Visual Analytics for Spatial Clusters of Air-Quality Data

Zhou, Zhiguang; Ye, Zhifei; Liu, Yanan; Liu, Fang; Tao, Yubo; Su, Weihua

doi:10.1109/mcg.2017.3621228

Cited by 38 publications

(18 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Treemaps are a common example of this technique that visualize hierarchy information of multidimensional data. Zhou et al [ZYL∗17] used treemap in the multiple coordinated view visualization system for air quality data to analyze hierarchy details of specified spatial cluster.…”

Section: Taxonomiesmentioning

confidence: 99%

The State of the Art in Visual Analysis Approaches for Ocean and Atmospheric Datasets

Afzal

Hittawe

Ghani

et al. 2019

Computer Graphics Forum

View full text Add to dashboard Cite

The analysis of ocean and atmospheric datasets offers a unique set of challenges to scientists working in different application areas. These challenges include dealing with extremely large volumes of multidimensional data, supporting interactive visual analysis, ensembles exploration and visualization, exploring model sensitivities to inputs, mesoscale ocean features analysis, predictive analytics, heterogeneity and complexity of observational data, representing uncertainty, and many more. Researchers across disciplines collaborate to address such challenges, which led to significant research and development advances in ocean and atmospheric sciences, and also in several relevant areas such as visualization and visual analytics, big data analytics, machine learning and statistics. In this report, we perform an extensive survey of research advances in the visual analysis of ocean and atmospheric datasets. First, we survey the task requirements by conducting interviews with researchers, domain experts, and end users working with these datasets on a spectrum of analytics problems in the domain of ocean and atmospheric sciences. We then discuss existing models and frameworks related to data analysis, sense‐making, and knowledge discovery for visual analytics applications. We categorize the techniques, systems, and tools presented in the literature based on the taxonomies of task requirements, interaction methods, visualization techniques, machine learning and statistical methods, evaluation methods, data types, data dimensions and size, spatial scale and application areas. We then evaluate the task requirements identified based on our interviews with domain experts in the context of categorized research based on our taxonomies, and existing models and frameworks of visual analytics to determine the extent to which they fulfill these task requirements, and identify the gaps in current research. In the last part of this report, we summarize the trends, challenges, and opportunities for future research in this area. (see http://www.acm.org/about/class/class/2012)

show abstract

Section: Taxonomiesmentioning

confidence: 99%

The State of the Art in Visual Analysis Approaches for Ocean and Atmospheric Datasets

Afzal

Hittawe

Ghani

et al. 2019

Computer Graphics Forum

View full text Add to dashboard Cite

show abstract

“…Guo et al [22] proposed Time-Correlation Partitioning (TCP) tree that represents the correlation of multiple pollutants and their evolutions. Zhou et al [21] proposed a storyline design to depict the evolution of different stations. Different from these works, we focus on the spatiotemporal multivariate patterns of air pollution propagation based on air quality and meteorological data.…”

Section: A Analysis and Visualization Of Air Pollution Propagationmentioning

confidence: 99%

Visual Analytics of Air Pollution Propagation Through Dynamic Network Analysis

Ren

Zhang

et al. 2020

IEEE Access

View full text Add to dashboard Cite

The propagation of pollutants between regions has become a noticeable factor affecting air pollution. Given the complicated propagation relationship, most of the existing works lack an effective perception mechanism of geographic correlations and time-varying features, which is crucial in exploring and understanding the propagation mechanism by integrating empirical knowledge and data inherent characteristics. In this paper, we abstract the complicated propagation relationship between regions as a dynamic network, and introduce visual analytics techniques to explore the spatiotemporal multivariate patterns of air pollution propagation. A particle tracking based model is first proposed to construct pollution propagation networks under multi-source factors. It combines numerical simulation and data characteristics simultaneously, and detects active pollution source areas based on long-term transport relationships and temporal correlations. Based on it, we extract propagation patterns and analyze the temporal evolution of diachronic propagation networks. Moreover, we design an interactive system to achieve an in-depth analysis of air pollution issues. Through elaborate multi-level glyphs and linkage views, the system facilitates users to perceive and explore propagation mechanism in spatiotemporal multivariate information, and compare propagation patterns from global and local perspectives. We present several case studies to demonstrate the usefulness of our work in air pollution propagation analysis.

show abstract

“…‘With the rapid development of industrial society, air pollution has become a major issue in the modern world that has attracted increasing attention from the public and governments because of its impact on human health and societal development’ [ZYL*17]. Numerous studies investigated how air pollution actually affects human health [DBR*97, Ped97] and thus demonstrate causal effects.…”

Section: Visual Analytics For Epidemiological Researchmentioning

confidence: 99%

A Survey of Visual Analytics for Public Health

Preim

Lawonn²

2019

Computer Graphics Forum

View full text Add to dashboard Cite

We describe visual analytics solutions aiming to support public health professionals, and thus, preventive measures. Prevention aims at advocating behaviour and policy changes likely to improve human health. Public health strives to limit the outbreak of acute diseases as well as the reduction of chronic diseases and injuries. For this purpose, data are collected to identify trends in human health, to derive hypotheses, e.g. related to risk factors, and to get insights in the data and the underlying phenomena. Most public health data have a temporal character. Moreover, the spatial character, e.g. spatial clustering of diseases, needs to be considered for decision‐making. Visual analytics techniques involve (subspace) clustering, interaction techniques to identify relevant subpopulations, e.g. being particularly vulnerable to diseases, imputation of missing values, visual queries as well as visualization and interaction techniques for spatio‐temporal data. We describe requirements, tasks and visual analytics techniques that are widely used in public health before going into detail with respect to applications. These include outbreak surveillance and epidemiology research, e.g. cancer epidemiology. We classify the solutions based on the visual analytics techniques employed. We also discuss gaps in the current state of the art and resulting research opportunities in a research agenda to advance visual analytics support in public health.

show abstract

Visual Analytics for Spatial Clusters of Air-Quality Data

Cited by 38 publications

References 9 publications

The State of the Art in Visual Analysis Approaches for Ocean and Atmospheric Datasets

The State of the Art in Visual Analysis Approaches for Ocean and Atmospheric Datasets

Visual Analytics of Air Pollution Propagation Through Dynamic Network Analysis

A Survey of Visual Analytics for Public Health

Contact Info

Product

Resources

About