Web-based Visualization System Research Articles

Coastal Hazards System: A Probabilistic Coastal Hazard Analysis Framework

Nadal-Caraballo, N.C.; Campbell, M.O.; Gonzalez, V.M.; Torres, M.J.; Melby, J.A., and Taflanidis, A.A., 2020. Coastal Hazards System: A Probabilistic Coastal Hazard Analysis Framework. In: Malvárez, G. and Navas, F. (eds.), Global Coastal Issues of 2020. Journal of Coastal Research, Special Issue No. 95, pp. 1211-1216. Coconut Creek (Florida), ISSN 0749-0208.Coastal hazards such as storm surge, waves, currents, wind, and rainfall associated with extratropical and tropical cyclones (e.g., hurricanes) can have devastating effects and threaten the lives of millions of people living along the world's coastlines. This has led to the development of the Coastal Hazards System (CHS), an ambitious program with the primary goal of quantifying extreme coastal hazards due to both tropical and extratropical cyclones. The CHS currently covers all U.S. hurricane-prone coastlines along the Gulf of Mexico, the Atlantic seaboard, Puerto Rico, and the U.S. Virgin Islands, as well as regions affected only by extratropical storms like the Great Lakes. The Probabilistic Coastal Hazard Analysis (PCHA) is an innovative statistical and probabilistic framework that serves as the foundation of the CHS. This framework includes comprehensive characterization of storm climatology, high-resolution numerical modeling, and advanced joint probability analysis of atmospheric forcing and primary storm responses, including associated aleatory and epistemic uncertainties. New PCHA advancements overcome limitations of previous joint probability methods by integrating meta-Gaussian copula for the computation of storm probabilities, spatially-varying bias and uncertainty quantification, and Gaussian process metamodel (GPM) based on existing CHS synthetic tropical cyclones. GPM emulates the response of computationally-expensive hydrodynamic models and enables the generation of augmented tropical cyclone suites, consisting of up to millions of storms, for optimal coverage of the parameter and probability spaces. The CHS also includes a database and a web-based data mining and visualization system for public distribution of high-fidelity probabilistic, atmospheric and hydrodynamic modeling results. PCHA results are distributed along with the corresponding metadata, in self-describing data formats for easy and efficient use by engineers, planners, economists, emergency managers, and researchers.

A Visual Analytics Approach for Structural Differences among Transportation Networks

Abstract Representing and analyzing structural differences among transportation networks help gain insight into the difference related patterns such as dynamic evolutions of transportation networks. Conventional solutions leverage representation learning techniques to encode structural information, but lack of an intuitive way of studying structural semantics of transportation networks. In this paper, we propose a representation-and-analysis scheme for structural differences among graphs. We propose a Delta2vec embedding technique to encode multiple graphs while preserving semantics of structural differences. We design and implement a web-based visual analytics system to support comparative study of features learned from the embeddings. One distinctive feature of our approach is that it supports semantics-aware construction, quantification, and investigation of latent relations encoded in graphs.

A Visual Analytics System for Route Planning and Emergency Crowd Evacuation

Emergency evacuation from crowded public spaces is of great importance to all authorities around the world. Many systems have been developed by researchers to address the optimization of emergency evacuation routing and planning. This paper presents a visual analytics system for route planning and emergency crowd evacuation; a web-based visualization and simulation system that allows stakeholders to develop and assess route planning and evacuation procedures for emergency scenar-ios. The system takes advantage of the available OpenStreetMap comprehensive spatial database to enable users to implement evacuation scenarios in almost anywhere OpenStreetMap dataset is available. Using multiple infrastructure-specific varying sce-narios, such as adjusting capacities of roads/pathways or their closure, the tool can identify bottleneck areas thus allowing the assessment of potential improvements to pedestrian and transportation network to relieve the bottleneck and improve evacuation time. As a case study, we use this system for the city of Makkah in Saudi Arabia and the city of Minnesota in the United States.

Location2vec: A Situation-Aware Representation for Visual Exploration of Urban Locations

Understanding the relationship between urban locations is an essential task in urban planning and transportation management. Although prior works have focused on studying urban locations by aggregating location-based properties, our scheme preserves the mutual influence between urban locations and mobility behavior, and thereby enables situation-aware exploration of urban regions. By leveraging word embedding techniques, we encode urban locations with a vectorized representation while retaining situational awareness. Specifically, we design a spatial embedding algorithm that is precomputed by incorporating the interactions between urban locations and moving objects. To explore our proposed technique, we have designed and implemented a web-based visual exploration system that supports the comprehensive analysis of human mobility, location functionality, and traffic assessment by leveraging the proposed visual representation. The case studies demonstrate the effectiveness of our approach.

