Collaborative Exploration and Sensemaking of Big Environmental Sound Data

Many ecologists are using acoustic monitoring to study animals and the health of ecosystems. Technological advances mean acoustic recording of nature can now be done at a relatively low cost, with minimal disturbance, and over long periods of time. Vast amounts of data are gathered yielding environmental soundscapes which requires new forms of visualization and interpretation of the data. Recently a novel visualization technique has been designed that represents soundscapes using dense visual summaries of acoustic patterns. However, little is known about how this visualization tool can be employed to make sense of soundscapes. Understanding how the technique can be best used and developed requires collaboration between interface, algorithm designers and ecologists. We empirically investigated the practices and needs of ecologists using acoustic monitoring technologies. In particular, we investigated the use of the soundscape visualization tool by teams of ecologists researching endangered species detection, species behaviour, and monitoring of ecological areas using long duration audio recordings. Our findings highlight the opportunities and challenges that ecologists face in making sense of large acoustic datasets through patterns of acoustic events. We reveal the characteristic processes for collaboratively generating situated accounts of natural places from soundscapes using visualization. We also discuss the biases inherent in the approach. Big data from nature has different characteristics from social and informational data sources that comprise much of the World Wide Web. We conclude with design implications for visual interfaces to facilitate collaborative exploration and discovery through soundscapes.     

Bird classification based on their sound patterns

In this paper we focus on automatic bird classification based on their sound patterns. This is useful in the field of ornithology for studying bird species and their behavior based on their sound. The proposed methodology may be used to conduct survey of birds. The proposed methods may be used to automatically classify birds using different audio processing and machine learning techniques on the basis of their chirping patterns. An effort has been made in this work to map characteristics of birds such as size, habitat, species and types of call, on to their sounds. This study is also part of a broader project that includes development of software and hardware systems to monitor the bird species that appear in different geographical locations which helps ornithologists to monitor environmental conditions with respect to specific bird species.     

Building a Virtual Ecosystem Dynamic Model for Root Research

Understanding the fundamental mechanistic processes within large environmental models has great implications in model interpretation and future improvement. However, obtaining a good understanding of these processes can be challenging due to the complexities in model structures and software configurations. This paper introduces a functional test framework - with unique approaches to tackling software complexities in large environmental models – to facilitate process-based model exploration and validation. A Virtual Ecosystem Dynamic Model is developed as a case study to better understand and validate root-related processes in the Community Land Model (CLM). The proposed framework could help empiricists better access the inner workings of large environmental models, and facilitate integrative collaborations among broad scientific communities including field scientists, environmental system modelers, and computer scientists.     

MizBee: A Multiscale Synteny Browser

In the field of comparative genomics, scientists seek to answer questions about evolution and genomic function by comparing the genomes of species to find regions of shared sequences. Conserve dsyntenic blocks are an important biological data abstraction for indicating regions of shared sequences. The goal of this work is to show multiple types of relationships at multiple scales in a way that is visually comprehensible in accordance with known perceptual principles. We present a task analysis for this domain where the fundamental questions asked by biologists can be understood by a characterization of relationships into the four types of proximity/location, size, orientation, and similarity/strength, and the four scales of genome, chromosome, block, and genomic feature. We also propose a new taxonomy of the design space for visually encoding conservation data. We present MizBee, a multiscale synteny browser with the unique property of providing interactive side-by-side views of the data across the range of scales supporting exploration of all of these relationship types. We conclude with case studies from two biologists who used MizBee to augment their previous automatic analysis work flow, providing anecdotal evidence about the efficacy of the system for the visualization of syntenic data, the analysis of conservation relationships, and the communication of scientific insights.     

SimilarityExplorer: A Visual Inter‐Comparison Tool for Multifaceted Climate Data

Inter‐comparison and similarity analysis to gauge consensus among multiple simulation models is a critical visualization problem for understanding climate change patterns. Climate models, specifically, Terrestrial Biosphere Models (TBM) represent time and space variable ecosystem processes, like, simulations of photosynthesis and respiration, using algorithms and driving variables such as climate and land use. While it is widely accepted that interactive visualization can enable scientists to better explore model similarity from different perspectives and different granularity of space and time, currently there is a lack of such visualization tools. In this paper we present three main contributions. First, we propose a domain characterization for the TBM community by systematically defining the domain‐specific intents for analyzing model similarity and characterizing the different facets of the data. Second, we define a classification scheme for combining visualization tasks and multiple facets of climate model data in one integrated framework, which can be leveraged for translating the tasks into the visualization design. Finally, we present SimilarityExplorer, an exploratory visualization tool that facilitates similarity comparison tasks across both space and time through a set of coordinated multiple views. We present two case studies from three climate scientists, who used our tool for a month for gaining scientific insights into model similarity. Their experience and results validate the effectiveness of our tool.     

