Street-level visual appearances play an important role in studying social systems, such as understanding the built environment, driving routes, and associated social and economic factors. It has not been integrated into a typical geographical visualization interface (e.g., map services) for planning driving routes. In this article, we study this new visualization task with several new contributions. First, we experiment with a set of AI techniques and propose a solution of using semantic latent vectors for quantifying visual appearance features. Second, we calculate image similarities among a large set of street-view images and then discover spatial imagery patterns. Third, we integrate these discovered patterns into driving route planners with new visualization techniques. Finally, we present VivaRoutes, an interactive visualization prototype, to show how visualizations leveraged with these discovered patterns can help users effectively and interactively explore multiple routes. Furthermore, we conducted a user study to assess the usefulness and utility of VivaRoutes.

Vision-based deep learning (DL) methods have made great progress in learning autonomous driving models from large-scale crowd-sourced video datasets. They are trained to predict instantaneous driving behaviors from video data captured by on-vehicle cameras. In this paper, we develop a geo-context aware visualization system for the study of Autonomous Driving Model (ADM) predictions together with large-scale ADM video data. The visual study is seamlessly integrated with the geographical environment by combining DL model performance with geospatial visualization techniques. Model performance measures can be studied together with a set of geospatial attributes over map views. Users can also discover and compare prediction behaviors of multiple DL models in both city-wide and street-level analysis, together with road images and video contents. Therefore, the system provides a new visual exploration platform for DL model designers in autonomous driving. Use cases and domain expert evaluation show the utility and effectiveness of the visualization system.

Abstract Layer‐wise Relevance Propagation (LRP) is an emerging and widely‐used method for interpreting the prediction results of convolutional neural networks (CNN). LRP developers often select and employ different relevance backpropagation rules and parameters, to compute relevance scores on input images. However, there exists no obvious solution to define a “best” LRP model. A satisfied model is highly reliant on pertinent images and designers' goals. We develop a visual model designer, named as VisLRPDesigner, to overcome the challenges in the design and use of LRP models. Various LRP rules are unified into an integrated framework with an intuitive workflow of parameter setup. VisLRPDesigner thus allows users to interactively configure and compare LRP models. It also facilitates relevance‐based visual analysis with two important functions: relevance‐based pixel flipping and neuron ablation. Several use cases illustrate the benefits of VisLRPDesigner. The usability and limitation of the visual designer is evaluated by LRP users.

Existing interactive visualization tools for deep learning are mostly applied to the training, debugging, and refinement of neural network models working on natural images. However, visual analytics tools are lacking for the specific application of x-ray image classification with multiple structural attributes. In this paper, we present an interactive system for domain scientists to visually study the multiple attributes learning models applied to x-ray scattering images. It allows domain scientists to interactively explore this important type of scientific images in embedded spaces that are defined on the model prediction output, the actual labels, and the discovered feature space of neural networks. Users are allowed to flexibly select instance images, their clusters, and compare them regarding the specified visual representation of attributes. The exploration is guided by the manifestation of model performance related to mutual relationships among attributes, which often affect the learning accuracy and effectiveness. The system thus supports domain scientists to improve the training dataset and model, find questionable attributes labels, and identify outlier images or spurious data clusters. Case studies and scientists feedback demonstrate its functionalities and usefulness.

Abstract Data containing geospatial semantics, such as geotagged tweets, travel blogs, and crime reports, associates natural language texts with geographical locations. This paper presents a lens‐based visual interaction technique, GTMapLens, to flexibly browse the geo‐text data on a map. It allows users to perform dynamic focus+context exploration by using movable lenses to browse geographical regions, find locations of interest, and perform comparative and drill‐down studies. Geo‐text data is visualized in a way that users can easily perceive the underlying geospatial semantics along with lens moving. Based on a requirement analysis with a cohort of multidisciplinary domain experts, a set of lens interaction techniques are developed including keywords control, path management, context visualization, and snapshot anchors. They allow users to achieve a guided and controllable exploration of geo‐text data. A hierarchical data model enables the interactive lens operations by accelerated data retrieval from a geo‐text database. Evaluation with real‐world datasets is presented to show the usability and effectiveness of GTMapLens.