RSATree: Distribution-Aware Data Representation of Large-Scale Tabular Datasets for Flexible Visual Query.

Analysts commonly investigate the data distributions derived from statistical aggregations of data that are represented by charts, such as histograms and binned scatterplots, to visualize and analyze a large-scale dataset. Aggregate queries are implicitly executed through such a process. Datasets are constantly extremely large; thus, the response time should be accelerated by calculating predefined data cubes. However, the queries are limited to the predefined binning schema of preprocessed data cubes. Such limitation hinders analysts' flexible adjustment of visual specifications to investigate the implicit patterns in the data effectively. Particularly, RSATree enables arbitrary queries and flexible binning strategies by leveraging three schemes, namely, an R-tree-based space partitioning scheme to catch the data distribution, a locality-sensitive hashing technique to achieve locality-preserving random access to data items, and a summed area table scheme to support interactive query of aggregated values with a linear computational complexity. This study presents and implements a web-based visual query system that supports visual specification, query, and exploration of large-scale tabular data with user-adjustable granularities. We demonstrate the efficiency and utility of our approach by performing various experiments on real-world datasets and analyzing time and space complexity.

SDG Viz: A Web-based System for Visualizing Sustainable Development Indicators

Abstract. To promote sustainable development, United Nations General Assembly (UNGA) has adopted “The 2030 Agenda for Sustainable Development”, which consists of 17 Sustainable Development Goals (SDGs) (e.g., “No poverty” and “Zero hunger”) with a set of 169 targets. To measure and track the progress, 230 indicators have been developed. It has been recognized by many international communities (e.g., ICA and ISPRS) that effective visualization of SDG indicators so as to communicate the SDG status to the public and decision-makers is one of the key issues. In this study, a web-based visualization system called “SDG Viz” has been developed for such a purpose. This system has two main components: visual representation and visual interaction. For visual representation, it has the capacity of producing thematic maps such as choropleth maps, proportional symbol maps, 3D maps and animated maps. For visual interaction, the framework of three stages cartographic interaction proposed by Roth was adopted as the basic for design of system functions. Some interaction functions have also been developed, such as identify, compare, rank, associate and delineate. It is believed that this system is potentially valuable for visualizing SDG indicators.

Informatics & Surveillance in Global Health: Informatics Capacity for Zika Outbreak

Informatics & Surveillance in Global Health: Informatics Capacity for Zika Outbreak

Analysis and visualization of multidimensional time series: Particulate matter (PM10) from São Carlos-SP (Brazil)

Data visualization techniques have been successfully used in a time series context for many years. In this work, a web-based data visualization system was developed to support the evolutionary analysis of atmospheric particulate matter (PM10) behavior, collected in the central region of São Carlos, São Paulo State, Brazil. The samples were acquired following two patterns: daily in the first stage and on alternate days in the second stage. Each measurement covered a period of 23 h and 30 min, using a large volume sampler (high-volume). To interrogate the acquired data, an interactive visualization tool was developed in JavaScript and D3 library. The tool is portable and can be used in all modern web browsers, not requiring any software installation. The values obtained from São Carlos’ PM10 monitoring campaign from 2014 to 2017 clearly showed a decrease of PM10 in the city atmosphere, in comparison with past data from the 1997 to 2005 campaign. This decreasing trend in PM10 concentrations could be due to an increase in the severity of law n° 997 of 05/31/1976 and decree 54.487 of 06/26/2009, which charges the anthropogenic agents with air pollution control.

