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Accumulations with Nicholas Felton

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Overview

For the past decade, Nicholas Felton has methodically captured and shared his life in a series of projects collectively known as the Feltron Annual Reports. These reports apply data visualization techniques to the age-old concerns of the journal and have contributed to the current field of lifelogging. In this talk, Nicholas will share his methods for data collection as well as his narrative and design concerns while creating his reports.

Objective

Accumulations is an introduction to the motivations and techniques behind the Feltron Annual Reports.

Target Audience

Designers. Anyone working with data or interested in storytelling.

Five Takeaways

The importance of data as a new design medium.
How to humanize statistics.
Successful strategies for collecting data.
Techniques for communicating a data set.
The sensitivity of meta-data.

Published in: Data & Analytics
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Accumulations with Nicholas Felton

  1. 1. Nicholas Felton ACCUMULATIONSFITC Toronto 2016
  2. 2. NYC BROOKLYN STUDIO
  3. 3. SIGNS OF WEAR
  4. 4. SIGNS OF WEAR
  5. 5. ACTIONS > PATTERNS > INFORMATIONVisualization without Intermediation
  6. 6. PHYSICAL PEDOMETER
  7. 7. REGULAR DISTRIBUTION
  8. 8. FELTRON ANNUAL REPORTS
  9. 9. BETWEEN FIVE BELLS
  10. 10. REPORTER APP
  11. 11. DESIGNER PROGRAMMER JOURNALIST STATISTICIAN color, typography, composition & texture data, scripting, & interactivity research, narrative & accountability analysis & correlation
  12. 12. 1. COLLECT2. COMPREHEND3. COMMUNICATE
  13. 13. 1. COLLECT2. COMPREHEND3. COMMUNICATE 1. Hoard 2. Wrangle 3. Design
  14. 14. Part One COLLECTQuestion > Data
  15. 15. Ask a QUESTIONWhat does a year look like? Can you visualize a wine?
  16. 16. YES NOHAS THE EVENT HAPPENED?
  17. 17. YES RESEARCH 1. BIKECYCLE 2. 2010 ANNUAL REPORT NOHAS THE EVENT HAPPENED?
  18. 18. 2015 BIKECYCLEQuestion > Data
  19. 19. DATA DUMP
  20. 20. 2010–2011 FAR 2010Question > Data
  21. 21. GORDON PAUL FELTON, 1929–2010
  22. 22. DAD’S MEMENTOS
  23. 23. Applications Audio Recordings Books Calendars Certificates Driver’s Licenses Expense Logs Family Trees Fast Trak Statements Itineraries Legal Documents Letters Lists Medical Records Newspaper Clippings Notebooks Passports Photos Police Certification Postcards Receipts School Reports Slides Tickets Union Registration Form AVAILABLE SOURCES4,348 items in 25 categories
  24. 24. 1 EKG
  25. 25. Jul 24, 1989: Vale of Rheidal & Devils bridge on old train out of Aberriswyth, walk to Devils cauldron. Nick checks out tidepools at Aber--. N&M move into camper. Jul 25, 1989: Oliver taken to beauty parlor. Jags over house low level. Meet Mervyn Evans at pub for lunch in local. Visit Newtown Montgomery & Welschpool. Interesting ruin of border fortress at Mowtgy. Jul 26, 1989: To Fistiniog to very visit to Slate Minesa Museum. Visit damm & locked in for 45mins. Beautiful drive by coast through Aberdovey. Drink with Russ & Anita & call Wilson’s Jul 27, 1989: Dep. Wales to Loboro. Visit aerospace museum at Cosford. Drop by Brush, David Harris. To Les & Gail & brief visit with Les Hollingsworth. Dinner for N&M at L’boro McDonald Jul 28, 1989: Lunch at White House Kegworth. Breakfast at Cafe. To Nottingham Castle Robinhood Adventure. ‘Out of Order.’ Visit still positive Dad ‘outlaw’ N&M are ‘Pardoned’ by King. Movie Karate Kid III. N&M call Carol 4,440 CALENDAR ENTRIES
