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Today, the opportunity for potential human activity has gone beyond physical space to virtual space. Based on a proposed conceptual framework that models the relationships between physical and virtual spaces, this paper presents an attempt to adjust the space-time prism concept of Hä gerstrand's time geography to identify potential activity opportunities in virtual space, focusing on the virtual space access channels available in physical space. A three-dimensional (3D) spatio-temporal Geographic Information System (GIS) design has been developed in this research to accommodate the adjusted space-time prism concept to support the representation, visualization, and analysis of potential human activities and interactions in physical and virtual spaces using the prism representation. Following the design, a prototype system has been successfully implemented in a 3D GIS environment. Such a system can provide powerful analytical tools for studies related to potential human activities and applications such as locationbased services (LBS) and accessibility analysis in the information age.

The increasing number of large individual‐based spatiotemporal datasets in various research fields has challenged the GIS community to develop analysis tools that can efficiently help researchers explore the datasets in order to uncover useful information. Rooted in Hägerstrand's time geography, this study presents a generalized space‐time path (GSTP) approach to facilitating visualization and exploration of spatiotemporal changes among individuals in a large dataset. The fundamental idea of this approach is to derive a small number of representative space‐time paths (i.e. GSTPs) from the raw dataset by identifying spatial cluster centers of observed individuals at different time periods and connecting them according to their temporal sequence. A space‐time GIS environment is developed to implement the GSTP concept. Different methods of handling temporal data aggregation and the creation of GSTPs are discussed in this article. Using a large individual‐based migration history dataset, this study successfully develops an operational space‐time GIS prototype in ESRI's ArcScene and ArcMap to provide a proof‐of‐concept study of this approach. This space‐time GIS system demonstrates that the proposed GSTP approach can provide a useful exploratory analysis and geovisualization environment to help researchers effectively search for hidden patterns and trends in such datasets.

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