An IoT based smart irrigation management system using Machine learning and open source technologies

The scarcity of clean water resources around the globe has generated a need for their optimum utilization. Internet of Things (IoT) solutions, based on the application specific sensors’ data acquisition and intelligent processing, are bridging the gaps between the cyber and physical worlds. IoT based smart irrigation management systems can help in achieving optimum water-resource utilization in the precision farming landscape. This paper presents an open-source technology based smart system to predict the irrigation requirements of a field using the sensing of ground parameter like soil moisture, soil temperature, and environmental conditions along with the weather forecast data from the Internet. The sensing nodes, involved in the ground and environmental sensing, consider soil moisture, soil temperature, air temperature, Ultraviolet (UV) light radiation, and relative humidity of the crop field. The intelligence of the proposed system is based on a smart algorithm, which considers sensed data along with the weather forecast parameters like precipitation, air temperature, humidity, and UV for the near future. The complete system has been developed and deployed on a pilot scale, where the sensor node data is wirelessly collected over the cloud using web-services and a web-based information visualization and decision support system provides the real-time information insights based on the analysis of sensors data and weather forecast data. The system has a provision for a closed-loop control of the water supply to realize a fully autonomous irrigation scheme. The paper describes the system and discusses in detail the information processing results of three weeks data based on the proposed algorithm. The system is fully functional and the prediction results are very encouraging.

Structure-Based Suggestive Exploration: A New Approach for Effective Exploration of Large Networks.

When analyzing a visualized network, users need to explore different sections of the network to gain insight. However, effective exploration of large networks is often a challenge. While various tools are available for users to explore the global and local features of a network, these tools usually require significant interaction activities, such as repetitive navigation actions to follow network nodes and edges. In this paper, we propose a structure-based suggestive exploration approach to support effective exploration of large networks by suggesting appropriate structures upon user request. Encoding nodes with vectorized representations by transforming information of surrounding structures of nodes into a high dimensional space, our approach can identify similar structures within a large network, enable user interaction with multiple similar structures simultaneously, and guide the exploration of unexplored structures. We develop a web-based visual exploration system to incorporate this suggestive exploration approach and compare performances of our approach under different vectorizing methods and networks. We also present the usability and effectiveness of our approach through a controlled user study with two datasets.

Modeling, validation and verification of three-dimensional cell-scaffold contacts from terabyte-sized images

BackgroundCell-scaffold contact measurements are derived from pairs of co-registered volumetric fluorescent confocal laser scanning microscopy (CLSM) images (z-stacks) of stained cells and three types of scaffolds (i.e., spun coat, large microfiber, and medium microfiber). Our analysis of the acquired terabyte-sized collection is motivated by the need to understand the nature of the shape dimensionality (1D vs 2D vs 3D) of cell-scaffold interactions relevant to tissue engineers that grow cells on biomaterial scaffolds.ResultsWe designed five statistical and three geometrical contact models, and then down-selected them to one from each category using a validation approach based on physically orthogonal measurements to CLSM. The two selected models were applied to 414 z-stacks with three scaffold types and all contact results were visually verified. A planar geometrical model for the spun coat scaffold type was validated from atomic force microscopy images by computing surface roughness of 52.35 nm ±31.76 nm which was 2 to 8 times smaller than the CLSM resolution. A cylindrical model for fiber scaffolds was validated from multi-view 2D scanning electron microscopy (SEM) images. The fiber scaffold segmentation error was assessed by comparing fiber diameters from SEM and CLSM to be between 0.46% to 3.8% of the SEM reference values. For contact verification, we constructed a web-based visual verification system with 414 pairs of images with cells and their segmentation results, and with 4968 movies with animated cell, scaffold, and contact overlays. Based on visual verification by three experts, we report the accuracy of cell segmentation to be 96.4% with 94.3% precision, and the accuracy of cell-scaffold contact for a statistical model to be 62.6% with 76.7% precision and for a geometrical model to be 93.5% with 87.6% precision.ConclusionsThe novelty of our approach lies in (1) representing cell-scaffold contact sites with statistical intensity and geometrical shape models, (2) designing a methodology for validating 3D geometrical contact models and (3) devising a mechanism for visual verification of hundreds of 3D measurements. The raw and processed data are publicly available from https://isg.nist.gov/deepzoomweb/data/ together with the web -based verification system.

Visual analytics and remote sensing imagery to support community-based research for precision agriculture in emerging areas

Agriculture in northern Ontario, Canada, has not yet reached the level of development of the southern regions of the province. In spite of the increasing desirability of the former region for agricultural expansion, northern agricultural producers – as well as other producers in “emerging” areas – have less access to information and decision support services relative to more established agricultural regions. At the same time, geographic information systems (GIS) are now being integrated into precision agriculture to assess field variability, to ensure optimal use of information, to maximize output, and to increase efficiency. To address this trend, a community-based research initiative based on an interactive web-based information visualization and GIS decision support system has been deployed with the aim of providing northern Ontario producers with access to the data they need to make the best possible decisions concerning their crops. This system employs citizen science and community-based participatory research to build a mutually beneficial partnership between agricultural producers, researchers, and other community stakeholders.

