H517 Visualization Design, Analysis, & Evaluation Week 15: - - PowerPoint PPT Presentation

H517 Visualization Design, Analysis, & Evaluation

Khairi Reda | redak@iu.edu School of Informa5cs & Compu5ng, IUPUI

Week 15: Example Vis Research projects

Scientific visualization techniques

BactoGeNIE (genomics) BMC Bioinformatics, 2015

Human Factors in Visual Analytics

Effects of Display Size on Insight

ACM CHI’14, CHI’15

Visualization for Science Education

Rain Table, 2009-2011

American Museum of Natural History Field Museum, Royal Ontario Museum

Communicate science narra5ves with interac5ve visualiza5on and real-5me simula5on

• Next week: final presentation
• Demo a working version of you visualization
• Just bring up the vis; no need for slides
• Presentation time: 6 minutes +1 min Q&A
• Upload your vis to a public URL so it can be

accessed from class computer

• Final deliverables due in Canvas: Dec 11 (midnight)

Administrativia…

Extra office hours Thursday 1pm - 3pm Friday 12pm - 1pm

• Genome sequencing

costs have decreased faster than Moore’s Law

• 1000s of complete

genomes sequences

• New opportunities for

comparative genomics research

Do existing genome analysis tools scale?

‘Omics Data

McKay et al. Using the Generic Synteny Browser (GBrowse_syn). Current protocols in Bioinformatics Hoboken, NJ, USA: John Wiley & Sons Fong et al. PSAT: a web tool to compare genomic neighborhoods

• f multiple prokaryotic genomes. BMC bioinformatics 9:1 (2008)

Y X

Ensemble encoding

Effects of Display Size on Insight

ACM CHI’14, CHI’15

data space

+ + =?

visualization user

Is temporal-separaJon of data detrimental to data analysis?

Are people be_er at analyzing informa5on, when informa5on is distributed spaJally?

“Space to think” Andrews et al., CHI’10

small display (temporal-separa5on) large display (spa5al-separa5on)

abc… 123… efg… 456…

How many insights users can come up with? And what is the nature of these insights?

• Volunteer graduate students were recruited to par5cipate. Had

basic knowledge in data analysis and experience with big displays

• Between subject design: par5cipants split evenly between two

condi5ons (small vs. large)

• Task: visually explore and analyze crime pa_erns in Chicago over

the last decade (~ 2.8 million data points)

• Think-aloud protocol
• Open-ended exploraJon: for a maximum of 2.5 hours

Study Design

Visualization Interface

• verview map

detail

2012 2009 2006 narcotics weapon violations

magic lenses

small

3 x 4 panels 12 Megapixels 40° FOV

large

13 x 4 panels 54 Megapixels 190° FOV

4.5 X

Two experimental conditions

• Insights
• Observa5ons
• Hypotheses
• Insight breadth score: 1 … 5

1 — “I can see a lot of non-serious crimes in downtown Chicago.” 5 — “A lot of people in the north-side are doing drugs, but they’re not fighKng - there are much fewer deaths resulKng from the narcoKcs trade [compared to the south-side]”

• ExploraJon Jme: how much 5me par5cipants choose to spend on

the task

Analysis

exploration time

minutes

Exploration time

p < .01

results

40 min extra time spent on task with the large display

Reported insights

• bservations

* * * *

small large

• bservation rates
• bservation / minute
• f analysis

* * *

results

distribution of breadth scores

74% more observations reported with the large display

p < .05

No significant difference in rate of insight acquisition

𝝍2(4,1327) = 263.3, p < .001

1 2 3 4 5

insight / min

minutes into activity commutative insights

Insights

• ver time

results

small large

cumulative insights

Are large displays better for exploratory data analysis?

Big displays help users…

• discover more insights
• integrate different pieces of informa5on
• engage with and spend more 5me on the analysis

On the other hand

• Big displays could discourage a narrower, more focused reading of the

data

• Big displays will increase the 5me of analyses

Visualization for data-

• riented storytelling

Informal Science Education

Flow of surface water

• Water flows toward other water
• Rivers curve due to the Earth’s spin
• Water flows south no ma_er what

Rain Table

Real-5me simula5on and visualiza5on of water flow

X Y 10.0 8.04 8.0 6.95 13.0 7.58 9.0 8.81 11.0 8.33 14.0 9.96 6.0 7.24 4.0 4.26 12.0 10.84 7.0 4.82 5.0 5.68 X Y 10.0 9.14 8.0 8.14 13.0 8.74 9.0 8.77 11.0 9.26 14.0 8.10 6.0 6.13 4.0 3.10 12.0 9.13 7.0 7.26 5.0 4.74 X Y 10.0 7.46 8.0 6.77 13.0 12.74 9.0 7.11 11.0 7.81 14.0 8.84 6.0 6.08 4.0 5.39 12.0 8.15 7.0 6.42 5.0 5.73 X Y 8.0 6.58 8.0 5.76 8.0 7.71 8.0 8.84 8.0 8.47 8.0 7.04 8.0 5.25 19.0 12.50 8.0 5.56 8.0 7.91 8.0 6.89

Set A Set B Set C Set D mean 9.0 7.5 9.0 7.5 9.0 7.5 9.0 7.5 variance 11.0 4.12 11.0 4.12 11.0 4.12 11.0 4.12 regression y = 3 + .5X y = 3 + .5X y = 3 + .5X y = 3 + .5X R2=0.82 R2=0.82 R2=0.82 R2=0.82

Set A Set B Set C Set D

Anscombe’s Quartet

Statistics / machine learning

Visualization

+

https://coursequestionnaire.iu.edu/blue

tinyurl.com/viscourse9

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