Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

Manipulation and Analysis01:21

Manipulation and Analysis

276
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...
276
Statistical Analysis: Overview01:11

Statistical Analysis: Overview

14.1K
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...
14.1K
Survival Curves01:18

Survival Curves

628
Survival curves are graphical representations that depict the survival experience of a population over time, offering an intuitive way to track the proportion of individuals who remain event-free at each time point. These curves are widely used in fields such as medicine, public health, and reliability engineering to visualize and compare survival probabilities across different groups or conditions.
The Kaplan-Meier estimator is the most common method for constructing survival curves. This...
628
Performing a Simple Data Analysis using MS-Excel Function01:17

Performing a Simple Data Analysis using MS-Excel Function

896
Microsoft Excel offers a suite of functions and tools ideal for statistical analysis, making it accessible to students and researchers. This article outlines fundamental Excel functions pivotal for data analysis.
SUM: This function calculates the total sum of a range of values. It's the foundation for aggregating data, essential for determining overall trends and totals in datasets.
AVERAGE: It computes the mean value of a given set of numbers, providing a quick insight into the central...
896
Levels of Use of a GIS01:29

Levels of Use of a GIS

326
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...
326
Bar Graph01:07

Bar Graph

21.3K
A bar graph is also called a bar chart and consists of bars that are separated from each other. It either uses horizontal or vertical bars to show comparisons among categories. The bars can be rectangles, or they can be rectangular boxes (used in three-dimensional plots). One axis of the graph represents the specific categories being compared, and the other axis shows a discrete value. In this graph, the length of the bar for each category is proportional to the number or percent of individuals...
21.3K

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

The Hue-Man Factor: An Empirical Evaluation of Visualization Perception and Accessibility Across Color Vision Profiles.

IEEE transactions on visualization and computer graphics·2025
Same author

Shifting Expectations for Encoding Rules Mitigates Misinterpretation of Connected Scatterplots.

IEEE transactions on visualization and computer graphics·2025
Same author

Write, Rank, or Rate: Comparing Methods for Studying Visualization Affordances.

IEEE transactions on visualization and computer graphics·2025
Same author

Cognitive Affordances in Visualization: Related Constructs, Design Factors, and Framework.

IEEE transactions on visualization and computer graphics·2025
Same author

Evaluating convergence between two data visualization literacy assessments.

Cognitive research: principles and implications·2025
Same author

PromptAid: Visual Prompt Exploration, Perturbation, Testing and Iteration for Large Language Models.

IEEE transactions on visualization and computer graphics·2025
Same journal

MesoSplats: Texture Synthesis with Gaussian Splatting.

IEEE transactions on visualization and computer graphics·2026
Same journal

GLLA: A Unified Force-Directed Graph Layout Framework Supporting Local Adjustments.

IEEE transactions on visualization and computer graphics·2026
Same journal

Multi-Perception Crowd: Learning to combine entity and implicit perception for diverse crowd simulation.

IEEE transactions on visualization and computer graphics·2026
Same journal

Hiding in Plain Sight: Camouflaging Real-world Objects.

IEEE transactions on visualization and computer graphics·2026
Same journal

RTF2Mesh: Restricted Tangent Face Based Mesh Compression With Neural Displacement Fields.

IEEE transactions on visualization and computer graphics·2026
Same journal

Practical Occluder Generation for Mobile Games.

IEEE transactions on visualization and computer graphics·2026
查看所有相关文章

相关实验视频

Updated: Jan 10, 2026

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

10.5K

在信息可视化中对文本功能的分析.

Chase Stokes, Anjana Arunkumar, Marti A Hearst

    IEEE transactions on visualization and computer graphics
    |November 21, 2025
    PubMed
    概括
    此摘要是机器生成的。

    文本在信息可视化设计中起着至关重要的作用. 这项研究引入了一个新的框架,确定了十个文本功能和四个设计策略,增强了我们在可视化中理解和使用文本的方式.

    更多相关视频

    Trajectory Data Analyses for Pedestrian Space-time Activity Study
    16:14

    Trajectory Data Analyses for Pedestrian Space-time Activity Study

    Published on: February 25, 2013

    14.1K
    Integrating Computerized Linguistic and Social Network Analyses to Capture Addiction Recovery Capital in an Online Community
    08:53

    Integrating Computerized Linguistic and Social Network Analyses to Capture Addiction Recovery Capital in an Online Community

    Published on: May 31, 2019

    5.5K

    相关实验视频

    Last Updated: Jan 10, 2026

    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

    10.5K
    Trajectory Data Analyses for Pedestrian Space-time Activity Study
    16:14

    Trajectory Data Analyses for Pedestrian Space-time Activity Study

    Published on: February 25, 2013

    14.1K
    Integrating Computerized Linguistic and Social Network Analyses to Capture Addiction Recovery Capital in an Online Community
    08:53

    Integrating Computerized Linguistic and Social Network Analyses to Capture Addiction Recovery Capital in an Online Community

    Published on: May 31, 2019

    5.5K

    科学领域:

    • 信息可视化 信息可视化
    • 人与计算机的交互
    • 视觉分析 视觉分析 视觉分析

    背景情况:

    • 文本是可视化设计中不可或缺但尚未研究的方面.
    • 之前的研究探讨了文本对理解和偏好的影响,但实际设计和使用仍然不清楚.

    研究的目的:

    • 引入一个全面的框架来理解信息可视化中的文本函数.
    • 在可视化设计中识别和分类文本扮演的不同角色.
    • 发现文本使用模式和相关的设计策略.

    主要方法:

    • 分析了120个真实世界的信息可视化和804个文本元素.
    • 识别十个不同的文本功能.
    • 因素分析揭示了总体的基于文本的设计策略.

    主要成果:

    • 确定了十个不同的文本功能,包括数据映射识别和副文本呈现.
    • 四个总体的文本知情设计策略出现了:归因和变量,注释为中心的设计,视觉装饰和叙事框架.
    • 文本被发现是多功能性的,甚至可以取代视觉元素.

    结论:

    • 拟议的框架为现有的可视化文本模型增加了细微差别.
    • 研究结果强调了文本在沟通,合成和框架视觉信息方面的灵活性.
    • 这项研究提供了对文本设计和实际使用的详细了解.