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Related Concept Videos

Multiple Bar Graph01:07

Multiple Bar Graph

10.4K
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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Bar Graph01:07

Bar Graph

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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...
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Time-Series Graph00:54

Time-Series Graph

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A time-series graph is a line graph with repeated measurements taken at successive intervals of time. It is also called a time series chart. To construct a time-series graph, one must look at both pieces of a paired data set. The horizontal axis is used to plot the time increments, and the vertical axis is used to plot the values of the variable that one is measuring. By using the axes in this way, each point on the graph will correspond to time and a measured quantity. The points on the graph...
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Ogive Graph01:07

Ogive Graph

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An ogive graph is sometimes called a cumulative frequency polygon. It is one type of frequency polygon that shows cumulative frequency. In other words, the cumulative percentages are added to the graph from left to right. An ogive graph plots cumulative frequency on the vertical y-axis and class boundaries along the horizontal x-axis. It’s very similar to a histogram; only instead of rectangles, an ogive displays a single point where the top right of the rectangle would be. Creating this...
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Scatter Plot01:15

Scatter Plot

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The most common and easiest way to display the relationship between two variables, x and y, is a scatter plot. A scatter plot shows the direction of a relationship between the variables. A clear direction happens when there is either:
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Pie Chart01:04

Pie Chart

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A pie chart (or a pie graph) is a circular graphical chart or a pictorial representation of categorical data. It is divided into slices of pie each indicating numerical proportions. It is also used to show the relative sizes of data in a single chart.
In a pie chart, the central angle, the arc length of each slice, and the area are directly proportional to the quantity or percentage it represents. Some real-world examples that can be depicted using pie charts include marks obtained by students...
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Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack
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PieceStack: Toward Better Understanding of Stacked Graphs.

Tongshuang Wu, Yingcai Wu, Conglei Shi

    IEEE Transactions on Visualization and Computer Graphics
    |January 24, 2017
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    Summary
    This summary is machine-generated.

    PieceStack enhances stacked graph analysis by interactively splitting and reconstructing visualizations. This technique reveals how individual layers form aggregations, improving understanding of complex temporal data.

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    Area of Science:

    • Data Visualization
    • Human-Computer Interaction
    • Information Visualization

    Background:

    • Stacked graphs are widely used for visualizing temporal sequences and their aggregations.
    • Visual illusions in stacked graphs obscure the connection between individual layers and aggregated views.
    • Existing methods fail to fully excavate the information contained within stacked graph formations.

    Purpose of the Study:

    • To introduce PieceStack, a novel visual analytic design for stacked graphs.
    • To reveal the relevance of stacked graphs in understanding the intrinsic details of their displayed shapes.
    • To address the limitations in interpreting the relationship between individual layers and aggregated data.

    Main Methods:

    • PieceStack interactively splits and reconstructs stacked graphs to interpret aggregation generation.
    • A clustering algorithm partitions stacked graphs into sub-aggregated pieces based on layer trend similarities.
    • Augmented encoding is used to visualize these pieces, aiding analyst exploration.

    Main Results:

    • The study demonstrates PieceStack's ability to clarify the formation of stacked graphs.
    • Case studies and user studies validate the technique's effectiveness.
    • Analysts can decompose and explore stacked graphs more effectively with PieceStack.

    Conclusions:

    • PieceStack offers a new approach to understanding the formation and details within stacked graphs.
    • The technique mitigates visual illusion issues, enabling deeper data excavation.
    • PieceStack enhances the analytical utility of stacked graph visualizations.