Design and evaluation of a geovisual analytics system for uncovering patterns in spatio-temporal event data

Robinson, Anthony C.; Peuquet, Donna J.; Pezanowski, Scott; Hardisty, Frank; Swedberg, Brian

doi:10.1080/15230406.2016.1139467

Cited by 28 publications

(21 citation statements)

References 44 publications

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“…Spatiotemporal visual analysis of activity diary data is visualized through VISUAL-TimePAcTS [102] on a coordinate plane of time and space. Robinson et al [83] developed STempo, a geovisualization application to facilitate the exploration of spatiotemporal patterns within event sequence data. Moreover, studies have also introduced visualization tools to cluster microarray sequences [41], [88], [91], [94].…”

Section: Visual Summarizationmentioning

confidence: 99%

Visual Progression Analysis of Event Sequence Data

Guo

Jin

Gotz

et al. 2019

IEEE Trans. Visual. Comput. Graphics

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Event sequence data is common to a broad range of application domains, from security to health care to scholarly communication. This form of data captures information about the progression of events for an individual entity (e.g., a computer network device; a patient; an author) in the form of a series of time-stamped observations. Moreover, each event is associated with an event type (e.g., a computer login attempt, or a hospital discharge). Analyses of event sequence data have been shown to help reveal important temporal patterns, such as clinical paths resulting in improved outcomes, or an understanding of common career trajectories for scholars. Moreover, recent research has demonstrated a variety of techniques designed to overcome methodological challenges such as large volumes of data and high dimensionality. However, the effective identification and analysis of latent stages of progression, which can allow for variation within different but similarly evolving event sequences, remain a significant challenge with important real-world motivations. In this paper, we propose an unsupervised stage analysis algorithm to identify semantically meaningful progression stages as well as the critical events which help define those stages. The algorithm follows three key steps: (1) event representation estimation, (2) event sequence warping and alignment, and (3) sequence segmentation. We also present a novel visualization system, ET2, which interactively illustrates the results of the stage analysis algorithm to help reveal evolution patterns across stages. Finally, we report three forms of evaluation for ET2: (1) case studies with two real-world datasets, (2) interviews with domain expert users, and (3) a performance evaluation on the progression analysis algorithm and the visualization design.

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Section: Visual Summarizationmentioning

confidence: 99%

Visual Progression Analysis of Event Sequence Data

Guo

Jin

Gotz

et al. 2019

IEEE Trans. Visual. Comput. Graphics

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“…Robinson et al [49] developed a visual analytics approach, called STempo, to support the discovery of patterns found in spatiotemporal events. STempo was designed to detect and analyze significant co-occurrences of real-world events.…”

Section: Manifold Lods Approachesmentioning

confidence: 99%

“…We also have that most VA approaches have been designed to follow a single Level of Detail (LoD) analysis [38,70]. Nevertheless, the LoD matters for the perception of patterns, and often there is no exclusive LoD to study phenomena [23,29,49,58]. Although the LoD plays a crucial role in the perception of patterns, users have been left with the choice of the LoD to look for patterns.…”

Section: Introductionmentioning

confidence: 99%

Visual analytics for spatiotemporal events

Silva

Pires

Datia

et al. 2019

Multimed Tools Appl

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Crimes, forest fires, accidents, infectious diseases, or human interactions with mobile devices (e.g., tweets) are being logged as spatiotemporal events. For each event, its geographic location, time and related attributes are known with high levels of detail (LoDs). The LoD plays a crucial role when analyzing data, as it can highlight useful patterns or insights and enhance the user' perception of phenomena. For this reason, modeling phenomena at different LoDs is needed to increase the analytical value of the data, as there is no exclusive LOD at which the data can be analyzed. Current practices work mainly on a single LoD of the phenomena, driven by the analysts' perception, ignoring that identifying the suitable LoDs is a key issue for pointing relevant patterns. This article presents a Visual Analytics approach called VAST, that allows users to simultaneously inspect a phenomenon at different LoDs, helping them to see in what LoDs do interesting patterns emerge, or in what LoDs the perception of the phenomenon is different. In this way, the analysis of vast amounts of spatiotemporal events is assisted, guiding the user in this process. The use of several synthetic and real datasets supported the evaluation and validation of VAST, suggesting LoDs with different interesting spatiotemporal patterns and pointing the type of expected patterns.

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“…In addition to interactive maps and essential query functions, this platform also expresses sentiment volumes and unusual topics. Robinson et al (2017) developed a system "STempo", supporting visual analysis and pattern discovery in terms of time, location, and content.…”

Section: Integrated Systemmentioning

confidence: 99%

Research on event perception based on geo-tagged social media data

Zhu

Zuo

Lin

2019

Proc. Int. Cartogr. Assoc.

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<p><strong>Abstract.</strong> Technological advancement makes information dissemination more convenient. When a notable event occurs, social media serves a popular platform for citizens to share event-related information. Therefore, in the information age, how to effectively observe the event and improve event management ability is an open question worthy of attention. Traditional social survey methods and various automatic sensors have been widely used to monitor the specific event. However, widely used social media service provides a unique approach for the event study with individuals as smart sensors. How to perceive an event through social media data has triggered a series of researches. Currently, we can find when, where what happened and induced impact based on geo-tagged social media data. However, event study based on social media is still in its infancy. This paper provides an overview of event study based on geo-tagged social media data. Firstly, we introduce the event model and the characteristics of social media data. Then, how to detect and trace event, how to analyze event impact and visually express obtained knowledge are discussed respectively. Subsequently, based on the existing researches, we propose further questions and conclude.</p>

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Design and evaluation of a geovisual analytics system for uncovering patterns in spatio-temporal event data

Cited by 28 publications

References 44 publications

Visual Progression Analysis of Event Sequence Data

Visual Progression Analysis of Event Sequence Data

Visual analytics for spatiotemporal events

Research on event perception based on geo-tagged social media data

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