Abstract
Hot spot analysis is the problem of identifying statistically significant spatial clusters and is typically applied on point data. The aim of this paper is to discover hot spots in an urban environment, namely road segments with statistically significant amount of traffic congestion, using massive GPS data of moving objects. To this end, we adjust the Getis-Ord index for hot spot discovery to become applicable for road networks. Then, we propose two data-parallel algorithms for hot spot discovery in road networks implemented in Apache Spark; an exact algorithm that has scalability limitations for very large data sets, and a scalable approximate algorithm that balances between performance and accuracy. We provide experiments over a large real-life data set that indicate the salient features of our approach.