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This paper presents a well-scaling parallel algorithm for the computation of Morse-Smale (MS) segmentations, including the region separators and region boundaries. The segmentation of the domain into ascending and descending manifolds, solely defined on the vertices, improves the computational time using path compression and fully segments the border region. Region boundaries and region separators are generated using a multi-label marching tetrahedra algorithm. This enables a fast and simple solution to find optimal parameter settings in preliminary exploration steps by generating an MS complex preview. It also poses a rapid option to generate a fast visual representation of the region geometries for immediate utilization. Two experiments demonstrate the performance of our approach with speedups of over an order of magnitude in comparison to two publicly available implementations. The example section shows the similarity to the MS complex, the useability of the approach, and the benefits of this method with respect to the presented datasets. We provide our implementation with the paper., Comment: Journal: IEEE Transactions on Visualization and Computer Graphics / Submitted: 22-Jun-2022 / Accepted: 13-Mar-2023

This article describes the research agenda for the Visual Analytics and Data Exploration Research (VADER) Lab at Arizona State University. Over the past decade, the VADER Lab has focused on creating novel algorithms, tools and visualizations for spatiotemporal data. This article will highlight past success in spatiotemporal analysis, explainable AI, graph mining, and mathematical topology. While, at first, these topics seem largely disjoint, we will describe how the underpinnings of spatiotemporal analysis has informed the various research directions in the VADER Lab, and how this research agenda has served to form a network of strong international collaborations. Finally, we will outline a vision for the Lab’s future research.

We introduce an innovative ensemble analysis framework for organizing, searching, and comparing results produced by hundreds of physical simulations. Our web-based approach is built on standard technologies, utilizes a scalable and modular design, and is suitable for displaying the results of in situ analysis and extreme-scale simulations.

Fundamental research into in situ visualization and analysis techniques is difficult to access for visualization researchers due to the overwhelming complexity and effort of constructing and managing in situ software stacks that allow reproducible evaluation of novel in situ visualization and analysis techniques. To address this problem, we describe EZ-ISAV, a work-in-progress towards a framework for easy construction of customizable in situ pipelines in container images. Designed for portability and ease of use, these images are intended to serve as proof-of-concept cases for in situ visualization and analysis research. Furthermore, we describe the EZ-ISAV repository, an open repository of turn-key, ready-made container images that can strongly reduce the overhead of developing and evaluating in situ techniques and provide improved reproducibility and portability of in situ visualization research.

Various research applications require detailed metrics to describe the form and composition of cities at fine scales, but the parameter computation remains a challenge due to limited data availability, quality, and processing capabilities. We developed an innovative big data approach to derive street-level morphology and urban feature composition as experienced by a pedestrian from Google Street View (GSV) imagery. We employed a scalable deep learning framework to segment 90-degree field of view GSV image cubes into six classes: sky, trees, buildings, impervious surfaces, pervious surfaces, and non-permanent objects. We increased the classification accuracy by differentiating between three view directions (lateral, down, and up) and by introducing a void class as training label. To model the urban environment as perceived by a pedestrian in a street canyon, we projected the segmented image cubes onto spheres and evaluated the fraction of each surface class on the sphere. To demonstrate the application of our approach, we analyzed the urban form and composition of Philadelphia County and three Philadelphia neighborhoods (suburb, center city, lower income neighborhood) using stacked area graphs. Our method is fully scalable to other geographic locations and constitutes an important step towards building a global morphological database to describe the form and composition of cities from a human-centric perspective.

Accepted for publication in the upcoming TopoInVis book (Springer).; International audience; This paper documents the organization, the execution, and the results of the Topology ToolKit (TTK) hackathon that took place at the TopoInVis 2019 conference. The primary goal of the hackathon was to promote TTK in our research community as a unified software development platform for topology-based data analysis algorithms. To this end, participants were first introduced to the structure and capabilities of TTK, and then worked on their own TTK-related projects while being mentored by senior TTK developers. Notable outcomes of the hackathon were first steps towards Python and Docker packages, further integration of TTK in Inviwo, the extension of TTK with new algorithms, and the discovery of current limitations of TTK as well as future development directions.

