In the big data era, robust solutions are obliged to be proposed to integrate and represent data from different formats and with different contents to assist the decision-making. Current cartographic and geographic information systems have limited capabilities for solving these problems. This paper describes an automatic and comprehensive system that conducts data fusion from all potentially related sources. In this system, a new Semantic Location Model (SemLM) is established to present the semantic concepts and location feature and demonstrate how locations are interrelated. In the SemLM, various types of location descriptors in different application scenarios can be analyzed and understood. Additionally, considering the challenges involved in data-intensive computation and visualization, this paper implements a Place-based Pan-Information System (P2S) as an innovative 4D system that dynamically associates and visualizes place-based information, using public security as the case study.@en

Stops along taxi trajectories, such as picking up and dropping off passengers, are spatially clustered and related to certain attributes of places where stops are made. To detect the hidden knowledge regarding these places, this article examines the semantics of massive taxi stops in a large city. Each taxi trajectory is modeled as a series of sequential semantic stops labeled by street names. All the trajectories can be examined as a document corpus, from which the hidden themes of the stops are identified through Latent Dirichlet Allocation model. Conventional GIS tools are coupled with topic modeling toolkit to visualize and analyze potential information of stop topics for understanding intra-city dynamics. The effectiveness of this approach is illustrated by a case study using a large dataset of taxi trajectories including approximately 4,000 taxis in Wuhan, China.@en

Taxi trajectories reflect human mobility over a road network. Pick-up and drop-off locations in different time periods represent origins and destinations of trips, respectively, demonstrating the spatiotemporal characteristics of human behavior. Each trip can be viewed as a displacement in the random walk model, and the distribution of extracted trips shows a distance decay effect. To identify the spatial similarity of trips at a finer scale, this paper investigates the distribution of trips through topic modeling techniques. Firstly, trip origins and trip destinations were identified from raw GPS data. Then, different trips were given semantic information, i.e., link identification numbers with a semantic enrichment process. Each taxi trajectory was composed of a series of trip destinations corresponding to the same taxi. Subsequently, each taxi trajectory was analogous to a document consisting of different words, and all taxi's trajectories could be regarded as document corpora, enabling a semantic analysis of massive trip destinations. Finally, we obtained different trip destination topics reflecting the spatial similarity and regional property of human mobility through LDA topic model training. The effectiveness of this approach was illustrated by a case study using a large dataset of taxi trajectories collected from 2 to 8 June 2014 in Wuhan, China.@en