Lecture 2: Design Rules of Thumb CS 7250 S PRING 2020 Prof. Cody Dunne N ORTHEASTERN U NIVERSITY Slides and inspiration from Michelle Borkin, Krzysztof Gajos, Hanspeter Pfister, 1 Miriah Meyer, Jonathan Schwabish, and David Sprague
P REVIOUSLY , ON CS 7250… 2
What is visualization anyway? 3
(static or interactive) (abstract or spatial) visualization: the visual representation of data to reinforce human cognition 4
computer graphics design HCI visualization art psychology statistics 5
S TAFF I NTRODUCTIONS Cody Dunne Sara Di Bartolomeo, CS Gayathri Raj Assistant Professor PhD UG Instructor TA Service-Learning TA 6
Course Homepage https://canvas.instructure.com/courses/1781732 • If you don’t have an account on our Canvas yet: https://canvas.instructure.com/enroll/CMAPDM • Use your name as known by the registrar and your @husky.neu.edu email. 7
Hall of Fame or Hall of Shame Prof. Krzystzof Gajos 8
9 http://hint.fm/wind/gallery/oct-29.js.html
10
Kathryn Busemeyer - https://www.nytimes.com/interactive/2017/09/09/us/hurricane-irma- 11 records.html
Matthew Kolosick - https://twitter.com/blkahn/status/905141924939649024/photo/1 12
In- Class Redesign — Hurricane Funnels (continued…) 5 min https://canvas.instructure.com/courses/1781732/assignments/13386302 https://www.nhc.noaa.gov/refresh/graphics_at5+shtml/ 155815.shtml?cone#contents
In- Class Redesign — Hurricane Funnels (continued…) 5 min https://canvas.instructure.com/courses/1781732/assignments/13386302 https://www.nhc.noaa.gov/refresh/graphics_at5+shtml/ 155815.shtml?cone#contents
R EADING Q UIZ 17
T HE N ESTED M ODEL FOR V ISUALIZATION D EVELOPMENT 18
T EXTBOOK Additional “recommended” books as resources in syllabus
“Nested Model” Example FAA (aviation) What is the busiest time of day at Logan Airport? Map vs. Scatter Plot vs. Bar Tamara Munzner
Nested Model 21
Nested Model Human-centered design Designer understands user Identified Abstract domain tasks Visualization design Designed Implementation 22
Nested Model Design Study Technique T OP - DOWN B OTTOM - UP “problem - “technique driven” - driven” Most difficult step! 23
Nested Model Mistakes propagate through model! 24
Threats to Validity 25
✓ Final Project validation Threats to Validity ✓ ✓ ✓ Final project follow-up 26
P ROJECTS (Using the nested model via design study “lite” methodology ) 27
S ERVICE -L EARNING P ROJECTS Why are we doing service learning? Design Study “Lite” Methodology ( Borkin et al. 2017) • Design studies are a growing and valuable research area. • Real-world data visualization experience. • Visualization for exploration and communication. • A more realistic experience of creating visualizations, and doing work in general. • Teaches design, interview, evaluation, communication, and feedback techniques difficult to replicate in a classroom. • Higher-stakes deliverables. • Professional development. • Make a positive impact in the community. • Publication? 29
S ERVICE -L EARNING P ROJECTS What are the challenges? • Real-world data is messy and difficult to gather and process. • Partners may not have clear goals and expectations. • There is communication and scheduling overhead, inc. for teaching staff to differentiate assignment grading if necessary. • Project areas may be too predefined. • Project areas may be too ambiguous. • May not actually make a meaningful impact. • Reduces time for white-room technical education. • More ambiguous expectations and grading challenges. • Possible variation in student workload. • Students may not know they are signing up for Service-Learning in advance (common problem with our registrar). 30
S ERVICE -L EARNING P ROJECTS Who to blame for getting you into this? 31
E XAMPLES OF S UCCESSFUL C OURSE P ROJECTS (Albeit with different requirements) 32
P ROJECT E XAMPLE — C EREBRO V IS CS 7260 F ALL 2017: V ISUALIZATION FOR N ETWORK S CIENCE Pandey et al. VIS 2019
P ROJECT E XAMPLE — C EREBRO V IS Pandey et al. VIS 2019
P ROJECT E XAMPLE — WWOV IS DS 4200 S PRING 2018: I NFORMATION P RESENTATION & V ISUALIZATION Cambpell et al. VIS4DH 2018
E XAMPLE OF A S UCCESSFUL D IFFERENTIATED C OURSE P ROJECT (Requires prior instructor approval to waive / alter requirements) 38
P ROJECT E XAMPLE — D IVERSIFORM T IMELINES CS 7340 F ALL 2018: T HEORY AND M ETHODS IN H UMAN C OMPUTER I NTERACTION Di Bartolomeo et al. CHI 2020 (submitted version)
P ROJECT E XAMPLE — D IVERSIFORM T IMELINES Di Bartolomeo et al. CHI 2020 (submitted version)
P ROJECT I DEAS : VIS + X Where X = (ML | SEC | NLP | HCC | GAM | NS | SYS | …) 42
P OTENTIAL V ENUE : IEEE VIS 2020 S HORT P APERS Deadline ~June 13, 2020 43
44
P ROJECTS In-class project pitches: M 2020.01.27 What questions do you have for me? 45
D ESIGN & R ULES OF T HUMB 46
Edward Tufte Tufte will be doing one of his one-day courses in Boston on 10/29, 30, and 31 2018. $220 for students includes these books. https://www.edwardtufte.com/tufte/courses 47
“Graphical Integrity” “Clear, detailed, and thorough labeling should be used to defeat graphical distortion and ambiguity. Write out explanations of the data on the graphic itself. Label important events in the data. ” (Axes and axis labels, titles, annotations, legends, etc.) Tufte, “Visual Display of Quantitative Information” 48 (1983)
“Distorted Scales” $(11,014) $3,549,385 y-axis baseline?! “Clear, detailed, and thorough labeling should be used to defeat graphical distortion and ambiguity. Write out explanations of the data on the graphic itself. Label important events in the data. ” Tufte, “Visual Display of Quantitative Information” 49 (1983)
Interest Rates 3.154 3.152 3.149 Percent % 3.147 3.145 3.142 3.140 2008 2009 2010 2011 2012 “Clear, detailed, and thorough labeling should be used to defeat graphical distortion and ambiguity. Write out explanations of the data on the graphic itself. Label important events in the data. ” Based on http://data.heapanalytics.com/how-to-lie-with-data- 50 visualization
Interest Rates 4.00 C ONTEXT ! 3.20 2.40 Percent % 1.60 0.80 0.00 2008 2009 2010 2011 2012 “Clear, detailed, and thorough labeling should be used to defeat graphical distortion and ambiguity. Write out explanations of the data on the graphic itself. Label important events in the data. ” Based on http://data.heapanalytics.com/how-to-lie-with-data- 51 visualization
Upcoming Assignments & Communication https://canvas.instructure.com/courses/1781732 If you don’t have an account on our Canvas yet: https://canvas.instructure.com/enroll/CMAPDM Use your name as known by the registrar and your @husky.neu.edu email. Look at the upcoming assignments and deadlines (12:01am)! Everyday Required Supplies: • 5+ colors of pen/pencil • White paper • Laptop and charger • Table tent Use Canvas Discussions for general questions, email the instructor/TAs for questions specific to you.
Recommend
More recommend
