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Questions and Answers
What is a target in data visualization?
What is an action in data visualization?
How you want to interact with the visualization and what you want to get out of it.
What does the 'discover' action involve?
Using visualization to find information you didn't have before.
What is an example of the 'discover' action?
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What is the 'present' action?
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What is an example of the 'present' action?
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What does the 'enjoy' action refer to?
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What is an example of the 'enjoy' action?
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What is the purpose of the 'annotate' action?
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What is an example of the 'annotate' action?
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What does the 'record' action allow you to do?
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What is an example of the 'record' action?
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What is the 'derive' action?
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What is an example of the 'derive' action?
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What defines a known target?
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What is an unknown target?
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Provide an example of a known target.
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Provide an example of an unknown target.
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What is a known location?
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What is an unknown location?
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What is an example of a known location?
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What is an example of an unknown location?
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What does the 'identify' action entail?
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What is an example of the 'identify' action?
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What does the 'compare' action involve?
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What is an example of the 'compare' action?
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What does the 'summarize' action refer to?
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What is an example of the 'summarize' action?
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What do we mean by 'trends' in data visualization?
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What are 'outliers' in data visualization?
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What are 'features' in data visualization?
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What targets can we look at when we have a single attribute?
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What targets can we examine with multiple attributes?
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What is 'arrange' in design choice?
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What is a scatterplot?
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What types of data are scatterplots well suited for?
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What types of data are scatterplots poorly suited for?
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How many different attributes can you convey with a scatterplot?
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What is list alignment?
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What is a bar chart?
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What types of data are bar charts well suited for?
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What types of data are bar charts poorly suited for?
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What is a stacked bar chart?
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What types of data are stacked bar charts well suited for?
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What types of data are stacked bar charts poorly suited for?
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What is a stream graph?
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What types of data are stream graphs well suited for?
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What types of data are stream graphs poorly suited for?
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What are dot charts?
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What are line charts?
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When should you choose a dot chart over a line chart?
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When should you choose a line chart over a dot chart?
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What is important regarding line chart aspect ratio?
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What is a matrix alignment?
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What is a heatmap?
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What types of data are heatmaps well suited for?
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What is a scatterplot matrix?
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What types of data are scatterplot matrices well suited for?
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What is a rectilinear layout?
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What is a parallel layout?
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What is a parallel coordinates plot?
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What are some advantages of a parallel coordinates plot?
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What are some disadvantages of a parallel coordinates plot?
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What is a radial layout?
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What is a pie chart?
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How do we often choose the position of graphical elements in spatial data?
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What is a choropleth map?
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What are choropleth maps well suited for?
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What are choropleth maps poorly suited for?
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What are three different ways of depicting a single scalar field?
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What is slicing in 3D scalar fields?
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What is direct volume rendering?
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What are isocontours?
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What factors should you consider when choosing rendering techniques?
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What is a transfer function in direct volume rendering?
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What are steady vector fields?
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What are unsteady vector fields?
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What are critical points?
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What is a streamline in a steady vector field?
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What are pie charts well suited for?
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What are pie charts poorly suited for?
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What is a normalized stacked bar chart?
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What are normalized stacked bar charts well suited for?
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What are normalized stacked bar charts poorly suited for?
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What is a rule of thumb?
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Why should you follow rules of thumb?
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What is unjustified 3D?
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What is unjustified 2D?
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What is occlusion?
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What are depth cues?
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What is perspective distortion?
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How can you decide whether to use 2D or 3D?
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What does it mean when we say 'eyes beat memory'?
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What is working memory?
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What is the conflict between resolution and immersion?
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Between resolution and immersion, which should we generally prefer?
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What is an overview?
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What does zoom and filter do?
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What is details on demand?
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What is latency in data visualization?
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Study Notes
Data Visualization Concepts
- Target refers to specific trends or outliers one is examining within the data.
- Action encompasses how a user wishes to interact with the visualization and what information they aim to extract.
- Discover Action involves utilizing a visualization to uncover new information previously unrecognized.
- Present Action signifies using visualizations to communicate insights to others, e.g., search results or TED talk visuals.
- Enjoy Action pertains to visualizations designed for entertainment or casual exploration.
- Annotate Action is the process of making notes or marks on a visualization to highlight important elements.
- Record Action allows users to save the current state of a visualization, often through screenshots.
- Derive Action means generating new insights or information from existing data.
Data Targets
- Known Target represents scenarios where the user has a defined search goal, such as a specific location.
- Unknown Target highlights uncertainty in what the user seeks, like looking for a restaurant without a name.
- Trends Target focuses on recognizing overall data movement over time, e.g., stock increases or seasonal changes.
- Outliers Target identifies values that do not conform to established trends and stand apart from the data.
- Features Target addresses smaller patterns within larger trends, such as local energy usage during specific events.
Data Relationships and Representation
- Comparison Action involves evaluating a limited number of items, such as product comparisons on shopping sites.
- Summarize Action means analyzing large data sets to identify broad patterns and trends.
- Distribution Analysis assesses how many items fall within specific value ranges, considering minimum and maximum values.
Visualization Types and Suitability
- Scatterplots are effective for showing correlations and outliers but poorly suited for categorical data.
- Bar Charts excel at making comparisons but lose spatial context, making them unsuitable for representation of spatial data.
- Stacked Bar Charts represent parts of a whole but can be ineffective if data segments are poorly distributed.
- Stream Graphs visualize continuous data over time; however, excessive data points can complicate readability.
- Heatmaps demonstrate the relationship between two groupings effectively with color coding.
- Scatterplot Matrices facilitate comparisons across multiple attributes, suitable for multivariate datasets.
Layout Principles
- Rectilinear Layout employs right-angle intersections common in most graphs.
- Parallel Coordinates Plot displays high-dimensional data but can be challenging to interpret due to complexity.
- Radial Layouts organize data in circular arrangements, resembling pie charts for comparative purposes.
Choosing Visualization Techniques
- Direct Volume Rendering captures the complexity of 3D scalar fields for medical imaging.
- Isocontours present 2D scalar fields effectively, displaying contours like those on a topographic map.
Visualization Challenges
- Occlusion occurs when stacked elements obscure data, complicating the readability of the visualization.
- Perspective Distortion describes how distant objects may appear misleading due to visual perspective cues.
- Latency measures response time delays in interactive systems, impacting user experience.
General Guidelines
- Rule of Thumb serves as fundamental principles established by past experience for creating effective visualizations.
- Resolution vs. Immersion presents a dilemma in visualization, with resolution usually preferred for clarity.
- Overview, Zoom, and Filter techniques allow users to navigate complex data more intuitively.
- Details on Demand gives users access to more granular information as needed, enhancing interaction and understanding.
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Description
Explore the key concepts of data visualization, including various actions users can take and types of targets they may focus on. Understand how these elements contribute to effective data analysis and communication. This quiz will enhance your knowledge about how to interact with visual data effectively.