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17th International Conference on Universal Access in Human-Computer Interaction (UAHCI) part of the 25th International Conference on Human-Computer Interaction (HCI)

作者： Petri, Alexis N. Ho, Duy H. Wang, Ye Lee, Yugyung Univ Missouri Kansas City Kansas City MO 64110 USA Auburn Univ NSF Eddie Bernice Johnson INCLUDES Initiat TAPDINTO S Auburn AL 36849 USA UMKC Sch Sci & Engn Kansas City MO USA UMKC Sch Humanities & Social Sci Kansas City MO USA

ISBN: (纸本)9783031358968;9783031358975

Description of the development and use of a dynamic portal for supporting an alliance of colleges and universities focused on supporting students with disabilities and transitioning to careers in science and technology. Called SOAR, the portal is designed to support separate institutes achieve collective impact through shared measures. Significant aspects of SOAR are the user-driven design with three different communication roles, dynamic generation of survey forms, the ability to schedule surveys, collecting data through the surveys, and data presentation through dynamic chart generation. SOAR utilizes and advances the best practices of Universal Access and is central to the alliance's ability to empower individuals with disabilities to live their best lives. One of the most interesting features is the ability for different institutes to customize their forms and collect campus-relevant data that can be changed and the application of machine learning to produce the dynamic chart generation. SOAR allows the alliance to meet individual campus needs and the reporting and evaluation needs of the National Science Foundation.

关键词： User-friendly Survey App Inclusive User Experience Tailored User Interface interactive data visualization

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Accessibility of a COVID-19 interactive display: An evaluation of the Johns Hopkins COVID-19 dashboard An evaluation of the Johns Hopkins COVID-19 dashboard 23

Accessibility of a COVID-19 interactive display: An evaluati...

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41st ACM International Conference on Design of Communication (SIGDOC)

作者： Deets, Stuart M. Univ Minnesota Twin Cities Dept Writing Studies Minneapolis MN 55455 USA

This paper highlights the importance of heuristic accessibility analysis by investigating the Johns Hopkins COVID-19 U.S. dashboard. It suggests possible future research directions for technical communicators to conti... 详细信息

ISBN: (纸本)9798400703362

关键词： COVID-19 interactive data visualization

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data Abstraction for Visual and Haptic Representations in Flow visualization 22

Data Abstraction for Visual and Haptic Representations in Fl...

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28th ACM Symposium on Virtual Reality Software and Technology (VRST)

作者： Bhardwaj, Ayush Kang, Sungjoo Kim, Jin Ryong Univ Texas Dallas Richardson TX 75083 USA ETRI AI Res Lab Daejeon South Korea

ISBN: (纸本)9781450398893

This paper presents a new way of data abstraction for visual and haptic representations in immersive analytics using a mid-air haptic display. Visual and haptic abstraction is proposed to transform raw data (wind tunnel data) into another form of data for effective visual and haptic data mapping. Three main features are extracted: (i) Magnitude of Velocity, (ii) Recirculation Region, and (iii) Vorticity. For each feature, visual and haptic abstractions are defined based on data characterization and data reduction. A preliminary study shows a promising direction toward multimodal data interaction in immersive analytics.

关键词： Immersive analytics haptic data abstraction data transformation interactive data visualization

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Bicycle Demand Prediction to Optimize the Rebalancing of a Bike Sharing System in Lisbon 26

Bicycle Demand Prediction to Optimize the Rebalancing of a B...

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26th International Conference Information Visualisation (IV)

作者： Afonso, Ana Sofia Pires, Joao Moura Datia, Nuno Birra, Fernando NOVA Sch Sci & Technol NOVA LINCS Monte De Caparica Portugal Politecn Lisboa ISEL Lisbon Sch Engn Future Internet Technol Lisbon Portugal

ISBN: (纸本)9781665490078

With urban development in cities, shared bicycle systems are increasingly used as a way to avoid traffic caused by cars, promoting sustainable mobility and contributing for traffic and pollution reduction in urban areas. The imbalance in the availability of bicycles and docks at the stations of the systems makes it impossible to rent and return bicycles, making it necessary to redistribute them across the network. However, this process has flaws, mainly during rush hours. In this paper, we analyse data provided by the Lisbon City Council regarding their bike sharing system, which has the rebalancing operations' influence. Since the original data was contaminated with the rebalancing operations, an analysis was conducted in an attempt to remove this influence from the data. Following this analysis, a new dataset was created using only the trip data to enable model development for each station and predict the bicycle demand. The plateaus in the created dataset were then analysed to determine if they're due to lack of demand from costumers, or due to stations being full or empty.

