Anatomy of a dataviz
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Accurat's data visualization for La Lettura dissected and explained / lectures for NYU ITP + Parsons Media and Technology Students, February and March 2013

Accurat's data visualization for La Lettura dissected and explained / lectures for NYU ITP + Parsons Media and Technology Students, February and March 2013

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  • 1. 140 118 4,1 1. ar 76 59 2,5 3. 4. 2,9 2. 5. 7. 0,5 rv 3,2 1,4 6. 8. 0,3 0,1 12. 13. 9.10. Ha 11.del l’as en ri za d for de app rsit ali e T del mom citoenen ion zona 0,1 0,5 17. 18. 14. MI al i vin art à temperata 5 an ch bel naz No seg to 1.785 11,5 bia 3.097 2.281 19. lte St ge lum rid Ca 24. 23. y mb Co 13.156 ele Ca rk 5. Londra 100 Be 6. Parigi 7. Berlino 9. Milano 10. Roma 308 53 a.C. 1237 8. Praga 500 a.C. 753 a.C. 885 209 125 231 103 15,1 109 5,9 1,5 8,4 3,7 3,7 7,1 1,2 0,1 0,9 0,5 0,2 zona 2,2 1,2 1,3 temperata 3,5 2,7 7. 8,1 2. 3.129 3.645 6.250 14. Dubai 6.398 14.862 1833 ANATOMY OF A DATA-VISUALIZATION 16. Shan 828 15. Pechino a 19.316 723 a.C. n scu tà cia i cit ti in citor 12. New York 330 de tale 11. Los Angeles 1613 na i vin 1781 to 381 13. Lisbona 5,1 310 7,7 6,9 stories, architecture, inspiration 138 a.C. 12 4,1 16,8 7 51 10 110 4,9 12 7 1,2 17 1,3 2,1 20 23 0,1 7 zona 10 8,2 0,5 13 temperata 3,7 8 1.760 7 2.680 2,1 19,6 17 10.019 3.003 4.238 5.192 4 6 91 20. Bangkok 18. Città del Messico 1769 21. Singapore 22. Giacarta 4 19 1 1521 1819 1619 17. Honolulu 19. Nairobi 2 1850 1899 304 225 4. 280 262 2 131 140 3 2,8 12,3 3 1,8 1,5 1,8 1,5 19,8 1,9 0,2 0,7 0,7 0,7 28 zona tropicale 0,3 10 3,1 5,3 8,8 8,2 3.638 674 1.572 1.561 2.244 pà: e pa ma gen, 12.246 am er di m inb ver 7 4. rgoglioolaas lTi ad arimo l 25. Sydney C L’o e Nik frate l (il p ndo 61 1788 La visualizzazione esplora le caratteristiche di venticinque tra le più importanti Jan unici Nobe il seco 5 1 Le città ordinate per latitudine (in cinque fasce) e per longitudine (allin 19 gli to un mia, 4 244 sono analizzate attraverso parametri demografici, territoriali, ambientali, ec 5 vin econo i a) 24. Johannesburg 6 23. BuenosnAires in medic 1536 1886 La forma del poligono e degli elementi che lo compongono restituisc 4 in 2,6 4 223 12,1 173ta: i, città 3 at con 5 . utodid o Marobel vere 2,2 anno di fondazione 1529 2 età L ’ a g l i e l m ra i N n o n a 2,9 0,2 0,5 Gu nico t ica a altezza edificio più alto (m) ta: zona l’u la fis ea 2,8 ia per a laur 1 rem temperata numero di visitatori 1. pluri-p rie, cere un 2.354 932 e turisti (mln) 1,2 sup La rie Cu a vin fisica t, rap Ma prima el (in 6 o . stumo: arlfeldre e 4,6 dimensioni possibili dim cen la e Nob ica) lK ve rt (kmq) Il p k Axe a rice la mo du chim Eri rimo dopo nu in 5 9 1 e I dati sono stati raccolti da:p bel 6.529 il o ia: 31 o: city-data.com, currentresults.com, il N om chi icz, con ica l 2 . iù veeuromonitor.com, globalpropertyguide.com,ll’el’un obe 19 5 1 c w a e precipitazioni temperatura r ad Hu arriv 7 . signorst r Il p nidskyscrapercenter.com, weatherbase.com,rom, e un N media annuale (mm) o ess i Leo uccwikipedia.org. a or O ince omico prezzo medio degli imm 2 L n 3 il s 0 ann v Eli na a o econ a9 2 donambit 2 ne: , in 3 . iù gioveaBragg Nobel I l p w re n c i è g i à La 5 ann a2
  • 2. 140 118 4,1 ar 76 1. 59 2,5 3. 4. 2,9 rv 0,5 3,2 2. 5. 7. 1,4 6. 8. Ha 0,3del ll’as men ori nza d 0,1 11. 12. 13. 9.10. for de app rsit ali e T ion mo ci ne zona MI 0,1 14. p 0,5 17. al i vin arteà 18. an ch 5 bel naz temperata No seg to bia t 1.785 11,5 lte 3.097 St di e ge 2.281 19. lum rid Ca 24. de y mb Co 23. ele 13.156 Ca rk 5. Londra Be 100 6. Parigi 7. Berlino 9. Milano 10. Roma 308 53 a.C. 1237 8. Praga 500 a.C. 753 a.C. 885 209 125 231 103 15,1 109 5,9 1,5 8,4 3,7 3,7 7,1 1,2 0,1 0,9 0,5 0,2 zona 2,2 1,2 1,3 3,5 7. temperata 8,1 2,7 2. 3.129 3.645 6.250 14. Dubai 6.398 14.862 1833 16. Shan 15. Pechino a (1) VISUAL DATA - La Lettura, Corriere della Sera 828 n 19.316 723 a.C. scu tà cia i cit ti in citor 12. New York 330 de tale na i vin 11. Los Angeles 1613 to 1781 381 13. Lisbona 5,1 6,9 12 310 138 a.C. 7,7 4,1 16,8 7 51 12 10 110 7 4,9 17 1,2 2,1 20 1,3 23 0,1 7 10 zona 13 temperata 8,2 0,5 3,7 8 1.760 7 2.680 17 2,1 19,6 10.019 3.003 4.238 5.192 4 6 91 20. Bangkok 18. Città del Messico 4 19 21. Singapore 22. Giacarta 1 1769 1521 1819 1619 17. Honolulu 19. Nairobi 2 1850 1899 304 4. 