  26. 26. Jun 8, 1959 Mr. Gordon Felton, c/o Mr. Ed. Brophy, 248-05 87th Avenue, Bellerose 6/ New York Oct 18, 1960 G. Felton, Esq. (??), Apt. 103, 451 Lee St., Oakland. California. Jun 14, 1962 Mr. Gordon Felton, 22 Terra Vista, San Francisco 05, California Sep 18, 1962 Mr. Gordon Felton, 22 Terra Vista Ap. E-2, San Francisco, California Sep 19, 1962 Mr. Gordon Felton, 22. Terra Vista Ap. E-2, San Francisco, California Sep 16, 1962 Mr. Gordon Felton, Terra Vista E-2, San Francisco, California Sep 26, 1962 Mr. Gordon Felton, 22 Terra Viesta, San Francisco, California 169 RECEIVED POSTCARDS
  27. 27. Jul 27, 1957 Havana, Cuba Aug 4, 1957 Fort Erie, Ontario, Canada Dec 2, 1957 Malton, Canada May 5, 1958 Melsbroek Air Base, De Kleetlaan, Machelen, Flemish Region, Belgium May 5, 1958 Berlin Tempelhof Airport, Germany May 9, 1958 Melsbroek Air Base, De Kleetlaan, Machelen, Flemish Region, Belgium May 9, 1958 Montreal Airport, Roméo-Vachon Boulevard North, Montreal, Quebec, Canada Sep 14, 1959 Toronto, Canada Oct 18, 1959 Malton, Canada Feb 24, 1960 Buffalo, NY Dec 12, 1961 San Francisco, CA Jan 2, 1962 San Francisco, CA Jan 2, 1962 San Francisco, CA Jan 2, 1962 San Francisco, CA Jan 2, 1962 San Francisco, CA Jan 3, 1962 San Francisco, CA 239 PASSPORT STAMPS
  28. 28. Montevideo Buenos Aires Cape of Good Hope Cape Town Johannesburg Iguacu Rio De Janeiro Ascotan - Chile La Paz Brasilia Cuzco Macchu Pichu Lima - Peru Arusha Ngorongoro Crater Manaos KNOWN CONTEXT
  29. 29. DISCOVERABLE CONTEXT
  30. 30. DISCOVERABLE CONTEXT
  31. 31. DISCOVERABLE CONTEXT
  32. 32. YES INSTRUMENT 1. 2009 2. 2013 3. 2014 ANNUAL REPORTS NOHAS THE EVENT HAPPENED?
  33. 33. 2009–2011 FAR 2009Question > Data
  34. 34. 2010/2011 CALENDAR
  35. 35. 2009 SURVEY CARD
  36. 36. 2009 SURVEY CARD
  37. 37. Encounter Date Jan 28, 2009 Name Adam B. Card No. 68 Relationship Friend Friendship Duration 5 years Encounter Length 1 hour Activities Talking, drinking, congratulating, planning sushi Mood happy, engaged Where Shebeen Elsewhere No Eat No Drink Stella(s) Transportation No Conversation Topics Work, wrestling in studio, our fathers, skiing, Feltron AR, letterpress and Willy, sushi, printing in China Favorite Moment seeing/receiving/ feeling business card & leaving happier than I came. Anything Else Nicholas instigated this encounter. Tangential encounters with people I knew already: 11 Tangential encounters with people I just met: 3 SAMPLE SURVEY RESPONSENo. 37 of 560
  38. 38. :) adventurous affable agitated agreeable alert amiable amicable animated anticipation anxious appreciated appreciative apprehensive approachable at ease attentive attuned awake awesome ball busted beatific beery bemused boozed bored bouyant bubbling bummed buoyant busy buzzy Californian calm calm-ish careful cautious celebratory charming chatty cheered up cheerful cheery chill chipper chirpy chopsticks chummy COLLECTIVE MOOD RESPONSESMoods 1–48 of 300
  39. 39. 2012–2014 FAR 2013Question > Data
