Design and Interpretability of Contour Lines for Visualizing Multivariate Data

Multivariate geospatial data are commonly visualized using contour plots, where the plots for various attributes are often examined side by side, or using color blending. As the number of attributes grows, however, these approaches become less efficient. This limitation motivated the use of glyphs, where different attributes are mapped to different pre-attentive features of the glyphs. Since both contour plot overlays and glyphs clutter the underlying map, in this paper we examine whether contour lines, which are already present in map space, can be leveraged to visualize multivariate geospatial data. We present five different designs for stylizing contour lines, and investigate their interpretability using three crowdsourced studies. We evaluated the designs through a set of common geospatial data analysis tasks on a four-dimensional dataset. Our first two studies examined how the contour line width and the number of contour intervals affect interpretability, using synthetic datasets where we controlled the underlying data distribution. Study 1 revealed that the increase of width improves the task performance in most of the designs, specially in completion time, except some scenarios where reducing width does not affect performance where the visibility of the background is critical. In Study 2, we found out that fewer contour intervals lead to less visual clutter, hence improved performance. We then compared the designs in a third study that used both synthetic and real-life meteorological data. The study revealed that the results found using synthetic data were generalizable to the real-life data, as hypothesized. Moreover, we formulated a design recommendation table tuned to give users task- and category-specific design suggestions under various environment constraints. At last, we discuss the comparison between the lab and online versions of study 1 with respect to display size (lab study was done on big screen and vice versa). Our studies show the effectiveness of stylizing contour lines to represent multivariate data, reveal trade-offs among design parameters, and provide designers with important insights into the factors that influence multivariate interpretability. We also show some real-life scenarios where our visualization approach may improve decision making

The possibilities of visual spatial data analysis methods on human migration data.

The theme of this Master’s thesis is to research the possible methods for visual analysis of human migration. Precedents of analysis methods are considered as viable techniques that would successfully achieve a rewarding result if conducted on Finland´s migration database. Human migration phenomenon can be analyzed with the visualization of spatial data. Spatial data visualization components such as spatial data, maps and methods are discussed with focus on human migration. Human migration data are a type of spatial data that are georeferenced so as to give a context in relation to a location in real world. Maps are geographical presentation interfaces used for visual analysis of migration data. Special purpose and thematic maps or the combination of both are deemed suitable for this task as they are able to portray the information desired. Spatial data visualization techniques such as flow lines, flowstrates, map animation and space time cube are researched as tools for fully analyzing migration data. Flowstrates is a specialized visualization method that analyses the spatial and temporal dimensions of migration data by utilizing a combination technique of flow lines, timelines, and origin-destination matrices for the visualization of migration data. With this novel method, detailed information can be visualized on individual migration flow/route. The result of this thesis are the examples of the most fitting visual analysis methods for migration data. These examples serve as the possible methods for analyzing actual migration data in Finland

Text-based Spatial and Temporal Visualizations and their Applications in Visual Analytics

Textual labels are an essential part of most visualizations used in practice. However, these textual labels are mainly used to annotate other visualizations rather than being a central part of the visualization. Visualization researchers in areas like cartography and geovisualization have studied the combination of graphical features and textual labels to generate map based visualizations, but textual labels alone are not the primary focus in these representations. The idea of using symbols in visual representations and their interpretation as a quantity is gaining more traction. These types of representations are not only aesthetically appealing but also present new possibilities of encoding data. Such scenarios regularly arise while designing visual representations, where designers have to investigate feasibility of encoding information using symbols alone especially textual labels but the lack of readily available automated tools, and design guidelines makes it prohibitively expensive to experiment with such visualization designs. In order to address such challenges, this thesis presents the design and development of visual representations consisting entirely of text. These visual representations open up the possibility of encoding different types of spatial and temporal datasets. We report our results through two novel visualizations: typographic maps and text-based TextRiver visualization. Typographic maps merge text and spatial data into a visual representation where text alone forms the graphical features, mimicking the practices of human map makers. We also introduce methods to combine our automatic typographic maps technique with spatial datasets to generate thema-typographic maps where the properties of individual characters in the map are modified based on the underlying spatial data. Our TextRiver visualization is composed of collection of stream-like shapes consisting entirely of text where each stream represents thematic strength variations over time within a corpus. Such visualization enables additional ways to encode information contained in temporal datasets by modifying text attributes. We also conducted a usability evaluation to assess the potential value of our text-based TextRiver design

Adjacent versus coincident representations of geospatial uncertainty: Which promote better decisions?

International audience3D geological models commonly built to manage natural resources are much affected by uncertainty because most of the subsurface is inaccessible to direct observation. Appropriate ways to intuitively visualize uncertainties are therefore critical to draw appropriate decisions. However, empirical assessments of uncertainty visualization for decision making are currently limited to two-dimensional map data, while most geological entities are either surfaces embedded in a 3D space or volumes. This paper first reviews a typical example of decision making under uncertainty, where uncertainty visualization methods can actually make a difference. This issue is illustrated on a real Middle East oil and gas reservoir, looking for the optimal location of a new appraisal well. In a second step, we propose a user study that goes beyond traditional 2D map data, using 2.5D pressure data for the purposes of well design. Our experiments study the quality of adjacent versus coincident representations of spatial uncertainty as compared to the presentation of data without uncertainty; the representations quality is assessed in terms of decision accuracy. Our study was conducted within a group of 123 graduate students specialized in geology

