Modeling Mobility and Dynamics of Scheduled Space-Time Activities—An RDF Approach

In this chapter, we present a semantic data modeling framework for representing and analyzing the movement dynamics of individuals that arise from following a schedule or plan of activities in a semantic-enriched environment, i.e., an environment for which an ontology-driven space-time activity knowledgebase has been constructed. The ontology-driven knowledgebase contains spatial, temporal, and semantic information about geospatial entities in the environment. The relations between geospatial entities in the environment are captured in the knowledgebase through the underlying ontology support. Movement by individuals on a university campus according to a semester-based course schedule is employed as a use case to demonstrate this framework. This work demonstrates an RDF-based semantic data model that is used for reasoning about movement, including the movement trajectories of students on campus based on weekly course schedules. Road network information is incorporated to generate movement trajectories more realistically and data from a campus course information system allows us to query and analyze aggregated movement dynamics on campus. This chapter discusses the advantages of a semantic data modeling approach over traditional data models for human activity and movement including the capability to incorporate different data sources into the analysis, and generate geographic visualizations of movement paths as well as the overall movement dynamics for a campus.