A Space-Time GIS Approach to Exploring Large Individual-based Spatiotemporal Datasets

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.