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Carlea Holl-Jensen||cholljen@umd.edu


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Search Results for: treemap (18 matches)

Yalcin, A., Elmqvist, N., Bederson, B. (October 2015)
Evaluating Multi-Column Bar Charts and Treemaps for Dense Visualization of Sorted Numeric Data
Under review
HCIL-2015-16

A single column bar chart can effectively visualize a sorted and labeled list of numeric records, such as salaries per employee. However, its height limits the number of visible records. As the number of records increase, scrolling requires interaction to see an overview, and using shorter bars hinders observing individual records. For dense visualization of sorted numeric data, we consider two multi-column bar chart designs, wrapped bars and piled bars, in addition to treemaps, a space-filling design that is commonly used to scale in the number of records. We evaluate their design characteristics and graphical perception performance by crowdsourcing under comparison, ranking and overview tasks. Our results suggest that multi-column designs can outperform the space-filling treemap design to show more records for comparison and overview tasks with training.


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Rodrigues, E., Milic-Frayling, N., Smith, M., Shneiderman, B., Hansen, D. (October 2011)
Group-In-a-Box Layout for Multi-faceted Analysis of Communities
Published in Proc. IEEE Conference on Social Computing, IEEE Press, Piscataway, NJ (October 2011).
HCIL-2011-24

Communities in social networks emerge from interactions among individuals and can be analyzed through a combination of clustering and graph layout algorithms. These approaches result in 2D or 3D visualizations of clustered graphs, with groups of vertices representing individuals that form a community. However, in many instances the vertices have attributes that divide individuals into distinct categories such as gender, profession, geographic location, and similar. It is often important to investigate what categories of individuals comprise each community and vice-versa, how the community structures associate the individuals from the same category. Currently, there are no effective methods for analyzing both the community structure and the category-based partitions of social graphs. We propose Group-In-a-Box (GIB), a metalayout for clustered graphs that enables multi-faceted analysis of networks. It uses the treemap space filling technique to display each graph cluster or category group within its own box, sized according to the number of vertices therein. GIB optimizes visualization of the network sub-graphs, providing a semantic substrate for category-based and cluster-based partitions of social graphs. We illustrate the application of GIB to multi-faceted analysis of real social networks and discuss desirable properties of GIB using synthetic datasets.


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Rios-Berrios, M., Sharma, P., Lee, T., Schwartz, R., Shneiderman, B. (May 2010)
TreeCovery : Coordinated Dual Treemap Visualization for Exploring the Recovery Act
HCIL-2010-06

The American Recovery and Reinvestment Act dedicated $787 billion to stimulate the US economy and mandated the release of the data describing the exact distribution of that money. The dataset is a large and complex one; one of its distinguishing features is its bi-hierarchical structure, arising from the distribution of money through agencies to specific projects and the natural aggregation of awards based on location. To offer a comprehensive overview of the data, a visualization must incorporate both these hierarchies. We present TreeCovery, a tool that accomplishes this through the use of two coordinated treemaps. The tool includes a number of innovative features, including coordinated zooming and filtering and a proportional highlighting technique across the two trees. TreeCovery was designed to facilitate data exploration, and initial user studies suggest that it will be helpful in insight generation. RATB(Recovery Accountability and Transparency Board) has tested TreeCovery and considering to include the concept into their visual analytics.


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Shneiderman, B. (November 2007)
Discovering Business Intelligence Using Treemap Visualizations
b-eye April 11, 2006.
HCIL-2007-20


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Chintalapani, G., Plaisant, C., Shneiderman, B. (April 2004)
Extending the Utility of Treemaps with Flexible Hierarchy
Proc. International Conference on Information Visualization, (2004), 335-344.
HCIL-2004-10, CS-TR-4663, ISR-TR-2005-52

Treemaps is a visualization technique for presenting hierarchical information on two dimensional displays. Prior implementations limit the visualization to pre-defined static hierarchies. Flexible hierarchy, a new capability of Treemap 4.0, enables users to define various hierarchies through dynamically selecting a series of data attributes so that they can discover patterns, clusters and outliers. This paper describes the design and implementation issues of flexible hierarchy. It then reports on a usability study which led to enhancements to the interface.


