Thanks to the ubiquitous cell phone use, we have never been so close to uncover population mobility patterns in urban area. While some researches utilize cellphone call records to mine population patterns, few works aim to depict population movement in adaptively spatial and temporal representations, i.e., from a community, a district in the city over an hour, a day to a week. In this paper, we construct a system which deciphers, transforms, queries, and visualizes the records from the millions of users in a city. In particular, we design a data structure, namely MobiHash, which collects phone call records over base stations and indexes them by utilizing a Voronoi division of the urban space. MobiHash supports responsive data queries so that users can interactively retrieve trajectories reflecting population flows in areas of interest. Moreover, population movement is represented as vector fields to reduce visual clutter and occlusions. Because of sparse moving points, a novel radiation model is proposed to interpolate population passing zones. Case studies and experts' feedback validate the utility and efficiency by comparing population moving patterns in different times by using our system.

Massive taxi trajectory data is exploited for knowledge discovery in transportation and urban planning. Existing tools typically require users to select and brush geospatial regions on a map when retrieving and exploring taxi trajectories and passenger trips. To answer seemingly simple questions such as "What were the taxi trips starting from Main Street and ending at Wall Street in the morning?" or "Where are the taxis arriving at the Art Museum at noon typically coming from?", tedious and time consuming interactions are usually needed since the numeric GPS points of trajectories are not directly linked to the keywords such as "Main Street", "Wall Street", and "Art Museum". In this paper, we present SemanticTraj, a new method for managing and visualizing taxi trajectory data in an intuitive, semantic rich, and efficient means. With SemanticTraj, domain and public users can find answers to the aforementioned questions easily through direct queries based on the terms. They can also interactively explore the retrieved data in visualizations enhanced by semantic information of the trajectories and trips. In particular, taxi trajectories are converted into taxi documents through a textualization transformation process. This process maps GPS points into a series of street/POI names and pick-up/drop-off locations. It also converts vehicle speeds into user-defined descriptive terms. Then, a corpus of taxi documents is formed and indexed to enable flexible semantic queries over a text search engine. Semantic labels and meta-summaries of the results are integrated with a set of visualizations in a SemanticTraj prototype, which helps users study taxi trajectories quickly and easily. A set of usage scenarios are presented to show the usability of the system. We also collected feedback from domain experts and conducted a preliminary user study to evaluate the visual system.

Studying human movement citywide is important for understanding mobility and transportation patterns. Rather than investigating the trajectories of individuals, we employ an Eulerian approach to analyze the crowd flows among a geographical network and a social network, which are extracted from mobile phone data. We design a suite of visualization techniques to illustrate the dynamic evolutions of the flow over the networks. We contribute the design and implementation of a visual analytics system, which is called Mobility Viewer, that supports situation-aware understanding and visual reasoning of human mobility. We exemplify our approach with a real citywide data set of seven million users in two months.