Visual Exploration of Vehicle Refueling Behavior with Geospatial and Temporal Features

The goal of this research is to study the general vehicle refueling behavior and investigate potential abnormal events through the analysis on massive gas refueling data within a region. Under this context, we design an interactive visualization analysis system based on vehicle refueling data collected from the whole region in Xinjiang province, China. First we extract the basic data features from the dataset and obtain the relationship between entities such as gas stations, vehicles, and drivers. Secondly, we employ multiple classical visualization models and meaningful composition of these models. On several view models, we append additional graphical elements in order to illustrate typical data features（such as geospatial and temporal features） from different perspectives under certain real-life application scenarios. In addition, the view models can describe the relations between different entities. Through two real-life case studies, we analyze the typical individual behaviors as well as statistical group features and finally realize the detection of abnormal refueling events with the assistance of domain experts.