COPE: Interactive Exploration of Co-Occurrence Patterns in Spatial Time Series

Li, Jie; Chen, S.; Zhang, Kang; Andrienko, Gennady; Andrienko, Gennady

doi:10.1109/tvcg.2018.2851227

Cited by 45 publications

(33 citation statements)

References 51 publications

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“…It is used to decrease the dimensions of a d-dimensional dataset by decreasing it to a k-dimensional subspace (where k < d). t-Distributed Stochastic Neighbor Embedding (t-SNE) is used [13,28,48,58] which helps to visualize high-dimensional data by giving each datapoint a location in a two-or threedimensional map. Huang et al [21] use deep convolutional auto-encoder (DCAE), based on deep convolutional neural network (CNN), to hierarchically model tfMRI time-series data in an unsupervised manner.…”

Section: Dimensionality Reduction Techniquesmentioning

confidence: 99%

TimeCluster: dimension reduction applied to temporal data for visual analytics

et al. 2019

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There is a need for solutions which assist users to understand long time-series data by observing its changes over time, finding repeated patterns, detecting outliers, and effectively labeling data instances. Although these tasks are quite distinct and are usually tackled separately, we present an interactive visual analytics system and approach that can address these issues in a single system. It enables users to visualize, understand and explore univariate or multivariate long time-series data in one image using a connected scatter plot. It supports interactive analysis and exploration for pattern discovery and outlier detection. Different dimensionality reduction techniques are used and compared in our system. Because of its power of extracting features, deep learning is used for multivariate time-series along with 2D reduction techniques for rapid and easy interpretation and interaction with large amount of time-series data. We deploy our system with different time-series datasets and report two real-world case studies that are used to evaluate our system.

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Section: Dimensionality Reduction Techniquesmentioning

confidence: 99%

TimeCluster: dimension reduction applied to temporal data for visual analytics

et al. 2019

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“…Li et al [11] do not use a fully animated approach, but neither do they commit to showing the full temporal data range in a single image. Instead, they use an interface termed the "Event View" to display images generated for discrete time intervals side-by-side.…”

Section: Spatiotemporal Data Visualizationmentioning

confidence: 99%

Using Animation to Visualize Spatio-Temporal Varying COVID-19 Data

Samet

Han

Kastner

et al. 2020

Proceedings of the 1st ACM SIGSPATIAL International Workshop on Modeling and Understanding the Spread of COVID-19

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CoronaViz (http://coronaviz.umiacs.io) is a research prototype developed by us to enable the dynamic map visualization of COVID-19 related variables including the number of confirmed cases, active cases, recoveries, and deaths all on a daily basis from the Johns Hopkins University web site at ter.ps/coronajhu, by allowing the underlying spatial region and the spanned time interval to vary. Any combination of the variables can be viewed. subject to a possibility of clutter which is avoided by the use of concentric circles (termed geo-circles) whose radius values correspond to the variable values. The variable values are provided both on cumulative and day-by-day bases. The visualization enables spatial and temporal variation.

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“…However, few works have paid attention to abnormal cases of air pollution. Li et al [16] extracted events of air quality data and detected various co-occurrence patterns among them. Although they could find pollution-related urban agglomeration, the lack of extracted temporal variation for the target city limited the determinacy of the discovered events.…”

Section: Visualization Of Air Quality Datamentioning

confidence: 99%

“…To the best of our knowledge, a fully quantitative comparison between AirInsight and other baseline systems is not feasible because few studies [12,16] have focused on the comprehensive exploration of regular patterns and anomalies in air quality data, and these few studies have objectives that differ from the goal of AirInsight. In order to further evaluate the effectiveness and powerfulness of our visual system, we developed a simple system as a baseline to compare with AirInsight.…”

Section: User Evaluationsmentioning

confidence: 99%

AirInsight: Visual Exploration and Interpretation of Latent Patterns and Anomalies in Air Quality Data

et al. 2019

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Nowadays, huge volume of air quality data provides unprecedented opportunities for analyzing pollution. However, due to the high complexity, most traditional analytical methods focus on abstracting data, so these techniques discard the original structure and limit the understanding of the results. Visual analysis is a powerful technique for exploring unknown patterns since it retains the details of the original data and gives visual feedback to users. In this paper, we focus on air quality data and propose the AirInsight design, an interactive visual analytic system for recognizing, exploring, and summarizing regular patterns, as well as detecting, classifying, and interpreting abnormal cases. Based on the time-varying and multivariate features of air quality data, a dimension reduction method Composite Least Square Projection (CLSP) is proposed, which allows appreciating and interpreting the data patterns in the context of attributes. On the basis of the observed regular patterns, multiple abnormal cases are further detected, including the multivariate anomalies by the proposed Noise Hierarchical Clustering (NHC) method, abruptly changing timestamps by Time diversity (TD) indicator, and cities with unique patterns by the Geographical Surprise (GS) measure. Moreover, we combine TD and GS to group anomalies based on their underlying spatiotemporal correlations. AirInsight includes multiple coordinated views and rich interactive functions to provide contextual information from different aspects and facilitate a comprehensive understanding. In particular, a pair of glyphs are designed that provide a visual representation of the temporal variation in air quality conditions for a user-selected city. Experiments show that CLSP improves the accuracy of Least Square Projection (LSP) and that NHC has the ability to separate noises. Meanwhile, several case studies and task-based user evaluation demonstrate that our system is effective and practical for exploring and interpreting multivariate spatiotemporal patterns and anomalies in air quality data.

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COPE: Interactive Exploration of Co-Occurrence Patterns in Spatial Time Series

Cited by 45 publications

References 51 publications

TimeCluster: dimension reduction applied to temporal data for visual analytics

TimeCluster: dimension reduction applied to temporal data for visual analytics

Using Animation to Visualize Spatio-Temporal Varying COVID-19 Data

AirInsight: Visual Exploration and Interpretation of Latent Patterns and Anomalies in Air Quality Data

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