NetVisia: Heat Map &amp;#x0026; Matrix Visualization of Dynamic Social Network Statistics &amp;#x0026; Content

Gove, Robert; Gramsky, Nick; Kirby, Rose; Sefer, Emre; Sopan, Awalin; Dunne, Cody; Shneiderman, Ben; Taieb-Maimon, Meirav

doi:10.1109/passat/socialcom.2011.216

Cited by 28 publications

(16 citation statements)

References 17 publications

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“…Similarly, the real-time analysis of online (social) media content can be used to produce "heat maps" of online activity surrounding a specific topic [34,35], such as flooding. Such maps show the spatial intensity of tweets in a color scheme, at a given time interval (min, h, days, months).…”

Section: Location Mappingmentioning

confidence: 99%

Early Flood Detection for Rapid Humanitarian Response: Harnessing Near Real-Time Satellite and Twitter Signals

Jongman

Wagemaker²,

Revilla-Romero

et al. 2015

IJGI

126

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Humanitarian organizations have a crucial role in response and relief efforts after floods. The effectiveness of disaster response is contingent on accurate and timely information regarding the location, timing and impacts of the event. Here we show how two near-real-time data sources, satellite observations of water coverage and flood-related social media activity from Twitter, can be used to support rapid disaster response, using case-studies in the Philippines and Pakistan. For these countries we analyze information from disaster response organizations, the Global Flood Detection System (GFDS) satellite flood signal, and flood-related Twitter activity analysis. The results demonstrate that these sources of near-real-time information can be used to gain a quicker understanding of the location, the timing, as well as the causes and impacts of floods. In terms of location, we produce daily impact maps based on both satellite information and social media, which can dynamically and rapidly outline the affected area during a disaster. In terms of timing, the results show that GFDS and/or Twitter signals flagging ongoing or upcoming flooding are regularly available one to several days before the event was reported to humanitarian organizations. 2247In terms of event understanding, we show that both GFDS and social media can be used to detect and understand unexpected or controversial flood events, for example due to the sudden opening of hydropower dams or the breaching of flood protection. The performance of the GFDS and Twitter data for early detection and location mapping is mixed, depending on specific hydrological circumstances (GFDS) and social media penetration (Twitter). Further research is needed to improve the interpretation of the GFDS signal in different situations, and to improve the pre-processing of social media data for operational use.

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Section: Location Mappingmentioning

confidence: 99%

Early Flood Detection for Rapid Humanitarian Response: Harnessing Near Real-Time Satellite and Twitter Signals

Jongman

Wagemaker²,

Revilla-Romero

et al. 2015

IJGI

126

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“…Another way to encode edge value changes over time is to use brightness changes on space-filling curves in matrix cells [30]. In addition to matrices, Netvisia [20] uses a heatmap, where one dimension is time and the other contains the vertices. Similar to FlowStrates [5], cells in the heatmap encode the value of one particular vertex attribute in the corresponding time step.…”

Section: Adjacency Matricesmentioning

confidence: 99%

Visualizing dynamic networks with matrix cubes

Bach

Pietriga

Fekete

2014

Proceedings of the SIGCHI Conference on Human Factors in Computing Systems

121

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Designing visualizations of dynamic networks is challenging, both because the data sets tend to be complex and because the tasks associated with them are often cognitively demanding. We introduce the Matrix Cube, a novel visual representation and navigation model for dynamic networks, inspired by the way people comprehend and manipulate physical cubes. Users can change their perspective on the data by rotating or decomposing the 3D cube. These manipulations can produce a range of different 2D visualizations that emphasize specific aspects of the dynamic network suited to particular analysis tasks. We describe Matrix Cubes and the interactions that can be performed on them in the Cubix system. We then show how two domain experts, an astronomer and a neurologist, used Cubix to explore and report on their own network data.

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“…We choose two methods whose effectiveness we would later compare. We decided on heatmap since it has been widely used in data visualizations for monitoring such as in and [3], [4], [5], [6], and [7], hence implicitly implies its effectiveness. We decided on the other method by process of elimination.…”

Section: General Design Conceptmentioning

confidence: 99%

Information system log visualization to monitor anomalous user activity based on time

Hanniel

Widagdo

Asnar

2014

2014 International Conference on Data and Software Engineering (ICODSE)

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As information systems start to manage the more crucial parts of human lives, their security cannot be neglected. One way to ensure the security is by analyzing their generated log files of anomalous user activity. Data visualization has become a common solution to help get around the problems in log analysis. In this paper, we tried to determine key characteristics of effective data visualization on detecting those anomalous user activity recorded in log files. First we analyzed the log data we have and derived 4 anomalies whose indicators are made into visualization topics. Hence we built 4 data visualizations to detect the 4 anomalies. Next, we transformed our data so that they can be visualized. After that, we analyzed the suitable time-based data visualization method to represent our data and decided on heatmap for its wide application on existing solutions and dot plot for it is able to accommodate all data variables needed on every visualization topic and has the suitable nuance for monitoring purposes. Next we decided on design concept of our data visualizations and implemented them as web-based data visualization. We conducted 2 tests in this paper to determine the key characteristics of effective data visualization. Even though the results are inconclusive, but they hinted that an effective data visualization on this matter should support large amount of perceived information through cognition and support focused exploration.

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NetVisia: Heat Map & Matrix Visualization of Dynamic Social Network Statistics & Content

Cited by 28 publications

References 17 publications

Early Flood Detection for Rapid Humanitarian Response: Harnessing Near Real-Time Satellite and Twitter Signals

Early Flood Detection for Rapid Humanitarian Response: Harnessing Near Real-Time Satellite and Twitter Signals

Visualizing dynamic networks with matrix cubes

Information system log visualization to monitor anomalous user activity based on time

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