Development of a visual e-learning system for supporting the semantic organization and utilization of open learning content

In an open e-learning content management environment, relation metadata is of benefit to improve semantic organization and reusability of learning content. Although the suggested relations defined in the SCORM and the extended relations proposed in the past studies can describe semantic relationships, there are some new requirements of semantic organization and utilization of open learning content. Based on existing models, this paper presents an extended relation metadata model for open knowledge communities. In order to help users to author and utilize the semantic relation, the visual authoring system named web-based visual authoring system for relation metadata (WVAS-RM) in the Learning Cell Knowledge Community is designed and implemented to assist the construction and utilization of semantic relations of Learning Cells. The paper presents an empirical evaluation of the teachers’ and learners’ acceptance and satisfaction of the proposed system using the adapted Technology Acceptance Model and System Usability Scale. The semi-structured interviews are also carried out with participants including teachers and students. It is concluded that students and teachers feel confident and satisfied with the system.

Korean Ministry of Environment's web-based visual consumer product exposure and risk assessment system (COPER).

With support from the Korean Ministry of the Environment (ME), our interdisciplinary research staff developed the COnsumer Product Exposure and Risk assessment system (COPER). This system includes various databases and features that enable the calculation of exposure and determination of risk caused by consumer products use. COPER is divided into three tiers: the integrated database layer (IDL), the domain specific service layer (DSSL), and the exposure and risk assessment layer (ERAL). IDL is organized by the form of the raw data (mostly non-aggregated data) and includes four sub-databases: a toxicity profile, an inventory of Korean consumer products, the weight fractions of chemical substances in the consumer products determined by chemical analysis and national representative exposure factors. DSSL provides web-based information services corresponding to each database within IDL. Finally, ERAL enables risk assessors to perform various exposure and risk assessments, including exposure scenario design via either inhalation or dermal contact by using or organizing each database in an intuitive manner. This paper outlines the overall architecture of the system and highlights some of the unique features of COPER based on visual and dynamic rendering engine for exposure assessment model on web.

A Web-Based Visual and Analytical Geographical Information System for Oil and Gas Data

With the development of strategic oil and gas assets, massive spatiotemporal oil and gas data have been accumulated. Application systems that assist in the storage and management of the voluminous and complex oil and gas datasets are in high demand. The voluminous and various data should be leveraged and turned into information for business decision-making and operation assistance. In this paper, we propose a set of visual analytic methods that specialize in oil and gas data; and, we develop a web-based oil and gas data management, visualization and analytical system, called Oil and Gas Visual Exploration System (OGVES). With OGVES, complex and multi-sourced oil and gas data can be stored, searched, filtered, and represented. As a web-based system, the OGVES provides more accessibility, convenience and efficiency than traditional desktop systems. Spatial scales and temporal primitives contained in oil and gas data are discussed. Different visualization methods are then presented to explore and represent spatiotemporal features of the oil and gas data. Various case studies demonstrate the usability of the system.

Daily Goals Formulation and Enhanced Visualization of Mechanical Ventilation Variance Improves Mechanical Ventilation Score.

The systematic implementation of evidence-based practice through the use of guidelines, checklists, and protocols mitigates the risks associated with mechanical ventilation, yet variation in practice remains prevalent. Recent advances in software and hardware have allowed for the development and deployment of an enhanced visualization tool that identifies mechanical ventilation goal variance. Our aim was to assess the utility of daily goal establishment and a computer-aided visualization of variance. This study was composed of 3 phases: a retrospective observational phase (baseline) followed by 2 prospective sequential interventions. Phase I intervention comprised daily goal establishment of mechanical ventilation. Phase II intervention was the setting and monitoring of daily goals of mechanical ventilation with a web-based data visualization system (T3). A single score of mechanical ventilation was developed to evaluate the outcome. The baseline phase evaluated 130 subjects, phase I enrolled 31 subjects, and phase II enrolled 36 subjects. There were no differences in demographic characteristics between cohorts. A total of 171 verbalizations of goals of mechanical ventilation were completed in phase I. The use of T3 increased by 87% from phase I. Mechanical ventilation score improved by 8.4% in phase I and 11.3% in phase II from baseline (P = .032). The largest effect was in the low risk VT category, with a 40.3% improvement from baseline in phase I, which was maintained at 39% improvement from baseline in phase II (P = .01). mechanical ventilation score was 9% higher on average in those who survived. Daily goal formation and computer-enhanced visualization of mechanical ventilation variance were associated with an improvement in goal attainment by evidence of an improved mechanical ventilation score. Further research is needed to determine whether improvements in mechanical ventilation score through a targeted, process-oriented intervention will lead to improved patient outcomes. (ClinicalTrials.gov registration NCT02184208.).