关键词： Shared Bicycle Systems Time Series Rebalancing Bicycle Demand Forecasting interactive data visualization

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Getting Started with D3, an Open-Source JavaScript Library for Creating interactive data visualizations for the Web

Journal of Computing Sciences in Colleges

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Journal of Computing Sciences in Colleges 2024年第2期40卷

作者： Karina Assiter STEM Department Landmark College Putney VT

D3 is a free, open-source JavaScript library for visualizing data [1]. The low-level approach of coding with D3 allows flexibility in creating dynamic, data-driven graphics; thus, D3 is behind the creation of groundbreaking and award-winning visualizations. Though the first version of D3 was released in 2011, it is still relevant for visualizing data in 2024 [4]. D3 is excellent for building data visualizations that are either standard and familiar (bar charts, line charts, force-based layouts, etc.) or non-standard and innovative [3]. In this tutorial we will start with a quick introduction to the field of data visualization [2], then we will provide an overview of D3, its benefits, structure, development environment, and methods of adding it to a JavaScript file. Then there will be opportunities for attendees to get hands-on experience with D3: setting up a simple server to host data files, adding code to D3 files, loading and building a visualization from an accessed data file, and adding interaction. Next, we will explore the myriad of ways that D3 can be (and has been) applied to advanced interactive data visualization problems. Finally, we will share resources that attendees can refer to as they get up-to-speed with D3.

关键词： D3 data visualization information visualization interactive data visualization

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Elaborator: A Novel App for Insights into Laboratory data of Clinical Trials

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THERAPEUTIC INNOVATION & REGULATORY SCIENCE 2021年第6期55卷 1220-1229页

作者： Janitza, Silke Majumder, Madhurima Mendolia, Franco Jeske, Steffen Kulmann, Hermann Bayer AG Res & Dev Pharmaceut Stat & Data Insights D-13342 Berlin Germany Bayer US LLC Whippany NJ USA Bayer AG Wuppertal Germany Chrestos Concept GmbH & Co KG Essen Germany

In clinical studies there are huge numbers of laboratory parameters available that are measured at several visits for several treatment groups. The status quo for presenting laboratory data in clinical trials consists in generating large numbers of tables and data listings. Such tables and listings are required for submissions to health authorities. However, reviewing laboratory data presented in the form of tables and listings is a lengthy and tedious process. Thus, to enable efficient exploration of laboratory data we developed elaborator, a comprehensive and easy-to-use interactive browser-based application. The elaborator app comprises three analyses types for addressing different questions, for example about changes in laboratory values that frequently occur, treatment-related changes and changes beyond the normal ranges. In this way, the app can be used by study teams for identifying safety signals in a clinical trial as well as for generating hypotheses that are further inspected with detailed analyses and possibly data from other sources. The elaborator app is implemented in the statistical software R. The R package elaborator can be obtained from https://***/package=elaborator. Patients' laboratory data need to be extracted from the clinical database and pre-processed locally for feeding into the app. For exploring data by means of the elaborator, the user needs some familiarity with R but no programming knowledge is required.

关键词： interactive data visualization Question-based visualization data exploration Clinical review Clinical trial Clinical laboratory data

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NYenviroScreen: An open-source data driven method for identifying potential environmental justice communities in New York State

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ENVIRONMENTAL SCIENCE & POLICY 2021年 124卷 348-358页

作者： Petroni, Michael Howard, Sarah Howell, I. Brielle Collins, Mary B. SUNY Coll Environm Sci & Forestry Ctr Environm Med & Informat 1 Forestry Dr Syracuse NY 13210 USA SUNY Coll Environm Sci & Forestry Grad Program Environm Sci 1 Forestry Dr Syracuse NY 13210 USA SUNY Coll Environm Sci & Forestry Dept Environm Studies 1 Forestry Dr Syracuse NY 13210 USA

In 2003, the New York State Department of Environmental Conservation began designating Potential Environmental Justice Areas (PEJA) for the purpose of providing public participation opportunities to disadvantaged communities during permitting deliberations. We developed NYenviroScreen to help stakeholders understand, review, and provide input for how future PEJA designation might be updated and improved, including for identifying disadvantaged communities under the newly enacted Climate Leadership and Community Protection Act (CLCPA). We present and compare three potential update methods and provide an interactive web application for investigating model components and composition. The three methods are: (i) three factor clustering using the Jenks natural breaks algorithm, (ii) a cumulative impact model adapted from CalEPA's CalEnviroScreen, and (iii) a hybrid approach that uses both methods and incorporates Federal and State recognized tribal land areas. NYenviroScreen brings together federal and state data sources related to population health, sociodemographics, environmental risk factors, and potential pollution exposures for 15,463 census block groups. We find that a hybrid approach provides the most robust coverage for both rural and urban areas of New York State. This publicly accessible innovative approach is an important, data driven effort toward the pursuit of environmental justice in New York State.

