Masahiro Takatsuka

GeoVISTA studio: a codeless visual programming environment for geoscientific data analysis and visualization

The fundamental goal of the GeoVISTA Studio project is to improve geoscientific analysis by providing an environment that operationally integrates a wide range of analysis activities, including those both computationally and visually based. Improving the infrastructure used in analysis has far-reaching potential to better integrate human-based and computationally based expertise, and so ultimately improve scientific outcomes. To address these challenges, some difficult system design and software engineering problems must be overcome.This paper illustrates the design of a component-oriented system, GeoVISTA Studio, as a means to overcome such difficulties by using state-of-the-art component-based software engineering techniques. Advantages described include: ease of program construction (visual programming), an open (non-proprietary) architecture, simple component-based integration and advanced deployment methods. This versatility has the potential to change the nature of systems development for the geosciences, providing better mechanisms to coordinate complex functionality, and as a consequence, to improve analysis by closer integration of software tools and better engagement of the human expert. Two example applications are presented to illustrate the potential of the Studio environment for exploring and better understanding large, complex geographical datasets and for supporting complex visual and computational analysis.

Introducing GeoVISTA Studio: an integrated suite of visualization and computational methods for exploration and knowledge construction in geography

One barrier to the uptake of geocomputation is that, unlike GIS, it has no system or toolbox that provides easy access to useful functionality. This paper describes an experimental environment, GeoVISTA Studio, that attempts to address this shortcoming. Studio is a Java-based, visual programming environment that allows for the rapid development of complex data exploration and knowledge construction applications to support geographic analysis. It achieves this by leveraging advances in geocomputation, software engineering, visualization and machine learning. At the time of writing, Studio contains full 3D rendering capability and has the following functionality: interactive parallel coordinate plots, scatterplot, visual classifier, 2D map and image viewer, sophisticated colour selection (including Munsell colour-space), spreadsheet, statistics package, and supervised and unsupervised neural networks. Through examples of Studio at work, this paper demonstrates the roles that geocomputation and visualization can play throughout the scientific cycle of knowledge creation, emphasising their supportive and mutually beneficial relationship. A brief overview of different types of inference used in such knowledge creation activities is given, and related to the exploratory analysis tools described. By way of results, a detailed account of the use of these tools is presented, and various findings and insights generated are pointed out. The domain of application is the process of uncovering useful categories by which a taxonomy of landuse/landcover can be created. The proposed categories are then evaluated using a combination of neural and visual methods, to ensure their viability.

Automatic Transfer Function Generation Using Contour Tree Controlled Residue Flow Model and Color Harmonics

Transfer functions facilitate the volumetric data visualization by assigning optical properties to various data features and scalar values. Automation of transfer function specifications still remains a challenge in volume rendering. This paper presents an approach for automating transfer function generations by utilizing topological attributes derived from the contour tree of a volume. The contour tree acts as a visual index to volume segments, and captures associated topological attributes involved in volumetric data. A residue flow model based on Darcys law is employed to control distributions of opacity between branches of the contour tree. Topological attributes are also used to control color selection in a perceptual color space and create harmonic color transfer functions. The generated transfer functions can depict inclusion relationship between structures and maximize opacity and color differences between them. The proposed approach allows efficient automation of transfer function generations, and exploration on the data to be carried out based on controlling of opacity residue flow rate instead of complex low-level transfer function parameter adjustments. Experiments on various data sets demonstrate the practical use of our approach in transfer function generations.

Low-cost interactive active monocular range finder

This paper describes a low-cost interactive active monocular range finder and illustrates the effect of introducing interactivity to the range acquisition process. The range finder consists of only one camera and a laser pointer to which three LEDs are attached. When a user scans the laser along surfaces of objects, the camera captures the image of spots (one from the laser and the others from LEDs), and triangulation is carried our using the cameras viewing direction and the optical axis of the laser. The user interaction allows the range finder to acquire range data in which the sampling rate varies across the object depending on the underlying surface structures. Moreover the processes of separating objects from the background and/or finding parts in the object can be achieved using the operators knowledge of the objects.

