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

Review and Preview01:13

Review and Preview

9.0K
Data are individual items of information obtained from a population or sample. Data may be classified as qualitative (categorical), quantitative continuous, or quantitative discrete. Because it is not practical to measure the entire population in a study, researchers use samples to represent the population. A random sample is a representative group from the population chosen by using a method that gives each individual in the population an equal chance of being included in the sample. Random...
9.0K
Levels of Use of a GIS01:29

Levels of Use of a GIS

56
Geographic Information Systems (GIS) operate across three levels of application, each representing an increasing degree of complexity: data management, analysis, and prediction. These levels reflect the expanding functionality and versatility of GIS technology in handling spatial data for diverse purposes.Data ManagementAt its foundational level, GIS serves as a tool for data management, enabling the input, storage, retrieval, and organization of spatial data. This level is often employed in...
56
Manipulation and Analysis01:21

Manipulation and Analysis

28
GIS manipulation and analysis functions are vital for decision-making and planning. These activities range from data retrieval tasks, such as selecting information based on specific criteria, to advanced analytical techniques that address complex spatial problems.One critical GIS analysis method is overlaying, which combines multiple data layers to examine impacts. For example, overlaying a river-dammed lake boundary with road networks can identify affected infrastructure. Another common...
28
Selected Data About Geographic Locations01:25

Selected Data About Geographic Locations

28
Geographic Information Systems (GIS) rely on two core types of data: spatial data and attribute data.Spatial DataSpatial data defines the physical location of features within a coordinate system, typically expressed in terms of latitude and longitude. It provides precise positioning for elements like roads, rivers, or buildings.Attribute DataAttribute data complements spatial data by adding descriptive information about these features. For example, a road's spatial data includes its start and...
28
Statistical Analysis: Overview01:11

Statistical Analysis: Overview

6.6K
When we take repeated measurements on the same or replicated samples, we will observe inconsistencies in the magnitude. These inconsistencies are called errors. To categorize and characterize these results and their errors, the researcher can use statistical analysis to determine the quality of the measurements and/or suitability of the methods.
One of the most commonly used statistical quantifiers is the mean, which is the ratio between the sum of the numerical values of all results and the...
6.6K
Archival Research01:40

Archival Research

16.0K
Some researchers gain access to large amounts of data without interacting with a single research participant. Instead, they use existing records to answer various research questions. This type of research approach is known as archival research. Archival research relies on looking at past records or data sets to look for interesting patterns or relationships. For example, a researcher might access the academic records of all individuals who enrolled in college within the past ten years and...
16.0K

You might also read

Related Articles

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

Sort by
Same author

Design Exploration of AI-Assisted Personal Affective Physicalization.

IEEE computer graphics and applications·2025
Same author

Struggles and Strategies in Understanding Information Visualizations.

IEEE transactions on visualization and computer graphics·2024
Same author

Challenges and Opportunities in Data Visualization Education: A Call to Action.

IEEE transactions on visualization and computer graphics·2023
Same author

Embracing Disciplinary Diversity in Visualization.

IEEE computer graphics and applications·2023
Same author

Computerised clinical decision support system for the diagnosis of pulmonary thromboembolism: a preclinical pilot study.

BMJ open quality·2023
Same author

KiriPhys: Exploring New Data Physicalization Opportunities.

IEEE transactions on visualization and computer graphics·2022

Related Experiment Video

Updated: Jul 12, 2025

Facilitating the Analysis of Immunological Data with Visual Analytic Techniques
10:58

Facilitating the Analysis of Immunological Data with Visual Analytic Techniques

Published on: January 2, 2011

10.2K

Enthusiastic and Grounded, Avoidant and Cautious: Understanding Public Receptivity to Data and Visualizations.

Helen Ai He, Jagoda Walny, Sonja Thoma

    IEEE Transactions on Visualization and Computer Graphics
    |October 23, 2023
    PubMed
    Summary

    Public open data and visualizations are not equally accessible. This study reveals "information receptivity" as a key factor influencing how diverse audiences engage with energy data, suggesting new inclusivity approaches.

    More Related Videos

    Using Generative Art to Convey Past and Future Climate Transitions
    06:10

    Using Generative Art to Convey Past and Future Climate Transitions

    Published on: March 31, 2023

    993
    Evaluating Usability Aspects of a Mixed Reality Solution for Immersive Analytics in Industry 4.0 Scenarios
    06:02

    Evaluating Usability Aspects of a Mixed Reality Solution for Immersive Analytics in Industry 4.0 Scenarios

    Published on: October 6, 2020

    2.3K

    Related Experiment Videos

    Last Updated: Jul 12, 2025

    Facilitating the Analysis of Immunological Data with Visual Analytic Techniques
    10:58

    Facilitating the Analysis of Immunological Data with Visual Analytic Techniques

    Published on: January 2, 2011

    10.2K
    Using Generative Art to Convey Past and Future Climate Transitions
    06:10

    Using Generative Art to Convey Past and Future Climate Transitions

    Published on: March 31, 2023

    993
    Evaluating Usability Aspects of a Mixed Reality Solution for Immersive Analytics in Industry 4.0 Scenarios
    06:02

    Evaluating Usability Aspects of a Mixed Reality Solution for Immersive Analytics in Industry 4.0 Scenarios

    Published on: October 6, 2020

    2.3K

    Area of Science:

    • Data Visualization
    • Human-Computer Interaction
    • Public Data Engagement

    Background:

    • Open data initiatives aim to inform the public, yet understanding of non-expert engagement with data visualizations remains limited.
    • Existing research often overlooks how individuals outside data analysis communities interact with public data.

    Purpose of the Study:

    • To explore lived experiences of diverse audiences with public open data and visualizations, specifically in the energy sector.
    • To identify factors influencing engagement beyond traditional data literacy and visualization engagement metrics.

    Main Methods:

    • Qualitative interview study with 19 participants from varied ethnic, occupational, and demographic backgrounds.
    • Analysis of participant experiences with open data and visualizations related to energy consumption, production, and transmission.

    Main Results:

    • Introduced "information receptivity" as a distinct concept, describing an individual's openness to information.
    • Identified four clusters of receptivity: Information-Avoidant, Data-Cautious, Data-Enthusiastic, and Domain-Grounded.
    • Highlighted diverse responses to data and visualization rhetoric among participants.

    Conclusions:

    • Recognizing information receptivity is crucial for the data visualization community.
    • Findings suggest opportunities to enhance the accessibility and inclusivity of open data initiatives for broad audiences.
    • Emphasizes the need to consider varying levels of audience openness to information.