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The 'grid' data type can be used to represent a runner's heartbeat sampled every 30 seconds.
The 'grid' data type can be used to represent a runner's heartbeat sampled every 30 seconds.
True
A 'link' data type represents the relationship between two runners who train together.
A 'link' data type represents the relationship between two runners who train together.
True
The 'attribute' data type describes a runner's position during a race.
The 'attribute' data type describes a runner's position during a race.
False
A 'table' data set type represents data using rows and columns, similar to a spreadsheet.
A 'table' data set type represents data using rows and columns, similar to a spreadsheet.
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The 'geometry' data set type focuses on representing continuous data, such as temperature readings across a region.
The 'geometry' data set type focuses on representing continuous data, such as temperature readings across a region.
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A 'network' data set type is suitable for representing relationships between items, like friendships in a social network.
A 'network' data set type is suitable for representing relationships between items, like friendships in a social network.
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The 'item' data type represents a specific runner participating in a hill race in Scotland.
The 'item' data type represents a specific runner participating in a hill race in Scotland.
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A 'grid' data type is an approach for collecting and storing data rather than a data type itself.
A 'grid' data type is an approach for collecting and storing data rather than a data type itself.
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The 'link' data type is used to represent a runner's club membership.
The 'link' data type is used to represent a runner's club membership.
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Adding the dimension of 'years' to a table, allows for tracking changes in clubs over time, making the table multidimensional.
Adding the dimension of 'years' to a table, allows for tracking changes in clubs over time, making the table multidimensional.
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Run-buddies are pairs that are constantly changing, forming and dissolving within different race events.
Run-buddies are pairs that are constantly changing, forming and dissolving within different race events.
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Fields are characterized by a finite number of measurements, making sampling and extrapolation unnecessary.
Fields are characterized by a finite number of measurements, making sampling and extrapolation unnecessary.
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A runner’s heartbeat can be represented in a two-column table, with the first column denoting the time intervals and the second column showing the heart rate at those intervals.
A runner’s heartbeat can be represented in a two-column table, with the first column denoting the time intervals and the second column showing the heart rate at those intervals.
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If the frequency of a runner's heartbeat is unknown, it must be included as an attribute of the data item, making the data more complex.
If the frequency of a runner's heartbeat is unknown, it must be included as an attribute of the data item, making the data more complex.
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A table with two columns can effectively represent a runner's heartbeat, with the first column denoting the time intervals and the second column showing the heart rate at those intervals.
A table with two columns can effectively represent a runner's heartbeat, with the first column denoting the time intervals and the second column showing the heart rate at those intervals.
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The example on the bottom right uses a polar coordinate style sampling of a continuous region of space, with 64 samples representing the region.
The example on the bottom right uses a polar coordinate style sampling of a continuous region of space, with 64 samples representing the region.
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The example on the bottom right utilizes a sampling technique that involves 5 measurements or calculations for each cell in the region, with one value highlighted within the cell.
The example on the bottom right utilizes a sampling technique that involves 5 measurements or calculations for each cell in the region, with one value highlighted within the cell.
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The example on the bottom right represents a sampling technique where the region is sampled based on four distances from a central point and an arc of 16 angles around that central point, resulting in 64 samples.
The example on the bottom right represents a sampling technique where the region is sampled based on four distances from a central point and an arc of 16 angles around that central point, resulting in 64 samples.
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Ordinal data can be added or subtracted to find meaningful distances between items.
Ordinal data can be added or subtracted to find meaningful distances between items.
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Categorical data can impose an ordering intrinsic to the data itself.
Categorical data can impose an ordering intrinsic to the data itself.
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The finisher's times of 1h40, 1h42, 1h53, and 1h54 demonstrate a quantitative scale.
The finisher's times of 1h40, 1h42, 1h53, and 1h54 demonstrate a quantitative scale.
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Elevation measurement can be categorized as nominal data.
Elevation measurement can be categorized as nominal data.
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Race dates are considered cyclic data.
Race dates are considered cyclic data.
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A race labeled as 'very easy' is considered ordinal data.
A race labeled as 'very easy' is considered ordinal data.
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The distances between finisher times in a race are consistent and measurable.
The distances between finisher times in a race are consistent and measurable.
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It is possible to determine the average of ordinal data.
It is possible to determine the average of ordinal data.
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Both '100m above' and '50m below' represent categorical data.
Both '100m above' and '50m below' represent categorical data.
