Hummingbird is an innovative, award-winning performance engaging participants in active storytelling that bridges live theater and collaborative interaction through virtual reality (VR). Hummingbird&rsquo;s story celebrates courage and coming of age through the eyes of a gutsy teen who must outsmart her mother&rsquo;s egotistic boss and survive a dangerous new technology in a live, immersive adventure. Developed at the University of Illinois Chicago by faculty and over 30 students from the departments of Computer Science and Design in partnership with professional theater producers, directors, actors, videographers and composers, this project advanced interdisciplinary collaboration, provided a unique learning environment and broadened the research experience for several cohorts of students. Over 500 people attended Hummingbird’s performances at the Tony Award-winning Goodman Theatre&rsquo;s New Stages Festival, Chicago Children&rsquo;s Theater and SIGGRAPH 2022 in Vancouver, Canada, with over 200 active VR participants. In each performance, five VR participants actively collaborate with each other and a lead actor within the VR adventure, contributing problem-solving, collaboration and teamwork, while a greater audience simultaneously follows the virtual performance aspects on a large video wall in real-time. Discussion sessions and audience evaluations followed each performance, informed the Hummingbird team on script, design and interactivity to improve future performances. Through qualitative analysis of audience experiences and insights from our collaboration, we discuss key considerations and design recommendations for integrating VR with live theater. Hummingbird demonstrates how VR can revolutionize theatrical storytelling by enabling traditional theater to narrate epic stories that were once considered too ambitious for traditional stage by extending live theater and making VR accessible to a broader audience. This project profit theater and interdisciplinary research institutions to increase opportunities for cross-disciplinary student education. 
CCS CONCEPTS: Computing methodologies; Computer Graphics; Graphics systems and interfaces; Virtual reality; Applied computing; Arts and humanities; Performing arts 
KEYWORDS Virtual reality, Storytelling, Theater, Multi-user, Performance, Interdisciplinary Collaboration, Design Process

VR participants interact in the Orchard scene while observers watch their interaction on the large screen.

Hummingbird: Live Theater Adventure Empowering Collaboration in Virtual Reality

Research in visual computing (VC) focuses on integrating visual representations of datasets with computing systems, thus leveraging the complementary abilities of humans and machines. The resulting VC systems may include machine learning (ML) techniques, which have remarkable abilities in terms of processing vast amounts of data. However, creating VC+ML systems that can operate on spatio-temporal data is a largely unexplored, difficult topic. Challenges in this area include: capturing domain characterizations for spatial VC+ML problems, in particular when serving both ML model builders and ML model clients; creating explainable VC+ML models that can operate on spatial data, including measures that can capture spatial structure similarity; designing visual encodings for ML models that use spatial information and that enhance the modeler and client understanding of the model; and measuring the effect of deploying such VC+ML systems in practice. br>
Based on several multi-year collaborations, in this dissertation I will document several instances where spatial and explainable VC+ML solutions are required, and what role spatiality plays in these situations. Throughout this process, I extract and describe design requirements for explainable spatial VC+ML systems, and I identify necessary VC advances, including spatially-aware similarity measures and spatial visual explainability. First, I construct spatially-aware similarity measures to support ML problems in precision oncology, patient risk stratification in data mining, explainable interactive regression modeling for hypothesis testing in social science, and digital twin interfaces for cancer treatment planning. Second, I introduce novel encodings for 3-dimensional and geospatial data and explore how
to integrate such encodings with ML model explanations. Finally, I implement and evaluate several VC+ML systems and several resulting models that are explicitly designed to integrate domain-specific structural spatial information and unsupervised ML. Throughout this process, I also articulate design lessons related to model actionability.

Development of the organ diagram used to encode organ-specific values. (Left) Organs that are located in the center of the head (Right) Bilateral organs that occur on both the left and right sides of the head.