Videos embedded with spatial coordinates, especially when combined with additional expert insights, offer the potential to acquire fine-scale multi-time period contextualized data for a variety of different environments. However, while these geospatial multimedia (GSMM) data include abundant spatiotemporal, semantic and visual information, the means to fully leverage their potential using a suite of visual and interactive analysis techniques and tools has thus far been lacking. In this paper, we address this gap by first identifying the types of tasks required of GSMM data, and then presenting a solution platform. This GeoVisuals system utilizes a visual analysis approach built on semantic data points that can be integrated spatially, which in turn enables management in a unified database with combined spatio-temporal and text querying. A set of visualization functions are integrated in two investigation modes: geo-video analysis and geo-location analysis.

In modern cities, service providers want to identify the regions that are hard to reach from multiple fire stations, a citizen wants to meet with friends in a restaurant close to everyone, and administrators want to find whether an area far from two bus stations needs a new one. Such tasks involve studying the dynamic accessibility of the urban structures over multiple geospatial and temporal constraints, which is an important topic in geographical sciences and urban transportation. In this paper, we present a new computational model and a visualization system that help domain users to interactively study the jointly constrained accessible regions, street segments, and Points of Interest (POIs). In particular, Urban Structure Accessibility Visualization system is built upon a new Min-Max Joint Set model, where specifically designed set operations not only represent the accessible regions but also compute the minimum and maximum access times to urban structures from the joint constraints. The computation and visualization are supported by a new graph model that accommodates the real-world dynamic traffic situation and the geographical settings of urban street segments and POIs. The visualization system allows the users to conveniently construct and manage accessible regions and visually explore the urban structures inside them.

Visual querying is essential for interactively exploring massive trajectory data. However, the data uncertainty imposes profound challenges to fulfill advanced analytics requirements. On the one hand, many underlying data does not contain accurate geographic coordinates, e.g., positions of a mobile phone only refer to the regions (i.e., mobile cell stations) in which it resides, instead of accurate GPS coordinates. On the other hand, domain experts and general users prefer a natural way, such as using a natural language sentence, to access and analyze massive movement data. In this paper, we propose a visual analytics approach that can extract spatial-temporal constraints from a textual sentence and support an effective query method over uncertain mobile trajectory data. It is built up on encoding massive, spatially uncertain trajectories by the semantic information of the POls and regions covered by them, and then storing the trajectory documents in text database with an effective indexing scheme. The visual interface facilitates query condition specification, situation-aware visualization, and semantic exploration of large trajectory data. Usage scenarios on real-world human mobility datasets demonstrate the effectiveness of our approach.

Sensing technologies, social media, and large-scale computing infrastructures have produced a variety of traffic and transportation data, e.g., human mobility, mobile trajectories, mobile phone calls, traffic, and geographical data. Despite the wealth of research on intelligent transportation systems, contemporary analytical tools are often inadequate for handling the data with the character of large volume, sparseness, and heterogeneity, let alone for supporting interactive visual analysis for data-intensive applications. Visual analytics can build bridges between the capability of data processing and human intelligence to promote addressing various transportation problems. On one hand, by employing visual channels to represent datasets and transforming various types of data into appropriate visual components, visualization can enhance understanding and analysis. On the other hand, an interactive interface allows users to investigate and directly access selected data points or features, discover interesting patterns or events, and engage in visual reasoning that allows users to gain insights, e.g., it is desirable to only show the most relevant portions of a dataset while giving directions for potential exploration.

