scholarly journals A Conceptual Model for Geo-Online Exploratory Data Visualization: The Case of the COVID-19 Pandemic

Information ◽  
2021 ◽  
Vol 12 (2) ◽  
pp. 69
Author(s):  
Anna Bernasconi ◽  
Silvia Grandi
Keyword(s):  
Data Visualization ◽  
Spatial Information ◽  
Online Communication ◽  
Spatial Dimension ◽  
Data Types ◽  
Related Data ◽  
Exploratory Data ◽  
Visualization Tools ◽  
Communication Needs ◽  
Entity Relationship

Responding to the recent COVID-19 outbreak, several organizations and private citizens considered the opportunity to design and publish online explanatory data visualization tools for the communication of disease data supported by a spatial dimension. They responded to the need of receiving instant information arising from the broad research community, the public health authorities, and the general public. In addition, the growing maturity of information and mapping technologies, as well as of social networks, has greatly supported the diffusion of web-based dashboards and infographics, blending geographical, graphical, and statistical representation approaches. We propose a broad conceptualization of Web visualization tools for geo-spatial information, exceptionally employed to communicate the current pandemic; to this end, we study a significant number of publicly available platforms that track, visualize, and communicate indicators related to COVID-19. Our methodology is based on (i) a preliminary systematization of actors, data types, providers, and visualization tools, and on (ii) the creation of a rich collection of relevant sites clustered according to significant parameters. Ultimately, the contribution of this work includes a critical analysis of collected evidence and an extensive modeling effort of Geo-Online Exploratory Data Visualization (Geo-OEDV) tools, synthesized in terms of an Entity-Relationship schema. The COVID-19 pandemic outbreak has offered a significant case to study how and how much modern public communication needs spatially related data and effective implementation of tools whose inspection can impact decision-making at different levels. Our resulting model will allow several stakeholders (general users, policy-makers, and researchers/analysts) to gain awareness on the assets of structured online communication and resource owners to direct future development of these important tools.

scholarly journals Graph structures for data visualizations

2021 ◽  
Vol 6 (2) ◽  
pp. 24-31
Author(s):  
Stefana Janićijević ◽  
Vojkan Nikolić
Keyword(s):  
Big Data ◽  
Data Visualization ◽  
Data Types ◽  
Current State ◽  
Graph Structures ◽  
Big Data Visualization ◽  
Visualization Tools ◽  
Graph System ◽  
Data Visualizations

Networks are all around us. Graph structures are established in the core of every network system therefore it is assumed to be understood as graphs as data visualization objects. Those objects grow from abstract mathematical paradigms up to information insights and connection channels. Essential metrics in graphs were calculated such as degree centrality, closeness centrality, betweenness centrality and page rank centrality and in all of them describe communication inside the graph system. The main goal of this research is to look at the methods of visualization over the existing Big data and to present new approaches and solutions for the current state of Big data visualization. This paper provides a classification of existing data types, analytical methods, techniques and visualization tools, with special emphasis on researching the evolution of visualization methodology in recent years. Based on the obtained results, the shortcomings of the existing visualization methods can be noticed.

scholarly journals Geo-online explanatory data visualization tools as crisis management and communication instruments

2021 ◽  
Vol 4 ◽  
pp. 1-8
Author(s):  
Silvia Grandi ◽  
Anna Bernasconi
Keyword(s):  
Data Visualization ◽  
Geographical Information Systems ◽  
Online Communication ◽  
Spatial Dimension ◽  
Decision Makers ◽  
Geographical Information ◽  
Disease Spread ◽  
Geospatial Information ◽  
Online Tools ◽  
Available Information

Abstract. Communication during emergency and crises times is a critical aspect. When available information contains a spatial dimension, maps and interactive localization features may help conveying strong messages to audiences that are otherwise difficult to reach. The COVID-19 pandemic has prompted the design and implementation of a great number of online tools to communicate data of the disease spread and its dynamics that are helpful to support informed decisions for both people in their everyday life and decision makers. Observing this phenomenon has inspired this conceptualization of the geo-Online Explanatory Data Visualization (geo-OEDV) tools, set in the context of available geospatial information, of statistical visualisation tools and of the solid tradition of Geographical Information Systems. Blending classical statistical tools, digital cartography, and the confluence of many elements into a single screen, has produced the currently most spread geo-OEDV instance, i.e., the geo-dashboard and geo-infographics. In particular this paper conceptualises geo-OEDV as a category of meta-cartography that blends online communication with cartographic representation and management principles.