  40. 40. Metadata Data EMAIL Google SMS Apple / ATT CHAT Facebook PHONE ATT MAIL USPS CONVERSATION COMM. SOURCES3 online / 3 offline
  41. 41. Who with Conversation Type Greeting, Non-Verbal, Pleasantry, Functional, Sporadic, Chat, Question, Answer, Meeting, Telephone, Video Conference, Breakfast, Brunch, Lunch, Dinner, Coffee, Drink, Other Non-Verbal Type Wave, Smile, Nod, Raise Eyebrows, Hand Shake, Hug, Kiss, Point, Wink, Fist Bump, Hi-Five, Other Greeting Type Good morning, What’s up?, Dude, Hey, Hi, Hello, How are you?, Nice to meet you, Bye, Goodbye, Good night, Take care, See you, Later, Other Pleasantry Type Thank you, Thanks, Bless you, Pardon me, Excuse me, Sorry, Other Description Location Time Duration Momentary, 1 minute, 2 minutes, 3 minutes, 5 minutes, 10 minutes, 15 minutes, 30 minutes, 1 hour, Other FULCRUM CONV. SURVEYFinal version
  42. 42. Who with: Olga Conversation Type: Greeting, Non-Verbal, Lunch Non-Verbal Type: Kiss Greeting Type: Hello Pleasantry Type: Description: Olga’s rehearsal schedule. delicious flat whole wheat ev- erything bagel with whitefish and avocado lunch. Dropbox camera roll backup. The definition of scrappy. Location: Home Coordinates: 40.713337, -73.949233 Date: September 24, 2015 Time: 13:07 – 13:45 Duration: 38m SAMPLE CONV. ENTRYNo. 9,640 of 12,707
  43. 43. 2013 CONVERSATIONS
  44. 44. 2013 CONVERSATIONS
  45. 45. 2013 CONVERSATIONS
  46. 46. book video
  47. 47. 2014–2015 FAR 2014Question > Data
  48. 48. Location Calendar Calendar Calendar Digital Analog Activity Weather Body CarWork Sleep Photos MEDIA Last.fm DATA SOURCES2005
  49. 49. Location Human App Day One Rove Lapka BAM Moves App Automatic Breeze Photo Exif Data Activity Fitbit Nike Fuelband Human App Trace App Moves App Basis Watch Nike Running Skitracks App Healthkit Weather Weather Underground Day One Body Withings Scale Lapka BAM Basis Watch Car Automatic Work Github RescueTime Dropbox Sleep Basis Watch Photos Narrative Clip Camera Roll MEDIA Netflix Kindle Last.fm Soundcloud Amazon Prime DATA SOURCES2014 Instagram
  50. 50. Camera Roll Instagram Skitracks App Moves App INSTRUMENTING PHONE
  51. 51. Last.fm RescueTime Netflix INSTRUMENTING LAPTOP
  52. 52. Photos GPS EXIF NARRATIVE CAMERA
  53. 53. Steps Heartrate Activity Types Sleep BASIS WATCH
  54. 54. Blood Alcohol LAPKA BAM Location
  55. 55. Driving Stats Location AUTOMATIC
  56. 56. Weight Body Fat WITHINGS SCALE
  57. 57. DATA ACCESSServices used Access via API Lapka BAM Instagram Fitbit Moves Hacks Required Basis Watch Netflix Direct Download Automatic RescueTime Day One Withings Scale Skitracks App Best Worse NO DATA Soundcloud Amazon Prime Kindle DropboxLast.fm
  58. 58. Part Two COMPREHENDData > Understanding
  59. 59. 2009–2011 FAR 2009Data > Understanding
  60. 60. BASIC DATA TYPES yes/no or 0/1 Boolean YES 1.0 HELLO!whole or decimal positive or negative Number free text or categories Text
  61. 61. SURVEY RESPONSES
  62. 62. Sad Happy -5 +5 Introverted Extroverted -5 +5 RATING MOOD RESPONSESWith Amazon Mechanical Turk Turk Interface
