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Related Concept Videos

Modified Boxplots00:57

Modified Boxplots

A standard box and whisker plot informs us about the spread of the data in a given sample. One can identify the minimum value, maximum value, first quartile value, second quartile or median value, and third quartile.
However, the box plot does not tell the reader about outliers - values that lie far from the center of the data. We can modify the standard box and whisker plot to identify the outliers and visualize the actual spread of the data in a sample.
Initially, we calculate the adjusted...
Outliers and Influential Points01:08

Outliers and Influential Points

An outlier is an observation of data that does not fit the rest of the data. It is sometimes called an extreme value. When you graph an outlier, it will appear not to fit the pattern of the graph. Some outliers are due to mistakes (for example, writing down 50 instead of 500), while others may indicate that something unusual is happening. Outliers are present far from the least squares line in the vertical direction. They have large "errors," where the "error" or residual is the vertical...
What Are Outliers?01:12

What Are Outliers?

Outliers are observed data points that are far from the least squares line. They have unusual values and need to be examined carefully. Though an outlier may result from erroneous data, at other times, it may hold valuable information about the population under study and should be included in the data. Hence, it is crucial to examine what causes a data point to be an outlier.
The z score is used to find outliers or unusual values. It should be noted that any values beyond -2 and +2 are...

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Related Experiment Video

Updated: May 21, 2026

Design and Implementation of an Automated Illuminating, Culturing, and Sampling System for Microbial Optogenetic Applications
11:13

Design and Implementation of an Automated Illuminating, Culturing, and Sampling System for Microbial Optogenetic Applications

Published on: February 19, 2017

Enhancing Line Density Plots with Outlier Control and Bin-Based Illumination.

Yumeng Xue, Bin Chen, Patrick Paetzold

    IEEE Transactions on Visualization and Computer Graphics
    |May 19, 2026
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a novel bin-based illumination model for visualizing line-based data, enhancing flow and revealing outliers. The method effectively highlights trends and anomalies in trajectories and time series data.

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    Area of Science:

    • Data Visualization
    • Computer Graphics

    Background:

    • Density plots are effective for point-based data but struggle with line-based datasets like trajectories.
    • Existing methods obscure path continuity, making it difficult to discern trends and outliers in time series data.

    Purpose of the Study:

    • To develop a visualization technique that enhances flow and reveals sparse outliers in line-based datasets.
    • To decouple structure from density for improved pattern recognition in trajectories and time series.

    Main Methods:

    • Proposed a bin-based illumination model that separates data structure from density.
    • Introduced a bin-based outlierness metric for ranking trajectories.
    • Developed a structural normal map with locally-adaptive lighting in the luminance channel.

    Main Results:

    • The method enhances flow and reveals sparse outliers while preserving the original colormap.
    • It effectively highlights dominant trends and atypical paths with minimal color distortion (lower CIEDE2000).
    • Demonstrated on real-world datasets, revealing details missed by simpler alternatives.

    Conclusions:

    • The interactive method allows analysts to prioritize trends, focus on outliers, or balance both.
    • It supports interactive updates for up to 10,000 lines, offering a significant improvement over existing visualization techniques.