Analysis of Mining, Visual Analytics Tools and Techniques in Space and Time

Space-time GIS emerged in the early 1990s to incorporate temporal information and analytical functions so that GIS technology could handle both spatial and temporal data. To do so, GIS technology has to embrace spatial and temporal data throughout the processes of conceptualization, representation, computation, and visualization. Conceptualization captures ontological constructs and how they manifest themselves and relate to each other in space and time meaningfully with respect to the geographic domain of interest. Representation formalizes the conceptualized ontological constructs based on their characteristics, behaviors, and relationships to organize spatial and temporal data effectively in accordance with the geographic domain. Computation operates on digital representations of the ontological constructs to measure spatial and temporal quantities, analyze patterns, model relationships, simulate possible scenarios, and make predictions in space and time. Finally, visualization creates visual means to inspect space-time data and analytical findings throughout GIS processing. Visual analytics, furthermore, utilizes an interactive visual interface to facilitate analytical reasoning, and hence engages visualization in computation. Advances in teal-time or near real-time geospatial data acquisition as well as data streaming and machine learning methods have significantly accelerated the development of space-time GIS since 2010.

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Can Interactive Map-Based Visualizations Reveal Contexts of Scientific Datasets?

Proceedings of the Annual Conference of CAIS / Actes du congrès annuel de l'ACSI ◽

10.29173/cais635 ◽

2013 ◽

Author(s):

Eva Fischer ◽

Olha Buchel

Keyword(s):

Visual Analytics ◽

Space And Time ◽

Scientific Datasets ◽

Interactive Map

Existing map-based visualizations of scientific datasets support a small number of tasks. The key reason is that visualizations do not show all properties present in datasets. Due to visualizing only locations in space and time, such visualizations have limited capabilities for visual analytics about contexts of scientific datasets. Visualizing other properties may enhance visual analytics of scientific contexts. The proposed approach is illustrated with a visualization prototype.Les techniques de visualisation actuelle par carte des ensembles de données scientifiques permettent un petit nombre de tâches. La principale raison est que la visualisation ne représente pas toutes les propriétés des ensembles de données. En visualisant uniquement des points à un temps et à un moment précis, une telle technique de visualisation a des capacités limitées aux fins d’analyse visuelle des contextes des ensembles de données scientifiques. La visualisation des autres propriétés peut améliorer l’analyse visuelle des contextes scientifiques. L’approche proposée est illustrée avec un prototype de visualisation.

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Interfaces to Scripting Languages in Visual Analytics Applications

10.31219/osf.io/9mjv5 ◽

2020 ◽

Author(s):

Johanna Schmidt

Keyword(s):

Visual Analytics ◽

Data Science ◽

Visual Analysis ◽

Future Research ◽

Research Directions ◽

Scripting Languages ◽

Daily Work ◽

Future Research Directions ◽

Tools And Techniques ◽

Levels Of Integration

The need to use data visualization and visual analysis in various fields has led to the development of feature-rich standalone applications such as Tableau and MS Power BI. These applications provide ready-to-use functionality for loading, analyzing and visualizing data, even for users who are not familiar with programming and scripting. Meanwhile, data scientists have to combine many different tools and techniques in their daily work, since no standalone application can yet cover the entire workflow. As a result, a rich landscape of open source libraries is available today, covering various tasks from data analysis to modeling and visualization. To combine the best of two worlds, interfaces for scripting languages have been integrated into standalone applications in recent years. We analyzed which interfaces to six common scripting languages are offered. The interfaces offer different levels of integration and therefore support different steps of the data science workflow. In this paper we investigated the integration levels of script languages in standalone applications and divided them into four groups. We used this classification to evaluate 13 standalone visual analysis applications currently available on the market. We then analyzed which groups of applications best support which steps in the data science workflow. We found that a tight integration of scripting languages can especially support the explorative analysis and modeling phase of the data science workflow. We also discuss our results in the light of visual analysis research and give suggestions for future research directions.

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The VAST Challenge: history, scope, and outcomes: An introduction to the Special Issue

Information Visualization ◽

10.1177/1473871613490678 ◽

2013 ◽

Vol 13 (4) ◽

pp. 301-312 ◽

Cited By ~ 11

Author(s):

Kristin Cook ◽

Georges Grinstein ◽

Mark Whiting

Keyword(s):

Cyber Security ◽

Visual Analytics ◽

Computer Network ◽

Coast Guard ◽

Special Issue ◽

Rfid Tag ◽

Large Numbers ◽

History Of ◽

Tools And Techniques ◽

Computer Network Attacks

The annual Visual Analytics Science and Technology (VAST) challenge provides Visual Analytics researchers, developers, and designers an opportunity to apply their best tools and techniques against invented problems that include a realistic scenario, data, tasks, and questions to be answered. Submissions are processed much like conference papers, contestants are provided reviewer feedback, and excellence is recognized with awards. A day-long VAST Challenge workshop takes place each year at the IEEE VAST conference to share results and recognize outstanding submissions. Short papers are published each year in the annual VAST proceedings. Over the history of the challenge, participants have investigated a wide variety of scenarios, such as bioterrorism, epidemics, arms smuggling, social unrest, and computer network attacks, among many others. Contestants have been provided with large numbers of realistic but synthetic Coast Guard interdiction records, intelligence reports, hospitalization records, microblog records, personal RFID tag locations, huge amounts of cyber security log data, and several hours of video. This paper describes the process for developing the synthetic VAST Challenge datasets and conducting the annual challenges. This paper also provides an introduction to this special issue of Information Visualization, focusing on the impacts of the VAST Challenge.

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