Urban Hotspots Mining Based on Improved FDBSCAN Algorithm

Abstract

Enormous human activities happen in modern cities nowadays. These activities generate trajectory information, which can play a big role in urban planning, transportation improvement, behavior analysis, and many location-based services. Currently, GPS devices are installed in many cities' taxies. These positioning devices are the ideal source of trajectory data. Urban hotspots can be extracted by applying spatial clustering algorithm to the taxi trajectory data. Since the classical DBSCAN clustering algorithm is short of execution efficiency and its variant FDBSCAN clustering algorithm fails to make fine-grained clustering on the taxi trajectory data, this paper proposes an improved FDBSCAN clustering algorithm, O-FDBSCAN clustering algorithm. The core of the O-FDBSCAN clustering algorithm is, it adds some coordinate offsets to the original trajectory data point based on some background geographic region knowledge. The background knowledge contains the collected functional domain information in the nearby area. By adding this kind of weight to the trajectory data point, the clustering result will be closer to the real target place and therefore give a more fine-grained clustering result. In this paper, we extract the urban hotspots and their spatiotemporal pattern by using the O-FDBSCAN clustering algorithm. The comparison result shows that, the proposed algorithm has better fine-grained clustering results than FDBSCAN. be taken in constructing both.