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Related Experiment Video

Updated: Apr 3, 2026

Trajectory Data Analyses for Pedestrian Space-time Activity Study
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The Volatility of Data Space: Topology Oriented Sensitivity Analysis.

Jing Du1, Arika Ligmann-Zielinska2

  • 1Department of Construction Science, Texas A&M University, Francis Hall 334, TAMU, College Station, TX 77840, United States of America.

Plos One
|September 15, 2015
PubMed
Summary

This study introduces Topology Oriented Sensitivity Analysis (TOSA), a novel method that measures model sensitivity by analyzing data topology. TOSA offers deeper insights into model behavior beyond traditional value-based approaches.

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Area of Science:

  • Computational Science
  • Data Analysis
  • Systems Modeling

Background:

  • Current Sensitivity Analysis (SA) methods are value-based, quantifying input/output uncertainties as numerical changes.
  • This value-centric approach offers limited understanding of underlying model and system dynamics.

Purpose of the Study:

  • To introduce an innovative SA method that explores the topological differences in data spaces.
  • To extend SA beyond value-based quantification into the realm of data topology.

Main Methods:

  • Developed Topology Oriented Sensitivity Analysis (TOSA).
  • Defined sensitivity as the volatility of data space.
  • Analyzed topological differences between pre-model and post-model data spaces.

Main Results:

  • Demonstrated that data topology offers richer model insights than value-based SA.
  • Established sensitivity as a measure of data space volatility.
  • Extended SA capabilities to a deeper, topological level.

Conclusions:

  • TOSA provides a more profound understanding of models and systems by analyzing data topology.
  • This topological approach enhances the depth and scope of sensitivity analysis.