Visual Analytics

Visual analytics is the science of analytic reasoning facilitated by the interactive visual interface. Even though this is a new science, the potential to have a positive impact on NGA is significant, moving NGA towards providing highly precise and relevant knowledge products for their stakeholders. Each of the five working groups offered detailed suggestions for developing the science of visual analytics for GEOINT.

The first working group stressed the importance of the development of the science of visual analytics and time-space narratives for geospatial sciences. This group stated that goals need to include optimizing analytical methods, decision cycles, and products through the use of fusion or synthesis; exploiting multi-source, multi-type, temporal and geospatial and dynamic active data; and developing new technologies.

The second working group discussed the need to address four topics within visual analytics. These were: (1) to address cognitive issues through cognitive models of human-computer systems and extension of the theory of human computer interaction; (2) to establish methods for evaluation, validation of reasoning, and analysis techniques; and (3) to extend current techniques to address uncertainty, scale, and time series. Lastly, it was thought important to enable mobile, collaborative, and distributed interaction.

The third working group suggested a set of areas specifically for integrative analytics. Their areas included developing four-dimensional space-time representation and analysis techniques; creating methods for dealing with multi-level data, heterogeneity, and uncertainty; and producing algorithms for statistical and machine learning. Interactive analytics were thought critical, that is efficiently coping with massive amounts of information and data, using visualization, haptics, sound, games, and modeling and simulation.

The fourth working group provided ideas surrounding integrated, interactive, and iterative spatial and temporal (visual) analytics. First was the need for dynamic information systems for geospatial intelligence—representation, modeling, and analysis issues—to support workflows and to focus on spatiotemporal data and analysis. Second, methods were desired for social mapping (social links and networks), which requires an improvement in the spatiotemporal component of social network analysis and the identification and representation of dynamic relationships over space and time. Next, the need for true, comprehensive and complete space-time analysis to address semantic and space-time scales was stressed, using scalable algorithms and infrastructure for large volumes of data. Fourth, means were thought necessary to represent and utilize and analyze data and information quality, reliability, and confidence. This implies the need to determine (1) what information is needed by particular users and the appropriate evaluations methods, and (2) how to make information on certainty or uncertainty useful and to support reasoning with uncertainty and with heterogeneous kinds of information. Lastly, the group discussed the need to develop adaptive visual analytic methods that support a range of users, uses, and devices across a range of interaction science issues; human-algorithm interaction; speed of response to support interaction; sensitivity assessment in real time; and uncertainty representation. This led the group to state a need to develop methods to determine what visual and other analytical methods are appropriate for specific problem contexts and how the success (or lack of success) of the methods and tools need to be evaluated.

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