Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Weighted Mean00:57

Weighted Mean

7.2K
While taking the arithmetic, geometric, or harmonic mean of a sample data set, equal importance is assigned to all the data points. However, all the values may not always be equally important in some data sets. An intrinsic bias might make it more important to give more weightage to specific values over others.
For example, consider the number of goals scored in the matches of a tournament. While computing the average number of goals scored in the tournament, it may be more important to...
7.2K
Thematic Layering in GIS01:30

Thematic Layering in GIS

394
In the past, planning projects such as schools or public facilities required extensive manual effort to gather and compile data. Information such as property boundaries, soil characteristics, road networks, zoning regulations, and flood zones had to be sourced individually from courthouses, utility providers, and registry offices. Assembling these datasets into a coherent format often took several months, delaying project timelines.The introduction of Geographic Information Systems (GIS)...
394
Plotting of Topographic Maps01:29

Plotting of Topographic Maps

680
Topographic maps represent the Earth's surface features using contour lines, which connect points of equal elevation to create a two-dimensional representation of three-dimensional terrain. Creating a topographic map requires a systematic approach.Begin by plotting a scaled grid and marking intersections corresponding to the survey's elevation data points. Assign elevation values at these intersections to build the base map. Next, determine contour levels using a consistent contour interval,...
680
Apparent Weight01:09

Apparent Weight

10.1K
True weight is the measure of the gravitational force acting on an object. However, if the object accelerates, its measured weight is different from its true weight. Similar observations can be made when the object is submerged in water. An object's weight in water is its apparent weight, which is equal to the difference between its true weight and the buoyant forces.
Consider a person standing on a bathroom scale inside an elevator. If the scale is accurate at rest, its reading equals the...
10.1K
Bias01:22

Bias

7.9K
Bias refers to any tendency that prevents a question from being considered unprejudiced. In research, bias occurs when one outcome or answer is selected or encouraged over others in sampling or testing. Bias can occur during any research phase, including study design, data collection, analysis, and publication.
In statistics, a sampling bias is created when a sample is collected from a population, and some members of the population are not as likely to be chosen as others (remember, each member...
7.9K
The Representativeness Heuristic02:13

The Representativeness Heuristic

17.0K
The representative heuristic describes a biased way of thinking, in which you unintentionally stereotype someone or something. For example, you may assume that your professors spend their free time reading books and engaging in intellectual conversation, because the idea of them spending their time playing volleyball or visiting an amusement park does not fit in with your stereotypes of professors.
17.0K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Crossing the Chasm: Bridging Visual Augmentations and Designer Intent.

IEEE transactions on visualization and computer graphics·2026
Same author

Data Augmentation for Visualization Design Knowledge Bases.

IEEE transactions on visualization and computer graphics·2025
Same author

Mosaic Selections: Managing and Optimizing User Selections for Scalable Data Visualization Systems.

IEEE transactions on visualization and computer graphics·2025
Same author

An Autoethnography on Visualization Literacy: A Wicked Measurement Problem.

IEEE transactions on visualization and computer graphics·2025
Same author

Visual Stenography: Feature Recreation and Preservation in Sketches of Noisy Line Charts.

IEEE transactions on visualization and computer graphics·2025
Same author

From Dashboard Zoo to Census: A Case Study With Tableau Public.

IEEE transactions on visualization and computer graphics·2024

Related Experiment Video

Updated: Mar 11, 2026

Applying Hyperspectral Reflectance Imaging to Investigate the Palettes and the Techniques of Painters
07:05

Applying Hyperspectral Reflectance Imaging to Investigate the Palettes and the Techniques of Painters

Published on: June 18, 2021

2.9K

Surprise! Bayesian Weighting for De-Biasing Thematic Maps.

Michael Correll, Jeffrey Heer

    IEEE Transactions on Visualization and Computer Graphics
    |November 23, 2016
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces Surprise Maps, a novel visualization method using Bayesian surprise to accurately display spatial event density. Surprise Maps reduce biases from base rates and sample sizes, improving the interpretation of spatial data.

    More Related Videos

    Measuring Attention and Visual Processing Speed by Model-based Analysis of Temporal-order Judgments
    13:00

    Measuring Attention and Visual Processing Speed by Model-based Analysis of Temporal-order Judgments

    Published on: January 23, 2017

    10.4K
    A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
    08:12

    A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

    Published on: March 1, 2022

    3.0K

    Related Experiment Videos

    Last Updated: Mar 11, 2026

    Applying Hyperspectral Reflectance Imaging to Investigate the Palettes and the Techniques of Painters
    07:05

    Applying Hyperspectral Reflectance Imaging to Investigate the Palettes and the Techniques of Painters

    Published on: June 18, 2021

    2.9K
    Measuring Attention and Visual Processing Speed by Model-based Analysis of Temporal-order Judgments
    13:00

    Measuring Attention and Visual Processing Speed by Model-based Analysis of Temporal-order Judgments

    Published on: January 23, 2017

    10.4K
    A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
    08:12

    A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

    Published on: March 1, 2022

    3.0K

    Area of Science:

    • Spatial data analysis
    • Geographic Information Systems (GIS)
    • Information visualization

    Background:

    • Thematic maps are common for visualizing spatial event density.
    • Traditional maps can be misleading due to base rates and sample size variations.
    • Existing methods struggle to accurately represent event significance without bias.

    Purpose of the Study:

    • To introduce Surprise Maps, a new visualization technique.
    • To counter biases in traditional thematic maps using Bayesian surprise.
    • To improve the accurate representation of spatial event density.

    Main Methods:

    • Adapting Bayesian surprise for spatial data visualization.
    • Developing a technique to weight event data against spatio-temporal models.
    • Utilizing Bayesian surprise to model human visual attention for information weighting.

    Main Results:

    • Surprise Maps effectively visualize event density by reducing biases.
    • The technique highlights unexpected events more prominently.
    • Demonstrated success with both synthetic and real-world datasets.

    Conclusions:

    • Surprise Maps offer a superior alternative to traditional event maps.
    • The method enhances the interpretability of spatial data by mitigating common biases.
    • Bayesian surprise provides a robust framework for unbiased spatial event visualization.