Proportional Symbol Mapping in R

Visualization of spatial data on a map aids not only in data exploration but also in communication to impart spatial conception or ideas to others. Although recent carto-graphic functions in R are rapidly becoming richer, proportional symbol mapping, which is one of the common mapping approaches, has not been packaged thus far. Based on the theories of proportional symbol mapping developed in cartography, the authors developed some functions for proportional symbol mapping using R, including mathematical and perceptual scaling. An example of these functions demonstrated the new expressive power and options available in R, particularly for the visualization of conceptual point data.

Integrated Visualization of Human Brain Connectome Data

Visualization plays a vital role in the analysis of multi-modal neuroimaging data. A major challenge in neuroimaging visualization is how to integrate structural, functional and connectivity data to form a comprehensive visual context for data exploration, quality control, and hypothesis discovery. We develop a new integrated visualization solution for brain imaging data by combining scientific and information visualization techniques within the context of the same anatomic structure. New surface texture techniques are developed to map non-spatial attributes onto the brain surfaces from MRI scans. Two types of non-spatial information are represented: (1) time-series data from resting-state functional MRI measuring brain activation; (2) network properties derived from structural connectivity data for different groups of subjects, which may help guide the detection of differentiation features. Through visual exploration, this integrated solution can help identify brain regions with highly correlated functional activations as well as their activation patterns. Visual detection of differentiation features can also potentially discover image based phenotypic biomarkers for brain diseases

Multiple Uncertainties in Time-Variant Cosmological Particle Data

Though the mediums for visualization are limited, the potential dimensions of a dataset are not. In many areas of scientific study, understanding the correlations between those dimensions and their uncertainties is pivotal to mining useful information from a dataset. Obtaining this insight can necessitate visualizing the many relationships among temporal, spatial, and other dimensionalities of data and its uncertainties. We utilize multiple views for interactive dataset exploration and selection of important features, and we apply those techniques to the unique challenges of cosmological particle datasets. We show how interactivity and incorporation of multiple visualization techniques help overcome the problem of limited visualization dimensions and allow many types of uncertainty to be seen in correlation with other variables

Hybrid-Dimensional Visualization and Interaction - Integrating 2D and 3D Visualization with Semi-Immersive Navigation Techniques

The integration of 2D visualization and navigation techniques has reached a state where the potential for improvements is relatively low. With 3D-stereoscopy-compatible technology now commonplace not only in research but also in many households, the need for better 3D visualization and navigation techniques has increased. Nevertheless, for the representation of many abstract data such as networks, 2D visualization remains the primary choice. But often such abstract data is associated with spatial data, thereby increasing the need for combining both 2D and 3D visualization and navigation techniques. Here, we discuss a new hybrid-dimensional approach integrating 2D and 3D (stereoscopic) visualization as well as navigation into a semi-immersive virtual environment. This approach is compared to classical 6DOF navigation techniques. Three scientific as well as educational applications are presented: an educational car model, a plant simulation data exploration, and a cellular model with network exploration, each of these combining spatial with associated abstract data. The software is available at: http://Cm4.CELLmicrocosmos.org

Brain explorer for connectomic analysis

Visualization plays a vital role in the analysis of multimodal neuroimaging data. A major challenge in neuroimaging visualization is how to integrate structural, functional, and connectivity data to form a comprehensive visual context for data exploration, quality control, and hypothesis discovery. We develop a new integrated visualization solution for brain imaging data by combining scientific and information visualization techniques within the context of the same anatomical structure. In this paper, new surface texture techniques are developed to map non-spatial attributes onto both 3D brain surfaces and a planar volume map which is generated by the proposed volume rendering technique, spherical volume rendering. Two types of non-spatial information are represented: (1) time series data from resting-state functional MRI measuring brain activation; (2) network properties derived from structural connectivity data for different groups of subjects, which may help guide the detection of differentiation features. Through visual exploration, this integrated solution can help identify brain regions with highly correlated functional activations as well as their activation patterns. Visual detection of differentiation features can also potentially discover image-based phenotypic biomarkers for brain diseases