scholarly journals Asubstratefor modular, extensible data-visualization

2017 ◽  
Author(s):  
Jordan K Matelsky ◽  
Joseph Downs ◽  
Hannah Cowley ◽  
Brock Wester ◽  
William Gray-Roncal
Keyword(s):  
Data Visualization ◽  
Three Dimensional ◽  
Large Data ◽  
Analysis Data ◽  
Relevant Information ◽  
Data Types ◽  
Seamless Integration ◽  
Computing Platform ◽  
Exploratory Data ◽  
High Level

AbstractAs the scope of scientific questions increase and datasets grow larger, the visualization of relevant information correspondingly becomes more difficult and complex. Sharing visualizations amongst collaborators and with the public can be especially onerous, as it is challenging to reconcile software dependencies, data formats, and specific user needs in an easily accessible package. We presentsubstrate, a data-visualization framework designed to simplify communication and code reuse across diverse research teams. Our platform provides a simple, powerful, browser-based interface for scientists to rapidly build effective three-dimensional scenes and visualizations. We aim to reduce the gap of existing systems, which commonly prescribe a limited set of high-level components, that are rarely optimized for arbitrarily large data visualization or for custom data types. To further engage the broader scientific community and enable seamless integration with existing scientific workflows, we also presentpytri, a Python library that bridges the use ofsubstratewith the ubiquitous scientific computing platform,Jupyter. Our intention is to reduce the activation energy required to transition between exploratory data analysis, data visualization, and publication-quality interactive scenes.

scholarly journals Human-Data Interaction in Healthcare

2020 ◽  
pp. 1148-1167
Author(s):  
Federico Cabitza ◽  
Angela Locoro
Keyword(s):  
Data Quality ◽  
Data Visualization ◽  
Interactive Systems ◽  
Evidence Based ◽  
Related Data ◽  
Human Data ◽  
Health Related ◽  
Visualization Tools ◽  
Oriented Research ◽  
User Centered

In this chapter, we focus on an emerging strand of IT-oriented research, namely Human-Data Interaction (HDI) and on how this can be applied to healthcare. HDI regards both how humans create and use data by means of interactive systems, which can both assist and constrain them and the operational level of data work, which is both work on data and by data. Healthcare is a challenging arena where to test the potential of HDI towards a new, user-centered perspective on how to support and assess “data work”. This is especially true in current times where data are becoming increasingly big and many tools are available for the lay people, including doctors and nurses, to interact with health-related data. This chapter is a contribution in the direction of considering health-related data through the lens of HDI, and of framing data visualization tools in this strand of research. The intended aim is to let the subtler peculiarities among different kind of data and of their use emerge and be addressed adequately. Our point is that doing so can promote the design of more usable tools that can support data work from a user-centered and data quality perspective and the evidence-based validation of these tools.

scholarly journals Human-Data Interaction in Healthcare

2017 ◽  
pp. 184-203 ◽  
Author(s):  
Federico Cabitza ◽  
Angela Locoro
Keyword(s):  
Data Quality ◽  
Data Visualization ◽  
Interactive Systems ◽  
Evidence Based ◽  
Related Data ◽  
Human Data ◽  
Health Related ◽  
Visualization Tools ◽  
Oriented Research ◽  
User Centered

In this chapter, we focus on an emerging strand of IT-oriented research, namely Human-Data Interaction (HDI) and on how this can be applied to healthcare. HDI regards both how humans create and use data by means of interactive systems, which can both assist and constrain them and the operational level of data work, which is both work on data and by data. Healthcare is a challenging arena where to test the potential of HDI towards a new, user-centered perspective on how to support and assess “data work”. This is especially true in current times where data are becoming increasingly big and many tools are available for the lay people, including doctors and nurses, to interact with health-related data. This chapter is a contribution in the direction of considering health-related data through the lens of HDI, and of framing data visualization tools in this strand of research. The intended aim is to let the subtler peculiarities among different kind of data and of their use emerge and be addressed adequately. Our point is that doing so can promote the design of more usable tools that can support data work from a user-centered and data quality perspective and the evidence-based validation of these tools.