  63. 63. :) 5 adventurous 3.2 affable 3.2 agitated -5 agreeable 5 alert 2.4 amiable 3 amicable 1.6 animated 4 anticipation -0.6 anxious -0.8 appreciated 3.2 appreciative 5 apprehensive -2 approachable 3.2 at ease 4 attentive 0 attuned 1.2 awake 0.6 awesome 5 ball busted -5 beatific 3.2 beery -0.4 bemused 5 boozed 0.6 bored -3.2 bouyant 5 bubbling 5 bummed -5 buoyant 3.2 busy 0 buzzy 3.2 Californian 0.4 calm 0.8 calm-ish 1 careful -0.6 cautious -0.8 celebratory 4 charming 5 chatty 1.8 cheered up 5 cheerful 5 cheery 5 chill 1.2 chipper 5 chirpy 2.4 chopsticks 0.8 chummy 4 INDEXED MOOD RESPONSESMoods 1–48 of 300
  64. 64. Processing seeks to ruin the careers of talented designers by tempting them away from their usual tools and into the world of programming and computation. Similarly, the project is designed to turn engineers and computer scientists to less gainful employment as artists and designers. PROCESSING MISSION STATEMENTwww.processing.org
  65. 65. PLOTTING MOODS
  66. 66. 2010–2011 FAR 2010Data > Understanding
  67. 67. people address entertainment food restaurant type CALENDAR TAGS
  68. 68. Montevideo -34.883335 -56.166668 2 Buenos Aires -34.608418 -58.37316 4 Cape of Good Hope -34.35869 18.475363 2 Cape Town -33.92479 18.429916 12 Johannesburg -26.204445 28.045555 1 Iguacu -25.683332 -54.433334 29 Rio De Janeiro -22.90354 -43.209587 20 Ascotan - Chile -22.442776 -68.92286 1 La Paz -16.49901 -68.14625 5 Brasilia -14.235004 -51.92528 14 Cuzco -13.525 -71.97222 13 Macchu Pichu -13.163611 -72.5459 12 Lima - Peru -12.043333 -77.028336 2 Arusha -3.365789 36.67445 4 Ngorongoro Crater -3.1740036 35.563892 53 Manaos -3.1071923 -60.026127 3 GEOCODING PHOTOS
  69. 69. MAPPING LOCATIONS
  70. 70. 2012–2014 FAR 2013Data > Understanding
  71. 71. SMS TIMESTAMPS
  72. 72. CLEANING CONVERSATIONSFulcrum data Dec 27, 2014 Jan 27, 2014 Jan 4: 9h 2m Cleaning
  73. 73. Corpus Word Count: 6,904,901 Unique Word Count: 117,194 Most frequent word is ‘the’ appearing 196,414 times Top Interrogatives: who: 4,080 what: 5,808 when: 6,476 where: 2,892 why: 1,593 how: 6,435 Profanities: hell 172 shit 112 damn 112 sex 82 fuck 78 dick 35 dong 33 fanny 30 screw 29 balls 29 negro 26 dyke 23 ass 23 xxx 21 crap 14 bastard 13 queer 12 titty 11 poop 11 bitch 9 fart 8 vibrator 7 pussy 7 porn 7 bugger 7 sperm 6 pimp 6 homo 6 piss 5 penis 5 boobs 5 WORD FREQUENCYWordCount_071714a
  74. 74. she 2696 whether 2453 him 1901 says 1740 tell 1657 going 1546 say 1464 ask 1348 asks 1330 working 943 getting 936 zoo 846 ill 838 being 767 yes 697 talk 665 dinner 642 nice 612 tomorrow 607 show 570 office 560 doing 556 king 542 drew 490 looking 474 went 473 wedding 472 trip 467 car 452 long 452 coming 436 can’t 428 place 421 little 420 happy 416 coffee 411 wants 405 food 400 building 388 down 387 yeah 387 trying 372 eat 371 cool 368 maybe 364 old 364 running 360 said 358 run 356 better 353 chat 353 soon 347 small 339 bad 337 really 335 something 335 water 334 morning 333 making 332 probably 329 tonight 329 give 328 put 328 why 327 2013 MOST SENT WORDSVocabularyPlot_071514a
  75. 75. click 15040 informa… 13765 address 13679 view 13499 percent 13056 price 11725 account 11462 code 11149 shipping 10750 emails 10609 offer 9985 sent 9693 payment 9634 customer 9577 transact… 9469 receive 9044 message 9021 twitter 8984 list 8927 details 8717 save 8306 sale 7948 change 7889 these 7456 privacy 7431 pm 7172 deal 7171 preferen… 6829 wrote 6680 follow 6632 contact 6470 read 6259 learn 6122 online 6099 music 6002 stock 5808 questions 5762 store 5725 received 5678 add 5664 reply 5479 policy 5471 available 5425 shop 5291 deals 5199 service 5188 tickets 4990 visit 4925 net 4895 image 4869 rights 4819 member 4637 share 4622 log 4618 page 4590 subject 4573 reserved 4554 terms 4494 amount 4451 valid 4441 card 4412 photos 4388 members 4296 est 4271 2013 MOST RECEIVED WORDSVocabularyPlot_071514a
