Recent work in data visualization has demonstrated that small, perceptually-distinct color palettes-such as those used in categorical mapping-can connote significant affective qualities. Data that are mapped or otherwise visualized are also often emotive in nature, either inherently (e.g., climate change, disease mortality rates), or by design, such as can be found in visual storytelling. However, little is known about how the affective qualities of color interact with those of data context in visualization design. This article describes the results of a crowdsourced study on the influence of affectively congruent versus incongruent color schemes on categorical map-reading response. We report both objective (pattern detection; area comparison) and subjective (affective quality; appropriateness; preference) measures of map-reader response. Our results suggest that affectively congruent colors amplify perceptions of the affective qualities of maps with emotive topics, affective incongruence may cause confusion, and that affective congruence is particularly influential in maps of positive-leaning data topics. Finally, we offer preliminary design recommendations for balancing color congruence with other design factors, and for synthesizing color and affective context in thematic map design.

Pattern analysis techniques currently common within geography tend to focus either on characterizing patterns of spatial and/or temporal recurrence of a single event type (e.g., incidence of flu cases) or on comparing sequences of a limited number of event types where relationships between events are already represented in the data (e.g., movement patterns). The availability of large amounts of multivariate spatiotemporal data, however, requires new methods for pattern analysis. Here, we present a technique for finding associations among many different event types where the associations among these varying event types are not explicitly represented in the data or known in advance. This pattern discovery method, known as T-pattern analysis, was first developed within the field of psychology for the purpose of finding patterns in personal interactions. We have adapted and extended the T-pattern method to take the unique characteristics of geographic data into account and implemented it within a geovisualization toolkit for an integrated computational-geovisual environment we call STempo. To demonstrate how T-pattern analysis can be employed in geographic research for discovering patterns in complex spatiotemporal data, we describe a case study featuring events from news reports about Yemen during the Arab Spring of 2011–2012. Using supplementary data from the Global Database of Events, Language, and Tone, we briefly summarize and reference a separate validation study, then evaluate the scalability of the T-pattern approach. We conclude with ideas for further extensions of the T-pattern technique to increase its utility for spatiotemporal analysis.

In most coordinated view geovisualization tools, a transient visual effect is used to highlight observations across views when brushed with a mouse or other input device. Most current geovisualization and information visualization systems use colored outlines or fills to highlight observations, but there remain a wide range of alternative visual strategies that can also be implemented and compared to color highlighting to evaluate user performance. This paper describes the results of an experiment designed to compare user performance with two highlighting methods; color and leader lines. Our study methodology uses eye-tracking to capture participant eye fixations while they answer questions that require attention to highlighted observations in multiple views. Our results show that participants extract information as efficiently from coordinated view displays that use leader line highlighting to link information as they do from those that use a specific color to highlight items. We also found no significant differences when changing the color of the highlighting effect from red to black. We conclude that leader lines show significant potential for use as an alternative highlighting method in coordinated multiple view visualizations, allowing color to be reserved for representing thematic attributes of data.

In this paper we present the GeoViz Toolkit, an open-source, internet-delivered program for geographic visualization and analysis that features a diverse set of software components which can be flexibly combined by users who do not have programming expertise. The design and architecture of the GeoViz Toolkit allows us to address three key research challenges in geovisualization: allowing end users to create their own geovisualization and analysis component set on-the-fly, integrating geovisualization methods with spatial analysis methods, and making geovisualization applications sharable between users. Each of these tasks necessitates a robust yet flexible approach to inter-tool coordination. The coordination strategy we developed for the GeoViz Toolkit, called Introspective Observer Coordination, leverages and combines key advances in software engineering from the last decade: automatic introspection of objects, software design patterns, and reflective invocation of methods.

Geovisualization tools are intended to support analysts in complex task domains like crisis management, disease surveillance, and threat analysis. It is likely that analysts in these domains will use geovisualizations to develop many analytical results over time. This calls for attention to the problem of collecting, organizing, and making sense out of groups of analytical results – a stage of analysis called synthesis. The research reported here aims to characterize the process of synthesis as it is conducted by analysts working alone, and to suggest design guidelines for new tools to support synthesis in that setting. We have developed a new experimental method for observing and characterizing the process of synthesis. This approach has participants work with a collection of physical data artifacts on a paper-covered workspace to devise hypotheses under the guise of a disease outbreak scenario. From experiment video recordings we identified and coded actions that participants undertook to complete the synthesis task. In this article we report results from synthesis experiments with analysts from Pacific Northwest National Laboratory and experts from The Pennsylvania State University. Experiment results are then distilled into a design framework that can be used to shape the development of geovisual synthesis tools.

This text comes at a time when the range of geospatial technologies is expanding rapidly, and their reach is increasing to the point that huge numbers of nonexperts are making use of geospatial tec...

Information synthesis is a key portion of the analysis process with visual analytics tools. This stage of work requires users to collect, organize, and add meaning to individual analytical results. This paper reports the results of a needs assessment study with technical and bio/chemical security analysts intended to characterize the ways in which users currently synthesize information,and to elicit ideas for future tools to support information synthesis. Our work used structured interviews to obtain knowledge from analysts. Responses indicate that synthesis is currently supported through the use of office productivity software, and current tools do not provide adequate support for the task of information synthesis.