225 280 262 2 3 131 140 2,8 12,3 3 1,8 1,5 1,8 1,5 19,8 1,9 0,2 0,7 0,7 0,7 28 zona tropicale 0,3 10 3,1 5,3 8,8 8,2 3.638 674 1.572 pà: 1.561 2.244 e pa ma gen, am er 12.246 di m inb ver 7 4. rgoglioolaas lTi ad arimo l C L’o e Nik frate l (il p ndo 25. Sydney 61 Jan unici Nobe il seco 1788 La visualizzazione esplora le caratteristiche di venticinque tra le più importanti 5 1 19 gli to un mia, Le città ordinate per latitudine (in cinque fasce) e per longitudine (allin 4 5 vin econo ina) 244 6 24. Johannesburg sono analizzate attraverso parametri demografici, territoriali, ambientali, ec i medic 23.nBuenos Aires 1886 La forma del poligono e degli elementi che lo compongono restituisc 4 in 1536 2,6 4 a: i, 12,1 3 223 173 t rcon dat 5. odi Ma el e città 2 ut o ob a ver anno di fondazione 1529 2,9 a glielm ra i N non L’ 2,2 età u G nicot 0,2 ica a 0,5 altezza edificio più alto (m) ta: l’u la fis ea miazona er laur 2,8 p a . luri-pree, temperata 1 p u r i c e re un 1 numero di visitatori sup 2.354 932 La rie C a vin fisica : ldt, e turisti (mln) 1,2 rap Ma prima el (in 6 . ostumloKarlfevere rte 4,6 dimensioni possibili dim la e Nob ica) p Axe rice mo Il k (kmq) cen du chim Eri rimo dopo a la e in 5 9 nu 1 il p I dati sono stati raccolti da: Nobe l ia: 31 6.529 o: il om chi icz, con ica l 2 . iù veuromonitor.com, globalpropertyguide.com, ll’el’un obe city-data.com, currentresults.com, 19 5 1 ec rw va e precipitazioni temperatura u Il p nid H so arr i ad 7 . signors , n N skyscrapercenter.com, weatherbase.com,tromere u co media annuale (mm) Leo ucces i a or O inc omi 2 L n prezzo medio degli imm 3 il s 0 wikipedia.org. ann v Eli na a o econ a9 2 donambit 2 ne: , in 3 . iù gioveaBragg Nobel I l p w re n c i è g i à La 5 ann a2
  • 3. 140 118 4,1 ar 76 1. 59 2,5 3. 4. 2,9 rv 0,5 3,2 2. 5. 7. 1,4 6. 8. Ha 0,3del ll’as men ori nza d 0,1 11. 12. 13. 9.10. for de app rsit ali e T ion mo ci ne zona MI 0,1 14. p 0,5 17. al i vin arteà 18. an ch 5 bel naz temperata No seg to bia t 1.785 11,5 lte 3.097 St di e ge 2.281 19. lum rid Ca 24. de y mb Co 23. ele 13.156 Ca rk 5. Londra Be 100 6. Parigi 7. Berlino 9. Milano 10. Roma 308 53 a.C. 1237 8. Praga 500 a.C. 753 a.C. 885 209 125 231 103 15,1 109 5,9 1,5 8,4 3,7 3,7 7,1 1,2 0,1 0,9 0,5 0,2 zona 2,2 1,2 1,3 3,5 7. temperata 8,1 2,7 2. 3.129 3.645 6.250 14. Dubai 6.398 14.862 1833 16. Shan 15. Pechino a (1) VISUAL DATA - La Lettura, Corriere della Sera 828 n 19.316 723 a.C. scu tà cia i cit ti in citor 12. New York 330 de tale na i vin 11. Los Angeles 1613 to 1781 381 13. Lisbona 5,1 6,9 12 310 138 a.C. 7,7 16,8 (2) ANATOMY - the architecture of a dataviz 4,1 7 51 12 10 110 7 4,9 17 1,2 2,1 20 1,3 23 0,1 7 10 zona 13 temperata 8,2 0,5 3,7 8 1.760 7 2.680 17 2,1 19,6 10.019 3.003 4.238 5.192 4 6 91 20. Bangkok 18. Città del Messico 4 19 21. Singapore 22. Giacarta 1 1769 1521 1819 1619 17. Honolulu 19. Nairobi 2 1850 1899 304 4. 225 280 262 2 3 131 140 2,8 12,3 3 1,8 1,5 1,8 1,5 19,8 1,9 0,2 0,7 0,7 0,7 28 zona tropicale 0,3 10 3,1 5,3 8,8 8,2 3.638 674 1.572 pà: 1.561 2.244 e pa ma gen, am er 12.246 di m inb ver 7 4. rgoglioolaas lTi ad arimo l C L’o e Nik frate l (il p ndo 25. Sydney 61 Jan unici Nobe il seco 1788 La visualizzazione esplora le caratteristiche di venticinque tra le più importanti 5 1 19 gli to un mia, Le città ordinate per latitudine (in cinque fasce) e per longitudine (allin 4 5 vin econo ina) 244 6 24. Johannesburg sono analizzate attraverso parametri demografici, territoriali, ambientali, ec i medic 23.nBuenos Aires 1886 La forma del poligono e degli elementi che lo compongono restituisc 4 in 1536 2,6 4 a: i, 12,1 3 223 173 t rcon dat 5. odi Ma el e città 2 ut o ob a ver anno di fondazione 1529 2,9 a glielm ra i N non L’ 2,2 età u G nicot 0,2 ica a 0,5 altezza edificio più alto (m) ta: l’u la fis ea miazona er laur 2,8 p a . luri-pree, temperata 1 p u r i c e re un 1 numero di visitatori sup 2.354 932 La rie C a vin fisica : ldt, e turisti (mln) 1,2 rap Ma prima el (in 6 . ostumloKarlfevere rte 4,6 dimensioni possibili dim la e Nob ica) p Axe rice mo Il k (kmq) cen du chim Eri rimo dopo a la e in 5 9 nu 1 il p I dati sono stati raccolti da: Nobe l ia: 31 6.529 o: il om chi icz, con ica l 2 . iù veuromonitor.com, globalpropertyguide.com, ll’el’un obe city-data.com, currentresults.com, 19 5 1 ec rw va e precipitazioni temperatura u Il p nid H so arr i ad 7 . signors , n N skyscrapercenter.com, weatherbase.com,tromere u co media annuale (mm) Leo ucces i a or O inc omi 2 L n prezzo medio degli imm 3 il s 0 wikipedia.org. ann v Eli na a o econ a9 2 donambit 2 ne: , in 3 . iù gioveaBragg Nobel I l p w re n c i è g i à La 5 ann a2
