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Related Experiment Video

Updated: May 23, 2026

Integrating Remote Sensing with Species Distribution Models; Mapping Tamarisk Invasions Using the Software for Assisted Habitat Modeling (SAHM)

Integrating Remote Sensing with Species Distribution Models; Mapping Tamarisk Invasions Using the Software for Assisted Habitat Modeling (SAHM)

Published on: October 11, 2016

Enhanced Spatial Stability with Hilbert and Moore Treemaps.

Susanne Tak, Andy Cockburn

IEEE Transactions on Visualization and Computer Graphics

|April 18, 2012

Summary

This study introduces a new location drift metric to better measure spatial stability in treemaps, outperforming the common distance change metric. New Hilbert and Moore treemap algorithms demonstrate high spatial stability.

Area of Science:

Computer Science
Data Visualization
Human-Computer Interaction

Background:

Treemaps are effective for visualizing hierarchical data using space-filling techniques.
Existing treemap algorithms aim to optimize performance, including spatial stability for user assistance.
Current spatial stability metrics may not fully capture user experience.

Purpose of the Study:

To introduce a new metric, location drift (LD), for a more comprehensive assessment of treemap spatial stability.
To evaluate the validity and utility of the LD metric compared to the traditional distance change (DC) metric.
To develop and assess novel treemap algorithms designed for enhanced spatial stability.

Main Methods:

Introduction of the location drift (LD) metric to quantify spatial stability in treemaps.

Related Experiment Videos

Last Updated: May 23, 2026

Integrating Remote Sensing with Species Distribution Models; Mapping Tamarisk Invasions Using the Software for Assisted Habitat Modeling (SAHM)

Integrating Remote Sensing with Species Distribution Models; Mapping Tamarisk Invasions Using the Software for Assisted Habitat Modeling (SAHM)

Published on: October 11, 2016

Empirical study to validate the LD metric and compare its explanatory power against the distance change (DC) metric.

Development of new treemap algorithms, specifically Hilbert and Moore, prioritizing high spatial stability.

Main Results:

The study demonstrates that the commonly used distance change (DC) metric inadequately captures spatial stability.
The new location drift (LD) metric shows improved ability to explain user performance variations.
The proposed Hilbert and Moore treemap algorithms exhibit strong performance across all evaluated spatial stability metrics.

Conclusions:

The location drift metric offers a more accurate and useful measure of treemap spatial stability than distance change.
Novel Hilbert and Moore treemap algorithms provide significant improvements in spatial stability.
These advancements contribute to more effective and user-friendly hierarchical data visualization.