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Fekete, J., Wang, D., Dang, N., Aris, A., Plaisant, C. (August 2003)
Overlaying Graph Links on Treemaps
In Information Visualization 2003 Symposium Poster Compendium, IEEE, (2003) 82-83
HCIL-2003-32, CS-TR-4686

Every graph can be decomposed into a tree structure plus a set of remaining edges. We describe a visualization technique that displays the tree structure as a Treemap and the remaining edges as curved links overlaid on the Treemap. Link curves are designed to show where the link starts and where it ends without requiring an explicit arrow that would clutter the already dense visualization. This technique is effective for visualizing structures where the underlying tree has some meaning, such as Web sites or XML documents with cross-references. Graphic attributes of the links such as color or thickness can be used to represent attributes of the edges. Users can choose to see all links at once or only the links to and from the node or branch under the cursor.


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Bederson, B., Shneiderman, B., Wattenberg, M. (July 2001)
Ordered and Quantum Treemaps: Making Effective Use of 2D Space to Display Hierarchies
ACM Transactions on Graphics (TOG), 21, (4), October 2002, 833-854.
HCIL-2001-18, CS-TR-4277, UMIACS-TR-2001-57, ISR-TR-2005-22

Treemaps, a space-filling method of visualizing large hierarchical data sets, are receiving increasing attention. Several algorithms have been proposed to create more useful displays by controlling the aspect ratios of the rectangles that make up a treemap. While these algorithms do improve visibility of small items in a single layout, they introduce instability over time in the display of dynamically changing data, fail to preserve order of the underlying data, and create layouts that are difficult to visually search. In addition, continuous treemap algorithms are not suitable for displaying quantum-sized objects within them, such as images. This paper introduces several new treemap algorithms, which address these shortcomings. In addition, we show a new application of these treemaps, using them to present groups of images. The ordered treemap algorithms ensure that items near each other in the given order will be near each other in the treemap layout. Using experimental evidence from Monte Carlo trials, we show that compared to other layout algorithms ordered treemaps are more stable while maintaining relatively favorable aspect ratios of the constituent rectangles. A second test set uses stock market data. The quantum treemap algorithms modify the layout of the continuous treemap algorithms to generate rectangles that are integral multiples of an input object size. The quantum treemap algorithm has been applied to PhotoMesa, an application that supports browsing of large numbers of images.


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Plaisant, C. (Editor) (June 2001)
2001 Human-Computer Interaction Laboratory Video Reports
HCIL-2001-12, CS-TR-4263, UMIACS-TR-2001-46

45 minute video of the lab's work over this year. Topics are:
  • PhotoFinder Goes Public: Redesigning for the CHI Community
  • PhotoMesa: A Zoomable Image Browser
  • Visual Specification of Queries for Finding Patterns in Time-Series Data
  • Fisheye Menus
  • Visualization for Production Management: Treemap and Fisheye Table Browser
  • Generalizing Query Previews
  • SearchKids: A Digital Library for Children
  • From MusicBlocks to AnimalBlocks: a case study in design
  • Designing the Classroom of the Future
  • Jesterbot: a Storytelling Robot for Pediatric Rehabilitation


[HTML] [Video] [Link to Report]

Bederson, B. (May 2001)
Quantum Treemaps and Bubblemaps for a Zoomable Image Browser
ACM Conference on User Interface and Software Technology (UIST 2001) as PhotoMesa: A Zoomable Image Browser using Quantum Treemaps and Bubblemaps, pp. 71-80.
HCIL-2001-10, CS-TR-4256, UMIACS-TR-2001-39

This paper describes two algorithms for laying out groups of objects in a 2D space-filling manner. Quantum Treemaps are a variation on existing treemap algorithms that are designed for laying out images or other objects of indivisible (quantum) size. They build on the Ordered Treemap algorithm, but guarantees that every generated rectangle will have a width and height that are an integral multiple of an input object size. Bubblemaps also fill space with groups of quantum-sized objects, but generate non-rectangular blobs, and utilize space more efficiently. Both algorithms have been applied to PhotoMesa, an application that supports browsing of large numbers of images. PhotoMesa uses a Zoomable User Interface with a simple interaction designed for novices and family use.