A Visual Analytics Approach to Understanding Spatiotemporal Hotspots

As data sources become larger and more complex, the ability to effectively explore and analyze patterns among varying sources becomes a critical bottleneck in analytic reasoning. Incoming data contain multiple variables, high signal-to-noise ratio, and a degree of uncertainty, all of which hinder exploration, hypothesis generation/exploration, and decision making. To facilitate the exploration of such data, advanced tool sets are needed that allow the user to interact with their data in a visual environment that provides direct analytic capability for finding data aberrations or hotspots. In this paper, we present a suite of tools designed to facilitate the exploration of spatiotemporal data sets. Our system allows users to search for hotspots in both space and time, combining linked views and interactive filtering to provide users with contextual information about their data and allow the user to develop and explore their hypotheses. Statistical data models and alert detection algorithms are provided to help draw user attention to critical areas. Demographic filtering can then be further applied as hypotheses generated become fine tuned. This paper demonstrates the use of such tools on multiple geospatiotemporal data sets.     

Visualizing the Evolution of Community Structures in Dynamic Social Networks

Social network analysis is the study of patterns of interaction between social entities. The field is attracting increasing attention from diverse disciplines including sociology, epidemiology, and behavioral ecology. An important sociological phenomenon that draws the attention of analysts is the emergence of communities, which tend to form, evolve, and dissolve gradually over a period of time. Understanding this evolution is crucial to sociologists and domain scientists, and often leads to a better appreciation of the social system under study. Therefore, it is imperative that social network visualization tools support this task. While graph‐based representations are well suited for investigating structural properties of networks at a single point in time, they appear to be significantly less useful when used to analyze gradual structural changes over a period of time. In this paper, we present an interactive visualization methodology for dynamic social networks. Our technique focuses on revealing the community structure implied by the evolving interaction patterns between individuals. We apply our visualization to analyze the community structure in the US House of Representatives. We also report on a user study conducted with the participation of behavioral ecologists working with social network datasets that depict interactions between wild animals. Findings from the user study confirm that the visualization was helpful in providing answers to sociological questions as well as eliciting new observations on the social organization of the population under study.     

Making Sense of Scientific Simulation Ensembles With Semantic Interaction

In the study of complex physical systems, scientists use simulations to study the effects of different models and parameters. Seeking to understand the influence and relationships among multiple dimensions, they typically run many simulations and vary the initial conditions in what are known as ‘ensembles’. Ensembles are then a number of runs that are each multi-dimensional and multi-variate. In order to understand the connections between simulation parameters and patterns in the output data, we have been developing an approach to the visual analysis of scientific data that merges human expertise and intuition with machine learning and statistics. Our approach is manifested in a new visualization tool, GLEE (Graphically-Linked Ensemble Explorer), that allows scientists to explore, search, filter and make sense of their ensembles. GLEE uses visualization and semantic interaction (SI) techniques to enable scientists to find similarities and differences between runs, find correlation(s) between different parameters and explore relations and correlations across and between different runs and parameters. Our approach supports scientists in selecting interesting subsets of runs in order to investigate and summarize the factors and statistics that show variations and consistencies across different runs. In this paper, we evaluate our tool with experts to understand its strengths and weaknesses for optimization and inverse problems.     

基于游戏引擎的机场鸟击风险三维可视化方法

鸟击事件是威胁航空安全以及导致航空事故发生的最主要原因之一.鸟类生态调查研究和鸟击风险防控是机场必要的日常业务.为了使机场鸟击防范工作更有针对性，对存在于三维空间中抽象的鸟击风险评估结果进行可视化具有重要应用价值.本文提出一种基于游戏引擎的鸟击风险三维可视化方法，并以厦门翔安国际机场为例，开发了一套鸟击风险可视化系统来实现该方法.构建了机场周边10×40平方公里上空的三维地理场景及鸟击风险三维标量场，叠加机场净空区三维模型，根据鸟情观测数据，模拟鸟类飞行路径，最后通过摄像头漫游与飞机飞行模拟的方式实现了风险场场景的沉浸式动态展示.该系统可为机场飞行安全管控提供辅助决策支持.     