Visual Computing Design for Explainable, Spatially-aware Machine Learning

With the decreasing cost of consumer display technologies making it easier for universities to have larger displays in classrooms, and the ubiquitous use of online tools such as collaborative whiteboards for remote learning during the COVID-19 pandemic, combining the two can be useful in higher education. This is especially true in visually intensive classes, such as data visualization courses, that can benefit from additional &ldquo;space to teach,&rdquo; coined after the &ldquo;space to think&rdquo; sense-making idiom. In this paper, we reflect on our approach to using SAGE3, a collaborative whiteboard with advanced features, in higher education to teach visually intensive classes, provide examples of activities from our own visually-intensive courses, and present student feedback. We gather our observations into usage patterns for using content-rich canvases in education.

A SAGE3 session can be shared across multiple locations: some locations may serve multiple users co-located in front of a large wall, while others may serve an individual with varied types of displays.

Space to Teach: Content-Rich Canvases for Visually-Intensive Education

Over the past decade, several urban visual analytics systems and tools have been proposed to tackle a host of challenges faced by cities, in areas as diverse as transportation, weather, and real estate. Many of these tools have been designed through collaborations with urban experts, aiming to distill intricate urban analysis workflows into interactive visualizations and interfaces. However, the design, implementation, and practical use of these tools still rely on siloed approaches, resulting in bespoke systems that are difficult to reproduce and extend. At the design level, these tools undervalue rich data workflows from urban experts, typically treating them only as data providers and evaluators. At the implementation level, they lack interoperability with other technical frameworks. At the practical use level, they tend to be narrowly focused on specific fields, inadvertently creating barriers to cross-domain collaboration. To address these gaps, we present Curio, a framework for collaborative urban visual analytics. Curio uses a dataflow model with multiple abstraction levels (code, grammar, GUI elements) to facilitate collaboration across the design and implementation of visual analytics components. The framework allows experts to intertwine data preprocessing, management, and visualization stages while tracking the provenance of code and visualizations. In collaboration with urban experts, we evaluate Curio through a diverse set of usage scenarios targeting urban accessibility, urban microclimate, and sunlight access. These scenarios use different types of data and domain methodologies to illustrate Curio&rsquo;s flexibility in tackling pressing societal challenges. Curio is available at <a=href"urbantk.org/curio.">urbantk.org/curio</a>. 
Index Terms - Urban analytics, urban data, spatial data, dataflow, provenance, visualization framework, visualization system.

Using Curio to build a dataflow to analyze the shadow impact of a proposed building in Boston.

Curio: A Dataflow-Based Framework for Collaborative Urban Visual Analytics

Unsupervised video anomaly detection (UVAD) aims to detect abnormal events in videos without any annotations. It remains challenging because anomalies are rare, diverse, and usually not well-defined. Existing UVAD methods are purely data-driven and perform unsupervised learning by identifying various abnormal patterns in videos. Since these methods largely rely on the feature representation and data distribution, they can only learn salient anomalies that are substantially different from normal events but ignore the less distinct ones. To address this challenge, this paper pursues a different approach that leverages data-irrelevant prior knowledge about normal and abnormal events for UVAD. We first propose a new normality prior for UVAD, suggesting that the start and end of a video are predominantly normal. We then propose normality propagation, which propagates normal knowledge based on relationships between video snippets to estimate the normal magnitudes of unlabeled snippets. Finally, unsupervised learning of abnormal detection is performed based on the propagated labels and a new loss re-weighting method. These components are complementary to normality propagation and mitigate the negative impact of incorrectly propagated labels. Extensive experiments on the ShanghaiTech and UCF-Crime benchmarks demonstrate the superior performance of our method. 
Funding: NSF award ECCS-2400900

Learning Anomalies with Normality Prior for Unsupervised Video Anomaly Detection

Deep learning approaches have made significant success in single-view 3D reconstruction, but they often rely on expensive 3D annotations for training. Recent efforts tackle this challenge by adopting an analysis-by-synthesis paradigm to learn 3D reconstruction with only 2D annotations. However, existing methods face limitations in both shape reconstruction and texture generation. This paper introduces an innovative Analysis-by-Synthesis Transformer that addresses these limitations in a unified framework by effectively modeling pixel-to-shape and pixel-to-texture relationships. It consists of a Shape Transformer and a Texture Transformer. The Shape Transformer employs learnable shape queries to fetch pixel-level features from the image, thereby achieving high-quality mesh reconstruction and recovering occluded vertices. The Texture Transformer employs texture queries for non-local gathering of texture information and thus eliminates incorrect inductive bias. Experimental results on CUB-200-2011 and ShapeNet datasets demonstrate superior performance in shape reconstruction and texture generation compared to previous methods. 
Funding: NSF award CNS-1828265, NSF award ECCS-2400900