  76. 76. “Ryan Case” - Conversation 195.1393 - ryan 142.47809 - ryans 101.06998 - minutiae 83.135025 - work 81.84209 - bonnie 75.954185 - facebook 61.653275 - app 52.95851 - going 52.938576 - new 51.793407 - lunch 4pm - All Communication 2445.5276 - | 2037.5258 - learn 1997.6616 - - 1216.178 - email 1206.7687 - new 1097.3185 - us 1080.9609 - will 1070.2032 - pm 1028.2096 - 2013 943.6468 - can TERM FREQUENCY INVERSE DOCUMENT FREQUENCYTF_IDF2_082714A
  77. 77. >>> conversation = nltk.Text(word- lists.words(‘AR13_conversation. txt’)) >>> conversation.collocations() Building collocations list new york; annual report; san francisco; say yes; last night; would like; dirty projectors; cross country; mill valley; moves app; fuel band; national geographic; small latte; reporter app; las vegas; palo alto; los angeles; hurricane sandy; ice cream; high five >>> NATURAL LANGUAGE TOOLKITPython
  78. 78. Person 78701 Company 49375 City 34545 FieldTerminology 16519 StateOrCounty 12239 Country 10987 Facility 10722 Organization 10389 JobTitle 8763 Technology 4322 PrintMedia 2798 GeographicFeature 2752 OperatingSystem 1738 Holiday 1103 Continent 764 Sport 410 Region 404 Degree 394 HealthCondition 323 FinancialMarketIndex 303 Crime 281 Product 267 Movie 235 EntertainmentAward 168 Drug 141 TelevisionStation 109 MusicGroup 83 TelevisionShow 79 Automobile 73 ProfessionalDegree 46 NaturalDisaster 44 SportingEvent 24 Anatomy 5 ALCHEMY APIwww.alchemyapi.com
  79. 79. TYPE RELEVANCE ENTITY SOURCE City 64% London email Person 88% Nicholas email Company 89% Facebook email Technology 16% iPhone email Country 43% Russia email Person 67% Joe Davis email Company 30% Apple email JobTitle 40% reporter facebook Person 85% Beyonce conversation Holiday 17% Christmas email JobTitle 38% Project Manager email PrintMedia 28% IL magazine email Continent 30% Europe email Anatomy 33% calf muscle sms Technology 69% iPhone email ALCHEMY APIwww.alchemyapi.com
  80. 80. warrens apt 38.9319 -77.0556 Williamsburg 37.2707 -76.7075 Japan 36.2048 138.253 Bowery 40.7253 -73.9903 Brussels 50.8503 4.35171 Richardson 32.9482 -96.7297 Japan. 36.2048 138.253 Brooklyn 40.65 -73.95 manhattan 40.7903 -73.9597 nyc. 40.7144 -74.006 portlandia 45.5101 -122.975 minneapolis 44.9833 -93.2667 Oslo 59.9139 10.7522 California 36.7783 -119.418 Australia -25.2744 133.775 royal tennenbaums. 36.8508 -76.2859 GEOCODINGGoogle Maps API
  81. 81. 2014–2015 FAR 2014Data > Understanding
  82. 82. AUTOMATIC BUGS
  83. 83. BASIS WATCH BUGS
  84. 84. LAPKA BAM BUGS
  85. 85. NARRATIVE PHOTO TAGGER
  86. 86. MOVES APP VISUALIZATION
  87. 87. MOVES APP VISUALIZATION
  88. 88. DAILY DATA GRAPHS
  89. 89. DAILY STEP GRAPHS
  90. 90. DAILY STEP GRAPHS
  91. 91. DAILY HEARTRATE GRAPH
  92. 92. TRIP DETECTOR
  93. 93. CORRELATION PLOT
  94. 94. 2015 BIKECYCLEHow do you visualize a year in New York’s bike-sharing program?