Visualizing visualizations. User interfaces for managing andexploring scientific visualization data

The process of scientific visualization is inherently iterative. A good visualization comes from experimenting with visualization, rendering, and viewing parameters to bring out the most relevant information in the data. A good data visualization system thus lets scientists interactively explore the parameter space intuitively. The more efficient the system, the fewer the number of iterations needed for parameter selection. Over the past 10 years, significant efforts have gone into advancing visualization technology (such as real-time volume rendering and immersive environments), but little into coherently representing the process and results (images and insights) of visualization. This information about the data exploration should be shared and reused. In particular, for types of data visualization with a high cost of producing images and less than obvious relationship between the rendering parameters and the image produced, a visual representation of the exploration process can make the process more efficient and effective. This visual representation of data exploration process and results can be incorporated into and become a part of the user interface of a data exploration system. That is, we need to go beyond the traditional graphical user interface (GUI) design by coupling it with a mechanism that helps users keep track of their visualization experience, use it to generate new visualizations, and share it with others. Doing so can reduce the cost of visualization, particularly for routine analysis of large-scale data sets     

Distribution-Driven Visualization of Volume Data

Feature detection and display are the essential goals of the visualization process. Most visualization software achieves these goals by mapping properties of sampled intensity values and their derivatives to color and opacity. In this work, we propose to explicitly study the local frequency distribution of intensity values in broader neighborhoods centered around each voxel. We have found frequency distributions to contain meaningful and quantitative information that is relevant for many kinds of feature queries. Our approach allows users to enter predicate-based hypotheses about relational patterns in local distributions and render visualizations that show how neighborhoods match the predicates. Distributions are a familiar concept to nonexpert users, and we have built a simple graphical user interface for forming and testing queries interactively. The query framework readily applies to arbitrary spatial data sets and supports queries on time variant and multifield data. Users can directly query for classes of features previously inaccessible in general feature detection tools. Using several well-known data sets, we show new quantitative features that enhance our understanding of familiar visualization results.     

A generic framework for deriving process stoichiometry in environmental models

It has been recognized for many decades that the stoichiometry of biological reactions is important for linking ecological and biogeochemical processes. However, only during the past decade has the scientific community become aware that “biological stoichiometry” may also help bridge evolutionary biology and ecosystem ecology. This awareness led to increasing attention to biological process stoichiometry in ecology during the past decade. Despite this trend in ecological data analysis and interpretation, ecological models are still predominantly formulated without sufficient attention to process stoichiometry. To support scientists in formulating stoichiometry in process models based on elemental mass conservation, we transfer techniques from chemical process engineering to achieve the following objectives: (i) develop a generic mathematical framework to formulate and solve stoichiometric equations; (ii) facilitate the extension of currently used process stoichiometries to consider more elements (e.g. adding S and/or Si to C, H, O, N and P); (iii) identify the need for stoichiometric constraints of biogeochemical processes in addition to elemental mass balances; (iv) unify approaches to characterize organic matter by elemental mass fractions, organic carbon, or chemical oxygen demand; (v) provide a small package of functions for the statistics and graphics software R (http://www.r-project.org) to support environmental model building, and (vi) show how to incorporate automatic stoichiometric calculations into next generation environmental simulation software. The small R package “stoichcalc” can be downloaded from http://www.eawag.ch/^～reichert or from the package repository of the R project (http://www.r-project.org).     

Cinema image-based in situ analysis and visualization of MPAS-ocean simulations

Due to power and I/O constraints associated with extreme scale scientific simulations, in situ analysis and visualization will become a critical component to scientific exploration and discovery. Current analysis and visualization options at extreme scale are presented in opposition: write files to disk for interactive, exploratory analysis, or perform in situ analysis to save data products about phenomena that a scientists knows about in advance. In this paper, we demonstrate extreme scale visualization of MPAS-Ocean simulations leveraging a third option based on Cinema, which is a novel framework for highly interactive, image-based in situ analysis and visualization that promotes exploration.     

Conservation planning to zone protected areas under optimal landscape management for bird conservation

This study proposes a two-stage conservation planning approach. Firstly, the Land-Use Pattern Optimization-library is used to maximize the suitability of habitats for target species by optimizing configuration based on the current landscape. Secondly, the systematic conservation planning tool, Marxan is used to identify protected areas based on the estimated species distributions from the optimal landscape configuration. We compared our conservation plan for three target bird species from a highland farm with the conservation plan produced using Marxan alone. Our comparison showed the effectiveness of our approach by selecting a reserve network with higher habitat suitability, better connection, and smaller size after relatively minor landscape modification. The proposed approach advances previous reserve site selection algorithms by considering optimal landscape configuration and potential species distributions for a reserve network design. Our approach yields priority maps to guide the design of a reserve network as well as identify landscape management for conservation.     