This paper describes a localized algorithm for the topological simplification of scalar data, an essential pre-processing step of topological data analysis (TDA). Given a scalar field $f$ and a selection of extrema to preserve, the proposed localized topological simplification (LTS) derives a function g that is close to $f$ and only exhibits the selected set of extrema. Specifically, sub- and superlevel set components associated with undesired extrema are first locally flattened and then correctly embedded into the global scalar field, such that these regions are guaranteed-from a combinatorial perspective-to no longer contain any undesired extrema. In contrast to previous global approaches, LTS only and independently processes regions of the domain that actually need to be simplified, which already results in a noticeable speedup. Moreover, due to the localized nature of the algorithm, LTS can utilize shared-memory parallelism to simplify regions simultaneously with a high parallel efficiency (70%). Hence, LTS significantly improves interactivity for the exploration of simplification parameters and their effect on subsequent topological analysis. For such exploration tasks, LTS brings the overall execution time of a plethora of TDA pipelines from minutes down to seconds, with an average observed speedup over state-of-the-art techniques of up to $\times 36$ . Furthermore, in the special case where preserved extrema are selected based on topological persistence, an adapted version of LTS partially computes the persistence diagram and simultaneously simplifies features below a predefined persistence threshold. The effectiveness of LTS, its parallel efficiency, and its resulting benefits for TDA are demonstrated on several simulated and acquired datasets from different application domains, including physics, chemistry, and biomedical imaging.

Author(s): Lohfink, AP; Wetzels, F; Lukasczyk, J; Weber, GH; Garth, C | Abstract: We describe a novel technique for the simultaneous visualization of multiple scalar fields, e.g. representing the members of an ensemble, based on their contour trees. Using tree alignments, a graph-theoretic concept similar to edit distance mappings, we identify commonalities across multiple contour trees and leverage these to obtain a layout that can represent all trees simultaneously in an easy-to-interpret, minimally-cluttered manner. We describe a heuristic algorithm to compute tree alignments for a given similarity metric, and give an algorithm to compute a joint layout of the resulting aligned contour trees. We apply our approach to the visualization of scalar field ensembles, discuss basic visualization and interaction possibilities, and demonstrate results on several analytic and real-world examples.

This paper presents a framework that fully leverages the advantages of a deferred rendering approach for the interactive visualization of large-scale datasets. Geometry buffers (G-Buffers) are generated and stored in situ, and shading is performed post hoc in an interactive image-based rendering front end. This decoupled framework has two major advantages. First, the G-Buffers only need to be computed and stored once---which corresponds to the most expensive part of the rendering pipeline. Second, the stored G-Buffers can later be consumed in an image-based rendering front end that enables users to interactively adjust various visualization parameters---such as the applied color map or the strength of ambient occlusion---where suitable choices are often not known a priori. This paper demonstrates the use of Cinema Darkroom on several real-world datasets, highlighting CD's ability to effectively decouple the complexity and size of the dataset from its visualization.

Urban morphology is an important multidimensional variable to consider in climate modeling and observations, because it significantly drives the local and micro-scale climatic variability in cities. Urban form can be described through urban canopy parameters (UCPs) that resolve the spatial heterogeneity of cities by specifying the 3-dimensional geometry, arrangement, and materials of urban features. The sky view factor (SVF) is a dimension-reduced UCP capturing 3-dimensional form through horizon limitation fractions. SVF has become a popular metric to parameterize urban morphology, but current approaches are difficult to scale up to global coverage. This study introduces a Big-Data approach to calculate SVFs for urban areas from Google Street View (GSV). 90-degree field-of-view GSV photos are retrieved and converted into hemispherical views through equiangular projection. The fisheyes are segmented into sky and non-sky pixels using image processing, and the SVF is calculated using an annulus method. Results are compared to SVFs retrieved from GSV images segmented using deep learning. SVF footprints are presented for urban areas around the world tallying 15,938,172 GSV locations. Two use cases are introduced: (1) an evaluation of a Google Earth Engine classified Local Climate Zone map for Singapore; (2) hourly sun duration maps for New York and San Francisco.