Analysis-by-Synthesis Transformer for Single-View 3D Reconstruction

In the biomedical domain, taxonomies organize the acquisition modalities of scientific images in hierarchical structures. Such taxonomies leverage large sets of correct image labels and provide essential information about the importance of a scientific publication, which could then be used in biocuration tasks. However, the hierarchical nature of the labels, the overhead of processing images, the absence or incompleteness of labeled data, and the expertise required to label this type of data impede the creation of useful datasets for biocuration. From a multi-year collaboration with biocurators and text-mining researchers, we derive an iterative visual analytics and active learning strategy to address these challenges. We implement this strategy in a system called BI-LAVA—Biocuration with Hierarchical Image Labeling through Active Learning and Visual Analytics. BILAVA leverages a small set of image labels, a hierarchical set of image classifiers, and active learning to help model builders deal with incomplete ground-truth labels, target a hierarchical taxonomy of image modalities, and classify a large pool of unlabeled images. BI-LAVA&rsquo;s front end uses custom encodings to represent data distributions, taxonomies, image projections, and neighborhoods of image thumbnails, which help model builders explore an unfamiliar image dataset and taxonomy and correct and generate labels. An evaluation with machine learning practitioners shows that our mixed human-machine approach successfully supports domain experts in understanding the characteristics of classes within the taxonomy, as well as validating and improving data quality in labeled and unlabeled collections. 
CCS Concepts: Human-centered computing -> Visual analytics; Computing methodologies -> Machine learning algorithms

Interaction during the case study: COVID-19 Image Modality Labeling.

BI-LAVA: Biocuration with Hierarchical Image Labeling through Active Learning and Visual Analytics

Over the past decade, there has been a significant increase in the development of visual analytics systems dedicated to addressing urban issues. These systems distill intricate urban analysis workflows into intuitive, interactive visual representations and interfaces, enabling users to explore, understand, and derive insights from large and complex data, including street-level imagery, street networks, and building geometries. Developing urban visual analytics systems, however, is a challenging endeavor that requires considerable programming expertise and interaction between various multidisciplinary stakeholders. This situation often leads to monolithic and isolated prototypes that are hard to reproduce, combine, or extend. Concurrently, there has been an increase in the availability of general and urban-specific toolkits, frameworks, and authoring tools that are open source and abstract away the need to implement low-level visual analytics functionalities. This paper provides a hierarchical taxonomy of urban visual analytics systems to contextualize how they are usually designed, implemented, and evaluated. We develop this taxonomy across three distinct levels (i.e., dimensions, categories, and tags), juxtaposing visualization with analytics, data, and system dimensions. We then assess the extent to which current open-source toolkits, frameworks, and authoring tools can effectively support the development of components tailored to urban visual analytics, identifying their strengths and limitations in addressing the unique challenges posed by urban data. In doing so, we offer a roadmap that can guide the effective employment of existing resources and chart a pathway for developing and refining future systems. 
Keywords: Visual Analytics, Visualization Toolkits, Visualization Grammars, Visualization Authoring, Urban Visual Analytics

Assessing the Landscape of Toolkits, Frameworks, and Authoring Tools for Urban Visual Analytics Systems