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The "Age category" column contains data that can be categorized as "ordered".
The "Age category" column contains data that can be categorized as "ordered".
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The "Finish time" data could be classified as a "position" data type.
The "Finish time" data could be classified as a "position" data type.
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The provided race data would be considered a "field" data set type.
The provided race data would be considered a "field" data set type.
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The "Club" column is an example of an attribute classified as "categorical" because the values can be categorized, but not ordered.
The "Club" column is an example of an attribute classified as "categorical" because the values can be categorized, but not ordered.
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The shape used to represent fields in the data might solely be based on rectangular shapes.
The shape used to represent fields in the data might solely be based on rectangular shapes.
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The "Name" column can be classified as "static" data because it is not expected to change over time for each runner.
The "Name" column can be classified as "static" data because it is not expected to change over time for each runner.
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The "Position" column is categorized as "ordered" because it represents a ranked order based on finish time.
The "Position" column is categorized as "ordered" because it represents a ranked order based on finish time.
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Spatial data can be identified solely by location names without the need for a coordinate system.
Spatial data can be identified solely by location names without the need for a coordinate system.
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The "Year" data could be classified as "sequential" data, considering the race results are in a specific time order.
The "Year" data could be classified as "sequential" data, considering the race results are in a specific time order.
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Data availability can refer to both static and dynamic streams.
Data availability can refer to both static and dynamic streams.
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The "Bib number" column is an example of a "link" data type because it connects runners with their results.
The "Bib number" column is an example of a "link" data type because it connects runners with their results.
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The average finish time for the Two Breweries race in 2018 is the same as the finish time over all time.
The average finish time for the Two Breweries race in 2018 is the same as the finish time over all time.
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The use of colour to highlight values in the cell is also used to indicate the location of that value.
The use of colour to highlight values in the cell is also used to indicate the location of that value.
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The "Finishing time" data in the Two Breweries Hill Race example is considered "dynamic" data, as it is expected to change depending on the year.
The "Finishing time" data in the Two Breweries Hill Race example is considered "dynamic" data, as it is expected to change depending on the year.
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Names of locations must be mapped onto a coordinate system to confirm their status as spatial data.
Names of locations must be mapped onto a coordinate system to confirm their status as spatial data.
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Data collected as a dynamic stream has fewer challenges compared to static data.
Data collected as a dynamic stream has fewer challenges compared to static data.
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Attribute types can include club names like Springly, Ludders, and Sharpford.
Attribute types can include club names like Springly, Ludders, and Sharpford.
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Data with a time dimension is the same as data that is available either online or offline.
Data with a time dimension is the same as data that is available either online or offline.
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Study Notes
Data Types
- There are five main data types: items, links, attributes, positions, and grids
- Items refer to objects or entities (e.g., a runner)
- Links represent relationships between items (e.g., two runners who are "run-buddies")
- Attributes are properties of items (e.g., a runner's club membership)
- Positions refer to locations in 2D or 3D space (e.g., the start point of a race)
- Grids are regular samplings of continuous data (e.g., a runner's heartbeat sampled every 30 seconds)
Data Set Types
- There are four main data set types: tables, networks and trees, fields, and geometry
- Tables consist of rows and columns (2D or multidimensional) used to store data
- Networks and trees represent relationships between items
- Fields represent continuous data that requires sampling or extrapolation (e.g., a runner's heartbeat)
- Geometry refers to spatial data (e.g., the location of hill races in Scotland)
Data Availability
- Data can be available at the same time (static) or collected as a dynamic stream
- Streaming data presents its own challenges
Attributes
- Attributes can be categorized into three types: categorical, ordinal, and quantitative
- Categorical attributes have no implicit ordering and can only be compared for equality (e.g., club membership)
- Ordinal attributes have an intrinsic order but with no defined distances between items (e.g., race difficulty)
- Quantitative attributes involve a metric space and can be added, subtracted, and (usually) divided (e.g., finisher's time)
Ordering Direction
- Ordering direction can be sequential, diverging, or cyclic
- Sequential ordering has a clear beginning and end (e.g., finisher's time)
- Diverging ordering has a central point with increasing or decreasing values (e.g., elevation)
- Cyclic ordering has no clear beginning or end and repeats itself (e.g., race dates)
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Description
This quiz covers the different data types in visualization analysis, including items, links, attributes, positions, and grids. Learn about these fundamental concepts from the book 'Visualization Analysis & Design' by T.Munzner.