Many cities and countries are now striving to create intelligent transportation systems that utilize the current abundance of multisource and multiform data related to the functionality and the use of transportation infrastructure to better support human mobility, interests, and lifestyles. Such intelligent transportation systems aim to provide novel services that can enable transportation consumers and managers to be better informed and make safer and more efficient use of the infrastructure. However, the transportation domain is characterized by both complex data and complex problems, which calls for visual analytics approaches. The science of visual analytics is continuing to develop principles, methods, and tools to enable synergistic work between humans and computers through interactive visual interfaces. Such interfaces support the unique capabilities of humans (such as the flexible application of prior knowledge and experiences, creative thinking, and insight) and couple these abilities with machines' computational strengths, enabling the generation of new knowledge from large and complex data. In this paper, we describe recent developments in visual analytics that are related to the study of movement and transportation systems and discuss how visual analytics can enable and improve the intelligent transportation systems of the future. We provide a survey of literature from the visual analytics domain and organize the survey with respect to the different types of transportation data, movement and its relationship to infrastructure and behavior, and modeling and planning. We conclude with lessons learned and future directions, including social transportation, recommender systems, and policy implications.

Thanks to the ubiquitous cell phone use, we have never been so close to uncover population mobility patterns in urban area. While some researches utilize cellphone call records to mine population patterns, few works aim to depict population movement in adaptively spatial and temporal representations, i.e., from a community, a district in the city over an hour, a day to a week. In this paper, we construct a system which deciphers, transforms, queries, and visualizes the records from the millions of users in a city. In particular, we design a data structure, namely MobiHash, which collects phone call records over base stations and indexes them by utilizing a Voronoi division of the urban space. MobiHash supports responsive data queries so that users can interactively retrieve trajectories reflecting population flows in areas of interest. Moreover, population movement is represented as vector fields to reduce visual clutter and occlusions. Because of sparse moving points, a novel radiation model is proposed to interpolate population passing zones. Case studies and experts' feedback validate the utility and efficiency by comparing population moving patterns in different times by using our system.

Massive taxi trajectory data is exploited for knowledge discovery in transportation and urban planning. Existing tools typically require users to select and brush geospatial regions on a map when retrieving and exploring taxi trajectories and passenger trips. To answer seemingly simple questions such as "What were the taxi trips starting from Main Street and ending at Wall Street in the morning?" or "Where are the taxis arriving at the Art Museum at noon typically coming from?", tedious and time consuming interactions are usually needed since the numeric GPS points of trajectories are not directly linked to the keywords such as "Main Street", "Wall Street", and "Art Museum". In this paper, we present SemanticTraj, a new method for managing and visualizing taxi trajectory data in an intuitive, semantic rich, and efficient means. With SemanticTraj, domain and public users can find answers to the aforementioned questions easily through direct queries based on the terms. They can also interactively explore the retrieved data in visualizations enhanced by semantic information of the trajectories and trips. In particular, taxi trajectories are converted into taxi documents through a textualization transformation process. This process maps GPS points into a series of street/POI names and pick-up/drop-off locations. It also converts vehicle speeds into user-defined descriptive terms. Then, a corpus of taxi documents is formed and indexed to enable flexible semantic queries over a text search engine. Semantic labels and meta-summaries of the results are integrated with a set of visualizations in a SemanticTraj prototype, which helps users study taxi trajectories quickly and easily. A set of usage scenarios are presented to show the usability of the system. We also collected feedback from domain experts and conducted a preliminary user study to evaluate the visual system.

Zoomable user interfaces are widely used in static visualizations and have many benefits. However, they are not well supported in animated visualizations due to problems such as change blindness and information overload. We propose the spot-tracking lens, a new zoomable user interface for animated bubble charts, to tackle these problems. It couples zooming with automatic panning and provides a rich set of auxiliary techniques to enhance its effectiveness. Our preliminary user studies suggested that, besides allowing users to examine detail information, it can be an engaging approach to exploratory analysis for dynamic data.

In the domain of epidemiology, logistic regression modeling is widely used to explain the relationships among explanatory variables and dichotomous outcome variables. However, logistic regression modeling faces challenges such as overfitting, confounding, and multicollinearity when there is a large number of explanatory variables. For example, in the birth defect study presented in this paper, variable selection for building high quality models to identify risk factors from hundreds of pollutant variables is difficult. To address this problem, we propose a novel visual analytics approach to logistic regression modeling for high-dimensional datasets. It leverages the traditional modeling pipeline by providing (1) intuitive visualizations for inspecting statistical indicators and the relationships among the variables and (2) a seamless, effective dimension reduction pipeline for selecting variables for inclusion in high quality logistic regression models. A fully working prototype of this approach has been developed and successfully applied to the birth defect study, which illustrates its effectiveness and efficiency. Its application in an insurance policy study and feedback from domain experts further demonstrate its usefulness.

