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.

1. Nicholas Felton
ACCUMULATIONSFITC Toronto 2016

2. NYC
BROOKLYN STUDIO

3. SIGNS OF WEAR

4. SIGNS OF WEAR

5. ACTIONS > PATTERNS > INFORMATIONVisualization without Intermediation

6. PHYSICAL PEDOMETER

7. REGULAR DISTRIBUTION

8. FELTRON ANNUAL REPORTS

9. BETWEEN FIVE BELLS

10. REPORTER APP

11. DESIGNER
PROGRAMMER
JOURNALIST
STATISTICIAN
color, typography,
composition & texture
data, scripting,
& interactivity
research, narrative
& accountability
analysis &
correlation

12. 1.
COLLECT2.
COMPREHEND3.
COMMUNICATE

13. 1.
COLLECT2.
COMPREHEND3.
COMMUNICATE
1. Hoard
2. Wrangle
3. Design

14. Part One
COLLECTQuestion > Data

15. Ask a
QUESTIONWhat does a year look like?
Can you visualize a wine?

16. YES NOHAS THE EVENT HAPPENED?

17. YES
RESEARCH
1.
BIKECYCLE
2.
2010 ANNUAL REPORT
NOHAS THE EVENT HAPPENED?

18. 2015
BIKECYCLEQuestion > Data

19. DATA DUMP

20. 2010–2011
FAR 2010Question > Data

21. GORDON PAUL FELTON, 1929–2010

22. DAD’S MEMENTOS

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. 1 EKG

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. 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. 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. 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. DISCOVERABLE CONTEXT

30. DISCOVERABLE CONTEXT

31. DISCOVERABLE CONTEXT

32. YES
INSTRUMENT
1.
2009
2.
2013
3.
2014 ANNUAL REPORTS
NOHAS THE EVENT HAPPENED?

33. 2009–2011
FAR 2009Question > Data

34. 2010/2011 CALENDAR

35. 2009 SURVEY CARD

36. 2009 SURVEY CARD

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. :)
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. 2012–2014
FAR 2013Question > Data

40. Metadata Data
EMAIL Google
SMS Apple / ATT
CHAT Facebook
PHONE ATT
MAIL USPS
CONVERSATION
COMM.
SOURCES3 online / 3 offline

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. 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. 2013 CONVERSATIONS

44. 2013 CONVERSATIONS

45. 2013 CONVERSATIONS

46. book video

47. 2014–2015
FAR 2014Question > Data

48. Location
Calendar Calendar Calendar Digital
Analog
Activity Weather Body CarWork Sleep Photos MEDIA
Last.fm
DATA SOURCES2005

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. Camera
Roll
Instagram
Skitracks
App
Moves App
INSTRUMENTING PHONE

51. Last.fm
RescueTime
Netflix
INSTRUMENTING LAPTOP

52. Photos
GPS EXIF
NARRATIVE CAMERA

53. Steps Heartrate
Activity
Types
Sleep
BASIS WATCH

54. Blood
Alcohol
LAPKA BAM
Location

55. Driving
Stats
Location
AUTOMATIC

56. Weight
Body Fat
WITHINGS SCALE

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. Part Two
COMPREHENDData > Understanding

59. 2009–2011
FAR 2009Data > Understanding

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. SURVEY RESPONSES

62. Sad Happy
-5 +5
Introverted Extroverted
-5 +5
RATING
MOOD
RESPONSESWith Amazon Mechanical Turk
Turk Interface

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. 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. PLOTTING MOODS

66. 2010–2011
FAR 2010Data > Understanding

67. people
address
entertainment
food
restaurant
type
CALENDAR
TAGS

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. MAPPING LOCATIONS

70. 2012–2014
FAR 2013Data > Understanding

71. SMS TIMESTAMPS

72. CLEANING CONVERSATIONSFulcrum data
Dec 27, 2014 Jan 27, 2014
Jan 4: 9h 2m Cleaning

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. 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. 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. “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. >>> 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. 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. 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. 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. 2014–2015
FAR 2014Data > Understanding

82. AUTOMATIC BUGS

83. BASIS WATCH BUGS

84. LAPKA BAM BUGS

85. NARRATIVE PHOTO TAGGER

86. MOVES APP VISUALIZATION

87. MOVES APP VISUALIZATION

88. DAILY DATA GRAPHS

89. DAILY STEP GRAPHS

90. DAILY STEP GRAPHS

91. DAILY HEARTRATE GRAPH

92. TRIP DETECTOR

93. CORRELATION PLOT

94. 2015
BIKECYCLEHow do you visualize a year in
New York’s bike-sharing program?

95. BIKECYCLE DATA PIPELINE

96. 322 STATIONS

97. 102,087 ROUTES

98. 8,080,863 RIDES

99. Part Three
COMMUNICATEData > Understanding

100. 2009–2011
FAR 2009Data > Understanding

101. DOCUMENT ORGANIZATION

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. 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. S/M/L SCALES
Entire Data Set
Large
Categories of Data
Medium
Individual Data Point
Small

105. COLOR ADJUSTMENTS

106. COLOR ADJUSTMENTS

107. FAR10
2010–2011
FAR 2010Understanding > Expression

108. VISUALIZATION INSPIRATION

109. 3D MAPPING EXPLORATION

110. TRIANGULATION EXPLORATIONS

111. FAR10 ATLAS

112. 2012–2014
FAR 2013Understanding > Expression

113. WAR GAMES

114. Borda OCRB Landmark
Miso SourceCode
Pro Monosten

115. Input Sans
Condensed + Compressed

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. FAR 13 VOLUME

118. FAR 13 VOCABULARY

119. 2014–2015
FAR 2014Understanding > Expression

120. PAGE LAYOUTS

121. SKETCH VISUALIZATION

122. SKETCH VISUALIZATION

123. SKETCH VISUALIZATION

124. SKETCH VISUALIZATION

125. SKETCH VISUALIZATION

126. VISUALIZATION ELEMENTS
Scale Connections Proximity Rotation Color
Shapes Symbols Type
318.2
Texas
Repetition Position

127. FINAL COVER

128. 2015
BIKECYCLEUnderstanding > Expression

129. WIREFRAME

130. BIKECYCLE DEVELOPMENT

131. CITIBIKES

133. Data is the
NEW WOOD

134. PHOTOVIZ BOOK

135. SKILLSHARE CLASSES

136. Nicholas Felton
THANK YOUfeltron.com / @feltron