Development of a geovisual analytics environment using parallel coordinates with applications to tropical cyclone trend analysis

A global transformation is being fueled by unprecedented growth in the quality, quantity, and number of different parameters in environmental data through the convergence of several technological advances in data collection and modeling. Although these data hold great potential for helping us understand many complex and, in some cases, life-threatening environmental processes, our ability to generate such data is far outpacing our ability to analyze it. In particular, conventional environmental data analysis tools are inadequate for coping with the size and complexity of these data. As a result, users are forced to reduce the problem in order to adapt to the capabilities of the tools. To overcome these limitations, we must complement the power of computational methods with human knowledge, flexible thinking, imagination, and our capacity for insight by developing visual analysis tools that distill information into the actionable criteria needed for enhanced decision support. In light of said challenges, we have integrated automated statistical analysis capabilities with a highly interactive, multivariate visualization interface to produce a promising approach for visual environmental data analysis. By combining advanced interaction techniques such as dynamic axis scaling, conjunctive parallel coordinates, statistical indicators, and aerial perspective shading, we provide an enhanced variant of the classical parallel coordinates plot. Furthermore, the system facilitates statistical processes such as stepwise linear regression and correlation analysis to assist in the identification and quantification of the most significant predictors for a particular dependent variable. These capabilities are combined into a unique geovisual analytics system that is demonstrated via a pedagogical case study and three North Atlantic tropical cyclone climate studies using a systematic workflow. In addition to revealing several significant associations between environmental observations and tropical cyclone activity, this research corroborates the notion that enhanced parallel coordinates coupled with statistical analysis can be used for more effective knowledge discovery and confirmation in complex, real-world data sets

Development of a geovisual analytics environment using parallel coordinates with applications to tropical cyclone trend analysis

A global transformation is being fueled by unprecedented growth in the quality, quantity, and number of different parameters in environmental data through the convergence of several technological advances in data collection and modeling. Although these data hold great potential for helping us understand many complex and, in some cases, life-threatening environmental processes, our ability to generate such data is far outpacing our ability to analyze it. In particular, conventional environmental data analysis tools are inadequate for coping with the size and complexity of these data. As a result, users are forced to reduce the problem in order to adapt to the capabilities of the tools. To overcome these limitations, we must complement the power of computational methods with human knowledge, flexible thinking, imagination, and our capacity for insight by developing visual analysis tools that distill information into the actionable criteria needed for enhanced decision support. In light of said challenges, we have integrated automated statistical analysis capabilities with a highly interactive, multivariate visualization interface to produce a promising approach for visual environmental data analysis. By combining advanced interaction techniques such as dynamic axis scaling, conjunctive parallel coordinates, statistical indicators, and aerial perspective shading, we provide an enhanced variant of the classical parallel coordinates plot. Furthermore, the system facilitates statistical processes such as stepwise linear regression and correlation analysis to assist in the identification and quantification of the most significant predictors for a particular dependent variable. These capabilities are combined into a unique geovisual analytics system that is demonstrated via a pedagogical case study and three North Atlantic tropical cyclone climate studies using a systematic workflow. In addition to revealing several significant associations between environmental observations and tropical cyclone activity, this research corroborates the notion that enhanced parallel coordinates coupled with statistical analysis can be used for more effective knowledge discovery and confirmation in complex, real-world data sets

Effective Visualizations of the Uncertainty in Hurricane Forecasts

The track forecast cone developed by the U.S. National Hurricane Center is the one most universally adopted by the general public, the news media, and governmental officials to enhance viewers\u27 understanding of the forecasts and their underlying uncertainties. However, current research has experimentally shown that it has limitations that result in misconceptions of the uncertainty included. Most importantly, the area covered by the cone tends to be misinterpreted as the region affected by the hurricane. In addition, the cone summarizes forecasts for the next three days into a single representation and, thus, makes it difficult for viewers to accurately determine crucial time-specific information. To address these limitations, this research develops novel alternative visualizations. It begins by developing a technique that generates and smoothly interpolates robust statistics from ensembles of hurricane predictions, thus creating visualizations that inherently include the spatial uncertainty by displaying three levels of positional storm strike risk at a specific point in time. To address the misconception of the area covered by the cone, this research develops time-specific visualizations depicting spatial information based on a sampling technique that selects a small, representative subset from an ensemble of points. It also allows depictions of such important storm characteristics as size and intensity. Further, this research generalizes the representative sampling framework to process ensembles of forecast tracks, selecting a subset of tracks accurately preserving the original distributions of available storm characteristics and keeping appropriately defined spatial separations. This framework supports an additional hurricane visualization portraying prediction uncertainties implicitly by directly showing the members of the subset without the visual clutter. We collaborated on cognitive studies that suggest that these visualizations enhance viewers\u27 ability to understand the forecasts because they are potentially interpreted more like uncertainty distributions. In addition to benefiting the field of hurricane forecasting, this research potentially enhances the visualization community more generally. For instance, the representative sampling framework for processing 2D points developed here can be applied to enhancing the standard scatter plots and density plots by reducing sizes of data sets. Further, as the idea of direct ensemble displays can possibly be extended to more general numerical simulations, it, thus, has potential impacts on a wide range of ensemble visualizations