Visualisation tasks
From “Visualisation Analysis & Design”, T. Munzner, CRC Press, 2015 (Chapter 3)
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Three different actions
• Given a visualisation of a data set, a user can:
– Analyse:
• consume or produce
• location/target is known/unknown?
• find specific information
The Analyse action
• Consuming: user simply accesses the data using the visualisation
• todiscoverinformationnotknownbefore • topresentinformationtoanotherperson • enjoy and have fun
• Producing: user actively creates something
• annotations of the data or the visualisation
• a persistant record of a visualisation (or aspects thereof)
• derivenewdatabasedonexistingdata
Running example: Analyse
• Discover: did anyone win both the TBHR and the Highland Fling in 2010?
• Present: here are the first Swandling club finishers for the TBHR in 2012
• Enjoy: gosh – I had no idea that so many people liked running up and down hills!
• Annotate: is the same person as
• Record: this chart on my wall shows how much faster I have become in the race over the past ten years
• Derive: calculate the percentage of active women in each club in each year
The Search action
Locating targest of interest in the visualisation
• Lookup:targetknown&locationknown(whereandwhat)
• Browse: target unknown & location known (where)
• Locate: target known & location unknown (what)
• Explore: target unknown & location unknown
45 40 35 30 25 20 15 10
Position in TBHR 2015
AD AS BH BH BH CN DF DG EF ES ET FD GR GR HH HJ HR HT HY JE JT KU LI LY NH NT OI PL PP RG SB SE SE UK WBWHWS XC XD YS
Running example: Search
• Lookup:whatpositiondidJohnThomas(JT)comein?(4) • Browse:whowontherace?(SB)
• Locate: did CG run this year? (no)
• Explore: is there any noticable pattern? (no)
The Query action Once you have found the data you are
interested in, what will you do with it?
– Identify: get all the information about it
– Compare: differences between more than one data item
– Summarise: produce an overview of more than one data item
Running example: Query
• Identify: What club was the TBHR 2015 winner from?
• Compare: Was ND faster than DF?
• Summarise: Of the first ten finishers, three were women
• Targets are the things of interest in a visualisation • Targets are not necessarily just the individual data
points (although this is common)
– for all data: trends, outliers, features
– for attributes: distributions, dependencies, correlations, similarities
– for network data: topology, paths – for spatial data: shape
Targets (things of interest) over all data • Trends:
– patterns: e.g. increase, decrease, plateau etc. • Outliers:
– data points that don’t fit into an obvious pattern • Features:
– other structures of interest, depending on the domain
Running example: Targets over all data • Trends:
– JD’s finishing time in the TBHR decreased suddenly in the early 2010’s , but recovered later in the decade
• Outliers:
– the winner’s time in 2015 was much slower than in all
other years
• Features:
– there are more females finishing in the first 25 places in the past four years than in the whole decade before that
Targets (things of interest) relating to
Attributes
• For the values of one attribute: – distribution
• For the values of more than one attribute – dependency, correlation, similarity
Running example: one Attribute Target • Distribution: the number of runners per age
• Extremes: the number of runners over 70yrs
50 40 30 20 10
Distribution of Age Categories, , 2018
Open V40 V50 V60 V70
Many Attribute Targets
• Dependency: the value of one attribute can be determined directly by the value of another
• Correlation: there is a tendency for the value of one attribute to be linked to the value of another
• Similarity: attributes ranked according to their similarly (as defined by quantitative aggregates)
Running example: many Attribute Targets
• Dependency: a runner’s category (e.g. M40) is directly dependent on age & gender (e.g 42yrs, male)
• Correlation: there is a trend for a runner’s finishing time to relate to their weight
• Similarity: the average finish time for the Race is closer to the average finish time for the Race than it is to the Race
Targets (things of interest) in Specific data sets
• For network data
– topology (structure of the network)
– paths (sequnces of connections between nodes)
• For spatial data – shape
Running example: Specific data set targets • Network of run-buddies
– topology: are there small groups of runners who always train with each other? If so, how many?
– paths: if JH has training advice that he gives to the people he trains with, will that advice get to BK?
• A race where runners have to pass through a set of checkpioints
– shape: what is the shape created by these checkpoints when they are connected by straight lines on a map?
Why is it useful to describe data types and visualisation tasks in such an abstract way?
Why is it useful to describe data types and visualisation tasks in such an abstract way?
• It is always good to pause and think about your data
• Decisions you make in your visualisation for one domain can be compared or used with those needed for another domain
Running example scenario
I want to join a Hill Running Club that has an equal balance of gender membership, a wide distribution of members with different age categories, some very fast runners, and a dense network of run-buddies.
Luckily, the VGVT (Very Clever Visusalisation Tool) allows me to find this information easily.
Looking for an data item (club) which is a categorical attribute of other data items (runners).
• Looking at the frequencies of an attribute (distribution of the genders of the members) for each of a set of data items in a table (clubs), and compare.
• Looking at the frequencies of an attribute (distribution of the age categories of the members) for each of a set of data items in a table (clubs), and compare.
• Deriving new data (calculating the average) of a quantitative attribute (finishing position) for each of a set of data items in a multi-dimensional table (runners in clubs), and compare.
• Looking at the structure of a set of networks (run-buddies) to identify information about them (the extent of connectivity), and compare.
Comparable example
I need to choose which companies to apply to for a job. I want a company that offers relatively high salaries for the sector and has strong social ties between its employees. I would like there to be an equal gender balance, and for there to be options for me to work in different countries.
If I load the relevant data into VGVT will I be able to find this information easily?
• Actions (verbs): things a user can do (3)
– analyse, search, query
• Targets (nouns): things a user can be interested in
– all data (3)
• trends, outliers, features
– attributes (4)
• distribution, dependency, correlation, similarity
– networks (2)
• topology, paths
– spatial data (1) • shape
Visualisation tasks
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