Seek for Success: A Visualization Approach for Understanding the Dynamics of Academic Careers

Wang, Yifang; Peng, Tai‐Quan; Lu, Huihua; Wang, Haoren; Xie, Xiao; Qu, Huamin; Wu, Yingcai

doi:10.1109/tvcg.2021.3114790

Cited by 16 publications

(1 citation statement)

References 65 publications

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“…Procedure . For the new researchers that had not used AirLens , we followed the interview procedure used by Wang et al [WPL∗21]. First, we introduced the data, problem and analysis tasks to them.…”

Section: Discussionmentioning

confidence: 99%

AirLens: Multi‐Level Visual Exploration of Air Quality Evolution in Urban Agglomerations

Lin

et al. 2022

Computer Graphics Forum

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The precise prevention and control of air pollution is a great challenge faced by environmental experts in recent years. Understanding the air quality evolution in the urban agglomeration is important for coordinated control of air pollution. However, the complex pollutant interactions between different cities lead to the collaborative evolution of air quality. The existing statistical and machine learning methods cannot well support the comprehensive analysis of the dynamic air quality evolution. In this study, we propose AirLens, an interactive visual analytics system that can help domain experts explore and understand the air quality evolution in the urban agglomeration from multiple levels and multiple aspects. To facilitate the cognition of the complex multivariate spatiotemporal data, we first propose a multi‐run clustering strategy with a novel glyph design for summarizing and understanding the typical pollutant patterns effectively. On this basis, the system supports the multi‐level exploration of air quality evolution, namely, the overall level, stage level and detail level. Frequent pattern mining, city community extraction and useful filters are integrated into the system for discovering significant information comprehensively. The case study and positive feedback from domain experts demonstrate the effectiveness and usability of AirLens.

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Section: Discussionmentioning

confidence: 99%

AirLens: Multi‐Level Visual Exploration of Air Quality Evolution in Urban Agglomerations

Lin

et al. 2022

Computer Graphics Forum

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What makes a scatterplot hard to comprehend: data size and pattern salience matter

Wang

Cai

et al. 2021

J Vis

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You are experienced: interactive tour planning with crowdsourcing tour data from web

Deng

Weng

2022

J Vis

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Seek for Success: A Visualization Approach for Understanding the Dynamics of Academic Careers

Cited by 16 publications

References 65 publications

AirLens: Multi‐Level Visual Exploration of Air Quality Evolution in Urban Agglomerations

AirLens: Multi‐Level Visual Exploration of Air Quality Evolution in Urban Agglomerations

What makes a scatterplot hard to comprehend: data size and pattern salience matter

You are experienced: interactive tour planning with crowdsourcing tour data from web

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