Podcast
Questions and Answers
A Chernoff Face glyph
A Chernoff Face glyph
- Is only good for visualising very low dimensional data sets
- Is made up of graphical elements that can interfere with each other, in representing independent dimensions (correct)
- Should only be used to represent information about a person
The Gestalt laws
The Gestalt laws
- Are ranked in order (top three): proximity, similarity, connectedness (correct)
- Only relate to static images
- Relate to bottom-up and top-down visual processing (correct)
An example of 'focus and context' system in a visualisation is
An example of 'focus and context' system in a visualisation is
- A fisheye distortion of a map (correct)
- A timeline view of data, plotting all occurrences of an event against a time axis (correct)
- A zoomed-in view of a scatterplot, where a subset of the data takes up the full screen (correct)
Colour
Colour
In relation to data encoding, which statement is correct?
In relation to data encoding, which statement is correct?
In the context of data collection, which statement is true?
In the context of data collection, which statement is true?
How many scatterplots are in a SPLOM, used to present 9-dimensional data?
How many scatterplots are in a SPLOM, used to present 9-dimensional data?
Which statement about large graphs is correct?
Which statement about large graphs is correct?
What does a single-axis composition generally mean?
What does a single-axis composition generally mean?
Which representation would be most effective for certain wildlife park data?
Which representation would be most effective for certain wildlife park data?
In the context of data collection, which statement is false?
In the context of data collection, which statement is false?
Roughly how long will each iteration of the spring model take if using 10,000 objects instead of 5,000?
Roughly how long will each iteration of the spring model take if using 10,000 objects instead of 5,000?
Stevens’ power law describes?
Stevens’ power law describes?
With respect to the things a viewer might find interesting in a visualization, which statement is true?
With respect to the things a viewer might find interesting in a visualization, which statement is true?
Successful methods of drawing graphs typically?
Successful methods of drawing graphs typically?
When creating a declarative query using a map to select a rectangular area of data, what will happen in a linked bar chart?
When creating a declarative query using a map to select a rectangular area of data, what will happen in a linked bar chart?
What does the Visualization Pipeline highlight?
What does the Visualization Pipeline highlight?
If you consider an additional data set option in your design, how much bigger will the design space be?
If you consider an additional data set option in your design, how much bigger will the design space be?
In semiotic terms, is a grey cloud on a weather map a?
In semiotic terms, is a grey cloud on a weather map a?
An example of 'query relaxation' in general selection is?
An example of 'query relaxation' in general selection is?
When depicting bivariate data, what is a good representation choice?
When depicting bivariate data, what is a good representation choice?
Which description applies to data sets A and B according to Munzner’s classification?
Which description applies to data sets A and B according to Munzner’s classification?
Which algorithm would not be a good choice for analyzing complex data with fine-grained local detail and high-level clusters?
Which algorithm would not be a good choice for analyzing complex data with fine-grained local detail and high-level clusters?
Data collected by questionnaire is easy to collect, but not always valuable?
Data collected by questionnaire is easy to collect, but not always valuable?
Is data collected by observation always subjective?
Is data collected by observation always subjective?
Which database attributes are most appropriate for managing qualitative data from interviews?
Which database attributes are most appropriate for managing qualitative data from interviews?
Summative evaluation focuses on which of the following?
Summative evaluation focuses on which of the following?
In justifying design, parallel coordinates visualizations are useful because?
In justifying design, parallel coordinates visualizations are useful because?
Using SQL to define queries over a large database allows the user to?
Using SQL to define queries over a large database allows the user to?
Bertin's visual variables can be characterized as?
Bertin's visual variables can be characterized as?
Study Notes
Quiz Overview
- Duration: 90 minutes time limit
- Format: 30 multiple choice questions, each with three options
- Scoring: Each question worth up to 2 marks; -1 penalty for incorrect answers
- Total score scaled to 70% of overall mark for the course
Data Collection and Analysis
- Performance data is often quantitative; preference and perception data are generally qualitative.
- A single axis composition involves aligning or overlaying visualizations based on one shared dimension.
- SPLOM (Scatterplot Matrix) displays multiple scatterplots to analyze high-dimensional data.
Visual Representation Techniques
- Heat maps and scatterplots are effective for visualizing relationships between variables.
- Large graphs can be simplified using clustering techniques while maintaining structural details.
- Query relaxation in data visualizations allows for broadening selection criteria, enhancing interactivity.
Evaluation Methodologies
- Summative evaluation assesses whether specified criteria are met, while formative evaluation focuses on improvement suggestions.
- Database attributes for qualitative analysis should include participant ID, interview text, and numerical measures for tracking efficacy.
Visualization Concepts
- Gestalt laws explain how viewers perceive visual data through principles like proximity and connectedness.
- Focus and context systems like fisheye distortion enable detailed analysis of specific data segments while maintaining an overview.
- Bertin's visual variables underlie many aspects of effective data representation.
Data Representation Challenges
- Bivariate data can have various suitable representation methods depending on if the variables are qualitative, quantitative, or categorical.
- Spring models are less effective when high-level clusters need to be preserved alongside fine-grained details.
- Chernoff faces visualize low-dimensional datasets using distinct graphical elements to represent different dimensions.
Cognitive and Perceptual Elements
- Stevens’ power law describes how physical intensity impacts perceived sensation differently across individuals.
- Interesting aspects in visualizations often include paths correlating attributes, focusing on specific targets, and investigating outliers.
Interaction Techniques
- SQL interactions allow users to see results of queries with varying detail levels, enhancing data exploration capabilities.
- Visualizations must consider audience perception and the cognitive load associated with interpreting complex data structures.
Miscellaneous Concepts
- Data classification may vary, with aspects like dimensional tables or simple relational tables being relevant for organizing data sets.
- A focus on detail versus macro-level understanding is crucial in designing algorithms for data analysis.
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