We propose TrajGraph, a new visual analytics method, for studying urban mobility patterns by integrating graph modeling and visual analysis with taxi trajectory data. A special graph is created to store and manifest real traffic information recorded by taxi trajectories over city streets. It conveys urban transportation dynamics which can be discovered by applying graph analysis algorithms. To support interactive, multiscale visual analytics, a graph partitioning algorithm is applied to create region-level graphs which have smaller size than the original street-level graph. Graph centralities, including Pagerank and betweenness, are computed to characterize the time-varying importance of different urban regions. The centralities are visualized by three coordinated views including a node-link graph view, a map view and a temporal information view. Users can interactively examine the importance of streets to discover and assess city traffic patterns. We have implemented a fully working prototype of this approach and evaluated it using massive taxi trajectories of Shenzhen, China. TrajGraph's capability in revealing the importance of city streets was evaluated by comparing the calculated centralities with the subjective evaluations from a group of drivers in Shenzhen. Feedback from a domain expert was collected. The effectiveness of the visual interface was evaluated through a formal user study. We also present several examples and a case study to demonstrate the usefulness of TrajGraph in urban transportation analysis.

A wide variety of real-world applications generate massive high dimensional categorical datasets. These datasets contain categorical variables whose values comprise a set of discrete categories. Visually exploring these datasets for insights is of great interest and importance. However, their discrete nature often confounds the direct application of existing multidimensional visualization techniques. We use measures of entropy, mutual information, and joint entropy as a means of harnessing this discreteness to generate more effective visualizations. We conduct user studies to assess the benefits in visual knowledge discovery.

A new methodology is developed to discover and analyze the hidden knowledge of massive taxi trajectory data within a city. This approach creatively transforms the geographic coordinates (i.e. latitude and longitude) to street names reflecting contextual semantic information. Consequently, the movement of each taxi is studied as a document consisting of the taxi traversed street names, which enables semantic analysis of massive taxi data sets as document corpora. Hidden themes, namely taxi topics, are identified through textual topic modeling techniques. The taxi topics reflect urban mobility patterns and trends, which are displayed and analyzed through a visual analytics system. The system integrates interactive visualization tools, including taxi topic maps, topic routes, street clouds and parallel coordinates, to visualize the probability-based topical information. Urban planners, administration, travelers, and drivers can conduct their various knowledge discovery tasks with direct semantic and visual assists. The effectiveness of this approach is illustrated by case studies using a large taxi trajectory data set acquired from 21, 360 taxis in a city.

Existing research efforts into tennis visualization have primarily focused on using ball and player tracking data to enhance professional tennis broadcasts and to aid coaches in helping their students. Gathering and analyzing this data typically requires the use of an array of synchronized cameras, which are expensive for non-professional tennis matches. In this paper, we propose TenniVis, a novel tennis match visualization system that relies entirely on data that can be easily collected, such as score, point outcomes, point lengths, service information, and match videos that can be captured by one consumer-level camera. It provides two new visualizations to allow tennis coaches and players to quickly gain insights into match performance. It also provides rich interactions to support ad hoc hypothesis development and testing. We first demonstrate the usefulness of the system by analyzing the 2007 Australian Open men's singles final. We then validate its usability by two pilot user studies where two college tennis coaches analyzed the matches of their own players. The results indicate that useful insights can quickly be discovered and ad hoc hypotheses based on these insights can conveniently be tested through linked match videos.

