Accommodating Big Data in Visual Analytics: Pairing Computation with Cognition

This talk covers opportunities and challenges related to big data visual analytics, with an emphasis on the kinds of questions developers, designers, and analysts alike must consider in the era of big data visualization.

Presented at AAG 2013 in Los Angeles, CA.

Transcript

1. Pairing Computation
   with Cognition
   JoshuaStevens AlanMacEachren
   Big Data
   Accommodating
   in Visual Analytics
   Plus: Big Data Social Science
   @ Penn State
   

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2. Outline
   • Introduction + Context
   • Computation: Considering End-users & Cognition
   • Big Data @ Penn State
   • Relating These Topics
   • Questions
   

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3. “
   Big data are more than big.
   “Big data is more than simply a matter of
   size; it is an opportunity to !nd insights
   in new and emerging types of data and
   content...”
   - IBM
   

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4. “
   Big data are more than big.
   Volume Variety Velocity Vinculation
   

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5. • The 4 v’s of ‘big data’ are the norm for
   GIScientists
   • Ex: Climate models, terrain, networks, mobility
   patterns...
   • Many platforms are ready for cloud, AWS, and
   HPC
   Introduction + Context
   

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6. Introduction + Context
   • Cartographers and their tools are ready, too.
   “Among many capabilities the
   HTML5 standard provides,
   there is one crucial for
   improving GIS, and that is
   HTML5 Canvas”
   Ravnić, D. HTML5 Canvas: An Open Standard for High
   Performing GIS Map Visualization in Web Browsers.
   Directions Magazine, April 5, 2012.
   

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7. Computation
   • Computation will in!uence visualization from (at
   least) 2 angles:
   1. Data wrangling, calculation, and storage
   2. Choosing the right technology for
   representation and visualization
   

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8. Computation: Calculation + Storage
   • “Each day, we create 2.5 quintillion bytes of data...” - IBM
   • 12 TB/day are tweets
   • ....more than 2 billion copies of Wikipedia, or
   • Algorithms and statistical techniques are essential
   • Basic example:
   • Clustering is simple and common in GIS, but scales
   poorly with N (i.e., not O(n) or O(n log n))
   • Typical solution: multiple machines (parallel and
   distributed GIS)
   (Stacked floppy disks x 19)
   

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9. Computation: Representation
   • Tools matter.
   • But...choose toolchains over tools.
   From Stevens, J., Smith, J., and M. Idris (2012). NVizABLE: A Network
   Visualization and Big Data Learning Environment.
   

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10. “Attack computation from 3
    perspectives:
    Find success in the middle.
    1: Machine-level
    2: HPC
    3: Representation technology
    

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11. “Attack computation from 3
    perspectives:
    Find success in the middle.
    (maybe)
    1: Machine-level
    2: HPC
    3: Representation technology
    

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12. Approaching Computation
    • Important questions (I think):
    1. Should we divide e"orts between machine-level
    and HPC? Or focus on both simultaneously?
    2. Bigger is (probably) not always better. How do we
    determine when big is big enough?
    3. How should visualization goals in!uence our
    answers?
    

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13. Computation Cognition
    • Many analytical evaluations and UI/UX case
    studies focus on carefully controlled data and
    settings (for obvious reasons)
    • But....such scenarios are rare in big data.
    Bakshy, E. Showing Support for Marriage Equality on Facebook. Facebook Data Science.
    March 29, 2013.
    

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14. • What this means for cognition...
    • Designers must expect the unexpected, then
    build tools that support edge cases (and beyond)
    • Think through computational issues,
    visualization goals, and users’ POV
    • Example: Incremental Visualization
    • Danyel Fisher, Igor Popov, Steven M. Drucker, and MC Schraefel, Trust
    Me, I'm Partially Right: Incremental Visualization Lets Analysts Explore
    Large Datasets Faster, in Proceedings of the 2012 Conference on
    Human Factors in Computing Systems (CHI 2012), 5 May 2012.
    Computation Cognition
    

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15. Fisher et al. (2012).
    Computation Cognition
    

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16. • Key considerations and questions emerge
    1. How do non-experts interpret visualizations that
    continuously change?
    2. Are incremental approaches e"ective in
    geographic displays?
    3. What should it be like to interact with ≥ millions
    of data points?
    • When/if points should be interactive? Tie to computation and
    representation (e.g., Canvas vs SVG)
    Computation Cognition
    

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17. “
    Overview !rst, zoom and !lter,
    then details on demand.
    - Shneiderman (1996)
    

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18. “
    Overview !rst, zoom and !lter,
    then details on demand.
    - Shneiderman (1996)
    Still applicable in the era of big
    data?
    

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19. Big Data @ Penn State
    • NSF IGERT in Big Data Social Science
    • PI: Burt Monroe (Poli Sci)
    • Co-PIs:
    • Alan MacEachren (Geog)
    • Lee Giles (IST)
    • Melissa Hardy (Soc and Demography)
    • Aleksandra Slavkovic (Stats and Public Health)
    • Project Coordinator: Dee Bagshaw
    • www.bdss.psu.edu
    

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20. Big Data @ Penn State
    • 7 PhD Fellows in initial cohort
    • Dual-degree program w/ BDSS-speci$c courses
    • Years 2 and 3
    • Research rotations in year 3
    • Summer externships (at least 1 non-academic)
    Beatrice
    Abiero,
    Health Policy
    and
    Demographics
    Molly
    Ariotti,
    Political Science
    Muhammed
    Idris,
    Political Science
    Jennifer
    (Smith) Mason
    Geography
    Joshua
    Stevens,
    Geography
    Stephanie
    Wilson,
    Human
    Development and
    Family Studies
    Mo Yu,
    Information
    Science and
    Technolgy
    

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21. Big Data @ Penn State
    • Workshops, demos, and hackathons
    • Projects and publications
    Stevens, J., Smith, J., and M. Idris (2012). NVizABLE: A
    Network Visualization and Big Data Learning
    Environment.
    Yanomine, J, and J. Stevens (2012). Political Events in
    Afghanistan: Analysis of 200 Million Events in the
    GDELT Database.
    Presented at AAG by M. Idris on Tuesday and
    upcoming NetMob @ MIT, May 1-3, 2013.
    Reported in Foreign Policy, April 10 2013.
    http://ideas.foreignpolicy.com/posts/2013/04/10/
    what_can_we_learn_from_the_last_200_million_th
    ings_that_happened_in_the_world
    

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22. How does this all relate?
    • Computation must consider visualization goals
    and cognitive impacts
    • No single approach is best, allocation of e"orts
    remains unclear
    • Must prepare students to deal with these issues
    early in their careers
    

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23. “ Thank you.
    josh.stevens@psu.edu | @jscarto
    https://speakerdeck.com/jscarto
    

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