Frontier of Information Visualization and Visual Analytics in 2016

Lü, Min; Chen, Siming; Lai, Chufan; Lin, Lijing; Yuan, Xiaoru

doi:10.1007/s12650-017-0431-9

Cited by 12 publications

(8 citation statements)

References 95 publications

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“…Fried and Kobourov [26] also introduced the heatmap to the study of relational data to extract the characteristic data. Social media data has been a very hot research focus in recent years [27], and related studies analyzed this typical relational data in the form of map-like visualization [28,29]. Cao et al [30] also applied the contour map to the study on social media data.…”

Section: Related Researchmentioning

confidence: 99%

Metaphor Representation and Analysis of Non-Spatial Data in Map-Like Visualizations

Rui

2018

IJGI

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Metaphors are rhetorical devices in linguistics that facilitate the understanding of an unfamiliar concept based on a familiar concept. Map representations are usually referred to as the second language of geo-science studies, and the metaphor method could be applied to maps to visualize non-spatial data via spatial element symbols. This study performs a cross-domain application of the map representation method through a map-like visualization. The procedure first designs the map layout with the aid of the Gosper curve. Under the guidance of the Gosper curve, the leaf data items without spatial attributes are arranged on the space plane. Through the bottom-up regional integration, one can complete the construction of the map framework. Then, the cartographic method is used to complete map-like renderings that reflect different data features through diverse visualizations. The map representation advantages, such as overview sensing and multi-scale representation, are also reflected in the map-like visualization and used to identify the characteristics of non-spatial data. Additionally, the electronic map provides a series of interactive convenience features for map observation and analysis. Using the help of map-like visualizations, one can perform a series of analyses of non-spatial data in a new form. To verify the proposed method, the authors conducted map-making experiments and data analyses using real data.

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Section: Related Researchmentioning

confidence: 99%

Metaphor Representation and Analysis of Non-Spatial Data in Map-Like Visualizations

Rui

2018

IJGI

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“…Analysts are unable to participate in the analysis process, and obtain intuitive visual feedbacks. Visual analytics methods combine the computational power of machine and the cognitive ability of human, supporting better knowledge discovery by providing various visual clues [3] [4]. Previous work usually focused on specific scale, such as system overview [5] [6], station clusters [7] [8], or single station status [9] [10].…”

Section: Introductionmentioning

confidence: 99%

Multi-scale and Interactive Visual Analysis of Public Bicycle System

2019

KSII TIIS

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Public bicycle system (PBS) is a new emerging and popular mode of public transportation. PBS data can be adopted to analyze human movement patterns. Previous work usually focused on specific scales, and the relationships between different levels of hierarchies are ignored. In this paper, we introduce a multi-scale and interactive visual analytics system to investigate human cycling movement and PBS usage condition. The system supports level-of-detail explorative analysis of spatio-temporal characteristics in PBS. Visual views are designed from global, regional and microcosmic scales. For the regional scale, a bicycle network is constructed to model PBS data, and an flow-based community detection algorithm is applied on the bicycle network to determine station clusters. In contrast to the previous used Louvain algorithm, our method avoids producing super-communities and generates better results. We provide two cases to demonstrate how our system can help analysts explore the overall cycling condition in the city and spatio-temporal aggregation of stations.

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“…Recent years have seen the surge of IML. However, most existing surveys either focus on a specific area of IML, including interactive dimensionality reduction (Sacha et al, 2017), interactive model analysis (Liu et al, 2017c), visual social data analytics (Wu et al, 2016), and predictive visual analytics (Lu et al, 2017b), or aim to summarize the visualization field that is too generic for IML, e.g., information visualization (Liu et al, 2014;Lu et al, 2017a). The most relevant work to ours is the recent survey by Endert et al (Endert et al, 2017), they reviewed visual analytics approaches that integrate machine learning models, with a focus on classification, clustering, regression and dimensionality reduction.…”

Section: Introductionmentioning

confidence: 99%

Recent research advances on interactive machine learning

Liu

Chen

2018

J Vis

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Interactive Machine Learning (IML) is an iterative learning process that tightly couples a human with a machine learner, which is widely used by researchers and practitioners to effectively solve a wide variety of real-world application problems. Although recent years have witnessed the proliferation of IML in the field of visual analytics, most recent surveys either focus on a specific area of IML or aim to summarize a visualization field that is too generic for IML. In this paper, we systematically review the recent literature on IML and classify them into a task-oriented taxonomy built by us. We conclude the survey with a discussion of open challenges and research opportunities that we believe are inspiring for future work in IML.

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Frontier of Information Visualization and Visual Analytics in 2016

Cited by 12 publications

References 95 publications

Metaphor Representation and Analysis of Non-Spatial Data in Map-Like Visualizations

Metaphor Representation and Analysis of Non-Spatial Data in Map-Like Visualizations

Multi-scale and Interactive Visual Analysis of Public Bicycle System

Recent research advances on interactive machine learning

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