Data Types
From “Visulisation Analysis & Design” T. Munzner,
CRC Press, 2015
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(Chapter 2)
Five different data types
• item: an object
• link: relationship between items
• attribute: property of an item
• position: a location in 2D or 3D space
• grid: regular sampling of continuous data
Running example
“Running” example
Hill running in Scotland Runners take part in races Races are held annually
Scottish Hill Racing: https://www.facebook.com/scottishhillracing/
Five different data types
• item: a runner
• link: two runners train together (“run-buddies”) • attribute: a runner belongs to a club
• position: the start point of a race
• grid: a runner’s heartbeat sampled every 30s
Four different data set types
• A data set type is a method for collecting data
– table: rows and columns (2D or multidimensional) – networks and trees: relationships between items
– fields: continuous data (conceptually there are an infinite number of measurements you could take, so sampling and exptrapolation is necessary)
– geometry: spatial data
Data set type: table
Table: rows and columns (2D or multidimensional)
Sara Ahmed
Bowlerside
Charles Ndlovu
Data set type: table
Table: rows and columns (2D or multidimensional)
Sara Ahmed
Bowlerside
Charles Ndlovu
Data set type: networks and trees Networks and trees: relationships between
Links show run-buddies Run-buddies are always only pairs
Data set type: fields Fields: continuous data.
Conceptually there are an infinite number of measurements you could take, so sampling and exptrapolation is necessary
Data set type: geometry Geometry: spatial data
Location of the annual Hill Running Races in Scotland, by start point
Data Availability
• Data is available at the same time, or collected as
as dynamic stream
• Not the same as ‘data with a time dimension’ • ‘Online’ or ‘Offline’
Average finish time for the Two Average finish time for the Two Breweries race in 2018 Breweries race over all time
Attributes
Attribute Types
club: Springly, Ludders, Bolderside, Sharpford.
race difficulty:
• very difficult
• difficult
• manageble by most runners
• very easy
finishers’ time: 1h40, 1hr42, 1hr53, 1hr54, 1hr58…
race date: 10th April, 15th Apr, 3rd May…
Ordering direction
finishers’ time for a race:
1h40, 1hr42, 1hr53, 1hr54, 1hr58…
elevation:
• 100m below
• 50m below
• 50m above
• 100m above
race date:
• 10th April
• 15th Apr
• 3rd May…
• …11th December • 8th April
• 10th example:
Two Breweries Hill Race (TBHR)
• Position
• Bib number
• Age category • Finishing time
Running example:
Two Breweries Hill Race (TBHR)
Year, Position, Bib number, Name, Club, Age category, Finish time
J Maitland
Aberdeen ACC
Horwich RMI
ARJ Curtis
Livingston & D
Horwich RMI
AW Spenceley
Carnethy HR
Carnethy HR
Running example:
Two Breweries Hill Race (TBHR)
Year, Position, Bib number, Name, Club, Age category, Finish time
J Maitland
Aberdeen ACC
Horwich RMI
ARJ Curtis
Livingston & D
Horwich RMI
AW Spenceley
Carnethy HR
Carnethy HR
• data types
• data set type
• data availability
• attribute types
• ordering direction
• Data types: nature of the data (5)
– items, attributes, links, positions, grids
• Data set types: how the data is arranged (4)
– tables, networks, fields, geometry • Whenthedataisavailable(2)
– static, dynamic
• Attributes: properties of the data (2)
– categorical, ordered (ordinal, quantitative) • Direction: ways of ordering (3)
– sequential, diverging, cyclic
Data Types
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