  • 4. 140 118 4,1 ar 76 1. 59 2,5 3. 4. 2,9 rv 0,5 3,2 2. 5. 7. 1,4 6. 8. Ha 0,3del ll’as men ori nza d 0,1 11. 12. 13. 9.10. for de app rsit ali e T ion mo ci ne zona MI 0,1 14. p 0,5 17. al i vin arteà 18. an ch 5 bel naz temperata No seg to bia t 1.785 11,5 lte 3.097 St di e ge 2.281 19. lum rid Ca 24. de y mb Co 23. ele 13.156 Ca rk 5. Londra Be 100 6. Parigi 7. Berlino 9. Milano 10. Roma 308 53 a.C. 1237 8. Praga 500 a.C. 753 a.C. 885 209 125 231 103 15,1 109 5,9 1,5 8,4 3,7 3,7 7,1 1,2 0,1 0,9 0,5 0,2 zona 2,2 1,2 1,3 3,5 7. temperata 8,1 2,7 2. 3.129 3.645 6.250 14. Dubai 6.398 14.862 1833 16. Shan 15. Pechino a (1) VISUAL DATA - La Lettura, Corriere della Sera 828 n 19.316 723 a.C. scu tà cia i cit ti in citor 12. New York 330 de tale na i vin 11. Los Angeles 1613 to 1781 381 13. Lisbona 5,1 6,9 12 310 138 a.C. 7,7 16,8 (2) ANATOMY - the architecture of a dataviz 4,1 7 51 12 10 110 7 4,9 17 1,2 2,1 20 1,3 23 0,1 7 10 zona 13 temperata 8,2 0,5 3,7 8 (3) INSPIRATION - get ideas from anything, anywhere, anytime 1.760 7 2.680 17 2,1 19,6 10.019 3.003 4.238 5.192 4 6 91 20. Bangkok 18. Città del Messico 4 19 21. Singapore 22. Giacarta 1 1769 1521 1819 1619 17. Honolulu 19. Nairobi 2 1850 1899 304 4. 225 280 262 2 3 131 140 2,8 12,3 3 1,8 1,5 1,8 1,5 19,8 1,9 0,2 0,7 0,7 0,7 28 zona tropicale 0,3 10 3,1 5,3 8,8 8,2 3.638 674 1.572 pà: 1.561 2.244 e pa ma gen, am er 12.246 di m inb ver 7 4. rgoglioolaas lTi ad arimo l C L’o e Nik frate l (il p ndo 25. Sydney 61 Jan unici Nobe il seco 1788 La visualizzazione esplora le caratteristiche di venticinque tra le più importanti 5 1 19 gli to un mia, Le città ordinate per latitudine (in cinque fasce) e per longitudine (allin 4 5 vin econo ina) 244 6 24. Johannesburg sono analizzate attraverso parametri demografici, territoriali, ambientali, ec i medic 23.nBuenos Aires 1886 La forma del poligono e degli elementi che lo compongono restituisc 4 in 1536 2,6 4 a: i, 12,1 3 223 173 t rcon dat 5. odi Ma el e città 2 ut o ob a ver anno di fondazione 1529 2,9 a glielm ra i N non L’ 2,2 età u G nicot 0,2 ica a 0,5 altezza edificio più alto (m) ta: l’u la fis ea miazona er laur 2,8 p a . luri-pree, temperata 1 p u r i c e re un 1 numero di visitatori sup 2.354 932 La rie C a vin fisica : ldt, e turisti (mln) 1,2 rap Ma prima el (in 6 . ostumloKarlfevere rte 4,6 dimensioni possibili dim la e Nob ica) p Axe rice mo Il k (kmq) cen du chim Eri rimo dopo a la e in 5 9 nu 1 il p I dati sono stati raccolti da: Nobe l ia: 31 6.529 o: il om chi icz, con ica l 2 . iù veuromonitor.com, globalpropertyguide.com, ll’el’un obe city-data.com, currentresults.com, 19 5 1 ec rw va e precipitazioni temperatura u Il p nid H so arr i ad 7 . signors , n N skyscrapercenter.com, weatherbase.com,tromere u co media annuale (mm) Leo ucces i a or O inc omi 2 L n prezzo medio degli imm 3 il s 0 wikipedia.org. ann v Eli na a o econ a9 2 donambit 2 ne: , in 3 . iù gioveaBragg Nobel I l p w re n c i è g i à La 5 ann a2
  • 5. (2011-2013) (2011- ) PhD in founder of design Accurat (2006) architectural me? studies(wanted to bea musician) (wanted to be a dancer) (2006-2010) interaction design exhibition design
  • 6. me? I ca nnot code I deal only with what I can handle
  • 7. (1) VISUAL DATA - La Lettura, Corriere della Sera
  • 8. I find limitsvery useful A PROJECT WITH MANY CONSTRAINTS Newspaper constraints - timing (4 days) - size (fixed) - background (same color) - fonts (only 2) + Our additional rules - fonts (1) - visual models (no pictorial) (experimental)
  • 9. I find limits very usefultrying to achievean aesthetic qualityonly throughspatial composition
  • 10. our team and processSimone: sociologist, qualitative analysis on dataGiorgia: design of the dataviz2 junior graphic designers: development of the dataviz
  • 11. non-linearstorytellingEverything folds within theconcept of layering,establishing hierarchies andmaking them clear,both for the data analysis andthe visual composition
  • 12. story is the key“What data do you have,and what can you ask of it?”is not the same as“What understanding do you want tobring, and do you have the data thatanswers those questions?”
  • 13. testing theefficiencytrying to look at howpeople read them
  • 14. testing theefficiency
  • 15. testing theefficiencycheck with friendsand family first(mailing my mum)
  • 16. testing theefficiencyour goals:telling somethingyou didn’t knowmaking you feelsomething
  • 17. after 16 slides,I can show youour dataviz
  • 18. Subterraneanveins ofEuropeHave you everconsidered travellingfrom London to Paris byTube? How long are theunderground “veins”that run below majorEuropean cities? Thisanalysis andvisualization comparesthe distances coveredand journey fares ofvarious undergroundsystems across thecontinent, revealingsome interestinginsights and comparingactual sizes on the map.
