Frequent route pattern mining from personal trajectory data is the basis of location awareness and location services. However, because personal trajectory data is highly uncertain, most existing approaches are only capable of finding short and incomplete route patterns. In this paper, a novel approach is proposed for the discovery of frequent route patterns based on trajectory abstraction. First, trajectory partition, location extraction, data simplification, and common segment discovery are used to abstract trajectory data, convert these trajectories into common segment temporal sequences (STS) and generate 1-frequent itemsets. Then, a pattern mining algorithm is proposed based on the spatial-temporal adjacency relationship. This algorithm uses the constraint mechanism and bidirectional projected database to mine frequent route patterns from STS. Based on the real GeoLife trajectory data, the experimental results indicate that the proposed method has better performance and can find longer route patterns than other currently available methods.

It is of interest to analyze urban spatial structure by identifying urban subcenters, for which published literature proposes many methods. Although these methods are widely applied, they demonstrate obvious shortcomings that restrict further application. Therefore, it is of great value to propose a new urban subcenter identification method that can overcome these shortcomings. In this paper, we introduce an alternative method. Unlike two-stage procedures and other arbitrary methods, our method is not based on arbitrary cutoff values and is entirely parameter-free. We first calculate the commuting fluxes for each pair of census tracts and use the fluxes to represent a local density. After that, the census tracts are partitioned into several clusters using a clustering algorithm. Finally, subcenters are derived from the clusters through a circularly shaped spatial scan statistic. We apply this procedure to 2010 and 2015 census datasets for Wuhan. The identification and comparison results demonstrate that our proposed method is effective and can be applied toward future research.

Classical spatial scan statistics are based on pre-defined shapes for scanning windows and specific distribution models and are used to detect latent cluster(s). However, the pre-defined windows (especially circularly shaped windows) may not be suitable for real situations, and the specific distribution models are inadequate for real clusters in which the exact distributions of the test statistics are only known in special cases. To generate more reasonable results, we propose a spatial scan statistic method with an irregularly shaped scanning window. A combinatorial particle swarm optimization method is used to optimize this window. A distribution-free concentration index is constructed to measure the difference between inside cluster and outside cluster. A compactness penalty function is employed to avoid generating clusters in a tree-structure. Simulation data sets are used to test the proposed method, and the results show the feasibility of our method.