencing application

Geo-fencing is a promising technique for emerging location-based services. Its two basic spatial predicates, INSIDE and WITHIN pairings between points and polygons, can be addressed by state-of-the-art methods such as the crossing number algorithm. In the era of big-data, however, geo-fencing has to process millions of points and hundreds of polygons or even more in real-time. In this paper, we propose an efficient algorithm to improve the scalability of geo-fencing, which consists of two main stages. At the first stage, an R-tree is used to quickly detect whether a point is inside the minimum bounding rectangle of a polygon. In the second stage, instead of an exhaustive search, we design an edge-based locality sensitive hashing scheme adapted to the crossing number algorithm. As for the case of WITHIN detection, a probing scheme is suggested to locate adjacent buckets so as to check all edges near to a target point. By further exploiting batch processing and multi-threading programming, our algorithm can achieve a fast speed while retaining 100% accuracy over all training datasets provided by the GIS Cup 2013 organizers.