Hypothesis generation in climate research with interactive visual data exploration

One of the most prominent topics in climate research is the investigation, detection, and allocation of climate change. In this paper, we aim at identifying regions in the atmosphere (e.g., certain height layers) which can act as sensitive and robust indicators for climate change. We demonstrate how interactive visual data exploration of large amounts of multi-variate and time-dependent climate data enables the steered generation of promising hypotheses for subsequent statistical evaluation. The use of new visualization and interaction technology--in the context of a coordinated multiple views framework--allows not only to identify these promising hypotheses, but also to efficiently narrow down parameters that are required in the process of computational data analysis. Two datasets, namely an ECHAM5 climate model run and the ERA-40 reanalysis incorporating observational data, are investigated. Higher-order information such as linear trends or signal-to-noise ratio is derived and interactively explored in order to detect and explore those regions which react most sensitively to climate change. As one conclusion from this study, we identify an excellent potential for usefully generalizing our approach to other, similar application cases, as well.     

VENUS: A Geometrical Representation for Quantum State Visualization

Visualizations have played a crucial role in helping quantum computing users explore quantum states in various quantum computing applications. Among them, Bloch Sphere is the widely-used visualization for showing quantum states, which leverages angles to represent quantum amplitudes. However, it cannot support the visualization of quantum entanglement and superposition, the two essential properties of quantum computing. To address this issue, we propose VENUS, a novel visualization for quantum state representation. By explicitly correlating 2D geometric shapes based on the math foundation of quantum computing characteristics, VENUS effectively represents quantum amplitudes of both the single qubit and two qubits for quantum entanglement. Also, we use multiple coordinated semicircles to naturally encode probability distribution, making the quantum superposition intuitive to analyze. We conducted two well-designed case studies and an in-depth expert interview to evaluate the usefulness and effectiveness of VENUS. The result shows that VENUS can effectively facilitate the exploration of quantum states for the single qubit and two qubits.     

石油勘探开发中的数据场可视化技术应用研究

石油勘探开发是石油工业的重要部分,在石油勘探开发过程中,会采集到大量的数据,形成三维数据场,使用这些数据的目的足为了准确地再现油藏分布以及石油勘探开发过程中的参数的动态变化。该文是将三维数据场可视化技术应用到石油勘探开发过程中的地质建模和数值模拟之中,利用可视化技术,尤其是三维数据场可视化技术,实现了从大量数据中构造出三维图像。该文在算法中使用了包围盒技术和分层存储结构来提高算法的速度,达到了较好的效果,直观地再现了石油在油气藏中的状态,指导设计人员进行井位的确定,提高石油生产的效益,辅助相关领域工作人员的分析、设计,具有广泛应用前景。     

Tasks,Techniques, and Tools for Genomic Data Visualization

Genomic data visualization is essential for interpretation and hypothesis generation as well as a valuable aid in communicating discoveries. Visual tools bridge the gap between algorithmic approaches and the cognitive skills of investigators. Addressing this need has become crucial in genomics, as biomedical research is increasingly data‐driven and many studies lack well‐defined hypotheses. A key challenge in data‐driven research is to discover unexpected patterns and to formulate hypotheses in an unbiased manner in vast amounts of genomic and other associated data. Over the past two decades, this has driven the development of numerous data visualization techniques and tools for visualizing genomic data. Based on a comprehensive literature survey, we propose taxonomies for data, visualization, and tasks involved in genomic data visualization. Furthermore, we provide a comprehensive review of published genomic visualization tools in the context of the proposed taxonomies.     

Sequential document visualization

Documents and other categorical valued time series are often characterized by the frequencies of short range sequential patterns such as n-grams. This representation converts sequential data of varying lengths to high dimensional histogram vectors which are easily modeled by standard statistical models. Unfortunately, the histogram representation ignores most of the medium and long range sequential dependencies making it unsuitable for visualizing sequential data. We present a novel framework for sequential visualization of discrete categorical time series based on the idea of local statistical modeling. The framework embeds categorical time series as smooth curves in the multinomial simplex summarizing the progression of sequential trends. We discuss several visualization techniques based on the above framework and demonstrate their usefulness for document visualization.