While cities around the world are looking for smart ways to channel new advances in data collection, management, and analysis to address their day-to-day problems, the complex nature of urban issues and the overwhelming amount of available structured and unstructured data have posed significant challenges in translating these efforts into actionable insights. In the past few years, urban visual analytics tools have significantly helped tackle these challenges. When analyzing a feature of interest, an urban scientist must transform, integrate and visualize different thematic (e.g., sunlight, flows) and physical (e.g., buildings, sidewalks) layers, oftentimes across multiple spatial and temporal scales. However, integrating and analyzing these layers requires expertise in different fields, increasing development time and effort. This makes the entire visual data exploration and system implementation hard for experienced programmers and also sets a high entry barrier for urban experts outside of computer science. With this in mind, in this paper, we present the Urban Toolkit (UTK), a flexible and extensible visualization framework that enables the easy authoring of web-based visualizations through a new high-level grammar specifically built with common urban use cases in mind. In order to facilitate the integration and visualization of different urban data, we also propose the concept of knots to merge thematic and physical urban layers. We evaluate our approach through use cases and a series of interviews with experts and practitioners from different domains, including urban accessibility, urban planning, architecture, and climate science. UTK is available at urbantk.org. 
Index Terms: Urban visual analytics, Urban analytics, Urban data, Visualization toolkit.

UTK abstracts spatial joins through the use of knots.

The Urban Toolkit: A Grammar-based Framework for Urban Visual Analytics

Weather forecasting is essential for decision-making and is usually performed using numerical modeling. Numerical weather models, in turn, are complex tools that require specialized training and laborious setup and are challenging even for weather experts. Moreover, weather simulations are data-intensive computations and may take hours to days to complete. When the simulation is finished, the experts face challenges analyzing its outputs, a large mass of spatiotemporal and multivariate data. From the simulation setup to the analysis of results, working with weather simulations involves several manual and error-prone steps. The complexity of the problem increases exponentially when the experts must deal with ensembles of simulations, a frequent task in their daily duties. To tackle these challenges, we propose ProWis: an interactive and provenance-oriented system to help weather experts build, manage, and analyze simulation ensembles at runtime. Our system follows a human-in-the-loop approach to enable the exploration of multiple atmospheric variables and weather scenarios. ProWis was built in close collaboration with weather experts, and we demonstrate its effectiveness by presenting two case studies of rainfall events in Brazil. 
Index Terms - Weather visualization, Ensemble visualization, Provenance management, WRF visual setup

Building, managing, and analyzing a Weather Research and Forecasting (WRF) model ensemble using PROWIS to study an extreme rainfall event in Maricá (RJ, Brazil) in April 2022.

ProWis: A visual approach for building, managing, and analyzing weather simulation ensembles in runtime

* Authors: 
Belcaid, M., Leigh, J., Theriot, R., Kirshenbaum, N., Tabalba, R., Rogers, M. University of Hawai’i at Manoa, Honolulu, HI 
Johnson, A., Brown, M., Renambot, L., Long, L., Nishimoto, A., University of Illinois Chicago, Chicago, IL 
North C., Harden, J., Virginia Tech, Blacksburg, VA 
Translational software research bridges the gap between scientific innovations and practical applications, driving impactful societal advancements. However, developing such software is challenging due to interdisciplinary collaboration, technology adoption, and post-funding sustainability. This article presents the experiences and insights of the Scalable Adaptive Graphics Environment (SAGE) team, which has spent two decades developing translational, cross-disciplinary, collaboration tools to benefit computational science research. With a focus on SAGE and its next-generation iterations, we explore the inherent challenges in translational research, such as fostering cross-disciplinary collaboration, motivating technology adoption, and ensuring post-funding product sustainability. We also discuss the roles of funding agencies, policymakers, and academic institutions in promoting translational research. Although the journey is fraught with challenges, the societal impact and satisfaction derived from translational research underscore its significance in the broader scientific landscape. This article aims to encourage further conversation and the development of effective models for
translational software projects.

The process that defines domain science, computer science (CS), and sustainable translational research.

