A Method for the Perceptual Optimization of Complex Visualizations

A common problem in visualization applications is the display of one surface overlying another. Unfortunately, it is extremely difficult to do this clearly and effectively. Stereoscopic viewing can help, but in order for us to be able to see both surfaces simultaneously, they must be textured, and the top surface must be made partially transparent. There is also abundant evidence that all textures are not equal in helping to reveal surface shape, but there are no general guidelines describing the best set of textures to be used in this way. What makes the problem difficult to perceptually optimize is that there are a great many variables involved. Both foreground and background textures must be specified in terms of their component colors, texture element shapes, distributions, and sizes. Also to be specified is the degree of transparency for the foreground texture components. Here we report on a novel approach to creating perceptually optimal solutions to complex visualization problems and we apply it to the overlapping surface problem as a test case. Our approach is a three-stage process. In the first stage we create a parameterized method for specifying a foreground and background pair of textures. In the second stage a genetic algorithm is applied to a population of texture pairs using subject judgments as a selection criterion. Over many trials effective texture pairs evolve. The third stage involves characterizing and generalizing the examples of effective textures. We detail this process and present some early results

Prototyping Information Visualization in 3D City Models: a Model-based Approach

When creating 3D city models, selecting relevant visualization techniques is a particularly difficult user interface design task. A first obstacle is that current geodata-oriented tools, e.g. ArcGIS, have limited 3D capabilities and limited sets of visualization techniques. Another important obstacle is the lack of unified description of information visualization techniques for 3D city models. If many techniques have been devised for different types of data or information (wind flows, air quality fields, historic or legal texts, etc.) they are generally described in articles, and not really formalized. In this paper we address the problem of visualizing information in (rich) 3D city models by presenting a model-based approach for the rapid prototyping of visualization techniques. We propose to represent visualization techniques as the composition of graph transformations. We show that these transformations can be specified with SPARQL construction operations over RDF graphs. These specifications can then be used in a prototype generator to produce 3D scenes that contain the 3D city model augmented with data represented using the desired technique.Comment: Proc. of 3DGeoInfo 2014 Conference, Dubai, November 201

The State of the Art in Cartograms

Cartograms combine statistical and geographical information in thematic maps, where areas of geographical regions (e.g., countries, states) are scaled in proportion to some statistic (e.g., population, income). Cartograms make it possible to gain insight into patterns and trends in the world around us and have been very popular visualizations for geo-referenced data for over a century. This work surveys cartogram research in visualization, cartography and geometry, covering a broad spectrum of different cartogram types: from the traditional rectangular and table cartograms, to Dorling and diffusion cartograms. A particular focus is the study of the major cartogram dimensions: statistical accuracy, geographical accuracy, and topological accuracy. We review the history of cartograms, describe the algorithms for generating them, and consider task taxonomies. We also review quantitative and qualitative evaluations, and we use these to arrive at design guidelines and research challenges

InferenceMAP: Mapping of Single-Molecule Dynamics with Bayesian Inference

Single-particle tracking (SPT) grants unprecedented insight into cellular function at the molecular scale [1]. Throughout the cell, the movement of single-molecules is generally heterogeneous and complex. Hence, there is an imperative to understand the multi-scale nature of single-molecule dynamics in biological systems. We have previously shown that with high-density SPT, spatial maps of the parameters that dictate molecule motion can be generated to intricately describe cellular environments [2,3,4]. To date, however, there exist no publically available tools that reconcile trajectory data to generate the aforementioned maps. We address this void in the SPT community with InferenceMAP: an interactive software package that uses a powerful Bayesian method to map the dynamic cellular space experienced by individual biomolecules.Comment: 56 page

Characterization of Two-Phase Flow in Microchannels

Aluminum multi-port microchannel tubes are currently utilized in automotive air conditioners for refrigerant condensation. Recent research activities are directed toward developing other air conditioning and refrigeration systems with microchannel condensers and evaporators. Three parameters are necessary to analyze a heat exchanger performance: heat transfer, pressure drop, and void fraction. The purpose of this investigation is the experimental investigation of void fraction and frictional pressure drop in microchannels. A flow visualization analysis is another important goal for two-phase flow behavior understanding and experimental analysis. Experiments were performed with a 6-port and a 14-port microchannel with hydraulic diameters of 1.54 mm and 1.02 mm, respectively. Mass fluxes from 50 to 300 kg/s.m2 (range of most typical automotive applications) are operated, with quality ranging from 0% to 100% for two-phase flow experiments. R410A, R134a, and air-water mixtures are used as primary fluids. The results from the flow visualization studies indicate that several flow configurations may exist in multi-port microchannel tubes at the same time while constant mass flux and quality flow conditions are maintained. Flow mapping of the fluid regimes is accomplished by developing functions that describe the fraction of time or the probability that the fluid exists in an observed flow configuration. Experimental analysis and flow observations suggest that pressure drop and void fraction in microchannel is dependent on the most probable flow regime at which the two-phase mixture is flowing. In general, correlations for void fraction and pressure drop predictions are based in a separated flow model and do not predict the experimental results in the range of conditions investigated. A flow regime based model is developed for pressure drop and void fraction predictions in microchannels.Air Conditioning and Refrigeration Project 10

Effective Visualization Approaches For Ultra-High Dimensional Datasets

Multivariate informational data, which are abstract as well as complex, are becoming increasingly common in many areas such as scientific, medical, social, business, and so on. Displaying and analyzing large amounts of multivariate data with more than three variables of different types is quite challenging. Visualization of such multivariate data suffers from a high degree of clutter when the numbers of dimensions/variables and data observations become too large. We propose multiple approaches to effectively visualize large datasets of ultrahigh number of dimensions by generalizing two standard multivariate visualization methods, namely star plot and parallel coordinates plot. We refine three variants of the star plot, which include overlapped star plot, shifted origin plot, and multilevel star plot by embedding distribution plots, displaying dataset in groups, and supporting adjustable positioning of the star axes. We introduce a bifocal parallel coordinates plot (BPCP) based on the focus + context approach. BPCP splits vertically the overall rendering area into the focus and context regions. The focus area maps a few selected dimensions of interest at sufficiently wide spacing. The remaining dimensions are represented in the context area in a compact way to retain useful information and provide the data continuity. The focus display can be further enriched with various options, such as axes overlays, scatterplot, and nested PCPs. In order to accommodate an arbitrarily large number of dimensions, the context display supports the multi-level stacked view. Finally, we present two innovative ways of enhancing parallel coordinates axes to better understand all variables and their interrelationships in high-dimensional datasets. Histogram and circle/ellipse plots based on uniform and non-uniform frequency/density mappings are adopted to visualize distributions of numerical and categorical data values. Color-mapped axis stripes are designed in the parallel coordinates layout so that correlations can be fully realized in the same display plot irrespective of axes locations. These colors are also propagated to histograms as stacked bars and categorical values as pie charts to further facilitate data exploration. By using the datasets consisting of 25 to 130 variables of different data types we have demonstrated effectiveness of the proposed multivariate visualization enhancements