关键词： Actionable science Environmental justice Environmental policy interactive data visualization Cumulative risk screening

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Combining 2D and 3D visualization with Visual Analytics in the Environmental Domain

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INFORMATION 2022年第1期13卷 7页

作者： Vuckovic, Milena Schmidt, Johanna Ortner, Thomas Cornel, Daniel VRVis Zentrum Virtual Real & Visualisierung Forsc A-1220 Vienna Austria

The application potential of Visual Analytics (VA), with its supporting interactive 2D and 3D visualization techniques, in the environmental domain is unparalleled. Such advanced systems may enable an in-depth interactive exploration of multifaceted geospatial and temporal changes in very large and complex datasets. This is facilitated by a unique synergy of modules for simulation, analysis, and visualization, offering instantaneous visual feedback of transformative changes in the underlying data. However, even if the resulting knowledge holds great potential for supporting decision-making in the environmental domain, the consideration of such techniques still have to find their way to daily practice. To advance these developments, we demonstrate four case studies that portray different opportunities in data visualization and VA in the context of climate research and natural disaster management. Firstly, we focus on 2D data visualization and explorative analysis for climate change detection and urban microclimate development through a comprehensive time series analysis. Secondly, we focus on the combination of 2D and 3D representations and investigations for flood and storm water management through comprehensive flood and heavy rain simulations. These examples are by no means exhaustive, but serve to demonstrate how a VA framework may apply to practical research.

关键词： visual analytics interactive data visualization environmental research flood simulation natural disaster management climate research

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The Geography of Mathematical (Dis)Advantage: An Application of Multilevel Simultaneous Autoregressive (MSAR) Models to Public data in Education Research

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AERA OPEN 2023年 9卷

作者： Canche, Manuel S. Gonzalez Univ Penn Philadelphia PA 19104 USA

Research has shown that mathematical proficiency gaps are related to students' and schools' indicators of poverty, with fewer studies on neighborhood effects on achievement gaps. Although this literature has accounted for students' nesting within schools, so far, methodological constraints have not allowed researchers to formally account for multilevel and spatial effects. I contribute to this discussion by simultaneously considering test-takers' own socioeconomic standing and the impact of their nesting schools and neighborhood structures. Multilevel simultaneous autoregressive (MSAR) models and population-level data of 2.09 million test-takers, whose standardized performances were measured at Grades 3-8 in New York State, revealed the presence of geography of mathematical (dis)advantage. Because mathematical performance is spatially dependent across schools and neighborhoods, moving forward, applied researchers should rely on MSAR to account for sources of spatially driven bias that cannot be handled with multilevel models alone. Full replication code and data are provided at https://***/N4zRstL.

关键词： achievement gap data reproducibility data science applications econometric analysis elementary schools geographical network modeling geography of mathematical (dis)advantage hierarchical linear modeling interactive data visualization machine learning applications multicolinearity in spatial or place-based indicators multilevel simultaneous autoregressive (MSAR) observational research performance assessment regression analyses research methodology secondary data analysis social class social context social stratification spatial dependence spatial socioeconometric modeling

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interactive statistical monitoring to optimize review of potential clinical trial issues during study conduct

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CONTEMPORARY CLINICAL TRIALS COMMUNICATIONS 2023年 33卷 101101-101101页

作者： Pau, David Lotz, Marie Grandclaude, Gaelle Jegou, Romain Civet, Alexandre Roche SAS Boulogne Billancourt France Keyrus Life Sci Nantes France

Background: Statistical monitoring involves the review of prospective study data collected in participating sites to detect intra/inter patients and sites inconsistencies. We report methods and results of statistical monitoring in a phase IV clinical ***: PRO-MSACTIVE is a study evaluating ocrelizumab in active relapsing multiple sclerosis (RMS) patients in France. Specific statistical methods (volcano plots, mahalanobis distance, funnel plot ...) have been applied to a SDTM database to detect potential issues. R-Shiny application was developed to generate an interactive web application in order to ease site and/or patients identification during statistical data review ***: The PRO-MSACTIVE study enrolled 422 patients in 46 centers between July 2018 and August 2019. Three data review meetings were held between April and October 2019 and 14 standard and planned tests were run on study data, with a total of 15 (32.6%) sites identified as needing review or investigation. Overall 36 findings were identified during the meetings: duplicate records, outliers, inconsistent delays between ***: Statistical monitoring is useful to identify unusual or clustered data patterns that might be revealing issues that could impact the data integrity and/or may potentially impact patients' safety. With anticipated and appropriate interactive data visualization, early signals can easily be identified or reviewed by the study team and appropriate actions be set up and assigned to the most appropriate function for a close follow-up and resolution. interactive statistical monitoring is time consuming to initiate using R-Shiny, but is time saving after the 1st data review meeting (DRV).(*** identifier: NCT03589105;EudraCT identifier: 2018-000780-91)

关键词： Statistical monitoring R shiny Clinical trial interactive data visualization

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