Coordinated Multiple Views for Exploratory GeoVisualization

Roberts, Jonathan C.

doi:10.1002/9780470987643.ch3

Cited by 20 publications

(37 citation statements)

References 64 publications

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“…The ability to interact with a map (e.g., zoom in for greater detail, zoom out for context, add or change map symbols) allows users to explore spatial patterns and trends and leads to understanding about the true nature of a geographic phenomenon or process [11]. In short, "insight is formed through interaction" [32]. However, there is also evidence that too many options for interaction may act to inhibit use of an interactive map or may confuse the point of a visualization or map [5,11].…”

Section: Web Mapping and Interactive Cartographymentioning

confidence: 99%

Digital Story Mapping to Advance Educational Atlas Design and Enable Student Engagement

Berendsen

Hamerlinck

Webster

2018

IJGI

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Abstract:Storytelling is recognized as a valid and important method of communicating information and knowledge gleaned from volumes of ever-accumulating data. Practices of data-driven storytelling in journalism and geovisual analytics have contributed to the development of geovisual stories; also called story maps. The benefits of student-focused multi-thematic atlases and digital storytelling methods in education can also be realized in story maps. An online, interactive version of the original paper version of the Wyoming Student Atlas was developed using story mapping technology. Studies on best practices for data-driven storytelling and web map interaction were used to inform the transition of the atlas from a traditional paper format to a collection of story maps. Evaluation of the atlas story maps for educational purposes was conducted by observing students from multiple classrooms as they used the story maps in a lesson. The students and educators responded to a survey after using the story maps. Results of the survey show positive responses to the atlas story maps, including ease of use and preference over a traditional paper atlas. However, certain types of interaction with the map resulted in increased negative or uncertain responses from students concerning their perception of the atlas story maps.

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Section: Web Mapping and Interactive Cartographymentioning

confidence: 99%

Digital Story Mapping to Advance Educational Atlas Design and Enable Student Engagement

Berendsen

Hamerlinck

Webster

2018

IJGI

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“…Apart from T2, map, PCP and table-oriented users were distinguished by tasks. Participants' willingness to work with a particular visualization method in various task types supports Roberts's (2008) claim that users might find different methods better for data manipulation. Response times and accuracy did not differ for users who focused on different views and those who used only one view in a particular task, confirming Edsall's (2003a) conclusion that there is no difference in response accuracy between visualization methods.…”

Section: Discussionmentioning

confidence: 98%

“…We selected these three visualization methods for the following reasons: maps are canonical in geographical displays (Roberts 2008), data displays such as interactive PCPs serve as components in most CMV tools, while tables are well known to users and are useful if equipped with the proper interaction techniques (Rao and Card 1994). We wanted to know whether users would willingly use a variety of displays that had different visualization methods, or reluctantly refer to different views in order to reduce the cognitive effort connected with context switching.…”

Section: Introductionmentioning

confidence: 99%

For your eyes only? Evaluating a coordinated and multiple views tool with a map, a parallel coordinated plot and a table using an eye-tracking approach

Gołębiowska

Opach

Rød

2016

International Journal of Geographical Information Science

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Geographic visualization tools with coordinated and multiple views (CMV) typically provide sets of visualization methods. Such configuration gives users the possibility of investigating data in various visual contexts; however, it can be confusing due to the multiplicity of visual components and interactive functions. We addressed this challenge and conducted an empirical study on how a CMV tool, consisting of a map, a parallel coordinate plot (PCP), and a table, is used to acquire information. We combined a task-based approach with eye-tracking and usability metrics since these methods provide comprehensive insights into users' behaviour. Our empirical study revealed that the freedom to choose visualization components is appreciated by users. The individuals worked with all the available visualization methods and they often used more than one visualization method when executing tasks.

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“…CMV, or MLV (Multiple Linked Views) in some papers (Erbacher & Frincke, 2007;Jern, Johansson, Johansson, & Franzen, 2007;Roberts, 2004), provides an exploratory visualization (EV) environment, "where each of the views are linked together such that any user manipulation in one view is automatically coordinated to that of any other" to promote "insight through interaction" (Roberts, 2008). In this interactive visualization construct, two or more data views of identical or related datasets are made interdependent in order to enhance the data exploration and information seeking behavior of the user (Buja, Cook, & Swayne, 1996;Buja, McDonald, Michalak, & Stuetzle, 1991).…”

Section: Introductionmentioning

confidence: 99%

Modeling coordinated multiple views of heterogeneous data cubes for urban visual analytics

Widjaja

Russo

Pettit

et al. 2014

International Journal of Digital Earth

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With the explosion of digital data, the need for advanced visual analytics, including coordinated multiple views (CMV), is rapidly increasing. CMV enable users to discover patterns and examine relationships across multiple visualizations of one or multiple datasets. CMV have been implemented in a web-based environment through the Australian Urban Research Infrastructure Network (AURIN) project. AURIN offers a platform providing seamless and secure access to an extensive range of distributed urban datasets across Australia. Visual exploration of these datasets is essential to support research endeavors. This paper focuses on the challenges in dealing with complexity and multidimensionality of datasets used in CMV. We rely on the concept of multidimensional data cubes as the theoretical framework for coordination across visualizations. Using the concept of data cubes and hierarchical dimensions, we present strategies to automatically build render groups. This provides an implicit coordination based on cube structures and a framework to establish links between a dataset with its aggregates in a one-to-many fashion. The CMV approach is demonstrated using aggregate-level data, which is provided through federated data services. The paper discusses the issues around our CMV implementation and concludes by reflecting on the challenges in supporting spatio-temporal urban data exploration. Modeling Coordinated Multiple Views of Heterogeneous Data Cubes for Urban Visual AnalyticsWith the explosion of digital data the need for advanced visual analytics, such as coordinated multiple views (CMV), is rapidly increasing. CMV enable users to discover patterns and examine relationships across multiple visualizations of one or multiple datasets. CMV have been implemented in a web-based environment known as the Australia Urban Research Infrastructure Network (AURIN) portal, a platform developed to support the visual exploration of urban datasets from distributed, heterogeneous sources in Australia. Specifically, the paper responds to the challenges in dealing with complexity and multidimensionality of datasets used in CMV. We rely on the concept of multidimensional data cubes as the theoretical frame for coordination across data cubes that underlie multiple visualizations. Using the concept of data cubes and hierarchical dimensions, we introduce strategies to automatically build render groups. This provides an implicit coordination based on cube structures and a framework to establish links between a dataset with its aggregates in one-to-many fashion. The CMV approach is demonstrated using aggregate-level data, which is provided through federated data services from across Australia. The paper discusses the issues around our CMV implementation and concludes by reflecting on the challenges in supporting spatio-temporal urban data exploration.

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Coordinated Multiple Views for Exploratory GeoVisualization

Cited by 20 publications

References 64 publications

Digital Story Mapping to Advance Educational Atlas Design and Enable Student Engagement

Digital Story Mapping to Advance Educational Atlas Design and Enable Student Engagement

For your eyes only? Evaluating a coordinated and multiple views tool with a map, a parallel coordinated plot and a table using an eye-tracking approach

Modeling coordinated multiple views of heterogeneous data cubes for urban visual analytics

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