1a - Data Types - Tagged.pdf

Data Types From “Visualization Analysis & Design” T. Munzner, CRC Press, 2015 (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...

Data Types From “Visualization Analysis & Design” T. Munzner, CRC Press, 2015 (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 together – 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/or extrapolation are necessary) – geometry: spatial data  Data set type: table Table: rows and columns (2D or multidimensional) John Dunne m M50 Springly Sara Ahmed f F60 Ludders Mei Chan f F40 Bowlerside Charles Ndlovu m M35 Ludders  Data set type: table Table: rows and columns (2D or multidimensional) ly Spring ly Spring ly ly Spring John Dunne m M50 Springly Spring ly Spring Ludde rs Panton Sara Ahmed f F30 Ludders Panton Panton Panton Mei Chan f F40 Bowlerside Ludde rs Charles Ndlovu m M30 Ludders Ludde rs Data set type: networks and trees Networks and trees: relationships between objects Links show run-buddies Run-buddies are static pairs, but those pairs can group together in race events  Data set type: fields Fields: continuous data. Conceptually there is an infinite number of measurements you could take, so sampling and extrapolation are necessary every 10s 90bpm 10s 90bpm 100bpm 25s 102bpm 105bpm 26s 103bpm 106bpm 40s 106bpm 110bpm 52s 112bpm 115bpm 58s 114bpm 135bpm 73s 137bpm 140bpm 80s 140bpm  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 managable by most runners easy very easy finishers’ time: 1h40, 1hr42, 1hr53, 1hr54, 1hr58… race date: 10th April, 15th Apr, 3rd May… Ordering direction Finisher’s time for a race: 1h40, 1h42, 1h53, 1h54, 1h58… elevation: 100m below 50m below 50m above race date: 100m above 10th April 15th Apr 3rd May… …11th December 8th April 10th April  Running example: Two Breweries Hill Race (TBHR) Year Position Bib number Name Club Age category Finishing time  Running example: Two Breweries Hill Race (TBHR) Year, Position, Bib number, Name, Club, Age category, Finish time 1984 1 69 J Maitland Aberdeen ACC MOPEN 2:44:36 1984 2 53 B Brinfle Horwich RMI M50 2:50:36 1984 3 64 ARJ Curtis Livingston & D W50 2:52:34 1984 4 24 S Moore Horwich RMI M40 2:53:01 1984 5 65 AW Spenceley Carnethy HR WOPEN 2:56:55 1984 6 77 M Lindsay Carnethy HR MOPEN 2:58:42  Running example: Two Breweries Hill Race (TBHR) Year, Position, Bib number, Name, Club, Age category, Finish time 1984 1 69 J Maitland Aberdeen ACC MOPEN 2:44:36 1984 2 53 B Brinfle Horwich RMI M50 2:50:36 1984 3 64 ARJ Curtis Livingston & D W50 2:52:34 1984 4 24 S Moore Horwich RMI M40 2:53:01 1984 5 65 AW Spenceley Carnethy HR WOPEN 2:56:55 1984 6 77 M Lindsay Carnethy HR MOPEN 2:58:42 data types data set type data availability attribute types ordering direction  Summary 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 When the data is available (2) – static, dynamic Attributes: properties of the data (2) – categorical, ordered (ordinal, quantitative) Direction: ways of ordering (3) – sequential, diverging, cyclic Data Types