Multimodal Coreference Resolution for Exploratory Data Visualization Dialogue: Context-Based Annotation and Gesture Identification

2017 ◽  
Author(s):  
Abhinav Kumar ◽  
Jillian Aurisano ◽  
Barbara Di Eugenio ◽  
Andrew Johnson ◽  
Abeer Alsaiari ◽  
...  
Keyword(s):  
Data Visualization ◽  
Coreference Resolution ◽  
Exploratory Data

Deep Learning in Disease Diagnosis: Models and Datasets

2020 ◽  
Vol 15 ◽  
Author(s):  
Deeksha Saxena ◽  
Mohammed Haris Siddiqui ◽  
Rajnish Kumar
Keyword(s):  
Biological Sciences ◽  
Machine Learning ◽  
Deep Learning ◽  
Disease Diagnosis ◽  
Learning Models ◽  
Data Types ◽  
Related Data ◽  
Abstract Level ◽  
Experimental Validations ◽  
Selection Of

Background: Deep learning (DL) is an Artificial neural network-driven framework with multiple levels of representation for which non-linear modules combined in such a way that the levels of representation can be enhanced from lower to a much abstract level. Though DL is used widely in almost every field, it has largely brought a breakthrough in biological sciences as it is used in disease diagnosis and clinical trials. DL can be clubbed with machine learning, but at times both are used individually as well. DL seems to be a better platform than machine learning as the former does not require an intermediate feature extraction and works well with larger datasets. DL is one of the most discussed fields among the scientists and researchers these days for diagnosing and solving various biological problems. However, deep learning models need some improvisation and experimental validations to be more productive. Objective: To review the available DL models and datasets that are used in disease diagnosis. Methods: Available DL models and their applications in disease diagnosis were reviewed discussed and tabulated. Types of datasets and some of the popular disease related data sources for DL were highlighted. Results: We have analyzed the frequently used DL methods, data types and discussed some of the recent deep learning models used for solving different biological problems. Conclusion: The review presents useful insights about DL methods, data types, selection of DL models for the disease diagnosis.

A Perioperative Care Display for Understanding High Acuity Patients

2021 ◽  
Vol 12 (01) ◽  
pp. 164-169
Author(s):  
Laurie Lovett Novak ◽  
Jonathan Wanderer ◽  
David A. Owens ◽  
Daniel Fabbri ◽  
Julian Z. Genkins ◽  
...  
Keyword(s):  
Data Visualization ◽  
Design Process ◽  
Cognitive Task ◽  
Development Project ◽  
Clinical Informatics ◽  
Data Display ◽  
Data Types ◽  
Iterative Design ◽  
High Acuity ◽  
Depth Interviews

Abstract Background The data visualization literature asserts that the details of the optimal data display must be tailored to the specific task, the background of the user, and the characteristics of the data. The general organizing principle of a concept-oriented display is known to be useful for many tasks and data types. Objectives In this project, we used general principles of data visualization and a co-design process to produce a clinical display tailored to a specific cognitive task, chosen from the anesthesia domain, but with clear generalizability to other clinical tasks. To support the work of the anesthesia-in-charge (AIC) our task was, for a given day, to depict the acuity level and complexity of each patient in the collection of those that will be operated on the following day. The AIC uses this information to optimally allocate anesthesia staff and providers across operating rooms. Methods We used a co-design process to collaborate with participants who work in the AIC role. We conducted two in-depth interviews with AICs and engaged them in subsequent input on iterative design solutions. Results Through a co-design process, we found (1) the need to carefully match the level of detail in the display to the level required by the clinical task, (2) the impedance caused by irrelevant information on the screen such as icons relevant only to other tasks, and (3) the desire for a specific but optional trajectory of increasingly detailed textual summaries. Conclusion This study reports a real-world clinical informatics development project that engaged users as co-designers. Our process led to the user-preferred design of a single binary flag to identify the subset of patients needing further investigation, and then a trajectory of increasingly detailed, text-based abstractions for each patient that can be displayed when more information is needed.

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