This study addresses the prevalent issue of missing data in patient-reported outcome datasets, particularly focusing on head and neck cancer patient symptom ratings sourced from the MD Anderson Symptom Inventory. Given that many data mining and machine learning algorithms necessitate complete datasets, the accurate imputation of missing data as an initial step becomes crucial. In this study we propose, for the first time, the use of collaborative filtering for imputing missing head and neck cancer patient symptom ratings. Two configurations of collaborative filtering, namely patient-based and symptom-based, leverage known ratings to infer the missing ones. Additionally, this study compares the performance of collaborative filtering with alternative imputation methods such as Multiple Imputation by Chained Equations, Nearest Neighbor Imputation, and Linear interpolation. Performance is compared using Root Mean Squared Error and Mean Absolute Error metrics. Findings demonstrate that collaborative filtering is a viable and comparatively superior approach for imputing missing patient symptom data. 
Keywords: Head and Neck Cancer, Imputation, Collaborative Filtering 
<a href="https://doi.org/10.1007/978-3-031-68309-1_20">https://doi.org/10.1007/978-3-031-68309-1_20</a>

Collaborative Filtering for the Imputation of Patient Reported Outcomes

September 2023 marks the 50th anniversary of the Electronic Visualization Laboratory (EVL) at University of Illinois Chicago (UIC). EVL&rsquo;s introduction of the CAVE Automatic Virtual Environment in 1992, the first widely replicated, projection-based, walk-in, virtual-reality (VR) system in the world, put EVL at the forefront of collaborative, immersive data exploration and analytics. However, the journey did not begin then. Since its founding in 1973, EVL has been developing tools and techniques for real-time, interactive visualizations - pillars of VR. But EVL&rsquo;s culture is also relevant to its successes, as it has always been an interdisciplinary lab that fosters teamwork, where each person&rsquo;s expertise contributes to the development of the necessary tools, hardware, system software, applications, and human interface models to solve problems. Over the years, as multidisciplinary collaborations evolved and advanced scientific instruments and data resources were distributed globally, the need to access and share data and visualizations while working with colleagues, local and remote, synchronous and asynchronous, also became important fields of study. This paper is a retrospective of EVL&rsquo;s past 50 years that surveys the many networked, immersive, collaborative visualization and VR systems and applications it developed and deployed, as well as lessons learned and future plans. 
Article appears in the journal PRESENCE: Virtual and Augmented Reality, in the Special Issue on Immersive Visualization Laboratories - Past, Present and Future, edited by S. Su and W. Sherman, <a href="https://direct.mit.edu/pvar/issue/volume/33">https://direct.mit.edu/pvar/issue/volume/33</a>. 
Article copyrighted by Massachusetts Institute of Technology (MIT). 
DOI: <a href="https://doi.org/10.1162/pres_a_00421">https://doi.org/10.1162/pres_a_00421</a>

EVL faculty, students, and collaborators 1975.

Electronic Visualization Laboratory&rsquo;s 50th Anniversary Retrospective: Look to the Future, Build on the Past

Meetings and conversations today are multimodal in nature, including the usage of diverse technologies in the form of text, video, images, voice, AI agents, and also immersive technologies. If it were possible to replay important multimodal conversations in their spatial and chronological contexts, it would benefit several audiences, such as - researchers to study and analyze multimodal conversations, novices in any field who could learn from experts, experts who have missed being part of crucial conversations. However, recreating such multimodal interactions requires integrating all of the modalities used in the conversation. Immersive environments provide a convenient setting to present data with its spatial and temporal referents through the use of situated analytics. We present MuSA (Multimodal Situated Analytics), an immersive environment prototype, as a way to recreate multimodal conversations for exploration and analysis in immersive environments. Our development pipeline consisted of various stages such as data capturing, data cleaning, data synchronization, prototype building, and deploying the final product to end-user hardware. We conducted two comprehensive user studies to evaluate the usability, user adoption, and space usage during the exploration of multimodal conversations with our application. In the first study, we explore conversations between seated participants (n=12), and in the second, we explore conversations between non-seated moving participants (n=13). We share insights gained during the development phase, as well as empirical results and feedback collected through both user studies.

Depiction of an analyst investigating a multimodal conversation that includes participants and other entities of the conversation through MuSA in Mixed Reality (HoloLens2).