  95. 95. BIKECYCLE DATA PIPELINE
  96. 96. 322 STATIONS
  97. 97. 102,087 ROUTES
  98. 98. 8,080,863 RIDES
  99. 99. Part Three COMMUNICATEData > Understanding
  100. 100. 2009–2011 FAR 2009Data > Understanding
  101. 101. DOCUMENT ORGANIZATION
  102. 102. Mood Happy Topics amount Thirty Six jan mar may jul sep novfeb apr jun aug oct dec jan mar may jul sep novfeb apr jun aug oct dec question 8. please describe his mood. Excellent! (In disguise of a hangover). warren, january 1 Somewhat tense, perhaps tired. kris, jan 7 Chummy! kenny, jan 21 Cheery, upbeat, Californian, unsullen. ellen w, jan 28 Unhurried and relaxed. jessica b, mar 20 Suffering from a cold, sore throat. carol, may 17 Earnestly industrious. matthew g, may 19 Very good – exuberant almost. More animated and smiley than usual. marie-claire, may 28 Pensive (but not in a lame way). nicholas b, july 18 Bored, oh God so bored. mariana, september 1 Cheerful to tipsy (?) hana, november 20 Jocular. khoi, december 3 Festive, happy, relieved?? olga, december 31 An assessment of demeanor. most frequent mood 76 reports, 8.8% of all moods unique moods 300 types of negative moods Eighty18% of all moods reports of being “upset” One average temperament Swell73.9% happy focused to distracted ratio 9:2 synonyms for tired Elevendrained, fading, jetlagged, exhausted, pooped, sleeeeeepy, sleepy, sreeepy, sweeepy, tired and yawny average personality An Ambivert59.9% extrovert / 40.1% introvert figure 11. reports of extroversion vs. introversion figure 10. reports of happiness vs. sadness happiness level average temperament sadness level degree of extroversion degree of introversionaverage personality question 9. what topics were discussed? AR08, encounters, Asian printing, fevers. john d, january 28 China, eggs, airplanes. zach, february 18 Music, work, Daytum. brian, march 21 Data visualization, ESPN, New York, Boston, IDEO, beer, keeping everything from looking the same vs. keeping everything from looking bad. gian, march 28 Work. Jewelry. Ikea. sam, april 14 Blogs, Pacman, Grand Theft Auto. thomas, june 12 Schools attended, Daedelus, Flying Lotus, bpm/ metronomes, Sarah Palin. olga, august 19 Mostly family background and computers. aunt ruth, august 29 Work, life, ideas, data. jordan, sepember 11 Life, the Universe and everything! No, seriously… warren, october 8 Eating solid foods versus soft foods and a fear of hard boiled eggs. nick b, october 21 Recording in the studio, computers, high fives. gunnar, november 20 The breadth and depth of conversation. figure 12. reported topics of conversation movies discussed Thirtyavatar, boys don’t cry, district 9, food inc., goonies, the hangover, ice age, legend, mad max, man on wire, milk, million dollar baby, nick and norah’s infinite playlist, ratatouille, runaway, t2, tell no one, terminator, the dark knight, the exorcist, the hurt locker, the neverending story, the wrestler, top gun, tropic thunder, watchmen, whip it, where the wild things are and zombieland board games discussed Fivebalderdash, dungeons & dragons, jenga, monopoly and scrabble typographic discussions 17an abandoned typeface, balloon letters, double spaces, drawing an s, kerning, letterforms, letterspacing, typography on bottles, organizing type, photo type composition, picas, points, ragging, shipflat, titling font families, type and type we like music discussed 25aaliyah, animal collective, bell, broken social scene, daedelus, department of eagles, doveman, edison, elliott smith, explosions