  • 19. Zoom
  • 20. Subterraneanveins ofEuropeHave you everconsidered travellingfrom London to Paris byTube? How long are theunderground “veins”that run below majorEuropean cities? Thisanalysis andvisualization comparesthe distances coveredand journey fares ofvarious undergroundsystems across thecontinent, revealingsome interestinginsights and comparingactual sizes on the map.
  • 21. Painters’ timeAt what stage of life didthe most celebratedartists paint theirmasterpieces? Werethey young andinexperienced ormature and established?And what about colour,technique andrepresentation styles?This analysis andvisualization attemptsto capture centuries ofart, artists and artistryin a double-page spread.
  • 22. Zoom
  • 23. (2) ANATOMY- the architecture of a dataviz have you ever thought about how you would describe (interpret) your own work?
  • 24. towards a non linear-layered storytelling(article - Parsons Journal for Information Mapping)
  • 25. towards a non linear-layered storytellinglayering and making hierarchies clear “ if we consider our collective presentation as composed of the pieces within a tale, we aim to build a singular “greater- story” built through the layering of sub stories, or story components.
  • 26. the layering process: (1) Composing the main architecture of the visualization (2) Positioning singular elements within the main framework. (3) Constructing shaped elements of dimensionality and form (4) Elucidating internal relationships between elements. (if any) (5) Labeling and Identifying elements (6) Supplementing the greater story through the addition of “minor or tangental tales” elements. (7) Providing small visual explanations such as a legend or key (8) Fine-tuning and stylizing of elements shapes, colors, and weights to make hierarchies pop out.
  • 27. (3) INSPIRATION- get ideas from anything, anywhere, anytime “it is not what you look at that matters, its what you see” H.D. Thoreau
  • 28. a step back:being “original”weve run out of methapors for visual models.Inspiration shouldn’t come fromDATAVIZ (or not only!)
  • 29. how totranslatewhat yousee in somethingelse?“an attempt to analyzethe aesthetic qualitiesof things that arenaturally pleasant to theeye,in order to understandhow they can beabstracted and re-usedas core principles andguidelines in buildingvisual compositions.”
  • 30. fiding yourown wayMy way is:drawing out everything thatcatches my attention“The act of reproducing thingsintroduces a level ofabstraction that helps focusingon the aspects of the compositionthat caught my attention. “
  • 31. Nobel Prizes Nobels, How to al y ize and laureates, read it ad e cip sit ns Pr gr evel riniveratio l at the 1901-2012 Each dot represents l D r P n li e u ffi ob tes ent d. Ph ste lor a f N ea m rde a Nobel laureate, an an a e o ur mo a d Visualized for each laureate are each recipient is positioned m m m c h e g re la he aw ar no degrees wo ba de t as v ar prize category, year the prize was according to the year the w prize was awarded (x axis) no awarded, and age of the recipient th io n H at the time. Visualized for each and age of the person ep at -d in IT at the time of in xam 12 d category are grade level, principal the award (y axis). e 20 M f or academic affiliations, and principal el ed o to ne an hometowns of the laureates. o b a rd t h a n St ch N w e a or on 2. 0 1 te 20 al m ers a p C bi m 91 lu ge 19 2. 7. Co id s br ar m ye Ca ey 10 19 8 1 k el ag d e B er en e tr 71 tim 19 61 or 19 ef h e ag eac RY ag or O e 51 er h f EG ag e 19 4. a v e a c CAT a g e e a t er ur av el la b 41 No 19 of f 1 l o e ty 3 ta at ci 19 to ure ach la r e 2 12 fo 1 7 21 20 51 19 7 4. 17 How much do you know 1 23 1 91 7 17 1 4 10 13 about Nobel prize 01 1 99 6 19 1. 1 8 7 winners and Nobel 4 T RY 2 graduates? IS s)CH EM 9 y (5 57 ar s ye ea r 6. 28 2 This visualization IC S O MC E a r s ) 1 7 3 3 explores Nobel Prizes O N E N ye a r s 96 E C S C I 6 69 y e (5 3. 1 1 5 and graduate S 5. Y SI C s) 1. 4 5 qualifications from 1901 PH ar s ye ea r 6 9 y (5 54 4 to 1912, by analysing the RE s 4 R AT U ar ) ye a r s 1 1 3 4. Sibling pride: age of recipients at the TE 6 4 ye 93 2 LI (5 9 1 5 9 Jan and Nikolaas Tinbergen, the only brothers to win time prizes were GY E a prize each 1 LO IN rs) O C S I D I yea a r s 5 (economics and medicine) awarded, average age H Y M E ( 5 95 7 y e 1. P R O CE s 2 3 Multiple awards: Marie Curie, the first 5. The self-taught: evolution through time E Ay e a r r s ) P 6 1 yea 1 2 2 recipient of two Nobel Prizes (chemistry Guglielmo Marconi, the only Nobel laureate (physics) without a degree and among categories, 90 9 and physics) (5 1 o 1 graduation grades, main ag 2. 6. hic ton C g k in Yor 11 The oldest: Leonid Hurwicz, The posthumous: Erik Axel Karlfeldt, university affiliations sh n Wa New osto n awarded at age 90 and the principal the first person to be awarded al B do 1 4 a Nobel Prize after his death cip ns n s rin tow tes Lo Pari p e ea o m r ho f lau ich un lln M er 4 3. The youngest: 7. The First Lady of Economics: hometowns of the B ien Lawrence Bragg, Elinor Ostrom, W est ap w awarded at age 25 the only female recipient of the Nobel Prize in economics graduates. ud sco