Reflecting on the Scalable Adaptive Graphics Environment Team’s 20-Year Translational Research Endeavor in Digital Collaboration Tools

Recent advances in precision medicine for treating Oropharyngeal cancer (OPC) have led to significant improvements in survival for patients. However, OPC survivors often are left with severe side effects that result from damage to vital tissues in the head and neck caused by secondary radiation received during treatment. These side effects can persist for months after treatment cessation, and diminish the patients’ quality of life. Existing guidelines for radiation therapy plans identify dose limits to individual organs with the goal to avoid or minimize permanent tissue damage and their related toxicity. However, existing tools fail to account for either the three-dimensional nature of dose distributions or the fact that critical organs often work as a system that can have multiple failure points. 
We present an unsupervised approach for stratifying patients based on three-dimensional dose distributions to multiple organs and evaluate their association with long-term patient-reported symptoms. We apply this method to predicting severe patient-reported symptoms 6 months after ceasing radiation therapy for OPC patients. For actionable interpretability, we introduce a rule-mining method that simplifies our strata into a set of dose thresholds, and demonstrate that these thresholds outperform existing dose treatment guidelines. 
Keywords: Radiation therapy; Oropharynx cancer; Head and Neck cancer; Stratification; Symptom Burden; Quality of Life

Results of a chi-squared test between covariates and membership in each set of clusters for each outcome.

Multi-Organ Spatial Stratification of 3-D Dose Distributions Improves Risk Prediction of Long-Term Self-Reported Severe Symptoms in Oropharyngeal Cancer Patients Receiving Radiotherapy: Development of a Pre-Treatment Decision Support Tool.

Motivation: Figures in biomedical papers communicate essential information with the potential to identify relevant documents in biomedical and clinical settings. However, academic search interfaces mainly search over text fields. 
Results: We describe a search system for biomedical documents that leverages image modalities and an existing index server. We integrate a problem-specific taxonomy of image modalities and image-based data into a custom search system. Our solution features a front-end interface to enhance classical document search results with image-related data, including page thumbnails, figures, captions, and image-modality information. We demonstrate the system on a subset of the CORD-19 document collection. A quantitative evaluation demonstrates higher precision and recall for biomedical document retrieval. A qualitative evaluation with domain experts further highlights our solution’s benefits to biomedical search. 
Availability and implementation: A demonstration is available at <a href="https://runachay.evl.uic.edu/scholar">https://runachay.evl.uic.edu/scholar</a>. Our code and image models can be accessed via <a href="https://github.com/uic-evl/bio-search">https://github.com/uic-evl/bio-search</a>. The dataset is continuously expanded. 
Image caption: Search interface displaying the results of a boolean query. (A) The color-coded legend displays the image modalities in the first level of the taxonomy. (B) The search bar, with a keyword box showing a boolean query, a time filter, and an image modalities filter limiting results to documents containing \textit{radiology} or \textit{fluorescence microscopy} figures. (C) The metadata of a result includes the document title, year, venue, authors, and the hits on the abstract and full text. Below is the count of figures and modalities in the document. (D) The enhanced document surrogate shows the image content, including the page thumbnail, extracted figure, subfigures and modalities, and caption. 
<a href="https://doi.org/10.1093/bioadv/vbad095">https://doi.org/10.1093/bioadv/vbad095</a>

Search interface displaying the results of a boolean query.

Enhancing biomedical search interfaces with images

Objective: Evaluate the effectiveness of machine learning tools that incorporate spatial information such as disease location and lymph node metastatic patterns-of-spread, for prediction of survival and toxicity in HPV+ oropharyngeal cancer (OPC). 
Materials & methods: 675 HPV+ OPC patients that were treated at MD Anderson Cancer Center between 2005 and 2013 with curative intent IMRT were retrospectively collected under IRB approval. Risk stratifications incorporating patient radiometric data and lymph node metastasis patterns via an anatomically-adjacent representation with hierarchical clustering were identified. These clusterings were combined into a 3-level patient stratification and included along with other known clinical features in a Cox model for predicting survival outcomes, and logistic regression for toxicity, using independent subsets for training and validation. 
Results: Four groups were identified and combined into a 3-level stratification. The inclusion of patient stratification in predictive models for 5-yr Overall survival (OS), 5-year recurrence free survival, (RFS) and Radiation associated dysphagia (RAD) consistently improved model performance measured using the area under the curve (AUC). Test set AUC improvements over models with clinical covariates, was 9 % for predicting OS, and 18 % for predicting RFS, and 7 % for predicting RAD. For models with both clinical and AJCC covariates, AUC improvement was 7 %, 9 %, and 2 % for OS, RFS, and RAD, respectively. 
Conclusion: Including data-driven patient stratifications considerably improve prognosis for survival and toxicity outcomes over the performance achieved by clinical staging and clinical covariates alone. These stratification generalize well to across cohorts, and sufficient information for reproducing these clusters is included. 
Keywords: Head & Neck cancer, Statistical and research methods, Machine learning, Radiation oncology, Health Informatics, Imaging, Risk stratification, Oropharyngeal cancer, Clustering, Radiomics

Average lymph node involvement and clinical staging categories of the 3 derived lymph node + radiomics clusters across both the training and validation datasets.