The local climate zone (LCZ) classification scheme is a standardization framework to describe the form and function of cities for urban heat island (UHI) studies. This study classifies and evaluates LCZs for two arid desert cities in the Southwestern United States – Phoenix and Las Vegas – following the World Urban Database and Access Portal Tools (WUDAPT) method. Both cities are classified into seven built type LCZs and seven land-cover type LCZs at 100-m resolution using Google Earth, Saga GIS, and Landsat 8 scenes. Average surface cover properties (building fraction, impervious fraction, pervious fraction) and sky view factors of classified LCZs are then evaluated and compared to pre-defined LCZ representative ranges from the literature, and their implications on the surface UHI (SUHI) effect are explained. Results suggest that observed LCZ properties in arid desert environments do not always match the proposed value ranges from the literature, especially with regard to sky view factor (SVF) upper boundaries. Although the LCZ classification scheme was originally designed to describe local climates with respect to air temperature, our analysis shows that much can be learned from investigating land surface temperature (LST) in these zones. This study serves as a substantial new resource laying a foundation for assessing the SUHI in cities using the LCZ scheme, which could inform climate simulations at local and regional scales.

Complex networks require effective tools and visualizations for their analysis and comparison. Clique communities have been recognized as a powerful concept for describing cohesive structures in networks. We propose an approach that extends the computation of clique communities by considering persistent homology, a topological paradigm originally introduced to characterize and compare the global structure of shapes. Our persistence-based algorithm is able to detect clique communities and to keep track of their evolution according to different edge weight thresholds. We use this information to define comparison metrics and a new centrality measure, both reflecting the relevance of the clique communities inherent to the network. Moreover, we propose an interactive visualization tool based on nested graphs that is capable of compactly representing the evolving relationships between communities for different thresholds and clique degrees. We demonstrate the effectiveness of our approach on various network types.

Volume 36 (2017), Number 3 Eurographics Conference on Visualization (EuroVis) 2017 J. Heer, T. Ropinski and J. van Wijk (Guest Editors) Nested Tracking Graphs Jonas Lukasczyk 1 , Gunther Weber 2,3 , Ross Maciejewski 4 , Christoph Garth 1 , and Heike Leitte 1 1 TU 2 Lawrence Kaiserslautern, Germany Berkeley National Laboratory, USA University of California, USA Arizona State University, USA Figure 1: Nested tracking graph for one ensemble member of the viscous finger dataset with highest particle resolution. In contrast to common tracking graphs—where edges only encode the evolution of components for a single level—nested tracking graphs additionally illustrate the nesting hierarchy of components across levels. The x-axis represents time and the y-axis is used to minimize edge crossings. The density levels 25, 30, and 35 are shown from dark to light red, and the width of edges encodes the size of their associated components. Abstract Tracking graphs are a well established tool in topological analysis to visualize the evolution of components and their properties over time, i.e., when components appear, disappear, merge, and split. However, tracking graphs are limited to a single level threshold and the graphs may vary substantially even under small changes to the threshold. To examine the evolution of fea- tures for varying levels, users have to compare multiple tracking graphs without a direct visual link between them. We propose a novel, interactive, nested graph visualization based on the fact that the tracked superlevel set components for different levels are related to each other through their nesting hierarchy. This approach allows us to set multiple tracking graphs in context to each other and enables users to effectively follow the evolution of components for different levels simultaneously. We demon- strate the effectiveness of our approach on datasets from finite pointset methods, computational fluid dynamics, and cosmology simulations. Categories and Subject Descriptors (according to ACM CCS) : Data [Computer Graphics]: Data Structures—Graphs and Networks 1. Introduction Detecting and tracking components of time varying scalar fields is a common approach in data analysis. Multiple techniques exist that identify components and visualize their evolution over time through so-called tracking graphs [WCPB12, ALS ∗ 16]. These tracking graphs are presented using layered graph drawing techniques where one axis represents time and the other axis serves to minimize edge crossings. Each edge represents a component, and nodes represent critical events such as when components appear, disappear, merge, c 2017 The Author(s) c 2017 The Eurographics Association and John Computer Graphics Forum Wiley & Sons Ltd. Published by John Wiley & Sons Ltd. or split. Thus, tracking graphs provide an overview and summa- rization of the evolution of components over time. Here, we focus on superlevel set components—connected areas in the scalar field that exceed a certain threshold—such as regions of a water filled can that exceed a specified salt concentration threshold, or parts of the universe where dark matter exceeds a certain density. To track components over time one can apply methods of topological feature tracking [ALS ∗ 16, BBD ∗ 07, BKL ∗ 11, EHMP04, MS09] or simply test for spatial overlaps [SB06, LMGH15, SW98, LBM ∗ 06]. One can also augment the resulting graphs with additional information

