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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...
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Statgraphics01:10

Statgraphics

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Statgraphics is a comprehensive statistical software suite designed for both basic and advanced data analysis. Originating in 1980 at Princeton University under Dr. Neil W. Polhemus, it was one of the pioneering tools for statistical computing on personal computers, with its public release in 1982 marking an early milestone in data science software. Over the years, it has evolved into a robust platform for data science, offering tools for regression analysis, ANOVA, multivariate statistics,...
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Review and Preview01:13

Review and Preview

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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...
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Multiple Bar Graph01:07

Multiple Bar Graph

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As the name suggests, a multiple bar graph is the same as a bar graph but has multiple bars to depict relationships between different data values. One can include as many parameters as possible. However, each parameter must have the same unit of measurement.
Each bar or column in the multiple bar graph represents a data value. These graphs are used primarily in interrelating two or more sets of data. The categories of different kinds of data are listed along the horizontal or x-axis, whereas...
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Design Example: Analyzing Capacity Contours for Flood Risk Assessment01:17

Design Example: Analyzing Capacity Contours for Flood Risk Assessment

270
Flood risk assessment involves careful planning and analysis to ensure the safety of communities near water retention structures. Capacity contours are a vital tool in this process, as they illustrate the potential spread of water at specific levels in a given area. In the context of building a bund across a small valley, these contours play a critical role in evaluating the safety of nearby residential areas.In this example, the bund is intended to store stormwater in the valley. The engineers...
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Selected Data About Geographic Locations01:25

Selected Data About Geographic Locations

249
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...
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Facilitating the Analysis of Immunological Data with Visual Analytic Techniques
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马赛克选择:管理和优化可扩展数据可视化系统的用户选择.

Jeffrey Heer, Dominik Moritz, Ron Pechuk

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    此摘要是机器生成的。

    马赛克选择优化大型数据集的交互可视化. 该模型能够在多个可视化中快速,低延迟的数据过和更新,改善数百万条记录的性能.

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    ExCYT: A Graphical User Interface for Streamlining Analysis of High-Dimensional Cytometry Data
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    科学领域:

    • 计算机科学 计算机科学
    • 数据可视化 数据可视化
    • 人与计算机的交互

    背景情况:

    • 交互式可视化与大量数据集 (数百万+记录) 的实时分析作斗争.
    • 用户对过数据的选择可能很复杂,需要低延迟的更新.

    研究的目的:

    • 介绍Mosaic Selections,这是一个用于管理和优化交互式可视化中用户选择的新型模型.
    • 为了实现高效,实时与大数据集的交互.

    主要方法:

    • 开发了Mosaic Selections,这是一个将过器预言集成到可视化和输入小部件的数据查询中的模型.
    • 实现自动优化,包括预先汇总数据,基于查询和选择预言分析.
    • 在开源的Mosaic架构中正式确定了选择模型和优化技术.

    主要成果:

    • 与未优化查询和现有的Vega优化器相比,实现了基于选择的优化延迟的数量级改进.
    • 证明了对复杂,多组件用户选择的高效处理.
    • 验证了数百万和数十亿条记录的可扩展性.

    结论:

    • 马赛克选择提供了一个灵活和可互操作的框架,用于在可视化中过数据.
    • 该模型的自动优化显著提高了使用大型数据集交互式可视化的性能.
    • 实现实时交互和分析大规模数据规模.