Multimodal Situated Analytics (MuSA) for Analyzing Conversations in Extended Reality

SAGE3 (the Smart Amplified Group Environment) is an open-source collaboration software platform designed for real-time interactive information and visualization sharing. Seamlessly complementing video conferencing tools like Zoom, Microsoft Teams, and Google Meets, it enhances group environments for information-rich collaboration. With SAGE3, users can collaborate on their laptops, meeting room projectors, and on large tiled display walls. SAGE3 on large display spaces has proven to enable people to think, brainstorm, discover and reach decisions with greater speed, accuracy, comprehensiveness and confidence. SAGE3 provides a large collaborative canvas for sharing and editing of media, data, code, visualizations, dashboards, and screen-shares. SAGE3 is ideally suiting for collaborative data science, project meetings, and classroom education scenarios. <a href="https://sage3.sagecommons.org/">https://sage3.sagecommons.org/</a>

SAGE3: Smart Amplified Group Environment for harnessing the data revolution (poster)

Digital twin models are of high interest to Head and Neck Cancer (HNC) oncologists, who have to navigate a series of complex treatment decisions that weigh the efficacy of tumor control against toxicity and mortality risks. Evaluating individual risk profiles necessitates a deeper understanding of the interplay between different factors such as patient health, spatial tumor location and spread, and risk of subsequent toxicities that can not be adequately captured through simple heuristics. To support clinicians in better understanding tradeoffs when deciding on treatment courses, we developed DITTO, a digital-twin and visual computing system that allows clinicians to analyze detailed risk profiles for each patient, and decide on a treatment plan. DITTO relies on a sequential Deep Reinforcement Learning digital twin (DT) to deliver personalized risk of both long-term and short-term disease outcomes and toxicity risk for HNC patients. Based on a participatory collaborative design alongside oncologists, we also implement several visual explainability methods to promote clinical trust and encourage healthy skepticism when using our system. We evaluate the efficacy of DITTO through quantitative evaluation of performance and case studies with qualitative feedback. Finally, we discuss design lessons for developing clinical visual XAI applications for clinical end users. 
Index Terms: Medicine; Machine Learning; Application Domains; High Dimensional data; Spatial Data; Activity Centered Design

Overview of DITTO.

DITTO: A Visual Digital Twin for Interventions and Temporal Treatment Outcomes in Head and Neck Cancer

Though the object detection performance on standard benchmarks has been improved drastically in the last decade, current object detectors are often vulnerable to domain shift between the training data and testing images. Domain adaptation techniques have been developed to adapt an object detector trained in a source domain to a target domain. However, they assume that the target domain is known and fixed and that a target dataset is available for training, which cannot be satisfied in many real-world applications. To close this gap, this paper investigates fully test-time adaptation for object detection. It means to update a trained object detector on a single testing image before making a prediction, without access to the training data. Through a diagnostic study of a baseline self-training framework, we show that a great challenge of this task is the unreliability of pseudo labels caused by domain shift. We then propose a simple yet effective method, termed the IoU Filter, to address this challenge. It consists of two new IoU-based indicators, both of which are complementary to the detection confidence. Experimental results on five datasets demonstrate that our approach could effectively adapt a trained detector to various kinds of domain shifts at test time and bring substantial performance gains. Code is available at https://github.com/XiaoqianRuan1/IoU-filter. Code is available at https://github.com/ XiaoqianRuan1/IoU-filter. 
Funding:: The COMPaaS DLV project (NSF award CNS-1828265)

Fully Test-time Adaptation for Object Detection

Unsupervised semantic segmentation (USS) aims at partitioning an image into semantically meaningful segments by learning from a collection of unlabeled images. The effectiveness of current approaches is plagued by difficulties in coordinating representation learning and pixel clustering, modeling the varying feature distributions of different classes, handling outliers and noise, and addressing the pixel class imbalance problem. This paper introduces a novel approach, termed Imbalance-Aware Dense Discriminative Clustering (IDDC), for USS, which addresses all these difficulties in a unified framework. Different from existing approaches, which learn USS in two stages (i.e., generating and updating pseudo masks, or refining and clustering embeddings), IDDC learns pixel-wise feature representation and dense discriminative clustering in an end-to-end and self-supervised manner, through a novel objective function that transfers the manifold structure of pixels in the embedding space of a vision Transformer (ViT) to the label space while tolerating the noise in pixel affinities. During inference, the trained model directly outputs the classification probability of each pixel conditioned on the image. In addition, this paper proposes a new regularizer, based on the Weibull function, to handle pixel class imbalance and cluster degeneration in a single shot. Experimental results demonstrate that IDDC significantly outperforms all previous USS methods on three real-world datasets, COCO-Stuff-27, COCO-Stuff-171, and Cityscapes. Extensive ablation studies validate the effectiveness of each design. Our code is available at https://github.com/MY-LIU100101/IDDC. Our code is available at https://github.com/MY-LIU100101/IDDC. 
Keywords: Unsupervised semantic segmentation · Imbalance-Aware Dense Discriminative Clustering · End-to-end training ·
Deep clustering 
Funding: The COMPaaS DLV project (NSF award CNS-1828265)