Understanding what causes proteins to change shape and how the resulting shape influences function will expedite the design of more narrowly focused drugs and therapies. Shape alterations are often the result of flexibility changes in a set of localized neighborhoods that may or may not act in concert. Computational models have been developed to predict flexibility changes under varying empirical parameters. In this paper, we tackle a significant challenge facing scientists when analyzing outputs of a computational model, namely how to identify, examine, compare, and group interesting neighborhoods of proteins under different parameter sets. This is a difficult task since comparisons over protein subunits that comprise diverse neighborhoods are often too complex to characterize with a simple metric and too numerous to analyze manually. Here, we present a series of novel visual analytics approaches toward addressing this task. User scenarios illustrate the utility of these approaches and feedback from domain experts confirms their effectiveness.

Community structure is an important characteristic of many real networks, which shows high concentrations of edges within special groups of vertices and low concentrations between these groups. Community related graph analysis, such as discovering relationships among communities, identifying attribute-structure relationships, and selecting a large number of vertices with desired structural features and attributes, are common tasks in knowledge discovery in such networks. The clutter and the lack of interactivity often hinder efforts to apply traditional graph visualization techniques in these tasks. In this paper, we propose PIWI, a novel graph visual analytics approach to these tasks. Instead of using Node-Link Diagrams (NLDs), PIWI provides coordinated, uncluttered visualizations, and novel interactions based on graph community structure. The novel features, applicability, and limitations of this new technique have been discussed in detail. A set of case studies and preliminary user studies have been conducted with real graphs containing thousands of vertices, which provide supportive evidence about the usefulness of PIWI in community related tasks.

Text streams demand an effective, interactive, and on-the-fly method to explore the dynamic and massive data sets, and meanwhile extract valuable information for visual analysis. In this paper, we propose such an interactive visualization system that enables users to explore streaming-in text documents without prior knowledge of the data. The system can constantly incorporate incoming documents from a continuous source into existing visualization context, which is “physically” achieved by minimizing a potential energy defined from similarities between documents. Unlike most existing methods, our system uses dynamic keyword vectors to incorporate newly-introduced keywords from data streams. Furthermore, we propose a special keyword importance that makes it possible for users to adjust the similarity on-the-fly, and hence achieve their preferred visual effects in accordance to varying interests, which also helps to identify hot spots and outliers. We optimize the system performance through a similarity grid and with parallel implementation on graphics hardware (GPU), which achieves instantaneous animated visualization even for a very large data collection. Moreover, our system implements a powerful user interface enabling various user interactions for in-depth data analysis. Experiments and case studies are presented to illustrate our dynamic system for text stream exploration.

Light-weight objects floating inside a flow play a significant role in the liveliness of our world (e.g., leaves, dust, snowflakes, bubbles). They follow the flow and show complex and chaotic motion. First, animators usually add simple random noise to the streaming path. However, this method yields low-quality floating behavior since the random noise does not take into account the spatial and temporal distribution of underlying flow turbulence. For example, obstacle-induced oscillation cannot be easily created as a major source of the unique motion. Second, floating objects can be passively advected by flow velocities from physically-based simulation. A critical challenge is that modeling the important jiggling motion requires turbulent flow field, which is hard to achieve by direct numerical simulation (DNS) due to limited computational resources and numerical dissipation. This situation deteriorates severely when realtime performance and interactivity are demanded in a 3D gaming environment. Moreover, the floating motion has intrinsic stochastic nature, i.e., the repeated executions result in nonidentical dynamics, which is not achievable with deterministic simulation. Third, adding noise to fluid solvers can introduce chaotic flow velocities (e.g. [Pfaff et al. 2010]). In an approach, special Langevin particles affect fluid solver with forces [Chen et al. 2011]. However, such methods rely on DNS and apply chaotic addition to the whole fluid domain, which is inefficient in handling a group of floating objects inside.

Abstract We develop a new Lagrangian primitive, named Langevin particle, to incorporate turbulent flow details in fluid simulation. A group of the particles are distributed inside the simulation domain based on a turbulence energy model with turbulence viscosity. A particle in particular moves obeying the generalized Langevin equation, a well known stochastic differential equation that describes the particle's motion as a random Markov process. The resultant particle trajectory shows self‐adapted fluctuation in accordance to the turbulence energy, while following the global flow dynamics. We then feed back Langevin forces to the simulation based on the stochastic trajectory, which drive the flow with necessary turbulence. The new hybrid flow simulation method features nonrestricted particle evolution requiring minimal extra manipulation after initiation. The flow turbulence is easily controlled and the total computational overhead of enhancement is minimal based on typical fluid solvers.

