Space, time and visual analytics

Tenth International Conference on Information Visualisation (IV'06), 2006

International Journal of Geographical Information Science, 2015

2014

The amount of spatio-temporal data produced everyday has sky rocketed in the recent years due to the commercial GPS systems and smart devices. Together with this, the need for tools and techniques to analyze this kind of data have also increased. A major task of spatio-temporal data analysis is to discover relationships and patterns among spatially and temporally scattered events. However, most of the existing visualization techniques implement a top-down approach i.e, they require prior knowledge of existing patterns. In this dissertation, I present my novel visualization technique called Storygraph which supports bottom-up discovery of patterns. Since Storygraph presents and integrated view, analysis of events can be done with losing either of time or spatial contexts. In addition, Storygraph can handle spatio-temporal uncertainty making it ideal for data being extracted from text. In the subsequent chapters, I demonstrate the versatility and the effectiveness of the Storygraph al...

Wien, 2021

2000

2002

It is widely recognized that data visualization may be a powerful instrument for exploratory analysis. In order to fulfill this claim, visualization software must be carefully designed taking into account two principal aspects: characteristics of the data to be visualized and the exploratory tasks to be supported. The tasks that may potentially arise in data exploration are, in their turn, dependent on the data. Developers of visualization software need comprehensive and operational typologies of data and tasks, where the task typology has an explicit connection to possible data components and characteristics. In the paper we present three examples of task-driven design of visualization software for different types of spatio-temporal data. We demonstrate that, first, different exploratory tasks may be anticipated in these three cases and, second, different sets of tools are required to properly support exploration of the data, although there are some common techniques and interface ...

Transactions in Gis, 2008

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.

Brazilian Symposium on GeoInformatics, 2003

Abstract. Visualization research deals with the use of graphical models to represent data, coupled with suitable interaction operations that support an active user exploration of the data representations. Visualization techniques can greatly enhance knowledge discovery processes involving geo-referenced data, and the study of visual displays to assist users of geographic information, which typically includes spatial and temporal attributes, motivates developments

2002

Computational Statistics & Data Analysis, 2003