Imbalance-Aware Discriminative Clustering for Unsupervised Semantic Segmentation

Studies have proven the effectiveness of mindfulness and forest-immersion therapy as an option for stress reduction. However, what happens when an individual is seeking stress relief but the outdoors is unavailable due to unpredictable weather or even a lack of accessibility? The purpose of this research project is to observe the effects of guided scenic meditation in a virtual reality environment on physiological stress. Additionally, it performs a platform comparison by deploying the same meditative experience and testing its effectiveness using a room-scale immersive environment and a desktop computer, as desktops ($400 - $1000) are far more affordable and widely available compared to room-scale immersive environments ($1 million+). Using each platform, twenty participants were asked to engage with a meditative application that simulates the sights, sounds, and smells of a forest environment. Based on the objective data collected, the participants experienced a decrease in both heart rate and blood pressure, demonstrating the effectiveness of the simulation in reducing physiological stress. However, the room-scale immersive environment proved to be more successful in reducing the heart rate than the desktop computer. Furthermore, according to the participants, their subjectively perceived stress levels decreased, resulting in a far more relaxed state than before. The results showcase that a virtual simulation of a real environment actively contributes to stress reduction and can be used in cases when the outdoors is unavailable. The platform comparison demonstrates that the product of the research - a guided scenic meditation application that simulates a forest - is accessible and does not require a million-dollar room-scale display in order to be effective in reducing stress.

The guided scenic meditation application deployed to the CAVE2, pictured on the left, and the desktop, pictured on the right.

The Effects of Virtual Reality Environments on Physiological Stress: A Platform Comparison Between Room-Scale Displays and Desktop Computers (MS Thesis Project)

This project was performed during the COVID-19 pandemic. It presents a visual computing framework (MOTIV) for analyzing moral rhetoric on social media around controversial topics such as Stay-at-Home orders. Using Moral Foundation Theory, it proposes a methodology for deconstructing and visualizing the when, where, and who behind each of these moral dimensions as expressed in microblog data. The project was developed in collaboration with experts from language processing, communications, and causal inference. Our approach integrates microblog data with multiple sources of geospatial and temporal data, and leverages unsupervised machine learning (generalized additive models) to support collaborative hypothesis discovery and testing. 
Abstract: We present a visual computing framework for analyzing moral rhetoric on social media around controversial topics. Using Moral Foundation Theory, we propose a methodology for deconstructing and visualizing the when, where, and who behind each of these moral dimensions as expressed in microblog data. We characterize the design of this framework, developed in collaboration with experts from language processing, communications, and causal inference. Our approach integrates microblog data with multiple sources of geospatial and temporal data, and leverages unsupervised machine learning (generalized additive models) to support collaborative hypothesis discovery and testing. We implement this approach in a system named MOTIV. We illustrate this approach on two problems, one related to Stay-at-home policies during the COVID-19 pandemic, and the other related to the Black Lives Matter movement. Through detailed case studies and discussions with collaborators, we identify several insights discovered regarding the different drivers of moral sentiment in social media. Our results indicate that this visual approach supports rapid, collaborative hypothesis testing, and can help give insights into the underlying moral values behind controversial political issues. 
Supplemental Material: <a href="https://osf.io/ygkzn/?view_only=6310c0886938415391d977b8aae8b749">https://osf.io/ygkzn/?view_only=6310c0886938415391d977b8aae8b749</a>

MOTIV visualization of moral framing on social media in 2020.

