Visual Analysis and Steering of Flooding Simulations

Ribicic, H.; Waser, Jürgen; Fuchs, Raphael; Blöschl, Günter; Gröller, Eduard

doi:10.1109/tvcg.2012.175

Cited by 37 publications

(31 citation statements)

References 16 publications

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“…Finally, examples for visualization systems tailored to the interactive exploration of natural hazard simulation data are described by Kunz et al (2011), Ribicic et al (2013), and Leskens et al (2015).…”

Section: Related Workmentioning

confidence: 99%

GPU-Accelerated Rendering Methods to Visually Analyze Large-Scale Disaster Simulation Data

et al. 2017

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Emerging methodologies for natural hazard risk assessments involve the execution of a multitude of different interacting simulation models that produce vast amounts of spatio-temporal datasets. This data pool is further enlarged when such simulation results are post-processed using GIS operations, for example to derive information for decisionmaking. The novel approach presented in this paper makes use of the GPU-accelerated rendering pipeline to perform such operations on-the-fly without storing any results on secondary memory and thus saving large amounts of storage space. Particularly, algorithms for three frequently used geospatial analysis methods are provided, namely for the computation of difference maps using map algebra and overlay operations, distance maps and buffers as examples for proximity analyses as well as kernel density estimation and inverse distance weighting as examples for statistical surfaces. In addition, a visualization tool is presented that integrates these methods using a node-based data flow architecture. The application of this visualization tool to the results of a real-world risk assessment methodology used in civil engineering shows that the memory footprint of post-processing datasets can be reduced at the order of terabytes. Although the technique has several limitations, most notably the reduced interoperability with conventional analysis tools, it can be beneficial for other use cases. When integrated into desktop GIS applications, for example, it can be used to quickly generate a preview of the results of complex analysis chains or it can reduce the amount of data to be transferred to web or mobile GIS applications.

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Section: Related Workmentioning

confidence: 99%

GPU-Accelerated Rendering Methods to Visually Analyze Large-Scale Disaster Simulation Data

et al. 2017

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“…Visual analytics techniques also support derivation of linear trend models from multivariate data [32], regression models [51], and time series models [13] [34]. Other works focus on supporting assessment of existing models, including classification [49], engineering simulation [47] [48], and spatio-temporal models [21][46] [55]. In particular, comparative assessments of results of multiple simulations are supported [47][48] [64].…”

Section: Visual Analytics Support To Modeling and Simulationmentioning

confidence: 99%

“…There are works on supporting the tasks 'forecast' and 'develop options' in planning pandemic response [1][46] and flood protection [55] [64]. Pre-existing simulation models are used to provide forecasts.…”

Section: Visual Analytics Support To Modeling and Simulationmentioning

confidence: 99%

Leveraging Spatial Abstraction in Traffic Analysis and Forecasting with Visual Analytics

Andrienko

Rinzivillo

2016

Machine Learning and Knowledge Discovery in Databases

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This is the accepted version of the paper.This version of the publication may differ from the final published version. Abstract-A spatially abstracted transportation network is a graph where nodes are territory compartments (areas in geographic space) and edges, or links, are abstract constructs, each link representing all possible paths between two neighboring areas. By applying visual analytics techniques to vehicle traffic data from different territories, we discovered that the traffic intensity (a.k.a. traffic flow or traffic flux) and the mean velocity are interrelated in a spatially abstracted transportation network in the same way as at the level of street segments. Moreover, these relationships are consistent across different levels of spatial abstraction of a physical transportation network. Graphical representations of the flux-velocity interdependencies for abstracted links have the same shape as the fundamental diagram of traffic flow through a physical street segment, which is known in transportation science. This key finding substantiates our approach to traffic analysis, forecasting, and simulation leveraging spatial abstraction. Permanent repository linkWe propose a framework in which visual analytics supports three high-level tasks, assess, forecast, and develop options, in application to vehicle traffic. These tasks can be carried out in a coherent workflow, where each next task uses the results of the previous one(s). At the 'assess' stage, vehicle trajectories are used to build a spatially abstracted transportation network and compute the traffic intensities and mean velocities on the abstracted links by time intervals. The interdependencies between the two characteristics of the links are extracted and represented by formal models, which enable the second step of the workflow, 'forecast', involving simulation of vehicle movements under various conditions. The previously derived models allow not only prediction of normal traffic flows conforming to the regular daily and weekly patterns but also simulation of traffic in extraordinary cases, such as road closures, major public events, or mass evacuation due to a disaster. Interactive visual tools support preparation of simulations and analysis of their results. When the simulation forecasts problematic situations, such as major congestions and delays, the analyst proceeds to the step 'develop options' for trying various actions aimed at situation improvement and investigating their consequences. Action execution can be imitated by interactively modifying the input of the simulation model. Specific techniques support comparisons between results of simulating different "what if" scenarios.

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“…Solutions providing sufficiently detailed geometric rendering and means for the analysis of multivariate attributes are often tailored to a specific use case, such as in disaster management [25], 3D visibility analysis [11,22], urban data analysis [7], or lighting design [30]. These works combine visualizations of multivariate attributes with a detailed 3D spatial visualization for supporting decision-making in heterogeneous scenarios.…”

Section: Related Workmentioning

confidence: 99%

“…Integrated solutions, which are tailored to a specific use case, typically focus on the analysis of a certain data entity: flooded buildings in disaster management [25], buildings [11] and lines-of-sight [22] in urban planning, illuminated surfaces [30] in interior lighting design, or tunnel cracks in the presented scenario. We generalize these entities as e = (g, a), where g is the geometric spatial representation and a = (a 1 , . .…”

Section: The Visar Frameworkmentioning

confidence: 99%

Visual analytics and rendering for tunnel crack analysis

et al. 2016

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The visual analysis of surface cracks plays an essential role in tunnel maintenance when assessing the condition of a tunnel. To identify patterns of cracks, which endanger the structural integrity of its concrete surface, analysts need an integrated solution for visual analysis of geometric and multivariate data to decide if issuing a repair project is necessary. The primary contribution of this work is a design study, supporting tunnel crack analysis by tightly integrating geometric and attribute views to allow users a holistic visual analysis of geometric representations and multivariate attributes. Our secondary contribution is Visual Analytics and Rendering, a methodological approach which addresses challenges and recurring design questions in integrated systems. We evaluated the tunnel crack analysis solution in informal feedback sessions with experts from tunnel maintenance and surveying. We substantiated the derived methodology by providing guidelines and linking it to examples from the literature.

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Visual Analysis and Steering of Flooding Simulations

Cited by 37 publications

References 16 publications

GPU-Accelerated Rendering Methods to Visually Analyze Large-Scale Disaster Simulation Data

GPU-Accelerated Rendering Methods to Visually Analyze Large-Scale Disaster Simulation Data

Leveraging Spatial Abstraction in Traffic Analysis and Forecasting with Visual Analytics

Visual analytics and rendering for tunnel crack analysis

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