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Related Concept Videos

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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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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Modified Boxplots00:57

Modified Boxplots

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A standard box and whisker plot informs us about the spread of the data in a given sample. One can identify the minimum value, maximum value, first quartile value, second quartile or median value, and third quartile.
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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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Interpreting R Charts01:22

Interpreting R Charts

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R chart, or range chart, is a fundamental tool in statistical process control used to monitor the variability within a process. It complements the X-bar (x̄) chart by focusing on the range of the data, rather than individual values, providing a clear picture of the process dispersion over time.
An R chart plots the range of subsets of measurements collected from a process. Each point on the chart represents the range—defined as the difference between the maximum and minimum...
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Design Example: Analyzing Capacity Contours for Flood Risk Assessment01:17

Design Example: Analyzing Capacity Contours for Flood Risk Assessment

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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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Facilitating the Analysis of Immunological Data with Visual Analytic Techniques
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Reclaiming the Horizon: Novel Visualization Designs for Time-Series Data with Large Value Ranges.

Daniel Braun, Rita Borgo, Max Sondag

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    |October 23, 2023
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    Summary
    This summary is machine-generated.

    New visualization designs effectively handle large value ranges in time-series data. The order of magnitude horizon graph excels at identification and discrimination tasks, improving data analysis for practitioners.

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    Area of Science:

    • Data Visualization
    • Human-Computer Interaction

    Background:

    • Analyzing time-series data with large value ranges presents significant challenges.
    • Existing visualization techniques often struggle to accurately represent data spanning several orders of magnitude.

    Purpose of the Study:

    • Introduce two novel visualization designs: the order of magnitude horizon graph and the order of magnitude line chart.
    • Evaluate the effectiveness of these designs for identification and discrimination tasks on large value ranges in time-series data.

    Main Methods:

    • Developed novel visualization designs by splitting values into mantissa and exponent (v=m·10^e).
    • Conducted an empirical user study comparing new designs against state-of-the-art visualizations.
    • Assessed performance across identification, discrimination, estimation, and trend detection tasks, measuring error, confidence, and response time.

    Main Results:

    • The order of magnitude horizon graph demonstrated superior or equal performance in identification, discrimination, and estimation tasks.
    • Traditional horizon graphs showed better performance specifically for trend detection tasks.
    • Results indicate domain-independent applicability for time-series data with extensive value ranges.

    Conclusions:

    • Novel order of magnitude visualizations enhance the analysis of time-series data with large value ranges.
    • These designs offer practical improvements for identification and discrimination tasks, crucial for data practitioners.
    • Further research may explore optimizations for specific tasks like trend detection.