Publications

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This summary of PANTHER Human Analytics work describes three of the team's major work activities: research with teams to elicit and document work practices; experimental studies of visual search performance and visual attention; and the application of spatio-temporal algorithms to the analysis of eye tracking data. Our intent is to provide basic introduction to the work area and a selected set of representative HA team publications as a starting point for readers interested our team's work.

Integrated intelligence is an accomplishment: a new holistic picture that explains what, why, and how. Integrated intelligence is also a process, a "unity of effort to produce the best intelligence possible" [1] that is fueled by sensors and computing but driven by informal practices [2] of human insight, discovery, creativity, and invention. To understand and potentially replicate these successes we studied coalition sites noted for integrated intelligence breakthroughs.

Integrated intelligence is a product wrapped in a service. As the intelligence needs of fighters and diplomats change rapidly and unpredictably, the service has to keep listening to feedback while changing the product. New thought and new behavior—innovation—is needed. Yet the ability to change is not free or cheap, and it rides on top of the ability to be the same

The purpose of the current study was to analyze the work of imagery analysts associated with Sagebrush, a Synthetic Aperture Radar (SAR) imaging system, using an adapted version of cognitive work analysis (CWA). This was achieved by conducting a work domain analysis (WDA) for the system under consideration. Another purpose of this study was to describe how we adapted the WDA framework to include a sequential component and a means to explicitly represent relationships between components. Lastly, we present a simplified work domain representation that we have found effective in communicating the importance of analysts' adaptive strategies to inform the research strategies of computational science researchers who want to develop useful algorithms, but who have little or no familiarity with sensor data analysis work. © 2014 Springer International Publishing.

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Effectiveness and efficiency of software development can be greatly increased by writing modularized code using informal (styles) and formal (standards) work approaches. Software development is about connecting pieces into a coherent whole. Thus consistent work approaches provide a structure that allows individuals and teams to minimize the time and thought put into making these connections. These investments in structure return even more benefits in the maintenance phase when old code has to be examined by new programmers, or after time has passed. We present some examples of coding style for Avenue: a simplified form of Hungarian notation (notationHungarian, stringCustomerName, etc.), script naming prefixes and suffixes, and options in script headers. We demonstrate several modular, object-like utility scripts that can be used alone or combined into other utilities. These include developer tools such as a System.Echo substitute for Windows, a Window inspector, and a script for detecting and dealing with multiple display resolutions.

Most routing problems depend on several important variables: transport distance, population exposure, accident rate, mandated roads (e.g., HM-164 regulations), and proximity to emergency response resources are typical. These variables may need to be minimized or maximized, and often are weighted. `Objectives` to be satisfied by the analysis are thus created. The resulting problems can be approached by combining spatial analysis techniques from geographic information systems (GIS) with multiobjective analysis techniques from the field of operations research (OR); we call this hybrid multiobjective spatial analysis` (MOSA). MOSA can be used to discover, display, and compare a range of solutions that satisfy a set of objectives to varying degrees. For instance, a suite of solutions may include: one solution that provides short transport distances, but at a cost of high exposure; another solution that provides low exposure, but long distances; and a range of solutions between these two extremes.

Both the Intelligent Transportation Systems (ITS) and National Information Infrastructure (NU) efforts have ambitious goals that are expected to improve the fundamental infrastructure, commerce, and society of the United States. Achieving these goals will require rapid development and deployment of information compatibility methods through technical and institutional standards. These standards will have to be scaleable and flexible to support new, and as-yet-undiscovered, data. Yet they will also need to accommodate our valuable data reserves. The area of geospatial data, and thus the creation of a National Spatial Data Infrastructure (NSDI), is particularly challenging due to the profoundly different forms, evolutionary histories, and meanings attached to spatial data. We discuss technical issues resulting from the different natures and inaccuracy of existing geodata, and areas where federal policy could lead the way to greater compatibility.

The Arc/Info GENERALIZE command implements the Douglas-Peucker algorithm, a well-regarded approach that preserves line ``character`` while reducing the number of points according to a tolerance parameter supplied by the user. The authors have developed an Arc Macro Language (AML) interface called MAGENCO that allows the user to browse workspaces, select a coverage, extract a sample from this coverage, then apply various tolerances to the sample. The results are shown in multiple display windows that are arranged around the original sample for quick visual comparison. The user may then return to the whole coverage and apply the chosen tolerance. They analyze the ergonomics of line simplification, explain the design (which includes an animated demonstration of the Douglas-Peucker algorithm), and discuss key points of the MAGENCO implementation.

Interest in the characteristics of urban street networks is increasing at the same time new monitoring technologies are delivering detailed traffic data. These emerging streams of data may lead to the dilemma that airborne remote sensing has faced: how to select and access the data, and what meaning is hidden in them? computer-assisted visualization techniques are needed to portray these dynamic data. Of equal importance are controls that let the user filter, symbolize, and replay the data to reveal patterns and trends over varying time spans. We discuss a prototype software system that addresses these requirements.