Spatially-aware clustering improves AJCC-8 risk stratification performance in oropharyngeal carcinomas

Honorable Mention in the EuroVis Best Paper Category 
Exploratory analysis of the chemical space is an important task in the field of cheminformatics. For example, in drug discovery research, chemists investigate sets of thousands of chemical compounds in order to identify novel yet structurally similar synthetic compounds to replace natural products. Manually exploring the chemical space inhabited by all possible molecules and chemical compounds is impractical, and therefore presents a challenge. To fill this gap, we present ChemoGraph, a novel visual analytics technique for interactively exploring related chemicals. In ChemoGraph, we formalize a chemical space as a hypergraph and apply novel machine-learning models to compute related chemical compounds. It uses a database to find related compounds from a known space and a machine learning model to generate new ones, which helps enlarge the known space. Moreover, ChemoGraph highlights interactive features that support users in viewing, comparing, and organizing computationally identified related chemicals. With a drug discovery usage scenario and initial expert feedback from a case study, we demonstrate the usefulness of ChemoGraph. 
Keywords: chemical space exploration, cheminformatics, multipartite graphs, data visualization 
CCS Concepts: Applied computing → Chemistry 
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An overview of ChemoGraph.

ChemoGraph: Interactive Visual Exploration of the Chemical Space

Most real-world networks are both dynamic and multivariate in nature, meaning that the network is associated with various attributes and both the network structure and attributes evolve over time. Visualizing dynamic multivariate networks is of great significance to the visualization community because of their wide applications across multiple domains. However, it remains challenging because the techniques should focus on representing the network structure, attributes, and their evolution concurrently. Many real-world network analysis tasks require the concurrent usage of the three aspects of the dynamic multivariate networks. In this paper, we analyze current techniques and present a taxonomy to classify the existing visualization techniques based on three aspects: temporal encoding, topology encoding, and attribute encoding. Finally, we survey application areas and evaluation methods; and discuss challenges for future research. 
CCS Concepts Human-centered computing → Graph drawings

Overview of the taxonomy of DMVN visualization techniques along with illustrations of the categories.

The State of the Art in Visualizing Dynamic Multivariate Networks

Developing applicable clinical machine learning models is a difficult task when the data includes spatial information, for example, radiation dose distributions across adjacent organs at risk. We describe the co-design of a modeling system, DASS,to support the hybrid human-machine development and validation of predictive models for estimating long-term toxicities related to radiotherapy doses in head and neck cancer patients. Developed in collaboration with domain experts in oncology and data mining, DASS incorporates human-in-the-loop visual steering, spatial data, and explainable AI to augment domain knowledge with automatic data mining. We demonstrate DASS with the development of two practical clinical stratification models and report feedback from domain experts. Finally, we describe the design lessons learned from this collaborative experience. 
CCS Concepts 
Human-centered computing→Scientific visualization; Computing methodologies→Machine learning; Applied computing→Life and medical sciences

DASS interactive model building for head/neck cancer.

DASS Good: Explainable Data Mining of Oncology Imaging and Toxicity Data

Methane the main component of natural gas is also a potent greenhouse gas that requires robust monitoring, so that scientists, educators, and the general public can get better informed of its effect(s) on local/regional air quality and current short-term mitigations for climate change. However, current data visualizations for methane, and other scientific data, are either static 2-D graphs/plots or animated through using videos/gifs. These data visualization approaches can be limiting in that they lack interactiveness for an end user to gain exploratory insights by comparing and contrasting different areas of a dataset. This report thereby showcases how to build a functional and interactive visualization as the solution to the previously mentioned limitation. 
Keywords: Interactive Scientific Data Visualization, Climate Data Visualization, Methane Visualization

This figure shows the respective volumetric/spheric plots for 07/02/2021 at 1000 hPa. Figure 2:

Visualizing the NOAA CarbonTracker-CH4 3-D Mole Fraction (Pressure Corrected) Dataset

Background: Existing studies have reported that forest therapy utilizing sensory stimuli increases the activity of the parasympathetic nervous system and decreases the activity of the sympathetic nervous system. 
Purpose: To observe the effects of guided scenic meditation in a virtual reality environment on physiological stress. 
To perform a platform comparison by deploying the same meditative experience and testing its effectiveness using a room-scale display and a desktop computer. 
Significance: Rather than using a headset, the study uses a room-scale display - the CAVE2 Hybrid-Reality Environment - to test the effects of virtual reality environments on stress reduction. 
The platform comparison intends to showcase that if proven effective, the product is accessible, as desktop computers are far more affordable ($400 - $1000) and widely available relative to room-scale displays ($1 million+).

Fig 1. On the left is the CAVE2 version of the application. Fig 2. On the right is the desktop version of the application.

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

In the past, existing studies have proven the effectiveness of forest therapy as an option for stress reduction, but 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 intends to perform a platform comparison by deploying the same meditative experience and testing its effectiveness using a room-scale display and a desktop computer, as the latter ($400 - $1000) is far more affordable and widely available relative to the former ($1 million+). Using both platforms, ten participants were asked to engage with a Unity-built meditative application that simulates the sights, sounds, and smells of a forest environment. Based on the data collected, the participants experienced a decrease in both heart rate and blood pressure, demonstrating the effectiveness of the simulation in reducing physiological stress. Similarly, 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 an 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 forest model developed in Unity.

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

While cities around the world are increasingly promoting streets and public spaces that prioritize pedestrians over vehicles, significant data gaps have made pedestrian mapping, analysis, and modeling challenging to carry out. Most cities, even in industrialized economies, still lack information about the location and connectivity of their sidewalks, making it difficult to implement research on pedestrian infrastructure and holding the technology industry back from developing accurate, location-based Apps for pedestrians, wheelchair users, street vendors, and other sidewalk users. To address this gap, we have designed and implemented an end-to-end open-source tool - TILE2NET - for extracting sidewalk, crosswalk, and footpath polygons from orthorectified aerial imagery using semantic segmentation. The segmentation model, trained on aerial imagery from Cambridge, MA, Washington DC, and New York City, offers the first open-source scene classification model for pedestrian infrastructure from sub-meter resolution aerial tiles, which can be used to generate planimetric sidewalk data in North American cities. TILE2NET also generates pedestrian networks from the resulting polygons, which can be used to prepare datasets for pedestrian routing applications. The work offers a low-cost and scalable data collection methodology for systematically generating sidewalk network datasets, where orthorectified aerial imagery is available, contributing to over-due efforts to equalize data opportunities for pedestrians, particularly in cities that lack the resources necessary to collect such data using more conventional methods. 
Keywords: Pedestrian network extraction, Semantic segmentation, Automated network generation, Pedestrian infrastructure

Model results showing detected sidewalk, crosswalk and footpath centerlines in a) Boston and Cambridge, b) Manhattan and parts of Brooklyn, c) Wash- ington, DC. The maps are shown at the same scale for comparison.

Mapping the walk: A scalable computer vision approach for generating sidewalk network datasets from aerial imagery

Personalized head and neck cancer therapeutics have greatly improved survival rates for patients, but are often leading to understudied long-lasting symptoms which affect quality of life. Sequential rule mining (SRM) is a promising unsupervised machine
learning method for predicting longitudinal patterns in temporal data which, however, can output many repetitive patterns that are difficult to interpret without the assistance of visual analytics. We present a data-driven, human-machine analysis visual system
developed in collaboration with SRM model builders in cancer symptom research, which facilitates mechanistic knowledge discovery in large-scale, multivariate cohort symptom data. Our system supports multivariate predictive modeling of post-treatment symptoms
based on during-treatment symptoms. It supports this goal through an SRM, clustering, aggregation back end, and a custom front
end to help develop and tune the predictive models. The system also explains the resulting predictions in the context of therapeutic
decisions typical in personalized care delivery. We evaluate the resulting models and system with an interdisciplinary group of modelers and head and neck oncology researchers. The results demonstrate that our system effectively supports clinical and symptom research. 
Index Terms: Temporal Data; Life Sciences; Mixed Initiative Human-Machine Analysis; Data Clustering and Aggregation