Visual analysis of document triage data

As part of the information seeking process, a large amount of effort is invested in order to study and understand how information seekers search through documents such that they can assess their relevance. This search and assessment of document relevance, known as document triage, is an important information seeking process, but is not yet well understood. Human-computer interaction (HCI) and digital library scientists have undertaken a series of user studies involving information seeking, collected a large amount of data describing information seekers' behavior during document search. Next to this, we have witnessed a rapid increase in the number of off-the-shelf visualization tools which can benefit document triage study. Here we set out to utilize existing information visualization techniques and tools in order to gain a better understanding of the large amount of user-study data collected by HCI and digital library researchers. We describe the range of available tools and visualizations we use in order to increase our knowledge of document triage. Treemap, parallel coordinates, stack graph, matrix chart, as well as other visualization methods, prove to be insightful in exploring, analyzing and presenting user behavior during document triage. Our findings and visualizations are evaluated by HCI and digital library researchers studying this proble

New insights into the suitability of the third dimension for visualizing multivariate/multidimensional data: a study based on loss of quality quantification

Most visualization techniques have traditionally used two-dimensional, instead of three-dimensional representations to visualize multidimensional and multivariate data. In this article, a way to demonstrate the underlying superiority of three-dimensional, with respect to two-dimensional, representation is proposed. Specifically, it is based on the inevitable quality degradation produced when reducing the data dimensionality. The problem is tackled from two different approaches: a visual and an analytical approach. First, a set of statistical tests (point classification, distance perception, and outlier identification) using the two-dimensional and three-dimensional visualization are carried out on a group of 40 users. The results indicate that there is an improvement in the accuracy introduced by the inclusion of a third dimension; however, these results do not allow to obtain definitive conclusions on the superiority of three-dimensional representation. Therefore, in order to draw further conclusions, a deeper study based on an analytical approach is proposed. The aim is to quantify the real loss of quality produced when the data are visualized in two-dimensional and three-dimensional spaces, in relation to the original data dimensionality, to analyze the difference between them. To achieve this, a recently proposed methodology is used. The results obtained by the analytical approach reported that the loss of quality reaches significantly high values only when switching from three-dimensional to two-dimensional representation. The considerable quality degradation suffered in the two-dimensional visualization strongly suggests the suitability of the third dimension to visualize data

DesignSense: A Visual Analytics Interface for Navigating Generated Design Spaces

Generative Design (GD) produces many design alternatives and promises novel and performant solutions to architectural design problems. The success of GD rests on the ability to navigate the generated alternatives in a way that is unhindered by their number and in a manner that reflects design judgment, with its quantitative and qualitative dimensions. I address this challenge by critically analyzing the literature on design space navigation (DSN) tools through a set of iteratively developed lenses. The lenses are informed by domain experts\u27 feedback and behavioural studies on design navigation under choice-overload conditions. The lessons from the analysis shaped DesignSense, which is a DSN tool that relies on visual analytics techniques for selecting, inspecting, clustering and grouping alternatives. Furthermore, I present case studies of navigating realistic GD datasets from architecture and game design. Finally, I conduct a formative focus group evaluation with design professionals that shows the tool\u27s potential and highlights future directions

Education in cartography: what is the status of young people's map-reading skills?

Due to recent technological progress, maps have become more popular than ever before. This is especially true for young people, who interact with these technologies on a daily basis. Therefore, it is essential that these potential map users possess sufficient knowledge and skills to process the content of cartographic products. A user study was conducted during which pupils (aged 11-18years) and geography students (>18years) had to solve a number of cartography questions using topographic maps. The data were analyzed statistically, taking into account a number of potentially influencing factors (user characteristics) on the participants' results: age, gender, youth club membership, knowledge about the area, among others. The results show a rising trend in the pupils' scores with increasing age, which can be explained by education in cartography at school. Geography students perform much better, but no influence of any other user characteristics was detected. For pupils, knowledge about the area and gender might be considered as influencing factors. However, the detected influence of gender depends on the scoring system

EMMIX-uskew: An R Package for Fitting Mixtures of Multivariate Skew t-distributions via the EM Algorithm

This paper describes an algorithm for fitting finite mixtures of unrestricted Multivariate Skew t (FM-uMST) distributions. The package EMMIX-uskew implements a closed-form expectation-maximization (EM) algorithm for computing the maximum likelihood (ML) estimates of the parameters for the (unrestricted) FM-MST model in R. EMMIX-uskew also supports visualization of fitted contours in two and three dimensions, and random sample generation from a specified FM-uMST distribution. Finite mixtures of skew t-distributions have proven to be useful in modelling heterogeneous data with asymmetric and heavy tail behaviour, for example, datasets from flow cytometry. In recent years, various versions of mixtures with multivariate skew t (MST) distributions have been proposed. However, these models adopted some restricted characterizations of the component MST distributions so that the E-step of the EM algorithm can be evaluated in closed form. This paper focuses on mixtures with unrestricted MST components, and describes an iterative algorithm for the computation of the ML estimates of its model parameters. The usefulness of the proposed algorithm is demonstrated in three applications to real data sets. The first example illustrates the use of the main function fmmst in the package by fitting a MST distribution to a bivariate unimodal flow cytometric sample. The second example fits a mixture of MST distributions to the Australian Institute of Sport (AIS) data, and demonstrate that EMMIX-uskew can provide better clustering results than mixtures with restricted MST components. In the third example, EMMIX-uskew is applied to classify cells in a trivariate flow cytometric dataset. Comparisons with other available methods suggests that the EMMIX-uskew result achieved a lower misclassification rate with respect to the labels given by benchmark gating analysis