MOTIV: Visual Exploration of Moral Framing in Social Media

Sunlight and shadow play critical roles in how urban spaces are utilized, thrive, and grow. While access to sunlight is essential to the success of urban environments, shadows can provide shaded places to stay during the hot seasons, mitigate heat island effect, and increase pedestrian comfort levels. Properly quantifying sunlight access and shadows in large urban environments is key in tackling some of the important challenges facing cities today. In this paper, we propose Dee Umbra, a novel computational framework that enables the quantification of sunlight access and shadows at a global scale. Our framework is based on a conditional generative adversarial network that considers the physical form of cities to compute high-resolution spatial information of accumulated sunlight access for the different seasons of the year. We use data from seven different cities to train our model, and show, through an extensive set of experiments, its low overall RMSE (below 0.1) as well as its extensibility to cities that were not part of the training set. Additionally, we contribute a set of case studies and a comprehensive dataset with sunlight access information for more than 100 cities across six continents of the world. Deep Umbra is available at <a href="https://urbantk.org/shadows">https://urbantk.org/shadows</a>. 
Index Terms - Urban computing, Urban analytics, Sunlight access, Shadow, Generative adversarial networks.

Example where shadows are cast across tiles.

Deep Umbra: A Generative Approach for Sunlight Access Computation in Urban Spaces

e-JUST - Environmental Justice using Urban Scalable Toolkit, a state-of-the-art GIS-based web platform, is being developed to inform local and regional decision-making regarding air quality and heat in the Chicago metropolitan area, utilizing Earth science and high-resolution climate model outputs. e-JUST contributes to mitigating risks associated with extreme heat and air pollution by improving decision-making related to heat and air quality stressors in the Chicago metropolitan region. e-JUST technology architecture involves ReactJS, Vega-Lite, and Urban Toolkit's functionalities. e-JUST integrates a variety of meteorological and air quality datasets. Among these are NASA&rsquo;s remote sensing observations, such as GOES 16 and ECOSTRESS, that measure surface temperatures at 100 m and hourly intervals. The environmental data layers include high-resolution (1 km and higher) outputs from urban climate models related to temperature, NO2 and PM2.5, ozone, and other pollutants. To account for demographic data, we use the 2020 US Census. Our data fusion approaches include further downscaling environmental data layers to street scale using machine learning algorithms for spatiotemporal interpolation, enabling more accurate predictions at scales relevant to communities. e-JUST&rsquo;s statistical view includes a heat matrix that illustrates correlations between several environmental and socioeconomic variables at a grid point in time, as well as a scatterplot that allows users to compare observed data and forecasts of two different variables. Overall, this toolkit provides information for the near past and near-real-time forecasting to aid in decision-making and emergency management preparedness for Chicago communities. Additionally, our platform is designed to provide users with easy access to data, enabling them to view and analyze data immediately. The e-JUST toolkit is anticipated to benefit a wide audience, ranging from the general public to academic researchers.

e-JUST - Environmental Justice using Urban Scalable Toolkit

Similarity detection seeks to identify similar, but distinct items over multivariate datasets. Often, similarity cannot be defined computationally, leading to a need for visual analysis, such as in cases with ensemble, computational, patient cohort, or geospatial data. In this work, we empirically evaluate the effectiveness of common visual encodings for multivariate data in the context of visual similarity detection. We conducted a user study with 40 participants to measure similarity detection performance and response time under moderate scale (16 items) and large scale (36 items). Our analysis shows that there are significant differences in performance between encodings, especially as the number of items increases. Surprisingly, we found that juxtaposed star plots outperformed superposed parallel coordinate plots. Furthermore, color-cues significantly improved response time, and attenuated error at larger scales. In contrast to existing guidelines, we found that filled star plots (Kiviats) outperformed other encodings in terms of scalability and error.

Similarity detection over multivariate data using five visual encodings (clockwise order from top left): Kiviat diagrams, Lines-Only glyphs, Color-cue Kiviat diagrams, Parallel Coordinate Plots, and Color-cue Parallel Coordinate Plots.