  • 32. How to l pa ity ss, read it e ad el gr ev i i nc ers ion l Pr niv liat el at t th Each dot represents D r u ffi ob tes en d Ph ste lor a f N ea m rde a Nobel laureate, an an a e o ur mo aureate are each recipient is positioned m m c h e g re la he aw according to the year the w om ba de t as e prize was w prize was awarded (x axis) n nohe recipient h o and age of the person e pt ati for each -d minel, principal at the time of the award (y axis). in xa 0 12 e 2 and principal o ereates. e l ed t on o b a rd t h a n N w e a or on 2. 0 1 m ers 20 p 9 91 2. 7. 1 r s y ea 1 10 19 8 e ag d n re et 7 1 tim 19 9 61 r 1 e fo h e ag eac RY a g fo r G O g e 1 r v e a c h AT E g e a a t e 1 95 4. a e C a e er laur av el b 1 No 19 4 of 9 31 1
  • 33. Nobel Prizes How to al y ize and laureates, read it ad e cip sit ns Pr gr evel riniveratio l at the 1901-2012 Each dot represents l D r P n li e u ffi ob tes ent d. Ph ste lor a f N ea m rde a Nobel laureate, an an a e o ur mo a d Visualized for each laureate are each recipient is positioned m m m c h e g re la he aw ar wo ba de t as v ar prize category, year the prize was according to the year the w prize was awarded (x axis) no awarded, and age of the recipient th io n H at the time. Visualized for each and age of the person ep at -d min IT at the time of in xa 12 d category are grade level, principal the award (y axis). e 20 M f or academic affiliations, and principal l to one an hometowns of the laureates. be ded an No war e th St ch a or on 2. 00 1 te 2 l m ers p Ca bi a 91 l um e 19 2. 7. Co i dg s br ar m ye Ca ey 10 98 1 k el er e 1 ag d B n t re e 71 tim 19 61 or 19 ef h ag eac RY ge r O e 51 ra f o G 4. g v e a c h AT E g e a a t e a e C 19 e ra u re av el la ob 41 of N 19 f l o e ty 31 ta at ci to ure ach 19 la r e 2 12 fo 1 7 21 20 51 19 7 4. 17 1 23 1 91 7 17 4 10 91 6 13 01 19 19 8 7 1. 1 4 Y TR 2 IS s) M ar s E ye ea r 6. 28CH 9 y (5 57 2 IC S 3 O MC E a r s ) 1 7 3 O N E N ye a r s 96 E C S C I 6 69 y e 3. 1 1 (5 5 CS 5. Y SI 4 5 s) PH ar s 1. 6 ye ea r 9 y 4 (5 54 U RE s 4 3 4. AT ar ) ye a r s ER 6 4 ye 31 1 2 Sibling pride: T 19 Jan and Nikolaas Tinbergen, LI (5 9 5 the only brothers to win 9 GY E a prize each 1 (economics and medicine) LO IN rs) 5 I O I C ea rs Y S E D5 9 y y e a 1. P HR M ( 5 7 2 Multiple awards: 5. O CE s 3 Marie Curie, the first The self-taught: E Ay e a r r s ) P 1 a 2 recipient of two Guglielmo Marconi, Nobel Prizes (chemistry the only Nobel laureate 6 ye 1 2 (physics) without a degree 90 9 and physics) (5 1 go 1 2. 6. ica n 11 Ch gto k The oldest: The posthumous: n or hi Y n Leonid Hurwicz, Erik Axel Karlfeldt, s Wa New osto n awarded at age 90 the first person to be awarded al B do 1 4 a Nobel Prize after his death cip s n s in owntes Lo Pari pr et ea 4 3. 7. m r ich ho f lau un lln The youngest: The First Lady of Economics: o M er Lawrence Bragg, Elinor Ostrom, B ien W est awarded at age 25 the only female recipient p of the Nobel Prize in economics da ow Bu osc
  • 34. contemporary musical notation
  • 35. John Cage, Fontana Mixcontemporary musical notation
  • 36. a graphic scoreconsisting of 10 sheets ofpaper, with curved linesand 12 transparencies,10 of which contain avaried number ofrandomly distributeddots, 1 with a straightline, and the last with agrid pattern.According to Cage’sinstructions these sheetscould be superimposedupon each other andthen interpreted so as toindicate differences insuch elements as tone,duration, or volume of avariety of differentsound events.
  • 37. (1)general idea
  • 38. (2)adding layers of information
  • 39. (3)pretty clear right now
  • 40. l à ne be it io no l rs az il d ve li lo re m i p su m do lor si t l o re m i p s um dolor sit o be ph ni affi u i nt l no lo re m i p su m do lor si t l o re m i p s um dolor sit vi i a d e e? a to nn lo re m i p su m do lor si t l o re m i p s um dolor sit m gg i h in do rd l o re m i p s um dolor sit C oi l e cu a v o va lo re m i p su m do lor si t c u ri o si tà c u r ios ità l o re m i p s um dolor sit s o in i h u uo m ar nn a c i H lo re m i p su m do lor si t )a à nn d lo re m i p s u m lo re m i ps u m l o re m i p s um dolor sit (x ) et ce 2. to a or lo re m i p su m do lor si t (y de en tic a nf ia am ma nd te St ge ed a m ic a tà id à at a e br et tem edi el am à m ob C it à n et mi rs e ve igi 1 901 1931 1961 1 99 1 2012 pr ni ar U P di ld t 4. bo A c ur io sit à um IC H IM l o re m ip s um H C ni an 7 5 IA M O 2. N 4. O Ha r vard c u ri os it à EC ni l o rem ip s u m an 6 6 A C 2. SI S tanfo rd FI 1. ni an c u r io si tà 1. 4 l o rem ip s um 5 Ca m br i d ge A 31 R U 19 AT ER U ni ve rsi tà ni TT A an d i Pa ri gi 21LE 19 IN 64 IC 11 ED H umb old t 19 ni an M 7 5 01 19 E C PA A IC ni I M7 a n( 5 1 ) CH ni 5 an 1 I An i 6 Cambridge N O Ma n 5 1 ) 6 6 ( Monaco E CO Boston Washington Londra Vienna Chicago New York Parigi Berlino I 10 PREMI NOBEL I 10 PREMI NOBEL PIÙ VECCHI: PIÙ GIOVANI: 1. Leonid Hurwicz 1. William Lawrence Bragg 2. Lloyd S. Shapley 2. Tsung-Dao Lee 3. Doris Lessing 3. Carl David Anderson ge id 4. Raymond Davis, Jr. 4. Paul Dirac br aco m 5. Yoichiro Nambu 5. Werner Heisenberg Ca Mon ton s 6. Vitaly Ginzburg 6. Tawakel Karman Bo gton n dra hi 7. Joseph Rotblat 7. Mairead Maguire as Lon na 8. Rudolf Mössbauer W en 8. Karl von Frisch Vi cago 9. Francis Peyton Rous 9. Frederick Banting i k Ch Yor i 10. Ferdinand Buisson 10. Rigoberta Menchú w rig Ne Pa no rli Be (-) intermediate steps
