Exploratory Visualization of Collective Mobile Objects Data Using Temporal Granularity and Spatial Similarity

摘要: Recent advances in location-aware technologies have produced vast amount of individual-based movement data, overwhelming the capacity traditional spatial analytical methods. There are growing opportunities for discovering unexpected patterns, trends and relationships that hidden massive mobile objects data. However, a lingering challenge is extracting meaningful information from data on multiple due to visual complexity these patterns even modest collection objects. This chapter describes visualization environments based temporal granularity, and/or attribute similarity measures exploring collective Reconstructing trajectories at user-defined levels granularity allows exploration different generality. At given level generality, individual can be combined into synthetic summary or classified groups locational similarity. A environment space-time cube concept exploits functionalities create user-interactive toolkit case study using wild chicken demonstrates potential system extract otherwise difficult comprehend collections trajectories.