in the sky, flying lotus, grizzly bear, here we go magic, itay talgam, jean louis, jean luc, jonathan richman, joy division, kevin drew, neil young, nico muhly, prefuse 73, sam amidon, the knife and tricky most mentioned pet Kingalso: francis, piper and pippin government agencies discussed Fivecia, fbi, homeland security, noaa and the nsa public personalities discussed andy warhol, ashley olsen, balloon boy, barack obama, bill hader, bill murray, brad bird, buckminster fuller, captain america, carl weathers, dan barber, douglas gordon, gene kaufmann, hillary swank, jason sudeikis, jasper johns, karim rashid, louis ck, malcolm gladwell, michael hoelke, michael pollan, mihaly csikszentmihalyi, mitch hedberg, morgan freeman, patrick swayze, peter arnell, robert downey jr., sarah palin, scarlett johansson, sol lewitt, steve jobs, tiger woods, van jones, venus & serena williams, werner herzog and wes anderson most discussed travel destination Mexico Cityreported 4 times places media food work nothing people ideas activities designthings variety MOOD PAGE
  103. 103. jan mar may jul sep novfeb apr jun aug oct dec figure 11. reports of extroversion vs. introversion figure 10. reports of happiness vs. sadness happiness level average temperament sadness level Cheerful to tipsy (?) hana, november 20 Jocular. khoi, december 3 Festive, happy, relieved?? olga, december 31 types of negative moods Eighty18% of all moods reports of being “upset” One average temperament Swell73.9% happy focused to distracted ratio 9:2 synonyms for tired Elevendrained, fading, jetlagged, exhausted, pooped, sleeeeeepy, sleepy, sreeepy, sweeepy, tired and yawny bivertmay jul sep novjun aug oct dec question 8. please describe his mood. Excellent! (In disguise of a hangover). warren, january 1 Somewhat tense, perhaps tired. kris, jan 7 Chummy! kenny, jan 21 Cheery, upbeat, Californian, unsullen. ellen w, jan 28 Unhurried and relaxed. jessica b, mar 20 Suffering from a cold, sore throat. carol, may 17 vs. introversion sadness happiness level t sadness level S/M/L SCALES
  104. 104. S/M/L SCALES Entire Data Set Large Categories of Data Medium Individual Data Point Small
  105. 105. COLOR ADJUSTMENTS
  106. 106. COLOR ADJUSTMENTS
  107. 107. FAR10 2010–2011 FAR 2010Understanding > Expression
  108. 108. VISUALIZATION INSPIRATION
  109. 109. 3D MAPPING EXPLORATION
  110. 110. TRIANGULATION EXPLORATIONS
  111. 111. FAR10 ATLAS
  112. 112. 2012–2014 FAR 2013Understanding > Expression
  113. 113. WAR GAMES
  114. 114. Borda OCRB Landmark Miso SourceCode Pro Monosten
  115. 115. Input Sans Condensed + Compressed
  116. 116. Y: 100 M: 15 C: 80 Y: 20 M: 80 Y: 60 K: 25 C: 15 Y: 100 C: 100 M: 100 Y: 100 K: 25
  117. 117. FAR 13 VOLUME
  118. 118. FAR 13 VOCABULARY
  119. 119. 2014–2015 FAR 2014Understanding > Expression
  120. 120. PAGE LAYOUTS
  121. 121. SKETCH VISUALIZATION
  122. 122. SKETCH VISUALIZATION
  123. 123. SKETCH VISUALIZATION
  124. 124. SKETCH VISUALIZATION
  125. 125. SKETCH VISUALIZATION
  126. 126. VISUALIZATION ELEMENTS Scale Connections Proximity Rotation Color Shapes Symbols Type 318.2 Texas Repetition Position
  127. 127. FINAL COVER
  128. 128. 2015 BIKECYCLEUnderstanding > Expression
  129. 129. WIREFRAME
  130. 130. BIKECYCLE DEVELOPMENT
  131. 131. CITIBIKES
  132. 132. Data is the NEW WOOD
  133. 133. PHOTOVIZ BOOK
  134. 134. SKILLSHARE CLASSES
  135. 135. Nicholas Felton THANK YOUfeltron.com / @feltron

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