  • 41. Nobel Prizes Nobels, How to al y ize and laureates, read it ad e cip sit ns Pr gr evel riniveratio l at the 1901-2012 Each dot represents l D r P n li e u ffi ob tes ent d. Ph ste lor a f N ea m rde a Nobel laureate, an an a e o ur mo a d Visualized for each laureate are each recipient is positioned m m m c h e g re la he aw ar no degrees wo ba de t as v ar prize category, year the prize was according to the year the w prize was awarded (x axis) no awarded, and age of the recipient th io n H at the time. Visualized for each and age of the person ep at -d in IT at the time of in xam 12 d category are grade level, principal the award (y axis). e 20 M f or academic affiliations, and principal el ed o to ne an hometowns of the laureates. o b a rd t h a n St ch N w e a or on 2. 0 1 te 20 al m ers a p C bi m 91 lu ge 19 2. 7. Co id s br ar m ye Ca ey 10 19 8 1 k el ag d e B er en e tr 71 tim 19 61 or 19 ef h e ag eac RY ag or O e 51 er h f EG ag e 19 4. a v e a c CAT a g e e a t er ur av el la b 41 No 19 of f 1 l o e ty 3 ta at ci 19 to ure ach la r e 2 12 fo 1 7 21 20 51 19 7 4. 17 How much do you know 1 23 1 91 7 17 1 4 10 13 about Nobel prize 01 1 99 6 19 1. 1 8 7 winners and Nobel 4 T RY 2 graduates? IS s)CH EM 9 y (5 57 ar s ye ea r 6. 28 2 This visualization IC S O MC E a r s ) 1 7 3 3 explores Nobel Prizes O N E N ye a r s 96 E C S C I 6 69 y e (5 3. 1 1 5 and graduate S 5. Y SI C s) 1. 4 5 qualifications from 1901 PH ar s ye ea r 6 9 y (5 54 4 to 1912, by analysing the RE s 4 R AT U ar ) ye a r s 1 1 3 4. Sibling pride: age of recipients at the TE 6 4 ye 93 2 LI (5 9 1 5 9 Jan and Nikolaas Tinbergen, the only brothers to win time prizes were GY E a prize each 1 LO IN rs) O C S I D I yea a r s 5 (economics and medicine) awarded, average age H Y M E ( 5 95 7 y e 1. P R O CE s 2 3 Multiple awards: Marie Curie, the first 5. The self-taught: evolution through time E Ay e a r r s ) P 6 1 yea 1 2 2 recipient of two Nobel Prizes (chemistry Guglielmo Marconi, the only Nobel laureate (physics) without a degree and among categories, 90 9 and physics) (5 1 o 1 graduation grades, main ag 2. 6. hic ton C g k in Yor 11 The oldest: Leonid Hurwicz, The posthumous: Erik Axel Karlfeldt, university affiliations sh n Wa New osto n awarded at age 90 and the principal the first person to be awarded al B do 1 4 a Nobel Prize after his death cip ns n s rin tow tes Lo Pari p e ea o m r ho f lau ich un lln M er 4 3. The youngest: 7. The First Lady of Economics: hometowns of the B ien Lawrence Bragg, Elinor Ostrom, W est ap w awarded at age 25 the only female recipient of the Nobel Prize in economics graduates. ud sco
  • 42. The phenomenon of so-called «brain drain» is explored through a map “brain showing incoming and outgoing flows of researchers in 16 countries. Using a series of parametres, the map is an attempt to discover k the motivations that move researchers from one country to another. mar 0 Each country is visualized through the representation of: GDP per capita, Den 6.39 female employment rate, overall unemployment rate, university rankings, drain?” percentage of foreign researchers, percentage of overall foreign population, percentage of emigrant researchers, percentage of overall emigrant population, percentage of researchers returning to their country of origin, and the main countries researchers come from and move to. re apo.833 Sing 5 n way Japa189 Nor 5.503 5. den Swe 5.017 rea Ko4.946 rg th bou 24 Usa 3 Sou Luxem 4.8 8 4.6 ada Can 4.334 gal tu4.307 ia Por tral 8 tria Aus 4.25 Aus 4.122 ain at Brit3.946 y Gre man 80 Ger 3.7 ce Fran.689 3 ium0 Belg 3.49 land nd tzer 3.319 Irela 372 3. Swi sia Rus3.091 ain The phenomenon of so- Sp2.931 nds Net her la 2.817 called «brain drain» is explored through a map showing incoming and fe l e mal oyment rate % (1 ) ity outgoing flows of vers 3) emp Uni kings ( ly >15 10 a 15 da 5 a 10 da ran 100 researchers in 16 Ita690 <5 1. D P pe r ca pita (1) countries. G nd Pola.597 50 Using a series of 1 a Destination Chi1n198 foreign researchers % (2) rkey . parametres, the map isy axis: n. of researchers per 1m people (1) Tu 803 ntry % foreigners in Courchers total population (1) Or igi n ase n. re eople ( mp 1) an attempt to discover er 1 zil Bra 695 try p % emigrants in total population (1) the motivations that C oun emigrant researchers %(2) move researchers from emigrant one country to another. 50 researchers returning ia How to read it 10 5 to country of origin % Ind135 20 (2) The countries are positioned according to: % of GDP invested in R&D (x axis) + n. of researchers per 1m people (y axis) 50 men ploy (1) t their country of origin, nemate (%) The analysis is based on the following data sources: (1) World Bank (2005-2010, worldbank.org) u r and the main countries (2) Foreign Born Scientists: Mobility Patterns for Sixteen Countries (2012 paper by Chiara Franzoni, Giuseppe Scellato and Paula Stephan, nber.org) researchers come from (3) Times Higher Education World University Rankings (2011-2012 and move to. 100 timeshighereducation.co.uk) x axis: % of GDP invested in R&D (1)
