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Review and Preview01:13

Review and Preview

10.0K
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...
10.0K
Design Example: Setting a Curve Using Design Data01:09

Design Example: Setting a Curve Using Design Data

98
Designing and plotting a curve using field data requires precise calculations and execution. A horizontal curve with a radius of 200 meters and an intersection angle of 20 degrees is established using the method of perpendicular offsets from the long chord. The long chord, which spans between the curve's endpoints, is calculated to be 69.46 meters in length. To maintain accuracy in plotting, intervals of 3 meters are selected along the chord.The engineer determines the offset distances for each...
98
Pie Chart01:04

Pie Chart

15.0K
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...
15.0K
Multiple Bar Graph01:07

Multiple Bar Graph

8.4K
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...
8.4K
Bar Graph01:07

Bar Graph

20.4K
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...
20.4K
Design Example: Designing Water Slide01:18

Design Example: Designing Water Slide

353
When designing a water slide, controlling the speed of water flow is crucial for rider safety while maintaining an exciting experience. As water flows down the slide, gravity causes it to accelerate, with its speed at the bottom depending on the height from which it starts. The higher the slide, the more potential energy the water has at the top, which is converted into kinetic energy as it descends, increasing its speed.
Bernoulli's principle determines the water's velocity along the...
353

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Understanding Data Visualization Design Practice.

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    Summary
    This summary is machine-generated.

    Data visualization practitioners use flexible, on-the-spot methods rather than systematic processes. Understanding this practice-led approach is key for researchers and educators in the growing field of data visualization design.

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    Area of Science:

    • Data Visualization Design
    • Human-Computer Interaction
    • Design Practice

    Background:

    • Growing popularity of professional data visualization design roles.
    • Limited understanding of real-world, professional design practices.
    • Disciplinary differences in how practitioners and researchers approach complex design problems.

    Purpose of the Study:

    • To explore data visualization design practice from the practitioners' perspective.
    • To understand the methods and decision-making processes used by visualization designers.
    • To investigate the gap between academic research and professional practice in data visualization.

    Main Methods:

    • Practice-led research approach.
    • Interviews with twenty professional data visualization designers.
    • Inquiry into design processes, decision-making, and methods.

    Main Results:

    • Practitioners do not adhere to highly systematic design processes.
    • Designers rely on situated knowing and acting, drawing from precedent.
    • Methods and principles are chosen based on immediate situational appropriateness.

    Conclusions:

    • Practitioner approaches differ significantly from typical research models.
    • Findings inform engagement between visualization researchers and practitioners.
    • Implications for curriculum development and training of future data visualization designers.