Fluid animation practitioners face great challenges from the complexity of flow dynamics and the high cost of numerical simulation. A major hindrance is the uncertainty of fluid behavior after simulation resolution increases and extra turbulent effects are added. In this paper, we propose to regulate fluid animations with predesigned flow patterns. Animators can design their desired fluid behavior with fast, low-cost simulations. Flow patterns are then extracted from the results by the Lagrangian Coherent Structure (LCS) that represents major flow skeleton. Therefore, the final high-quality animation is confined towards the designed behavior by applying the patterns to drive high-resolution and turbulent simulations. The pattern regulation is easily computed and achieves controllable variance in the output. The method makes it easy to design special fluid effects, which increases the usability and scalability of various advanced fluid modeling technologies.

Mutual funds are probably the most important investment instruments for investors. Their performance is mainly affected by their characteristics, such as asset size, turnover and fee structure. It is thus of the investors' highest priorities to understand the relation between fund performance and these properties. Typically, financial researchers use the linear regression technique to statistically assess the relation from massive fund performance data. Unfortunately, the prevailing methods may be confounded by the existence of on-linearity and outliers, and give conflicting conclusions. In this paper, we propose a visualization-based method to improve the mutual fund analysis, where a new visual analytical tool, the density-based distribution map, is applied. The new visual representations greatly help to understand the critical relations, reveal the deficiency of current analytical algorithms, and support mutual funds selection. The tool is used to perform an expert financial analysis, and establish a fund selection strategy from a real-world database of the US stock funds. Our method gains the admiration and support from professionals in the finance field, they embrace it as a potential investment analysis tool for mutual fund managers, financial planners, and investors.

We propose a novel distance field transform method based on an iterative method adaptively performed on an evolving active band. Our method utilizes a narrow band to store active grid points being computed. Unlike the conventional fast marching method, we do not maintain a priority queue, and instead, perform iterative computing inside the band. This new algorithm alleviates the programming complexity and the data-structure (e.g. a heap) maintenance overhead, and leads to a parallel amenable computational process. During the active band propagating from a starting boundary layer, each grid point stays in the band for a lifespan time, which is determined by analyzing the particular geometric property of the grid structure. In this way, we find the Face-Centered Cubic (FCC) grid is a good 3D structure for distance transform.We further develop a multiple-segment method for the band propagation, achieving the computational complexity of O(m · N) with a segment-related constant m.

We simulate 3D fluid flow by a locally-refined lattice Boltzmann method (LBM) on graphics hardware. A low resolution LBM simulation running on a coarse grid models global flow behavior of the entire domain with low consumption of computational resources. For regions of interest where small visual details are desired, LBM simulations are performed on fine grids, which are separate grids superposed on the coarse one. The flow properties on boundaries of the fine grids are determined by the global simulation on the coarse grid. Thus, the locally refined fine-grid simulations follow the global fluid behavior, and model the desired small-scale and turbulent flow motion with their denser numerical discretization. A fine grid can be initiated and terminated at any time while the global simulation is running. It can also move inside the domain with a moving object to capture small-scale vortices caused by the object. Besides the performance improvement due to the adaptive simulation, the locally-refined LBM is suitable for acceleration on contemporary graphics hardware (GPU), since it involves only local and linear computations. Therefore, our approach achieves fast and adaptive 3D flow simulation for computer games and other interactive applications.