  • 43. ce Fran.689 3 ium0 B elg 49 3. nd land itz erla3.319 I re 3.37 2 Sw ds rlan.817 th e 2 Ne (1) t rate % ale fem loymen sity emp iver gs (3) Un kin >15 10 a 15 da a 10 ran da 5 100 ) <5 pita (1 p er ca GDP 50 a DestinationChi1n198 foreign researchers % (2) . try % foreigners in n ournhers C c total population (1) igi sea 1) Or n. re eople ( mp per 1 % emigrants in ntry total population (1) Cou emigrant researchers %(2) emigrant 50 researchers returning How to read it 10 5 to country of origin % 20 (2) The countries are positioned according to: % of GDP invested in R&D (x axis) 50 t men + n. of researchers per 1m people (y axis) m ploy %) (1) The analysis is based on the following data sources: une rate ( (1) World Bank (2005-2010, worldbank.org) (2) Foreign Born Scientists: Mobility Patterns for Sixteen Countries (2012 paper by Chiara Franzoni, Giuseppe Scellato and Paula Stephan, nber.org) (3) Times Higher Education World University Rankings (2011-2012 100 timeshighereducation.co.uk)(1)
  • 44. MOMA, INVENTINGABSTRACTION1910-1925 INVENTING ABSTRACTION
  • 45. (1)playing with singular elements
  • 46. disoccupazione disoccupazione rca documenti documenti ranking ranking ima donne donne Dan sil sil Bra sil sil Bra 123.456 Bra Bra n ppo Gia 123.456 Usa sil Bra ada ia Can tral Aus Uk ia man cia Ger Fran io sil Belg Bra zera Sviz * Spa gna nda Ola sil sil silBra Bra Bra a itor nan o Itali rito he r chia tutti lli c ) ho s i ra) que rde? itiva qui acità d catori chia e pos le op i ricer ale piu in v na cosa tr e ann princip (E’ u ria (sto sil le Bra Bra si * Ind ia (-) intermediate steps
  • 47. The phenomenon of so-called «brain drain» is explored through a map “brain showing incoming and outgoing flows of researchers in 16 countries. Using a series of parametres, the map is an attempt to discover k the motivations that move researchers from one country to another. mar 0 Each country is visualized through the representation of: GDP per capita, Den 6.39 female employment rate, overall unemployment rate, university rankings, drain?” percentage of foreign researchers, percentage of overall foreign population, percentage of emigrant researchers, percentage of overall emigrant population, percentage of researchers returning to their country of origin, and the main countries researchers come from and move to. re apo.833 Sing 5 n way Japa189 Nor 5.503 5. den Swe 5.017 rea Ko4.946 rg th bou 24 Usa 3 Sou Luxem 4.8 8 4.6 ada Can 4.334 gal tu4.307 ia Por tral 8 tria Aus 4.25 Aus 4.122 ain at Brit3.946 y Gre man 80 Ger 3.7 ce Fran.689 3 ium0 Belg 3.49 land nd tzer 3.319 Irela 372 3. Swi sia Rus3.091 ain The phenomenon of so- Sp2.931 nds Net her la 2.817 called «brain drain» is explored through a map showing incoming and fe l e mal oyment rate % (1 ) ity outgoing flows of vers 3) emp Uni kings ( ly >15 10 a 15 da 5 a 10 da ran 100 researchers in 16 Ita690 <5 1. D P pe r ca pita (1) countries. G nd Pola.597 50 Using a series of 1 a Destination Chi1n198 foreign researchers % (2) rkey . parametres, the map isy axis: n. of researchers per 1m people (1) Tu 803 ntry % foreigners in Courchers total population (1) Or igi n ase n. re eople ( mp 1) an attempt to discover er 1 zil Bra 695 try p % emigrants in total population (1) the motivations that C oun emigrant researchers %(2) move researchers from emigrant one country to another. 50 researchers returning ia How to read it 10 5 to country of origin % Ind135 20 (2) The countries are positioned according to: % of GDP invested in R&D (x axis) + n. of researchers per 1m people (y axis) 50 men ploy (1) t their country of origin, nemate (%) The analysis is based on the following data sources: (1) World Bank (2005-2010, worldbank.org) u r and the main countries (2) Foreign Born Scientists: Mobility Patterns for Sixteen Countries (2012 paper by Chiara Franzoni, Giuseppe Scellato and Paula Stephan, nber.org) researchers come from (3) Times Higher Education World University Rankings (2011-2012 and move to. 100 timeshighereducation.co.uk) x axis: % of GDP invested in R&D (1)
  • 48. Mosca latitude 1100 San Pietroburgo 302 Reykjavik 1703 874 Dublino 140 urbanism 118 4,1 76 59 2,5 2,9 0,5 3,2 1,4 0,3 0,1zona 0,1 0,5temperata 5 1.785 3.097 11,5 2.281 13.156 Londra 100 Parigi Milano Roma Berlino 308 53 a.C. 500 a.C. 753 a.C. 1237 Praga 885 209 231 103 125 15,1 5,9 109 1,5 8,4 7,1 1,3 3,7 3,7 0,1 0,2 0,9 0,5zona 2,2 1,3 1,2temperata 2,7 3,5 8,1 3.129 3.645 6.250 6.398 Dubai 1833 14.862 828 Pechino Shanghai 1075 Visualised are 25 among 19.316 the most important 723 a.C. New York 492 330 Los Angeles 1613 1781 381 Lisbona 5,1 worlds metropolis. 6,9 310 138 a.C. 110 7,7 4,1 16,8 6,3 Cities are grouped into 5 10 main temperature zones 4,9 1,3 1,2 2,1 0,1zonatemperata 3,7 8,2 1.760 0,5 according to their 2.680 10.019 2,1 19,6 23 latitude; and 3.003 4.238 5.192 horizontally ordered Città del Messico Bangkok according to their 1769 Singapore Giacarta Honolulu 1850 1521 225 Nairobi 1899 304 1819 1619 longitude. 280 262 131 2,8 140 12,3 1,9 Each city identity is 1,8 1,5 1,8 1,5 19,8zona 0,2 0,7 0,7 0,7 represented by a 0,3tropicale 3.638 8,8 674 3,1 8,2 5,3 1.572 10,1 multiform polygon: 1.561 2.244 12.246 illustrating and Sydney Come si legge? correlating its area 1788 244 La visualizzazione esplora le caratteristiche di venticinque tra le più importanti metropoli internazionali. Le città ordinate per latitudine (in cinque fasce) e per longitudine (allinterno della stessa fascia) sono analizzate attraverso parametri demografici, territoriali, ambientali, economici e architettonici. (square meter), number Johannesburg Buenos Aires 1536 1886 2,6 12,1 La forma del poligono e degli elementi che lo compongono restituiscono l’identità della città. of inhabitants, number 223 2,9 173 2,2 anno di fondazione età città 1529 of turists per year, tallestzona 0,2 2,8 0,5 altezza edificio più alto (m) buildings heights, and 1temperata 2.354 932 numero di visitatori e turisti (mln) 1,2 superficie (migliaia di kmq) rappresentata con dieci possibili average cost of houses 4,6 dimensioni del quadrato centrale according to real estate numero di abitanti (mln)I dati sono stati raccolti da:city-data.com, currentresults.com, 6.529 parameters(squareeuromonitor.com, globalpropertyguide.com,skyscrapercenter.com, weatherbase.com, precipitazioni media annuale (mm) temperatura media annule (°C) prezzo medio degli immobili di 120 mq meeter) characteristics .wikipedia.org. in zone urbane centrali (euro/mq)