This work describes an approach for the interactive visual analysis of large-scale simulations, where numerous superlevel set components and their evolution are of primary interest. The approach first derives, at simulation runtime, a specialized Cinema database that consists of images of component groups, and topological abstractions. This database is processed by a novel graph operation-based nested tracking graph algorithm (GO-NTG) that dynamically computes NTGs for component groups based on size, overlap, persistence, and level thresholds. The resulting NTGs are in turn used in a feature-centered visual analytics framework to query specific database elements and update feature parameters, facilitating flexible post hoc analysis.

This software paper gives an overview of the features supported by the Topology ToolKitTopology ToolKit (TTK), which is an  Open-sourceopen-source library for  Topological data analysistopological data analysis (TDA). TTK implements, in a generic and efficient way, a substantial collection of reference algorithms in TDA. Since its initial public release in 2017, both its user and developer bases have grown, resulting in a significant increase in the number of supported features. In contrast to the original paper introducing TTK [40] (which detailed the core algorithms and data structures of TTK), the purpose of this Softwaresoftware paper is to describe the list of features currently supported by TTK, ranging from image segmentation tools to advanced topological analysis of high-dimensional data, with concrete usage examples available on the TTK website [42].

The Sky View Factor (SVF) is a dimension-reduced representation of urban form and one of the major variables in radiation models that estimate outdoor thermal comfort. Common ways of retrieving SVFs in urban environments include capturing fisheye photographs or creating a digital 3D city or elevation model of the environment. Such techniques have previously been limited due to a lack of imagery or lack of full scale detailed models of urban areas. We developed a web based tool that automatically generates synthetic hemispherical fisheye views from Google Earth at arbitrary spatial resolution and calculates the corresponding SVFs through equiangular projection. SVF results were validated using Google Maps Street View and compared to results from other SVF calculation tools. We generated 5-meter resolution SVF maps for two neighborhoods in Phoenix, Arizona to illustrate fine-scale variations of intra-urban horizon limitations due to urban form and vegetation. To demonstrate the utility of our synthetic fisheye approach for heat stress applications, we automated a radiation model to generate outdoor thermal comfort maps for Arizona State University’s Tempe campus for a hot summer day using synthetic fisheye photos and on-site meteorological data. Model output was tested against mobile transect measurements of the six-directional radiant flux density. Based on the thermal comfort maps, we implemented a pedestrian routing algorithm that is optimized for distance and thermal comfort preferences. Our synthetic fisheye approach can help planners assess urban design and tree planting strategies to maximize thermal comfort outcomes and can support heat hazard mitigation in urban areas.

Analysis of spatio-temporal event data is of central importance in many domains of science and policy making. Current visualization methods rely on animation, small multiples, and space-time cubes to enable spatio-temporal data exploration. These methods require the user to remember state spaces or deal with layout occlusions when exploring their data. To overcome such issues, we propose a novel visualization technique for such data that applies the topological notion of Reeb graphs to identify hotspots as areas of relatively high event density within kernel density estimates. We illustrate that the topological identification of hotspots proposed in this paper is able to elucidate lifetime, properties, and relationships of hotspots by visualizing their temporal evolution based on the spatio-temporal Reeb graph. To validate our approach, we demonstrate our method on an epidemiological and a crime dataset. The resulting visualizations assist users in quickly identifying and comprehending important dates, events, hotspot properties, and relationships between hotspots.