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Shneiderman, B., Wattenberg, M. (April 2001)
Ordered Treemap Layouts
Proc. IEEE Symposium on Information Visualization 2001, 73-78. IEEE Press, Los Alamitos, CA (October 2001).
HCIL-2001-06, CS-TR-4237, UMIACS-TR-2001-26, ISR-TR-2005-16

Treemaps, a space-filling method of visualizing large hierarchical data sets, are receiving increasing attention. Several algorithms have been proposed to create more useful displays by controlling the aspect ratios of the rectangles that make up a treemap. While these algorithms do improve visibility of small items in a single layout, they introduce instability over time in the display of dynamically changing data, and fail to preserve an ordering of the underlying data. This paper introduces the ordered treemap, which addresses these two shortcomings. The ordered treemap algorithm ensures that items near each other in the given order will be near each other in the treemap layout. Using experimental evidence from Monte Carlo trials, we show that compared to other layout algorithms ordered treemaps are more stable while maintaining relatively low aspect ratios of the constituent rectangles. A second test set uses stock market data.


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Asahi, T., Turo, D., Shneiderman, B. (January 1995)
Visual Decision-Making: Using Treemaps for the Analytic Hierarchy Process
Video in CHI '95 Video Program, ACM, New York. A two page video summary also appears in ACM CHI '95 Conference Companion, (Denver, Colorado, May 7-11, 1995) 405-406. Video also available through HCIL as part of the 1994 HCIL Video Report.
HCIL-95-04

The Analytic Hierarchy Process (AHP), a decision-making method based upon division of problem spaces into hierarchies, is visualized through the use of treemaps, which pack large amounts of hierarchical information into small screen spaces. Two direct manipulation tools, presented metaphorically as a "pump" and a "hook," were developed and applied to the treemap to support AHP sensitivity analysis. The problem of construction site selection is considered in this video. Apart from its traditional use for problem/ information space visualization, the treemap also serves as a potent visual tool for "what if" type analysis.


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Turo, D. (April 1994)
Hierarchical Visualization with Treemaps: Making Sense of Pro Basketball Data
Video in CHI '94 Video Program, ACM, New York. A two page video summary also appears in ACM CHI '94 Conference Companion, (Boston, MA, April 24-28, 1994) 441-442. Video also available through HCIL as part of the 1993 HCIL Video Report.
HCIL-94-15

Treemaps support visualiztion of large hierarchical information spaces. The treemap generation algorithm is straightforward and application prototypes have only minimal hardware requirements. Given primary graphical encodings of area color and enclosure, treemaps are best suited for the tasks of outlier detection, cause-effect analysis and location of specific nodes--satisfying user-specified critera--in their hierarchical context. Distortion effects extend treemap capabilities by emphasizing node relationships in the diagram.


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Carr, D., Jog, N., Kumar, H., Teittinen, M. (September 1994)
Using Interaction Object Graphs to Specify and Develop Graphical Widgets
HCIL-94-09, CS-TR-3344, CAR-TR-734, ISR-TR-94-69

This document describes five widgets that have been developed at the Human-Computer Interaction Laboratory of the University of Maryland. These widgets are: a range selection slider, a two-level alpha-slider, a secure switch, a tree viewer, and a treemap viewer. The last two use the same tree representation and can be used as alternate visualizations of the same hierarchy. In addition, a system for widget specification is introduced and each widget is specified using this system.


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Asahi, T., Turo, D., Shneiderman, B. (June 1994)
Using Treemaps to Visualize the Analytic Hierarchy Process
Information Systems Research, vol. 6, 4 (Dec. 1995) 357-375.
HCIL-94-08, CS-TR-3293, CAR-TR-719, ISR-TR-94-57.