  • 49. Bangkok 1769 Singapore Giacarta 1819 1619 304 280 2622,3 1,5 19,8 1,9 0,7 0,7 5,3 10,1 8,2 1.572 2.244 12.246 Come si legge? La visualizzazione esplora le caratteristiche di venticinque tra le più importanti metropoli internazionali. Le città ordinate per latitudine (in cinque fasce) e per longitudine (allinterno della stessa fascia) sono analizzate attraverso parametri demografici, territoriali, ambientali, economici e architettonici. La forma del poligono e degli elementi che lo compongono restituiscono l’identità della città. città anno di fondazione 1529 età altezza edificio più alto (m) numero di visitatori superficie (migliaia di kmq) e turisti (mln) 1,2 rappresentata con dieci possibili 4,6 dimensioni del quadrato centrale numero di abitanti (mln) precipitazioni temperatura media annule (°C) media annuale (mm) prezzo medio degli immobili di 120 mq in zone urbane centrali (euro/mq)
  • 50. Mosca latitude 1100 San Pietroburgo 302 Reykjavik 1703 874 Dublino 140 urbanism 118 4,1 76 59 2,5 2,9 0,5 3,2 1,4 0,3 0,1zona 0,1 0,5temperata 5 1.785 3.097 11,5 2.281 13.156 Londra 100 Parigi Milano Roma Berlino 308 53 a.C. 500 a.C. 753 a.C. 1237 Praga 885 209 231 103 125 15,1 5,9 109 1,5 8,4 7,1 1,3 3,7 3,7 0,1 0,2 0,9 0,5zona 2,2 1,3 1,2temperata 2,7 3,5 8,1 3.129 3.645 6.250 6.398 Dubai 1833 14.862 828 Pechino Shanghai 1075 Visualised are 25 among 19.316 the most important 723 a.C. New York 492 330 Los Angeles 1613 1781 381 Lisbona 5,1 worlds metropolis. 6,9 310 138 a.C. 110 7,7 4,1 16,8 6,3 Cities are grouped into 5 10 main temperature zones 4,9 1,3 1,2 2,1 0,1zonatemperata 3,7 8,2 1.760 0,5 according to their 2.680 10.019 2,1 19,6 23 latitude; and 3.003 4.238 5.192 horizontally ordered Città del Messico Bangkok according to their 1769 Singapore Giacarta Honolulu 1850 1521 225 Nairobi 1899 304 1819 1619 longitude. 280 262 131 2,8 140 12,3 1,9 Each city identity is 1,8 1,5 1,8 1,5 19,8zona 0,2 0,7 0,7 0,7 represented by a 0,3tropicale 3.638 8,8 674 3,1 8,2 5,3 1.572 10,1 multiform polygon: 1.561 2.244 12.246 illustrating and Sydney Come si legge? correlating its area 1788 244 La visualizzazione esplora le caratteristiche di venticinque tra le più importanti metropoli internazionali. Le città ordinate per latitudine (in cinque fasce) e per longitudine (allinterno della stessa fascia) sono analizzate attraverso parametri demografici, territoriali, ambientali, economici e architettonici. (square meter), number Johannesburg Buenos Aires 1536 1886 2,6 12,1 La forma del poligono e degli elementi che lo compongono restituiscono l’identità della città. of inhabitants, number 223 2,9 173 2,2 anno di fondazione età città 1529 of turists per year, tallestzona 0,2 2,8 0,5 altezza edificio più alto (m) buildings heights, and 1temperata 2.354 932 numero di visitatori e turisti (mln) 1,2 superficie (migliaia di kmq) rappresentata con dieci possibili average cost of houses 4,6 dimensioni del quadrato centrale according to real estate numero di abitanti (mln)I dati sono stati raccolti da:city-data.com, currentresults.com, 6.529 parameters(squareeuromonitor.com, globalpropertyguide.com,skyscrapercenter.com, weatherbase.com, precipitazioni media annuale (mm) temperatura media annule (°C) prezzo medio degli immobili di 120 mq meeter) characteristics .wikipedia.org. in zone urbane centrali (euro/mq)
  • 51. (1)general idea
  • 52. p p p p p p p p Pechino Pechino o Pechin ha senso che uno dei 4 assi (esempio popolazione) sia integrato nella superficie principale? (stesso colore?) MOSCA LONDRA 140 d.C. 140 d.C. MOSCA LONDRA 140 d.C. Se un dato (tipo quanto ha senso usare due colori 100 a.C. vecchia è la città) venisse confrontabili fusi orari? per parte sopra e sotto? dato dall’opacità totale oppure dal pattern MOSCA 140 d.C. (sporco interno) totale? p p p p PECHINO 570 d.C PECHINO 570 d.C Mosca trattare i paralleli 140 d.C. Londra 100 a.C. Mosca Mosca come linee cosi? o Pechin fondazione? PECHINO temperatura linea sotto 570 d.C p pioggia? ppp p MOSCA 140 d.C. pprovare a dare un pattern interno ai colori?(come REf a sinistra?) MOSCA 140 d.C. LONDRA 100 a.C. MOSCA 140 d.C. LONDRA pioggia? pioggia? pallino e linea che va indietro 100 a.C. = fondazione MOSCA 140 d.C. LONDRA MOS 100 a.C. 140 d MOSCA LONDRA 140 d.C. 140 d.C. MOSCA 140 d.C. (-) LONDRA 100 a.C. intermediate steps
  • 53. (1)general idea
  • 54. fiding yourown wayMy way is:drawing out everything thatcatches my attention“The act of reproducing thingsintroduces a level ofabstraction that helps focusingon the aspects of the compositionthat caught my attention. “
  • 55. fiding yourown way
  • 56. (3) INSPIRATION- get ideas from anything, anywhere, anytime “it is not what you look at that matters, its what you see” H.D. Thoreau giorgia.lupi@accurat.it www.giorgialupi.net www.accurat.it tw: @giorgialupi