TopicLens: Efficient Multi-Level Visual Topic Exploration of Large-Scale Document Collections.

Topic modeling, which reveals underlying topics of a document corpus, has been actively adopted in visual analytics for large-scale document collections. However, due to its significant processing time and non-interactive nature, topic modeling has so far not been tightly integrated into a visual analytics workflow. Instead, most such systems are limited to utilizing a fixed, initial set of topics. Motivated by this gap in the literature, we propose a novel interaction technique called TopicLens that allows a user to dynamically explore data through a lens interface where topic modeling and the corresponding 2D embedding are efficiently computed on the fly. To support this interaction in real time while maintaining view consistency, we propose a novel efficient topic modeling method and a semi-supervised 2D embedding algorithm. Our work is based on improving state-of-the-art methods such as nonnegative matrix factorization and t-distributed stochastic neighbor embedding. Furthermore, we have built a web-based visual analytics system integrated with TopicLens. We use this system to measure the performance and the visualization quality of our proposed methods. We provide several scenarios showcasing the capability of TopicLens using real-world datasets.

Development of Web-Based Information System for Visualization of Three-dimensional Models of Museum Exhibits

In the following article main results are going to be summarized about three-dimensional web development and technologies. An introduction to 3D representation methods that are applied on the featured three-dimensional webpage is going to be presented. Transformation techniques will be described, which convert a traditional two-dimensional webpage into a fully functional three-dimensional platform. The methods and concepts used are based on technologies such as HTML, CSS, and JavaScript. The principle of automatic conversion is going to be showed, upon which almost any two-dimensional webpage can be converted into a 3D website, including minimal manual interaction. Future concept and conclusions are also described.

Visual analytics for supply network management: System design and evaluation

We propose a visual analytic system to augment and enhance decision-making processes of supply chain managers. Several design requirements drive the development of our integrated architecture and lead to three primary capabilities of our system prototype. First, a visual analytic system must integrate various relevant views and perspectives that highlight different structural aspects of a supply network. Second, the system must deliver required information on-demand and update the visual representation via user-initiated interactions. Third, the system must provide both descriptive and predictive analytic functions for managers to gain contingency intelligence. Based on these capabilities we implement an interactive web-based visual analytic system. Our system enables managers to interactively apply visual encodings based on different node and edge attributes to facilitate mental map matching between abstract attributes and visual elements. Grounded in cognitive fit theory, we demonstrate that an interactive visual system that dynamically adjusts visual representations to the decision environment can significantly enhance decision-making processes in a supply network setting. We conduct multi-stage evaluation sessions with prototypical users that collectively confirm the value of our system. Our results indicate a positive reaction to our system. We conclude with implications and future research opportunities.

3D web visualization of huge CityGML models

Abstract. Nowadays, rapid technological development into acquiring geo-spatial information; joined to the capabilities to process these data in a relative short period of time, allows the generation of detailed 3D textured city models that will become an essential part of the modern city information infrastructure (Spatial Data Infrastructure) and, can be used to integrate various data from different sources for public accessible visualisation and many other applications. One of the main bottlenecks, which at the moment limit the use of these datasets to few experts, is a lack on efficient visualization systems through the web and interoperable frameworks that allow standardising the access to the city models. The work presented in this paper tries to satisfy these two requirements developing a 3D web-based visualization system based on OGC standards and effective visualization concepts. The architectural framework, based on Services Oriented Architecture (SOA) concepts, provides the 3D city data to a web client designed to support the view process in a very effective way. The first part of the work is to design a framework compliant to the 3D Portrayal Service drafted by the of the Open Geospatial Consortium (OGC) 3D standardization working group. The latter is related to the development of an effective web client able to render in an efficient way the 3D city models.