Interactive 3D visualization for theoretical Virtual Observatories

Virtual Observatories (VOs) are online hubs of scientific knowledge. They encompass a collection of platforms dedicated to the storage and dissemination of astronomical data, from simple data archives to e-research platforms offering advanced tools for data exploration and analysis. Whilst the more mature platforms within VOs primarily serve the observational community, there are also services fulfilling a similar role for theoretical data. Scientific visualization can be an effective tool for analysis and exploration of datasets made accessible through web platforms for theoretical data, which often contain spatial dimensions and properties inherently suitable for visualization via e.g. mock imaging in 2d or volume rendering in 3d. We analyze the current state of 3d visualization for big theoretical astronomical datasets through scientific web portals and virtual observatory services. We discuss some of the challenges for interactive 3d visualization and how it can augment the workflow of users in a virtual observatory context. Finally we showcase a lightweight client-server visualization tool for particle-based datasets allowing quantitative visualization via data filtering, highlighting two example use cases within the Theoretical Astrophysical Observatory.Comment: 10 Pages, 13 Figures, Accepted for Publication in Monthly Notices of the Royal Astronomical Societ

A semantically adaptable integrated visualization and natural exploration of multi-scale biomedical data

The exploration of biomedical data which involves heterogeneous sources coming from different spatial scales and medical domains is a challenging topic in current research. In this work, we combine efforts regarding multi-scale visualization, multimodal interaction and knowledge formalization for the exploration of multi-scale biomedical data. The knowledge formalization stores and organizes the information sources, the integrated visualization captures all relevant information for the domain expertise of the user and the multimodal interaction provides a natural exploration. We present a concrete example of use of the proposed exploratory system designed for a biologist investigating multi-scale pathologies.This work was supported from the EU Marie Curie ITN MultiScaleHuman (FP7-PEOPLE-2011-ITN, Grant agreement no.: 289897). The authors would like to thank all the partners for providing biomedical data sets.info:eu-repo/semantics/publishedVersio

Exploring Road Incident Data with Heat Maps

This research seeks to determine whether heat mapping is an effective technique for the visual exploration of road incident data. Four software prototypes, which adopted map, treemap and spatial treemap layouts, were developed using open source software. Whilst the visualization process described by Fry (2007) informed the development effort, the evaluation methodology was based on the Nested Process Model (Munzner, 2009). The results of two evaluation methods-a design study and the presentation and discussion of results with domain experts-confirm heat mapping's validity and provide requirements for further software development

VISUALIZATION-BASED DECISION SUPPORT FOR OPTIMIZING SITE SELECTION:QUARRIES IN LEBANON; WHERE TO?

Traditionally the term visualization has been used to describe the process of graphically conveying or presenting end results. This paper argues that the utility of visualization approaches extends beyond these limits as it plays key role in fields of exploration, analysis and presentation, which enhances planner\u27s capabilities to solve complex planning problems. It proposes a transdisciplinary method that combines visualization approaches to site selection, integrated with spatial scenario planning, and stakeholder participation. However, it focuses on visualization as it relates to spatial data, to be applied to all the stages of problem-solving in geographical analysis, from development of initial hypotheses, through knowledge discovery, analysis, presentation and evaluation. It uses three different spatial scenarios – nature conservation, residential expansion, and sustainable development- to investigate the potentials of GIS based visualization to develop maps of a range of plausible future for possible quarrying locations in Lebano

Interactive visual exploration of a large spatio-temporal dataset: Reflections on a geovisualization mashup

Exploratory visual analysis is useful for the preliminary investigation of large structured, multifaceted spatio-temporal datasets. This process requires the selection and aggregation of records by time, space and attribute, the ability to transform data and the flexibility to apply appropriate visual encodings and interactions. We propose an approach inspired by geographical 'mashups' in which freely-available functionality and data are loosely but flexibly combined using de facto exchange standards. Our case study combines MySQL, PHP and the LandSerf GIS to allow Google Earth to be used for visual synthesis and interaction with encodings described in KML. This approach is applied to the exploration of a log of 1.42 million requests made of a mobile directory service. Novel combinations of interaction and visual encoding are developed including spatial 'tag clouds', 'tag maps', 'data dials' and multi-scale density surfaces. Four aspects of the approach are informally evaluated: the visual encodings employed, their success in the visual exploration of the clataset, the specific tools used and the 'rnashup' approach. Preliminary findings will be beneficial to others considering using mashups for visualization. The specific techniques developed may be more widely applied to offer insights into the structure of multifarious spatio-temporal data of the type explored here