Time use is crucial for human beings in social science and public health studies as an indicator of daily behavior, resource allocation and social relationships. However, they are also limited without integrating indicators of cognitive, emotional, and physiological states, geospatial coordinates, and social networks, which are crucial for identifying associations between time use pattern and human behaviors. Spatial trajectory data can compensate this disadvantage by providing location and background information. However, fusing time and space data is fundamentally challenging. In this study, we aim to design a web-based GIS platform to unobtrusively collect and visualize location trajectory data and daily time use diary collected from mobile devices in urban areas. In this way, trajectory data is integrated with time use data to enhance the study of spatiotemporal behavior pattern analysis. Though designed for understanding human behavior pattern, this platform can also be utilized in a smaller spatiotemporal scale to visualize and analyze the behavior pattern of agents from agent-based models which are developed to simulate human movement. Specifically, trajectory clustering method is applied to identify the agents' trajectory pattern generated from an agent-based model for simulating human movement.
He, Jiaying, Cheng Fu, Paul Torrens, Liana Sayer, and Jae-In Lee. 2015. "Utilizing Web-based GIS Platform to Visualize and Explore the Spatiotemporal Trajectory Behavior Pattern." Presented at the Association of American Geographers Annual Meeting, Chicago. http://meridian.aag.org/callforpapers/program/AbstractDetail.cfm?AbstractID=67136