Bowdoin: Data Driven Socities 2014 - Code/Spaced 3/26/14
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Data Driven Societies Digital & Computational Studies Bowdoin College March 26, 2014 Professor Gieseking Lecture Slides "Code/Spaced"
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Bowdoin: Data Driven Socities 2014 - Code/Spaced 3/26/14
- 1. Data Driven Societies: Code/Spaced Professors Gaze & Gieseking
- 2. Multiple Intelligences Theory ✦ Musical–rhythmic and harmonic ✦ Visual–spatial ✦ Verbal–linguistic ✦ Logical–mathematical ✦ Bodily–kinesthetic ✦ Interpersonal ✦ Intrapersonal ✦ Naturalistic
- 5. Mental Mapping Milgram & Jodelet 1970
- 6. Create a map of your Bowdoin experience. Map whatever you think pertinent to explain your time at Bowdoin. ! Create a legend. ! Groups: First-years Sophomores Juniors Seniors (three groups)
- 7. What does code have to do with space?
- 8. Code/Space: Software-Sorting The term software-sorting captures the crucial and often ignored role of code in directly, automatically and continuously allocating social or geographical access to all sorts of critical goods, services, life chances or mobility opportunities to certain social groups or geographical areas, often at the direct expense of others. -Graham (2005, 3)
- 9. ✦ Over half the world’s population lives in cities ✦ Little attention is paid to the billions of lines of code that run our cities — why is code invisible? ✦ Why does visualization take off at this moment? ✦ Examples: mobilities, GIS, CCTV ✦ Calls for a “spatial politics of code” Code/Space
- 10. Code/Space: Mobilities NYTimes & scoop.it ✦ 171 LED lights at Newark Airport (top) recognize long lines *and* suspicious activity and alert staff ✦ Issues: airport security, pay-per- journey roads, packet switching by class and corporation, call center rankings ✦ Outcomes: reproduces data rich vs. data poor ✦ Possible solutions: regulate the Internet through FCC like a utility
- 11. Code/Space: GIS Baltimore land use 1986 v2000 - NASA ✦ NASA “SLEUTH” urban land use remote sensing satellite —> predict & design city development ✦ Issues: predictive GIS assumes data is only reality, neighborhoods overtake Internet presence ✦ Outcomes: reproduces data rich vs. data poor, modeling via predictive algorithms only leaves little room for change
- 12. Code is… GIS Layers ✦ Points ✦ Lines ✦ Polygons ! ! ! ✦ Layers ! ✦ Geographic Information Systems (GIS) !
- 13. Code/Space: CCTV ✦ 4.2 million CCTV in the UK (1 for every 14 people) ✦ Issues: algorithmically driven biometrics (facial recognition, micro-expressions) with little accuracy yet great trust ✦ Outcomes: invisibility behind the cameras and algorithms behind them, normalize those who tend to be excluded, decontextualize observations wealthwire.com
- 14. How can visualization make more transparent uses of and better the experience of different spaces?
- 19. Jabbour on Map Design ✦ Prioritize your aim(s) & audience ✦ Include the essentials & leave out the clutter ✦ Use families of color and related shapes to create relational notions of time and space ✦ Select the scale your users should relate to the visualization ✦ Test out your visualizations on the right audience ✦ Work through the trade-offs for visualization outcome
- 20. Guess: which of these maps is the most popular NYC subway map among young New Yorkers right now?
- 21. The Weekender
- 23. Next Class: Mar. 31 ✦ Today: code/spaced ! ✦ Readings: Lingel, Nakamura ! ✦ Lecture: 3/31 @ 7pm Lancaster “Fb is Anti-Drag” ! ✦ Blog post: due Wed the 2nd - reflect on maps ! (Rejoice: soon this snow image and the snow itself will be gone, gone, gone)