Exploratory geospatial analysis using GeoVISTA Studio: from a desktop to the Web

The main objective of the GeoVISTA Studio project is to improve geoscientific analysis by providing an environment that operationally integrates a wide range of analysis activities, including those both computationally and visually based. We argue that improving the infrastructure used in analysis has far-reaching potential to better integrate human-based and computationally-based expertise, whether locally or remotely available, and so ultimately improve scientific outcomes. The improvements to research infrastructure proposed have implications for Web-based deployment of analysis methods. This paper illustrates various features of GeoVISTA Studio, such as ease of program construction (visual programming), an open (non-proprietary) architecture, simple component-based integration and advanced deployment methods, which allow us to rapidly construct and share applications that combine geocomputation and geovisualization methods. It also demonstrates that GeoVISTA Studio can be used as both a Web GIS application and a Web GIS application builder.

Estimating virtual touchscreen for fingertip interaction with large displays

Large displays are everywhere. However, the computer mouse remains the most common interaction tool for such displays. We propose a new approach for fingertip interaction with large display systems using monocular computer vision. By taking into account the location of the user and the interaction area available, we can estimate an interaction surface - virtual touchscreen - between the display and the user. Users can use their pointing finger to interact with the display as if it was brought forward and presented directly in front of them, while preserving viewing angle. An interaction model is presented to describe the interaction with the virtual touchscreen, using the head-hand line method. Initial results, in the form of a work-in-progress prototype, demonstrate the feasibility of this concept.

Visualizing Multivariate Networks: A Hybrid Approach

Multivariate networks are data sets that describe not only the relationships between a set of entities but also their attributes. In this paper, we present a new technique to determine the layout of a multivariate network using geodesic self-organizing map (GeoSOM). During the training process of a GeoSOM, graph distances are non-linearly combined with attribute similarities based on the networks graph distance distribution. The resulted layout has less edge crossings than those generated by the previous methods. We conducted a user study to evaluate the effectiveness of this hybrid approach. The results were compared against the most commonly used glyph-based technique. The user study shows that the hybrid approach helps users draw conclusions from both the relationship and vertex attributes of a multivariate network more quickly and accurately. In addition, users found it easier to compare different relationships of the same set of entities. Finally, the capability of the hybrid approach is demonstrated using the world military expenditures and weapon transfer networks.

Parallel batch training of the self-organizing map using openCL

The Self-Organizing Maps (SOMs) are popular artificial neural networks that are often used for data analyses through clustering and visualisation. SOMs mathematical model is inherently parallel. However, many implementations have not successfully exploited its parallelism because previous attempts often required cluster-like infrastructures. This article presents the parallel implementation of SOMs, particularly the batch map variant using Graphics Processing Units (GPUs) through the use of Open Computing Language (OpenCL).

Low-cost interactive active range finder

Abstract. This paper describes a new type of low-cost interactive active range finder and illustrates the effect of introducing interactivity to the range-acquisition process. The new range finder consists of only one camera and a laser pointer to which three LEDs are attached. When a user scans the laser, the camera captures the image of spots (one from the laser and the others from LEDs), and triangulation is carried out using the cameras viewing direction and the optical axis of the laser. The user interaction allows the range finder to acquire range data in which the sampling rate varies across the object depending on the underlying surface structures. Moreover, the processes of separating objects from the background and/or finding parts in the object can be achieved using the operators knowledge of the objects.

A multi-dimensional importance metric for contour tree simplification

Real-world data sets produce unmanageably large contour trees because of noise. Contour Tree Simplification (CTS) would remove small scale branches, and maintain essential structure of data. Despite multiple measures of importance (MOIs) available, conventional CTS approaches often use a single MOI, which is not enough in evaluating the importance of branches in the CTS. This paper proposes an importance-driven CTS approach. The proposed approach combines multiple MOIs through the introduction of various concepts to maximize advantages of each MOI. In the attribute space, various attributes of a branch are organized in a single space. The concept of the importance triangle is used to evaluate the importance of a branch by size of the importance triangle. It considers the whole attribute space and gives better evaluation of importance. Finally, new importance values of branches are compared in the importance space to make simplification decisions.Graphical Abstract