We provide a physically-based framework for simulating the natural phenomena related to heat interaction between objects and the surrounding air. We introduce a heat transfer model between the heat source objects and the ambient flow environment, which includes conduction, convection, and radiation. The heat distribution of the objects is represented by a novel temperature texture. We simulate the thermal flow dynamics that models the air flow interacting with the heat by a hybrid thermal lattice Boltzmann model (HTLBM). The computational approach couples a multiple-relaxation-time LBM (MRTLBM) with a finite difference discretization of a standard advection-diffusion equation for temperature. In heat shimmering and mirage, the changes in the index of refraction of the surrounding air are attributed to temperature variation. A nonlinear ray tracing method is used for rendering. Interactive performance is achieved by accelerating the computation of both the MRTLBM and the heat transfer, as well as the rendering on contemporary graphics hardware (GPU).

We present a method for simulating front propagation over 3D surfaces with arbitrary topology. Our approach, which can handle the simultaneous spreading of multiple fronts, is implemented by applying a level set method on the surface. A special data structure, called the surrounding grid, assists in advancing the fronts without triangulation or parameterization of the surface. The surrounding grid consists of discretized grid points in the vicinity of the surface. An enhanced distance field provides the bridge between the surrounding grid and the surfaces on which the fronts travel. By manipulating speed functions in the level set method, the front propagation dynamics can be conveniently controlled. We illustrate the applicability and flexibility of our method with three example applications: texture generation, water flooding, and fire propagation.

The size and resolution of volume datasets in science and medicine are increasing at a rate much greater than the resolution of the screens used to view them. This limits the amount of data that can be viewed simultaneously, potentially leading to a loss of overall context of the data when the user views or zooms into a particular area of interest. We propose a focus+context framework that uses various standard and advanced magnification lens rendering techniques to magnify the features of interest, while compressing the remaining volume regions without clipping them away completely. Some of these lenses can be interactively configured by the user to specify the desired magnification patterns, while others are feature-adaptive. All our lenses are accelerated on the GPU. They allow the user to interactively manage the available screen area, dedicating more area to the more resolution-important features.

We present a system for simulating and visualizing the propagation of dispersive contaminants with an application to urban security. In particular, we simulate airborne contaminant propagation in open environments characterised by sky-scrapers and deep urban canyons. Our approach is based on the multiple relaxation time lattice Boltzmann model (MRTLBM), which can efficiently handle complex boundary conditions such as buildings. In addition, we model thermal effects on the flow field using the hybrid thermal MRTLBM. Our approach can also accommodate readings from various sensors distributed in the environment and adapt the simulation accordingly. We accelerate the computation and efficiently render many buildings with small textures on the GPU. We render streamlines and the contaminant smoke with self-shadowing composited with the textured buildings.

We present an approach for simulating the natural dynamics that emerge from the interaction between a flow field and immersed objects. We model the flow field using the Lattice Boltzmann Model (LBM) with boundary conditions appropriate for moving objects and accelerate the computation on commodity graphics hardware (GPU) to achieve real-time performance. The boundary conditions mediate the exchange of momentum between the flow field and the moving objects resulting in forces exerted by the flow on the objects as well as the back-coupling on the flow. We demonstrate our approach using soap bubbles and a feather. The soap bubbles illustrate Fresnel reflection, reveal the dynamics of the unseen flow field in which they travel, and display spherical harmonics in their undulations. Our simulation allows the user to directly interact with the flow field to influence the dynamics in real time. The free feather flutters and gyrates in response to lift and drag forces created by its motion relative to the flow. Vortices are created as the free feather falls in an otherwise quiescent flow.

We introduce a method for the animation of fire propagation and the burning consumption of objects represented as volumetric data sets. Our method uses a volumetric fire propagation model based on an enhanced distance field. It can simulate the spreading of multiple fire fronts over a specified isosurface without actually having to create that isosurface. The distance field is generated from a specific shell volume that rapidly creates narrow spatial bands around the virtual surface of any given isovalue. The complete distance field is then obtained by propagation from the initial bands. At each step multiple fire fronts can evolve simultaneously on the volumetric object. The flames of the fire are constructed from streams of particles whose movement is regulated by a velocity field generated with the hardware-accelerated Lattice Boltzmann Model (LBM). The LBM provides a physically-based simulation of the air flow around the burning object. The object voxels and the splats associated with the flame particles are rendered in the same pipeline so that the volume data with its external and internal structures can be displayed along with the fire.