Treemaps, a visualization method for large hierarchical data spaces, are used to augment the capabilities of the Analytic Hierarchy Process (AHP) for decision-making. Two direct manipulation tools, presented metamorphically as a "pump" and a "hook," were developed and applied to the treemap to support AHP sensitivity analysis. A usability study was conducted using a prototype AHP application; results showed that treemap representation of decision-support tools was acceptable for AHP users from both a visualization and data operation standpoint. Subjective preferences were high for AHP treemaps.


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Johnson, B. (August 1993)
Treemaps: Visualizing Hierarchical and Categorical Data
HCIL-94-04, UMI-94-25057.

200 page Doctoral dissertation. Treemaps are a graphically based method for the visualization of hierarchical or categorical data spaces. Treemap presentations of data shift mental workload from the cognitive to the perceptual systems, taking advantage of the human visual processing system to increase the bandwidth of the human-computer interface. Efficient use of display space allows for the simultaneous presentation of thousands of data records, as well as facilitating the presentation of semantic information. Treemaps let users see the forest and the trees by providing local detail in the context of a global overview, providing a visually engaging environment in which to analyze, search, explore and manipulate large hierarchical and categorical data spaces. The treemap method of hierarchical visualization, at its core, is based on the property of containment. This property of containment is a fundamental idea which powerfully encapsulates many of our reasons for constructing information hierarchies. All members of the treemap family of algorithms partition multi-dimensional display spaces based on weighted hierarchical data sets. In addition to generating treemaps and standard traditional hierarchical diagrams, the treemap algorithms extend non-hierarchical techniques such as bar and pie charts into the domain of hierarchical presentation. Treemap algorithms can be used to generate bar charts, outlines, traditional 2-D node and link diagrams, pie charts, cone trees, cam trees, drum trees, etc. Generating existing diagrams via treemap transformations is an excercise meant to show the power, ease, and generality with which alternative presentations can be generated from the basic treemap algorithms. Controlled experiments with novice treemap users and real data highlight the strengths of treemaps and provide direction for improvement. Experimental results show that treemaps are a powerful visualization tool for large data sets, significantly reducing user performance times for global comparison tasks. Effective visualizations of large data sets can help users gain insight into relevant features of the data, construct accurate mental models of the information, and locate regions of particular interest. Treemaps are based on simple, fundamental ideas, but they are the building blocks with which an entire world of unique and exciting visualizations can be built.


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Jungmeister, W., Turo, D. (Nov. 1992)
Adapting treemaps to stock portfolio visualization
HCIL-92-14, CS-TR-2996, CAR-TR-648, SRC-TR-92-120.

Treemap visualization techniques are extended and applied to stock market portfolios via a prototype application. Designed to facilitate financial decision-making, the prototype provides an overview of large amounts of hierarchical financial data and al lows users to alter aspects of the visual display dynamically. Treemap concepts are illustrated via examples which address common portfolio management needs.


[Link to Report]

Johnson, B. (May 1992)
TreeViz: Treemap visualization of hierarchically structured information
Demonstration summary appears in ACM CHI `92 Conference Proc. (Monterey, CA, May 3-7, 1992) 369-370.
HCIL-92-10


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Turo, D., Johnson, B. (May 1992)
Improving the visualization of hierarchies with treemaps: Design issues and experimentation
Proc. Visualization `92 (Boston, MA, Oct. 19-23,1992) 124-131.
HCIL-92-06, CS-TR-2901, CAR-TR-626, SRC-TR-92-62.

Controlled experiments with novice treemap users and real data highlight the strengths of treemaps and provide direction for improvement. Issues discussed include experimental results, layout algorithms, nesting offsets, labeling, animation and small mu ltiple displays. Treemaps prove to be a potent tool for hierarchy display. The principles discussed are applicable to many information visualization situations.


[HTML] [Link to Report]


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